U.S. patent application number 09/729299 was filed with the patent office on 2001-04-26 for air-conditioning device.
This patent application is currently assigned to DENSO Corporation. Invention is credited to Ichishi, Yoshinori, Ito, Yuji, Kajino, Yuichi, Kawai, Takayoshi, Samukawa, Katsuhiko.
Application Number | 20010000407 09/729299 |
Document ID | / |
Family ID | 27469994 |
Filed Date | 2001-04-26 |
United States Patent
Application |
20010000407 |
Kind Code |
A1 |
Ichishi, Yoshinori ; et
al. |
April 26, 2001 |
Air-conditioning device
Abstract
Temperatures in a Dr side air-conditioning zone and a Pa side
air-conditioning zone are controlled highly independently of each
other without temperature interference between each zone. A room
internal air temperature sensor and a room external air temperature
sensor are provided. Dr side and Pa side temperature setters
separately set room setpoint temperatures (Tset(Dr), Tset(Pa)) in
each zone. First and second target blow-out temperature calculating
portions, which include neural network, input the room setpoint
temperatures and the temperature data. Then it calculates Dr side
and Pa side target blow-out temperatures (TAO(Dr), TAO(Pa))
relative to each air-conditioning zones by using a neural network.
Air-mixing doors separately adjusts the temperatures of conditioned
air blown out from Dr side air passage and Pa side air passage to
be the first and second target blow-out temperatures. Here, the
neural network has the learning function, which adjusts its output
to be desired data (teacher signal). Therefore, the output at a
specific input condition can be adjusted without temperature
interference between each zone.
Inventors: |
Ichishi, Yoshinori;
(Kariya-city, JP) ; Kawai, Takayoshi; (Hoi-gun,
JP) ; Ito, Yuji; (Okazaki-city, JP) ;
Samukawa, Katsuhiko; (Obu-city, JP) ; Kajino,
Yuichi; (Nagoya-city, JP) |
Correspondence
Address: |
Harness, Dickey & Pierce, P.L.C.
P.O. Box 828
Bloomfield Hills
MI
48303
US
|
Assignee: |
DENSO Corporation
|
Family ID: |
27469994 |
Appl. No.: |
09/729299 |
Filed: |
December 4, 2000 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
09729299 |
Dec 4, 2000 |
|
|
|
09295997 |
Apr 21, 1999 |
|
|
|
Current U.S.
Class: |
236/13 ;
165/203 |
Current CPC
Class: |
B60H 1/00814 20130101;
F24F 11/63 20180101; B60H 1/00735 20130101 |
Class at
Publication: |
236/13 ; 236;
165/203 |
International
Class: |
G05D 023/13; B60H
001/00 |
Foreign Application Data
Date |
Code |
Application Number |
Apr 22, 1998 |
JP |
10-112435 |
Apr 24, 1998 |
JP |
10-115419 |
Apr 24, 1998 |
JP |
10-115420 |
Apr 27, 1998 |
JP |
10-117416 |
Claims
What is claimed is:
1. An air-conditioning device in which a first air-conditioning
zone and a second air-conditioning zone are set in a room, and
respective temperatures in the first and the second
air-conditioning zones are separately adjusted by conditioned air,
comprising: first and second air passages provided relative to the
first and the second air-conditioning zones, respectively; first
and second temperature setters to separately set setpoint
temperatures in the first and the second air-conditioning zones; a
temperature data detector to detect temperature data including a
room internal air temperature and a room external air temperature;
first and second target blow-out temperature calculating portions
including a neural network, that receive the setpoint temperatures
and the temperature data, and calculate first and second target
blow-out temperatures for the first and the second air-conditioning
zones via the neural network; and first and second temperature
adjusters arranged in the first and second air passages,
respectively, to separately adjust the temperatures of conditioned
air blown out from the first and second air passages to the first
and the second target blow-out temperatures.
2. An air-conditioning device according to claim 1, wherein each of
the first and second target blow-out temperature calculating
portions comprises: a temporary target temperature calculating
portion that receive the setpoint temperatures and the temperature
data, and calculate first and second temporary target temperatures
for the first and the second air-conditioning zones via the neural
network; a radiation amount correction calculating portion that
receive at least a radiation data, and calculate radiation amount
corrections via the neural network; a target temperature
calculating portion to calculate the first and second target
blow-out temperatures relative to the first and the second
air-conditioning zones based on the first and second temporary
target temperatures and the radiation amount corrections.
3. An air-conditioning device according to claim 1, wherein the
first air-conditioning zone is a driver seat side air-conditioning
zone, the second air-conditioning zone is a passenger seat side
air-conditioning zone, and the first and second temperature
adjusters separately adjust the temperatures of the driver seat
side air-conditioning zone and the passenger seat side
air-conditioning zone.
4. An air-conditioning device in which a first air-conditioning
zone and a second air-conditioning zone are set in a room, and the
temperatures in the first and the second air-conditioning zones are
separately adjusted by the conditioned air, comprising: first and
second air passages respectively provided relative to the first and
the second air-conditioning zones; first and second temperature
setters to separately set setpoint temperatures in the first and
the second air-conditioning zones; a temperature data detector to
detect temperature data including a room internal air temperature
and a room external air temperature; first and second target
blow-out temperature calculating portions including a fuzzy control
system, that receive the set setpoint temperatures and the
temperature data, and calculate first and second target blow-out
temperatures relative to each air-conditioning zones via the neural
network; and first and second temperature adjusters arranged in the
first and second air passages, respectively, to separately adjust
the temperatures of conditioned air blown out from the first and
second air passages to the first and second target blow-out
temperatures.
5. An air-conditioning device, comprising: an air passage in which
air flows; a blower disposed in the air passage; a heat exchanger
disposed in the air passage, for exchanging heat with air flowing
through the air passage; a face blow-out port arranged at a
downstream side of the air passage, for blowing air in a FACE mode;
a foot blow-out port arranged at a downstream side of the air
passage, for blowing air in a FOOT mode; a temperature setter to
set a room setpoint temperature; a temperature data detector to
detect temperature data including a room internal air temperature
and a room external air temperature; a radiation amount detector to
detect a radiation amount to the room; and an air amount
calculating portion including a neural network, for calculating an
air amount blown by the blower, wherein the air amount calculating
portion receives input signals including the room setpoint
temperature, the room internal air temperature, the room external
air temperature and the radiation amount, independently calculates
an air amount in the FACE mode and an air amount in the FOOT mode
via the neural network, selects one of the air amount in the FACE
mode and the FOOT mode, and outputs a final air amount based on a
mode signal input thereto.
6. An air-conditioning device according to claim 5, wherein both of
the face blow-out port and the foot blow-out port output air in a
B/L mode, and a control characteristic of the air amount at the B/L
mode is equal to that of the FACE mode.
7. An air-conditioning device according to claim 5, wherein the
neural network inputs a previously calculated temperature
difference between the room internal air temperature and the room
setpoint temperature.
8. An air-conditioning device according to claim 5, wherein the
foot blow-out port outputs air to the foot of a passenger, and the
face blow-out port outputs air to an upper body of the
passenger.
9. An air-conditioning device according to claim 5, wherein: the
air passage includes a driver seat side air passage for a driver
seat side air-conditioning zone and a passenger seat side air
passage for a passenger seat side air-conditioning zone, a driver
seat side temperature adjuster and a passenger seat side
temperature adjuster are arranged in the driver seat side air
passage and the passenger seat side air passage, respectively, the
face blow-out port and the foot blow-out port are arranged in both
of the driver seat side air passage and the passenger seat side air
passage, respectively, and temperatures of the first and the second
air-conditioning zones are controlled independently by air blown
from both the driver seat side air passage and the passenger seat
side air passage.
10. An air-conditioning device, comprising: an air passage in which
air flows; a blower disposed in the air passage; a heat exchanger
disposed in the air passage, for exchanging heat with air; a face
blow-out port disposed at a downstream side of the air passage, for
blowing air in a FACE mode; a foot blow-out port disposed at a
downstream side of the air passage, for blowing air in a FOOT mode;
a temperature setter to set a room setpoint temperature; a
temperature data detector to detect temperature data including a
room internal air temperature and a room external air temperature;
a radiation amount detector to detect a radiation amount to the
room; and an air amount calculating portion for calculating an air
amount blown from the blower, wherein the air amount calculating
portion receives input signals including the room setpoint
temperatures, the room internal air temperature, the room external
air temperature and the radiation amount, reduces the air amount
blown by the blower in proportion to lowering of the room internal
air temperature, wherein: when an air-conditioning load based on
the room external air temperature and the radiation amount is
large, a decreasing point, at which the amount of air blown by the
blower starts to decrease from a maximum area to the small amount
area, is set to a high temperature side with respect to the room
internal air temperature, and when an air-conditioning load based
on the room external air temperature and the radiation amount is
small, the decreasing point is set to a low temperature side with
respect to the room internal air temperature.
11. An air-conditioning device according to claim 10, wherein the
air amount calculating portion includes a neural network.
12. An air-conditioning device, comprising: an air passage in which
air flows; a first and second blow-out port disposed at a
downstream side in the air passage, for blowing conditioned air; a
temperature setter to set a room setpoint temperature; a
surrounding data detector to detects a surrounding physical
quantity data in the room; an air amount calculating portion
includes a neural network, for calculating an air amount blown to
the room, wherein the air amount calculating portion receives the
surrounding physical quantity data as input data, and independently
calculates an air amount to be supplied to the first and the second
air passages, via the neural network.
13. An air-conditioning device, comprising: an air passage in which
air flows; a blower disposed in the air passage; a heat exchanger
disposed in the air passage, to exchange heat with air; a plurality
of blow-out ports disposed at a downstream side in the air passage,
to blow air to different directions each other; a blow-out port
changer to control an-opening and a closing of each the blow-out
ports; a temperature adjuster to adjust temperatures of air blown
from the blow-out ports; a temperature setter to set a room
setpoint temperature; a temperature data detector to detects
temperature data including a room internal air temperature and a
room external air temperature; a target blow-out temperature
calculating that receive the room setpoint temperature and the
temperature data, and calculate a target blow-out temperature of
air blown to the room; a blow-out port mode calculating portion
including a neural network, that receive the target blow-out
temperature and a heat indication factor that influences a heating
condition as input data, and calculate a blow-out port mode signal
to control the blow-out port changer via the neural network.
14. An air-conditioning device according to claim 13, wherein the
heat indication factor includes a radiation amount in the room.
15. An air-conditioning device according to claim 13, wherein: the
heat exchanger includes a heating heat exchanger for heating air by
using hot water, and the heat indication factor includes the room
external air temperature and a hot water temperature.
16. An air-conditioning device according to claim 13, wherein the
heat indication factor includes the room internal air
temperature.
17. An air-conditioning device according to claim 13, wherein the
heat indication factor includes user skin temperature.
18. An air-conditioning device according to claim 13, wherein the
signal indicating surrounding factor includes the room external air
temperature.
19. An air-conditioning device for automobile, comprising: an air
passage in which air flows; a blower disposed in the air passage; a
heat exchanger disposed in the air passage, to exchange heat with
air; a foot blow-out port disposed at a downstream side in the air
passage, for blowing air in a FOOT mode; a face blow-out port
disposed at a downstream side in the air passage, for blowing air
in a FACE mode; a blow-out port changer to control an opening and a
closing of each the blow-out ports; a temperature adjuster to
adjust temperatures of air blown from the blow-out ports; a
temperature setter to set a room setpoint temperature; a
temperature data detector to detects temperature data including a
room internal air temperature and a room external air temperature;
a target blow-out temperature calculating portion that receives the
room setpoint temperature and the temperature data, and calculates
a target blow-out temperature of air blown to the room; and a
blow-out port mode calculating portion includes a neural network,
that receives the target blow-out temperature and a heat indication
factor that influences a heating condition, and calculates a
blow-out port mode signal to control the blow-out port changer via
the neural network, wherein the blow-out port mode signal includes
a FOOT mode signal for blowing air from the foot blow-out port, a
FACE mode signal for blowing air from the face blow-out port, and a
B/L mode signal for blowing air from both the foot blow-out port
and the face blow-out port.
20. An air-conditioning device, comprising: an air passage in which
air flows; a heat exchanger disposed in the air passage, to
exchange heat with air; a radiation correction coefficient
calculating portion including a neural network, that receives
signals including a room setpoint temperature, a room internal air
temperature and a room external air temperature, and to calculate a
radiation correction coefficient via the neural network; a
radiation amount correction calculating portion to calculate a
radiation amount correction based on a radiation correction
coefficient and a radiation amount; a target temperature
calculating portion to calculate a target blow-out temperature
based on the room setpoint temperature, the room internal air
temperature, the room external air temperature and the radiation
correction coefficient; and a temperature adjuster to adjust
temperature of air blown from the air passage, and being controlled
so that temperature of air blown from the air passage approaches
the target blow-out temperature.
21. An air-conditioning device, comprising: an air passage in which
air flows; a heat exchanger disposed in the air passage, to
exchange heat with air; a radiation amount correction calculating
portion includes a neural network, that receives signals including
a room setpoint temperature, a room internal air temperature, a
room external air temperature and an amount of solar radiation, and
calculates a radiation amount correction via the neural network; a
target temperature calculating portion to calculate a target
blow-out temperature based on the room setpoint temperature, the
room internal air temperature, the room external air temperature
and the radiation correction coefficient; and a temperature
adjuster to adjust temperature of air blown from the air passage,
and being controlled so that temperature of air blown from the air
passage approaches the target blow-out temperature.
22. An air-conditioning device, comprising: an air passage in which
air flows; a heat exchanger disposed in the air passage, to
exchange heat with air; a radiation correction coefficient
calculating portion including a neural network, that receives
signals including a room setpoint temperature, a room internal air
temperature and a room external air temperature, and calculates a
radiation correction coefficient via the neural network; a
radiation amount correction calculating portion to calculate a
radiation amount correction based on a radiation correction
coefficient and a radiation amount; a temporary target temperature
calculating portion to calculate a temporary target blow-out
temperature based on the room setpoint temperature, the room
internal air temperature, the room external air temperature and the
radiation correction coefficient; a target temperature calculating
portion to calculate a target blow-out temperature based on the
radiation amount correction and the temporary target blow-out
temperature; and a temperature adjuster to adjust temperature of
air blown from the air passage, and being controlled so that
temperature of air blown from the air passage approaches the target
blow-out temperature.
23. An air-conditioning device according to claim 20, wherein the
neural network inputs a previously calculated temperature
difference between the room internal air temperature and the room
setpoint temperature.
24. An air-conditioning device according to claim 20, wherein the
radiation correction coefficient calculating portion further inputs
the radiation amount.
25. An air-conditioning device according to claim 20, wherein the
radiation amount correction is calculated by multiplying a
predetermined proportional gain and the-radiation amount.
26. An air-conditioning device according to claim 20, wherein
further comprises: a foot blow-out port disposed in the air passage
to blow air to foot area of a user; and a face blow-out port
disposed in the air passage to blow air to face area of the user,
wherein the air-conditioning device selects one of a FOOT mode to
blow air from the foot blow-out port, a FACE mode to blow air from
the face blow-out port, and a B/L mode to blow air from both the
foot blow-out port and the face blow-out port, and wherein when the
room internal air temperature reaches the room setpoint temperature
at a heating in winter, the radiation correction coefficient is
increased as the room external air temperature falls.
27. An air-conditioning device according to claim 20, wherein: the
air passage includes a driver seat side air passage for a driver
seat side air-conditioning zone and a passenger seat side air
passage for a passenger seat side air-conditioning zone, a driver
seat side temperature adjuster and a passenger seat side
temperature adjuster are disposed in the driver seat side air
passage and the passenger seat side air passage, respectively, the
face blow-out port and the foot blow-out port are disposed in both
of the driver seat side air passage and the passenger seat side air
passage, respectively, and temperatures of each the first and the
second air-conditioning zones are controlled independently by air
blown from both of the driver seat side air passage and the
passenger seat side air passage.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
1. This application is based upon Japanese Patent Applications No.
Hei. 10-112435 filed Apr. 22, 1998, No. Hei. 10-115419 filed Apr.
24, 1998, No. Hei. 10-115420 filed Apr. 24, 1998, and No. Hei.
10-117416 filed Apr. 27, 1998, the contents of which are
incorporated herein by reference.
BACKGROUND OF THE INVENTION
2. 1. Field of the Invention
3. The present invention related to an air-conditioning device for
automatically controlling the temperature in a room such as a
passenger component of a vehicle or a room in a building.
4. 2. Description of Related Art
5. Some air-conditioning devices for controlling two or more
different air-conditioning zones independently of each other have
heretofore been proposed in an air-conditioning device for
automobile field. When the temperatures, which set in a driver seat
(Dr) side air-conditioning zone and in a passenger seat (Pa) side
air-conditioning zone, are controlled independently, since there is
no partition wall between two air-conditioning zones, temperature
interference between the two air-conditioning zones may occur.
6. As an air-conditioning device for automobiles, which is capable
of controlling independently between the two air-conditioning
zones, Japanese Laid-open Patent No. 7-32854 has proposed. In this
air-conditioning device, when a Dr side target blow-out temperature
and a Pa side target blow-out temperature are calculated, a
calculation term of difference between a Dr side setpoint
temperature and a Pa side setpoint temperature are corrected by
correction gain, which is decided based on an external temperature,
so as to realize desired temperatures in each of the Dr side and Pa
side air-conditioning zones.
7. This air-conditioning device aims at prevent practical
temperatures of each zones from deviating from predetermined
setpoint temperatures due to an influence of the external
temperature, by the collection described the above.
8. However, the temperature interference between the two
air-conditioning zones can not be conjectured only based on the
external temperature and the difference between the two setpoint
temperatures. Actually, the temperature interference is related to
an internal temperature, a blow-out temperature, and an amount of
air or the like at every timing. Therefore, the independent
temperature controlling can not be operated accurately by only the
correction described above.
9. FIGS. 12A, 12B are temperature characteristics of the
independent controlling, which are experimentally confirmed. FIG.
12A shows a characteristic of a temperature of area in which
surrounding passengers when the Pa side setpoint temperature is set
to constant and the Dr side setpoint temperature varies from
22.degree.C. to 28.degree.C. FIG. 12B shows a characteristic of
opposite relation.
10. As shown in FIG. 12A, during a varying the setpoint temperature
of Dr side, the temperature interference, which is a phenomenon,
that the temperature of area surrounding passengers in Pa side is
dragged by temperature changes of Dr side. Hence, a controllability
of temperature of both Dr side and Pa side has no inconvenient.
11. However, as shown in FIG. 12B, when the setpoint temperature of
the Pa side is varied, the temperature of area surrounding
passengers in Pa side is decreased slightly with respect to normal
temperature increasing due to an influence on Dr side.
Specifically, when the Pa side setpoint temperature
(Tset(Pa))=28.degree.C., the temperature of area surrounding
passengers in Pa side reaches only around 25.5.degree.C.
12. In the Japanese Laid-open Patent No. 7-32854, for the purpose
of offsetting the temperature interference, a correction value is
calculated by multiplying the difference between two setpoint
temperatures, and is added to the target blow-out temperature.
FIGS. 13A, 13B are temperature characteristics when this correction
is adopted. As shown in FIG. 13B, when the Pa side setpoint
temperature varies, the control characteristic at the condition
where Tset(Pa)=28.degree.C. is improved, however, the correction
term influences to other condition on the contrary. This is because
the correction term depends on the difference between two setpoint
temperatures.
13. In other words, from the temperature difference point of view,
both the condition of which the characteristic should be improved
(Dr side setpoint temperature Tset(Dr)=25.degree.C., Pa side
setpoint temperature Tset(Pa)=28.degree.C.) and the condition of
which the characteristic should be maintained
(Tset(Dr)=25.degree.C., Tset(Dr)=22.degree.C.) are the identical
(each of them is 3.degree.C.). Therefore, the correction is adapted
to other condition.
14. Then, an inconvenient that the temperature of area surrounding
passengers in Pa side is decreased below 22.degree.C., as shown in
FIG. 13B, in the condition of which the characteristic should be
maintained (Tset(Dr)=25.degree.C., Tset(Dr)=22.degree.C.) may
occur.
15. Similarly, as shown in FIG. 13A, an inconvenient that the
temperature of area surrounding passengers in Pa side is deviated
from 25.degree.C. due to temperature varying in Dr side may
occur.
16. FIG. 14A shows a characteristic of a Dr side correction gain
KDr according to the related art described the above. When the
external temperature rises from T1 to T2, KDr decreases from K1 to
K2. A Pa side correction gain Kpa has a similar characteristic. If
the Pa side correction gain Kpa is changed from K3 to K4 at
external temperature =10.degree.C., since the relation Kpa=K4 is
adopted to other condition during external temperature
=10.degree.C., the inconvenient shown in FIGS. 13A, 13B may
occur.
17. Therefore, in order to cancel the inconvenient, it is necessary
to change the Pa side correction gain Kpa to K4 in only a
particular condition, and to maintain the Pa side correction gain
with K3 without change in the other conditions.
18. In other words, a control logic, which can change the
correction gain in only the particular condition, is needed.
However, environment conditions, of which the air-conditioning
device for automobiles faces, include a wide variety of parameters
such as the external temperature, an amount of solar radiation
(hereinafter, radiation amount), a speed of the automobile and the
like. Therefore, it is extremely difficult to investigate a
relationship of factors at which these environment conditions
influence to the temperature control characteristic one by one, to
quantify the influence of the relationship, and to decide the
blow-out temperature control logic corresponds to the influence,
because it needs huge processes.
19. On the other hand, an another conventional automatically
control air-conditioning device for vehicles, as shown in Japanese
Laid-open Patent No. 6-195323, calculates an air amount by using a
neural network based on an internal air temperature and an external
air temperature of the vehicle, a setpoint temperature, and a
radiation amount.
20. In this kind of air-conditioning device, during a normal
operation after the internal air temperature reaches around the
setpoint temperature, when a blow-out port mode is either in a FACE
mode or in a BI-LEVEL (B/L) mode, the air amount is increased in
proportion to the radiation amount so as to increase an cooled air
feeling (felt by a driver or a passenger), during high solar
radiation.
21. Here, when the blow-out port mode is in a FOOT mode, since the
temperature in a passenger component rises due to the radiation, an
increase of amount of conditioned air (hereinafter, air amount)
does not needed. Therefore, the air amount is not increased in
proportion to the radiation amount.
22. According to the above-mentioned conventional device, when the
air amount is changed in proportion to radiation during a normal
operation, the following inconvenience may occur.
23. The number of output of the air amount, which is calculated by
the neural network, is only one independent of the blow-out port
mode. Therefore, when the blow-out port mode is switched among the
FACE mode, the B/L mode and the FOOT mode, the air amount needs to
be changed step by step during high radiation.
24. This inconvenience will be explained in detail with reference
to FIG. 25. In FIG. 25, the ordinate represents a blower voltage
which determines the air amount to the passenger component, the
abscissa represents a difference TD (=Tr-Tset) between the internal
air temperature Tr in the passenger component and the setpoint
temperature Tset. This difference TD is zero around center of an
area A on the abscissa, and is in a plus at the right side on the
abscissa and in a minus at the left side on the abscissa.
25. In the FOOT mode, when the difference TD is in around zero in
the normal operation area A, the blower voltage is set to the
minimum voltage E.sub.2 independent of radiation. On the other
hand, in the FACE mode or the B/L mode, the blower voltage is
increased from E.sub.2to E.sub.4in proportion to radiation. Since
this changes (increasing), which is an amount of changes .DELTA.E
of the blower voltage due to mode switching, does not have
continuously (step by step changes), a learning of the neural
network becomes difficult.
26. Further the conventional device inputs data such as the
internal air temperature, the external temperature, the setpoint
temperature and the radiation amount to the neural network. The
total number of data is desired to reduce so as to reduce the
number of intermediate layers and neurons in the neural network,
and to reduce the total calculation time of the neural network
enough to converge the learning of link coefficients between the
neurons.
27. A further another conventional automatically control
air-conditioning device for vehicles, as shown in Japanese
Laid-open Patent No. 56-86815, calculates an target blow-out
temperature TAO, which is used for maintaining a temperature on a
passenger component. Then it controls a temperature adjuster (e.g.,
air-mixing door or hot water valve) so that a temperature of air
blown to the passenger component approaches the target blow-out
temperature TAO. The target blow-out temperature TAO is calculated
as follows:
TAO=Kset.times.Tset-Kr.times.Tr-Kam.times.Tam-Ks.times.Ts+C
28. Here, Tr is an internal air temperature, Tam is an external air
temperature, Ts is a radiation amount to the passenger component,
Kset is a temperature set gain, Kr is an internal air temperature
gain, Kam is an external air temperature gain, Ks is a radiation
amount gain, and C is an correction constant value.
29. One of a Face mode for blowing air to a face area of the
passenger, a FOOT mode for blowing air to a foot area of the
passenger, and bi-level (B/L) mode for blowing air to both the face
area and the foot area of the passenger, is selected based on the
target blow-out temperature TAO.
30. FIGS. 46A-46C show blow-out port control based of the TAO. The
blow-out port mode is changed to the FACE mode.fwdarw.the B/L mode
.fwdarw.the FOOT mode, in proportion to a rising of TAO.
31. According to this conventional device, if heat load conditions
for the vehicle are same, the TAO will be same value. Therefore, in
this case, the blow-out port mode will be set to be same mode.
However, a heat feeling of the passenger due to surrounding
condition is different from the heat load condition. Hence, a
uniform switching of the blow-out port mode based on the TAO may
make an air-conditioning feeling worse.
32. These air-conditioning feeling will be explained with reference
to FIGS. 46A-46C. FIG. 46A shows a condition of the external air
temperature Tam is 10.degree.C. (rather warm), and cloudiness (less
solar radiation). FIG. 46B shows a condition of the external air
temperature Tam is 0.degree.C. (rather cold), and fairy (much solar
radiation). In these two conditions, both of the TAO will be the
same value "a", therefore, the FOOT mode is selected uniformly.
33. However, in the case of FIG. 46B, the passenger will feel hot
due to the radiation to the upper body, even if the external air
temperature is rather low, and will want more cooled air to the
upper body. That is, in this case, it is desired to select the B/L
mode to improve the air-conditioning feeling. Therefore, the
conventional device could not control the blow-out port mode in
view of the radiation.
34. Furthermore, the external air temperature and a temperature of
hot water to a heat exchanger also influence to the
air-conditioning feeling. However, the conventional device also
could not control the blow-out port mode in view of these
factors.
35. In order to resolve the above-mentioned inconvenience, it can
be thought the following structure as shown in FIGS. 47A, 47B. That
is, two maps including a no radiation map (FIG. 47A) and a
radiation map (FIG. 47B) are provided as a characteristic switching
map between the blow-out port mode and the TAO. When it is in the
radiated condition, as shown in FIG. 47B, a switching point of the
B/L mode will be changed to a high temperature side. Similarly, the
same method can be adopted for the external temperature, and the
hot water temperature.
36. However, the structure in FIGS. 47A, 47B may increase memory
portion (ROM) of an air-conditioning electrical control unit,
because it needs additional maps.
37. Further, environment conditions, of which the air-conditioning
device for automobiles faces, include a wide variety of parameters
such as the external temperature, an amount of solar radiation, a
speed of the automobile and the like. Therefore, it is extremely
difficult to investigate a relationship of factor at which these
environment conditions influence to the temperature control
characteristic one by one, to quantify the influence of the
relationship, and to decide the blow-out temperature control logic
corresponds to the influence, because it needs huge processes.
38. Then, another inconvenience of Japanese Laid-open Patent No.
56-86815 will be explained. In this conventional device, the amount
radiation Ts is included as a calculation term in the equation of
the target blow-out temperature TAO. Therefore, even if it is at
the timing just after the air-conditioning device starts in winter
(warm-up), TAO is calculated as low temperature. Then, a warm-up
time may be long time. The warm-up time corresponds a period
between the temperature adjuster is adjusted from maximum heating
position to temperature region and a room temperature rises to the
setpoint temperature.
39. In order to solve the above-mentioned inconvenience, Japanese
Laid-open Patent No. 4-163223 is proposed. In this device, when a
temperature difference (Tr-Tset) between the internal air
temperature Tr of the passengers component and the setpoint
temperature Tset is minus, the radiation amount Ts as the
calculation term (amount on radiation correction) is decreased in
proportion to an increasing of the absolute value of the
temperature difference.
40. According to an investigation, it is found the following facts.
That is, when the radiation amount correction is decided only based
on the temperature difference (Tr-Tset), it may be impossible to
calculate the radiation amount correction for various of
surrounding conditions. That is, even if the temperature difference
is equal, the TAO is desired to be high temperature by decreasing
the radiation amount correction when the external air temperature
is extremely low like in winter, so as to shorten the warm-up
time.
41. Similarly, even if the temperature difference is equal, the TAO
is desired to be low temperature by decreasing the radiation amount
correction when there is little solar radiation, so as to shorten
the warm-up time.
42. In winter, since an angle of the sun is rather small, the solar
radiation is likely to be radiated to upper body of the passenger.
In such a case, when the internal air temperature reaches the
setpoint temperature (Tr-Tset.apprxeq.0) as the result of heating,
an operation of the will be normal operation. Then, the passenger
may feel hot due to the radiation. Therefore, it is desired to set
TAO low temperature by increasing the radiation amount correction
during much radiation in the normal operation so as to set the
blow-out port mode to B/L mode to blow cooled air from a face
blow-out port.
SUMMARY OF THE INVENTION
43. The present invention was accomplished in view of the
above-mentioned circumstances. First object is to provide an
air-conditioning device for controlling the air-conditioning
temperatures in a first air-conditioning zone and in a second
air-conditioning zone highly independently of each other by
restricting temperature interference between the first
air-conditioning zone and in the second air-conditioning zone.
44. A neural network, which is one of information process system,
has a characteristic to correct its output to be desired data
(teacher signal) automatically, by adjusting link coefficients
(synapse weights) between each neurons in each layers in the neural
network automatically (i.e., learning function). The present
inventions aim at correcting the target blow-out temperature only
at a specific condition without increasing an engineer's process,
by using the automatic adjusting function of link coefficients
between the neurons in the neural network.
45. Furthermore, second object is to provide an air-conditioning
device, which calculate an air amount by using a neural network, of
which learning can be simplified.
46. Also, third object is to provide an air-conditioning device,
which calculate an air amount by using a neural network, of which
total calculation time can be decreased.
47. Furthermore, fourth object is to provide an air-conditioning
device, which can control a blow-out port mode finely in accordance
to air-conditioning feeling of user.
48. Furthermore, fifth object is to provide an air-conditioning
device, which can calculate a radiation amount correction
accurately to improve an air-conditioning feeling of user.
49. In order to accomplish one of the above-mentioned object, the
present invention provides an air-conditioning device includes a
first and a second temperature adjusters, and a first and a second
target blow-out temperature calculating portions for input setpoint
temperatures (Tset(Dr), Tset(Pa)) of a first and a second
air-conditioning zones, an internal air temperature (Tr) detected
by a temperature data detector, and an external air temperature
(Tam) detected by the temperature data detector, to calculate a
first and a second target blow-out temperatures (TAO(Dr), TAO(Pa))
of each air-conditioning zones by using a neural network. Here, the
first and the second temperature adjusters are controlled so that
the blow-out temperatures of air-conditioned air from each air
passages relative to the first and the second air-conditioning
zones can correspond to the first and the second target blow-out
temperatures (TAO(Dr), TAO(Pa)).
50. According to the present invention, the first and the second
target blow-out temperatures (TAO(Dr), TAO(Pa)) related to each
air-conditioning zones are calculated via the neural network. The
neural network has the learning function, which adjusts the link
coefficients (synapse weights) between each neurons in each layers
in the neural network automatically to correct its output to be
desired data (teacher signal). Therefore, the output at a specific
input condition can be adjusted, by changing the teacher signal at
the specific input condition and then adjusting the link
coefficients (synapse weights) automatically in advance.
51. Furthermore, since the neural network adjusts its whole link
coefficients so that the desired outputs (teacher signal) at the
other input condition are maintained even if the output at the
specific input condition is changed. Therefore, the change of the
output at the specific input condition does not influence the
outputs at the other input conditions.
52. Hence, when the setpoint temperatures of the first and the
second air-conditioning zones are changed, both temperatures of the
area surrounding passengers in the first and the second
air-conditioning zones are highly independent controlled with
accurately, without the temperature interference between each
zones.
BRIEF DESCRIPTION OF THE DRAWINGS
53. FIG. 1 is a diagram illustrating the constitution of whole
system of an air-conditioning device according to a first
embodiment of the present invention;
54. FIG. 2 is a block diagram of main functions of the first
embodiment;
55. FIG. 3 is a schematic diagram of a neural network of temporary
target temperature calculating portion of the first embodiment;
56. FIGS. 4A, 4B are diagram and graph for explaining a output
calculating procedure of neural network;
57. FIG. 5 is a schematic diagram of a neural network of radiation
amount correction calculating portions of the first embodiment;
58. FIGS. 6A-6C are diagram of characteristics illustrating the
radiation amount correction calculating portions;
59. FIG. 7 is a schematic diagram of a neural network of blow-out
port mode calculating portion of the first embodiment;
60. FIG. 8 is a schematic diagram of a neural network of air amount
calculating portion of the first embodiment;
61. FIG. 9 is a flow chart illustrating a control flow according to
the first embodiment;
62. FIG. 10 is a diagram of characteristics illustrating a
calculation of the blow-out port mode according to the first
embodiment;
63. FIGS. 11A, 11B are diagrams of control characteristics of
temperatures of area surrounding passengers in Dr side and Pa side
according to the first embodiment;
64. FIG. 11C is a diagram illustrating a relationship between input
signals and output signal of neural network;
65. FIGS. 12A, 12B are diagrams of control characteristics of
temperatures of area surrounding passengers in Dr side and Pa
side;
66. FIGS. 13A, 13B are diagrams of control characteristics of
temperatures of area surrounding passengers in Dr side and Pa side
according to related art;
67. FIGS. 14A, 14B are diagrams illustrating a setting procedure of
correction gain of a target blow-out temperature according to the
related art;
68. FIGS. 15A, 15B are diagrams illustrating a setting procedure of
correction gain according to the related art;
69. FIG. 16 is a diagram illustrating the constitution of whole
system of an air-conditioning device according to a second
embodiment of the present invention;
70. FIG. 17 is a block diagram of main functions of the second
embodiment;
71. FIG. 18 is a schematic diagram of a neural network of air
amount calculating portion of the second embodiment;
72. FIGS. 19A, 19B are diagram and graph for explaining a output
calculating procedure of neural network;
73. FIG. 20 is a schematic diagram of a neural network of temporary
target temperature calculating portion of the second
embodiment;
74. FIG. 21 is a schematic diagram of a neural network of radiation
amount correction calculating portion of the second embodiment;
75. FIG. 22 is a schematic diagram of a neural network of blow-out
port mode calculating portion of the second embodiment;
76. FIG. 23 is a flow chart illustrating a control flow according
to the second embodiment;
77. FIG. 24 is a diagram of characteristics illustrating a
calculation of the blow-out port mode according to the second
embodiment;
78. FIG. 25 is a diagram of characteristic illustrating a
relationship between a switching of blow-out port mode and a blower
voltage (air amount);
79. FIGS. 26-29 are diagrams of characteristic illustrating
relationships between the blower voltage (air amount) and a
temperature difference TD just after starting the air-conditioning
in summer;
80. FIG. 30 is a diagram illustrating the constitution of whole
system of an air-conditioning device according to a third
embodiment of the present invention;
81. FIG. 31 is a block diagram of main functions of the third
embodiment;
82. FIG. 32 is a schematic diagram of a neural network of air
amount calculating portion of the third embodiment;
83. FIG. 33 is a schematic diagram of a neural network of temporary
target temperature calculating portion of the third embodiment;
84. FIG. 34 is a schematic diagram of a neural network of radiation
amount correction calculating portion of the third embodiment;
85. FIG. 35 is a schematic diagram of a neural network of blow-out
port mode calculating portion of the third embodiment;
86. FIG. 36 is a schematic diagram of an another neural network of
blow-out port mode calculating portion of the third embodiment;
87. FIG. 37 is a diagram illustrating the constitution of whole
system of an air-conditioning device according to a fourth
embodiment of the present invention;
88. FIG. 38 is a block diagram of main functions of the fourth
embodiment;
89. FIG. 39 is a schematic diagram of a neural network of blow-out
port mode calculating portion of the fourth embodiment;
90. FIGS. 40A, 40B are diagram and graph for explaining a output
calculating procedure of neural network;
91. FIG. 41 is a schematic diagram of a neural network of temporary
target temperature calculating portion of the fourth
embodiment;
92. FIG. 42 is a schematic diagram of a neural network of radiation
amount correction calculating portion of the fourth embodiment;
93. FIG. 43 is a schematic diagram of a neural network of air
amount calculating portion of the fourth embodiment;
94. FIG. 44 is a flow chart illustrating a control flow according
to the fourth embodiment;
95. FIG. 45 is a diagram of characteristics illustrating a
calculation of the blow-out port mode according to the fourth
embodiment;
96. FIGS. 46A-46C and 47A-47B are diagrams illustrating a switching
characteristic of a blow-out port mode according to a related
art;
97. FIG. 47C is a diagram illustrating a switching characteristic
of a blow-out port mode according to fourth embodiment;
98. FIG. 48 is a diagram of characteristic illustrating a
relationship between input conditions and a blow-out port mode
signal output;
99. FIG. 49 is a diagram of characteristic illustrating a
relationship between a comfort in a B/L mode and an external air
temperature and a temperature of hot water;
100. FIG. 50 is a diagram of characteristic illustrating a
relationship between input conditions and a blow-out port mode
signal output;
101. FIG. 51 is a schematic diagram of a neural network of blow-out
port mode calculating portion of a fifth embodiment;
102. FIG. 52 is a schematic diagram of a neural network of blow-out
port mode calculating portion of a sixth embodiment;
103. FIG. 53 is a diagram of characteristic illustrating a
relationship between input a blow-out port mode and a target
blow-out temperature according to a seventh embodiment;
104. FIG. 54 is a diagram illustrating the constitution of whole
system of an air-conditioning device according to a eighth
embodiment of the present invention;
105. FIG. 55 is a block diagram of main functions of the eighth
embodiment;
106. FIG. 56 is a schematic diagram of a neural network of blow-out
port mode calculating portion of the eighth embodiment;
107. FIG. 57 is a diagram illustrating the constitution of whole
system of an air-conditioning device according to a ninth
embodiment of the present invention;
108. FIG. 58 is a schematic diagram of a neural network of a
radiation correction coefficient calculating portion of the ninth
embodiment;
109. FIGS. 59A, 59B are diagram and graph for explaining a output
calculating procedure of neural network;
110. FIG. 60 is a flow chart illustrating a control flow according
to the ninth embodiment;
111. FIG. 61 is a diagram of characteristics illustrating a
calculation of a blower voltage according to the ninth
embodiment;
112. FIG. 62 is a diagram of characteristics illustrating a
calculation of a suction port mode according to the ninth
embodiment;
113. FIG. 63 is a diagram of characteristics illustrating a
calculation of a blow-out port mode according to the ninth
embodiment;
114. FIG. 64 is a diagram of characteristics illustrating a
calculation of a radiation correction coefficient according to the
ninth embodiment;
115. FIGS. 65A, 65B are diagrams of characteristics illustrating
calculations of a radiation correction coefficient according to the
tenth embodiment;
116. FIG. 66 is a diagram of characteristics illustrating a
calculation of a radiation correction coefficient according to the
eleventh embodiment;
117. FIG. 67 is a schematic diagram of a neural network of a
radiation correction coefficient calculating portion of the
eleventh embodiment;
118. FIG. 68 is a diagram of characteristic illustrating a
relationship between input conditions and a radiation correction
coefficient output of the eleventh embodiment;
119. FIG. 69 is a schematic diagram of a neural network of a
radiation correction coefficient calculating portion of the twelfth
embodiment;
120. FIG. 70 is a diagram illustrating the constitution of whole
system of an air-conditioning device according to a thirteenth
embodiment of the present invention;
121. FIG. 71 is a schematic diagram of a neural network of a
radiation correction coefficient calculating portion of the
thirteenth embodiment.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
122. (First Embodiment)
123. A first embodiment of the present invention will be described
hereinafter with reference to FIGS. 1-11B. FIG. 1 is a diagram
illustrating the constitution of whole system of an
air-conditioning device for automobiles (vehicles), which is
capable of controlling independently between a driver seat (Dr)
side air-conditioning zone and a passenger seat (Pa) side
air-conditioning zone in a passenger component. In FIG. 1, an
internal air 1 external air change-over door 1001 is arranged at
the most upstream side of air flow in the air-conditioning device.
One of an external air and an internal air is selectively
introduced into an air duct 1002 by moving the door 1001.
124. A blower 1003, an evaporator 1004 and a heater core 1005 are
arranged from upstream side to downstream side in the air duct
1002. The evaporator 1004 is a cooling heat exchanger for cooling
an air by absorbing an evaporating latent heat of a refrigerant in
a refrigerating cycle from air. The heater core 1005 is a heating
heat exchanger for heating air with heat from a hot water (engine
coolant) from a vehicle engine (not shown).
125. A partitioning wall 1006 is arranged from the heater core 1005
portion to its downstream in the air duct 1002 to divide the air
duct 1002 into a Dr side air passage 1007 and a Pa side air passage
1008.
126. A Dr side air-mixing door 1009 is arranged at the upstream of
the heater core 1005 in the Dr side air passage 1007. The Dr side
air-mixing door 1009 adjusts a ratio of two air amount in the Dr
side air passage 1007, wherein an amount of heated air passed
through the heater core 1005 and an amount of cooled air by-passed
the heater core 1005. A Pa side air-mixing door 1010 is arranged at
the upstream of the heater core 1005 in the Pa side air passage
1008, and adjusts a ratio of two air amount in the Pa side air
passage 1008. Here, the two air amount are an amount of heated air
passed through the heater core 1005 and an amount of cooled air
by-passed the heater core 1005.
127. At the most downstream side of the Dr side air passage 1007
and the Pa side air passage 1008, foot blow-out ports 1007a, 1008a,
face blow-out ports 1007b, 1007c, 1008b, 1008c, and a defroster
blow-out port 11 are provided. Here, the foot blow-out ports 1007a,
1008a are provided to blow the conditioned air onto the feet of the
passengers. The face blow-out ports 1007b, 1007c, 1008b, 1008c are
provided at each of center portion and side portion in the
passenger component to blow the conditioned air to the upper half
of the body of the passengers. The defroster blow-out port 1011 is
provided to blow the conditioned air to a windshield.
128. In the Dr side air passage 1007 and the Pa side air passage
1008, blow-out port change-over doors 1012-1016 are arranged to
selectively open/close the blow-out ports 1007a-1007c, 1008a 1008c
and 1011. Each of predetermined blow-out modes, such as a FACE mode
(FACE), a BI-LEVEL mode (B/L mode), and a FOOT mode (FOOT) or the
like can be set with respective to each ports 1007, 1008,
independently, by changing the open/close condition of the doors
1012-1016.
129. Here, a control system for controlling the air-conditioning
device will be explained. The internal air/external air changing
door 1001, the Dr side air-mixing door 1009, the Pa side air-mixing
door 1010, and the blow-out port change-over doors are driven by
servomotors 1017-1022. The servomotors 1017-1022 are controlled by
outputs of an air-conditioning electric control device 1023
(hereinafter, called "ECU 1023"). A motor 1003a of the blower 1003
is also controlled by the output of the ECU 1023 via a control
circuit (motor applied voltage control circuit) 1024. The ECU 1023
includes a microcomputer and its peripheral circuits.
130. A Dr side temperature setter (first temperature setter) 1025
is provided to set a Dr side setpoint temperature Tset(Dr) of the
Dr side air-conditioning zone relative to the Dr side air passage
1007, and output the setpoint temperature Tset(Dr) to the ECU 1023.
A Pa side temperature setter (second temperature setter) 1026 is
provided to set a Pa side setpoint temperature Tset(Pa) of the Pa
side air-conditioning zone relative to the Pa side air passage
1008, and output the setpoint temperature Tset(Pa) to the ECU 1023.
Both temperature setters 1025, 1026 are provided independently each
other.
131. As temperature data detectors, an internal air sensor 1027 is
arranged to detect an internal air temperature Tr. An external air
sensor 1028 is arranged to detect an external air temperature Tam.
An evaporator temperature sensor 1029 is arranged to detect a
cooling temperature (blow-out air temperature) of the evaporator
1004. A water temperature sensor 1030 is arranged to detect a
temperature Tw of hot water flowing into the heater core 1005.
132. Furthermore, a Dr side radiation sensor 1031 is arranged to
detect a (solar) radiation amount TsDr to the Dr side
air-conditioning zone, and a Pa side radiation sensor 1032 is
arranged to detect a (solar) radiation amount TsPa to the Pa side
air-conditioning zone.
133. Control functions processed by the microcomputer in the ECU
1023 are generally divided as shown in FIG. 2. The ECU 1023
includes first and second temporary target temperature calculating
portions 1033, 1034, first and second radiation amount correction
calculating portions 1035, 1036, first and second blow-out port
mode calculating portions 1037, 1038, first and second air amount
calculating portion 1039, 1040, first and second target temperature
calculating portions 1041, 1042, an air amount calculating portion
1043, a Dr side air-mixing door opening degree calculating portion
1044, a Pa side air-mixing door opening degree calculating portion
1045.
134. Here, the first temporary target temperature calculating
portion 1033 calculates a Dr side temporary target blow-out
temperature TAOB(Dr). The second temporary target temperature
calculating portion 1034 calculates a Pa side temporary target
blow-out temperature TAOB(Pa).
135. The first temporary target temperature calculating portion
1033 includes a neural network 1100 as shown FIG. 3, and inputs the
signals including the internal air temperature Tr, the external air
temperature Tam, setpoint temperature Tset(Dr) from the Dr side
temperature setter 1025 and a setpoint temperature difference A
TsetDr, which is a difference of the both setpoint temperatures
Tset(Dr) and Tset(Pa). And the first temporary target temperature
calculating portion 1033 calculates the Dr side temporary target
blow-out temperature TAOB(Dr) based on the input signals via the
neural network 1100.
136. Similarly, the second temporary target temperature calculating
portion 1034 includes a neural network 1100 as shown FIG. 3, inputs
the signals including the internal air temperature Tr, the external
air temperature Tam, setpoint temperature Tset(Pa) from the Pa side
temperature setter 1026, and a setpoint temperature difference
.DELTA.TsetPa, which is a difference of the both setpoint
temperatures Tset(Dr) and Tset(Pa). The second temporary target
temperature calculating portion 1034 calculates the Pa side
temporary target blow-out temperature TAOB(Pa) based on the input
signals via the neural network 1100.
137. The first radiation amount correction calculating portion 1035
calculates a Dr side radiation amount correction TAOS(Dr). The
second radiation amount correction calculating portion 1036
calculates a Pa side radiation amount correction TAOS(Pa).
138. The first blow-out port mode calculating portion 1037
calculates a Dr side blow-out port mode signal TMODE(Dr). The
second blow-out port mode calculating portion 1038, calculates a Pa
side blow-out port mode signal TMODE(Pa).
139. Furthermore, the first air amount calculating portion 1039
calculates an blower voltage level TBLO(Dr) which decides a Dr side
air amount. The second air amount calculating portion 1040
calculates an blower voltage level TBLO(Pa) which decides a Pa side
air amount.
140. The first and the second radiation amount correction
calculating portions 1035, 1036 include a neural network 1200 as
shown in FIG. 5. The first and the second blow-out port mode
calculating portions 1037, 1038 include a neural network 1300 as
shown in FIG. 7. The first and the second air amount calculating
portions 1039, 1040 include a neural network 1400 as shown in FIG.
8.
141. The first target temperature calculating portion 1041
calculates a Dr side final target blow-out temperature TAO (Dr)
based on the output of the first temporary target temperature
calculating portion 1033 and the output of the first radiation
amount correction calculating portion 1035. The second target
temperature calculating portion 1042 is provided to calculate a Pa
side final target blow-out temperature TAO(Pa) based on the output
of the second temporary target temperature calculating portion 1034
and the output of the second radiation amount correction
calculating portion 1036.
142. The air amount calculating portion 1043 calculates blower
voltage level TBLO corresponding to the final amount on air, based
on an average of the blower voltage levels TBLO(Dr), TBLO(Pa),
which are calculated by the first and second air amount calculating
portions 1039, 1040.
143. The Dr side air-mixing door opening degree calculating portion
1044 calculates a Dr side air-mixing door opening degree SW(Dr)
based on the TAO(Dr) output from the first target temperature
calculating portion 1041, and a Pa side air-mixing door opening
degree calculating portion 1045 to calculate a Pa side air-mixing
door opening degree SW(Pa) based on the TAO(Pa) output from the
second target temperature calculating portion 1042.
144. The constitutions of the neural networks 1100-1400 will be
explained. Since the constitutions of the neural networks 1100-1400
are substantially equal, these constitutions are explained based on
the neural network 1100 with reference to FIGS. 3, 4A, 4B.
145. As shown in FIG. 3, the neural network 1100 includes an input
layer 1101, a first intermediate layer 1102, a second intermediate
layer 1103, and output layer 1104. Each of the input layer 1101,
the first and the second intermediate layers 1102, 1103 has a
plurality of neurons 1105, and output layer has one neuron 1105.
Each of neurons 1105 in the input layer 1101 is connected to each
of neurons 1105 in the first intermediate layer 1102 with a certain
link coefficient (synapse weight) 1106. Similarly, each of neurons
1105 in the first intermediate layer 1102 is connected to each of
neurons 1105 in the second intermediate layer 1103 with a certain
link coefficient 1106. Each of neurons 1105 in the second
intermediate layer 1103 is connected to the neuron 1105 in the
output layer 1104 with a certain link coefficient 1106. These
neurons 1105 are formed by memories in the ECU 1023 in an actual
circuit, and the neural network 1100 is formed by combined
memories.
146. The neural network 1100, which is a network having a layered
structure, has a Back Propagation Learning Function that can
automatically correct the link coefficients 1106 between each
neurons among the input layer 1101, the first and second
intermediate layers 1102, 1103, and output layer, so as to adjust
its output to a desired value (teacher signal) related to input
signals such as Tseti, Tr, Tam, .DELTA.Tseti.
147. When the teacher signal is changed, the link coefficients 1106
are adjusted by repeatedly "learning" so as to correct the output
to be the changed teacher signal related to the input signals. In
other words, based on a large number of desired data (teacher
signals), a correlation function (link coefficients 1106) is
automatically generated. The teacher signals are determined to
desired values obtained by an experience or the like (desired
output for the input signals).
148. In the layered structure neural network 1100, there is no
connection among each of the neurons 1105 in an identical layer,
but the neurons 1105 in each layer are connected to only neurons
1105 in forward and backward layers. The link coefficients 1106
between each neuron 1105 in each layer represent weights of
connections (magnitude). The larger the weight of connection, the
larger an amplitude of signal, which is output from each neuron
1105 and propagated to neurons 1105 in backward layer.
149. Here, the input signals (Tseti, Tr, Tam, .DELTA.tseti) are
normalized to 0-1 value before inputting to the neural network 1100
by a normalize portion 1107. The output signal from the neural
network 1100 is transformed in reverse procedure of the normalize
portion 1107 by an output transform portion 1108. For example,
actual detected internal temperature Tr detected by the internal
air sensor is normally in a range from 0.degree.C. to 50.degree.C.
This detected value is assigned to a certain value in a range from
0 to 1 in the normalize portion 1107, and is output to the input
layer 1101 in the neural network 1100. Since the output signal from
the output layer 1104 is a value in the range from 0 to 1, this
output signal is transformed to the actual value corresponding to
the sensor signal in the reverse procedure by using a predetermined
transformation map in the output transform portion 1108.
150. The environment conditions, of which the air-conditioning
device for vehicle faces, are variable, as described above.
Therefore, the number of the teacher signals, which are desired
values related to the various environment conditions, will be
enormously.
151. Therefore, in a designing process of the ECU 1023, the
learning is executed by a high-speed calculator including a neural
network, which is the same as the neural network 1100, before being
mounted to the vehicle. The high-speed calculator calculates the
link coefficients 1106 so that the output of the neural network is
to be desired teacher signals for various input signals. Then, the
calculated coefficients 1106 are memorized in a memory portion
(ROM) corresponding to each neural network 1100, 1200, 1300, 1400
in each of the calculating portions of the ECU 1023 shown in FIG.
2.
152. Therefore, when the ECU 1023 is mounted on the vehicle, the
link coefficients 1106 among the neurons 1105 in each layer of the
neural networks 1100-1400 have set to predetermined value.
153. After being mounted on the vehicle, the neural networks
1100-1400 in each layer calculate an output for input signals as
shown in FIG. 4A. Specifically, in each of the neurons 1105, each
of the input signals O.sub.1-O.sub.n is multiplied by each of the
corresponding coefficients 1106 (W.sub.1-W.sub.n), and multiplied
values are adapted to a so-called sigmoid function as shown FIG.
4B. The calculated results of the sigmoid function are output to
the neurons 1105 in backward layer. These calculations are repeated
between each layer to decide a final output.
154. Since the neural network 1100 in each calculating portion of
mounted ECU 1023 is previously set so that the desired value for
the various input signals O.sub.1-O.sub.n can be obtained, learning
for correction of the link coefficients does not needed.
155. Therefore, the neural network 1100 of the first and second
temporary target temperature calculating portions 1033, 1034 output
the desired teacher signals as the temporary target blow-out
temperature TAOBi related to the change of the input signals
(Tseti, Tr, Tam, .DELTA.tseti). Here, a subscript "i" represents
either "Dr" or "Pa", and will be used hereinafter as the same
meaning.
156. The Dr side temporary target blow-out temperature TAOB(Dr)
calculated by the neural network 1100 of the first temporary target
temperature calculating portions 1033 is input to the first target
temperature calculating portion 1041 which calculates the Dr side
final target blow-out temperature TAO(Dr). The Pa side temporary
target blow-out temperature TAOB(Pa) calculated by the neural
network 1100 of the second temporary target temperature calculating
portions 1034 is input to the first target temperature calculating
portion 1042 which calculates the Pa side final target blow-out
temperature TAO(Pa).
157. The neural network 1200 of the first and second radiation
amount correction calculating portions 1035, 1036 output the
desired teacher signals as the radiation amount correction TAOSi
relate to the change of the input signals (Tseti, Tr, Tam,
.DELTA.tseti). Next, a calculation procedure of the radiation
amount correction TAOSi will be explained with reference to FIGS.
6A-6C. In FIGS. 6A-6C, the abscissa represents the internal air
temperature Tr.
158. FIG. 6A shows that the radiation amount correction TAOSi is
set to zero, independent of the setpoint temperature Tseti, the
external air temperature Tam, and the water temperature Tw, when
the radiation amount Tsi=0.
159. FIG. 6B shows a characteristic of the radiation amount
correction TAOSi when the radiation amount Tsi=1 kW/m.sup.2, the
setpoint temperature Tseti=25.degree.C., the external air
temperature Tam=<-10.degree. C. The radiation amount correction
TAOSi is risen from TAOSi=0 at the point the internal air
temperature Tr=T3, and is saturated with Tal at the point the
internal air temperature Tr=T4, independent of the water
temperature Tw,
160. Further, FIG. 6C shows the characteristic of the radiation
amount correction TAOSi when the radiation amount Tsi=1 kW/m.sup.2,
the setpoint temperature Tseti=25.degree.C., the external air
temperature Tam=>10.degree.C. The radiation amount correction
TAOSi is risen from TAOSi=0 at the point the internal air
temperature Tr=T1. Then, it is saturated with Tal at the point the
internal air temperature Tr=T2, independent of the water
temperature Tw, Here, the internal air temperature has a
relationship of T1<T2<T3<T4.
161. When the external air temperature Tam is within a range of
-10.degree.C. and 10.degree.C., the internal air temperature Tr at
the timing the radiation amount correction TAOSi rises is set to
intermediate temperature between T1 and T3, and the internal air
temperature Tr at the timing the radiation amount correction TAOSi
saturates is set to intermediate temperature between T2 and T4.
162. According to the characteristic of the radiation amount
correction TAOSi shown in FIGS. 6A-6C, when the internal air
temperature Tr is in a low temperature field (warm-up control field
when a heating is started in winter), the internal air temperature
is risen mainly by the radiation without the radiation correction.
On the other hand, when the internal air temperature Tr is in a
high temperature field which is higher that a given temperature,
the radiation correction is executed so that the variation on
internal air temperature control of the passenger component due to
the radiation can be restricted.
163. In the first and the second target temperature calculating
portions 1041, 1042, the final target blow-out temperatures TAOi
are calculated by the following equation:
TAOi=TAOBi-TAOS (1)
164. In the Dr side air-mixing door opening degree calculating
portion 1044 and the Pa side air-mixing door opening degree
calculating portion 1045, the Dr side air-mixing door opening
degree SW(Dr) and the Pa side air-mixing door opening degree SW(Pa)
are calculated based on the TAO(Dr) and TAO(Pa), which are output
from the first and the second target temperature calculating
portion 1041, 1042, the temperature Te of the evaporator 1004, and
the water temperature Tw of the heater core 1005, by using the
following equation:
Swi(%)=(TAOi-Te) / (Tw-Te).times.100 (2)
165. The neural networks 1300 as shown in FIG. 7, which constitutes
either of the first and the second blow-out port mode calculating
portions 1037, 1038, calculate and output the desired value as the
blow-out port mode signal TMODE(Dr) on the Dr side and the blow-out
port mode signal TMODE (Pa) on the Pa side, respectively, in
relation to the changes of input signals (TAOi, Tsi, Tam, Tw).
166. The neural networks 1400 as shown in FIG. 8, which constitutes
either of the first and the second air amount calculating portions
1039, 1040, calculate and output the desired value as the Dr side
blower voltage level TBLO(Dr), the Pa side blower voltage level
TBLO(Pa), respectively, in relation to the changes of input signals
(Tseti, Tr, Tam, Tsi).
167. The control flow according to the preferred embodiment will be
explained with reference to a flow chart shown in FIG. 9. This
control routine starts from a control switch signal, which starts
the air-conditioning device.
168. At step 1510, the air-conditioning ECU 1023 is reset. At step
1520, the ECU 1023 inputs the output signals from each sensors such
as the internal air temperature Tr, the external air temperature
Tam, and the signals from the Dr side temperature setter 1025 and
the Pa side temperature setter 1026.
169. At step 1530, the temporary target blow-out temperatures TAOBi
are calculated via the neural network 1100, shown in FIG. 3. The
step 1530 corresponds to the first and the second temporary target
temperature calculating portions 1033, 1034 in FIG. 2.
170. At step 1540, the amounts of radiation corrections TAOSi are
calculated via the neural network 1200, shown in FIG. 5. The step
1540 corresponds to the first and the second radiation amount
correction calculating portions 1035, 1036 in FIG. 2.
171. At step 1550, the final target blow-out temperatures TAOi are
calculated by the equation (1). The step 1550 corresponds to the
first and the second target temperature calculating portions 1041,
42.
172. At step 1560, the air-mixing door opening degrees SWi are
calculated by the equation (2). The step 1560 corresponds to the Dr
side air-mixing door opening degree calculating portion 1044 and
the Pa side air-mixing door opening degree calculating portion
1045.
173. At step 1570, the blow-out port mode signals TMODEi are
calculated via the neural network 1300, shown in FIG. 7. The step
1570 corresponds to the first and the second blow-out port mode
calculating portions 1037, 1038. Here, outputs in the blow-out port
mode signals TMODEi have characteristics that these outputs
increase in proportion to an increasing of the target blow-out
temperature TAOi. One of the FACE mode (FACE), the B/L mode, and
the FOOT mode (FOOT), which are shown in FIG. 10, is determined
based on the output TMODEi from the neural network 1300.
174. At step 1580, the Dr side blower voltage level TBLO(Dr) and
the Pa side blower voltage level TBLO(Pa) are calculated via the
neural network 1400, shown in FIG. 8. Then, both of the blower
voltage levels TBLO(Dr), TBLO(Pa) are averaged to obtain final
blower voltage levels TBLO. The step 1580 corresponds to the air
amount calculating portions 1039, 1040, and 1043.
175. At steps 1590-1610, each of actuators such as motors 1017-1022
and the motor applied voltage control circuit 1024 as shown in FIG.
1 are controlled so as to correspond to the above-mentioned
calculated values.
176. Next, merits of this preferred embodiment, in which the target
blow-out temperatures TAOi are calculated via the neural networks
1100, 1200, will be explained in detail.
177. FIG 11C is a diagram illustrating a relationship between input
signals 1-4 and output signal (teacher signal) of neural network
1100. When the input signals 1-4 are 28.degree.C., 25.degree.C.,
10.degree.C., 3.degree.C., and the output signal is 50.degree.C.,
the output signal is changed from 50.degree.C to 35.degree.C. in
case the inconvenience (the temperature of area surrounding
passengers in Pa side is 25.5.degree.C.), shown in FIG. 12,
occurs.
178. Here, the neural network has the learning function, which
adjusts the link coefficients (synapse weights) 1106 between each
neurons in each layers in the neural network automatically to
correct its output to be desired data (teacher signal). Therefore,
the output at a specific input condition can be adjusted, by
changing the teacher signal at the specific input condition and
then adjusting the link coefficients 1106 automatically in
advance.
179. By above-mentioned changing of output (change from
50.degree.C. to 35.degree.C.), the temperature of area surrounding
passengers in Pa side can be controlled to substantially
28.degree.C. as set, when the Pa side setpoint temperature
Tset(Pa)=28.degree.C.
180. Furthermore, since the neural network adjusts its whole link
coefficients 1106 so that the desired outputs (teacher signal) at
the other input condition are maintained even if the output at the
specific input condition is changed. Therefore, the output change
at the specific input condition does not influence the outputs at
the other input conditions.
181. As shown in FIGS. 11A, 11B, when both the Dr side and Pa side
setpoint temperatures are changed, both temperatures of the area
surrounding passengers in the Dr side and Pa side are highly
independent controlled with accurately, without the temperature
interference between each sides.
182. Furthermore, since the correction of the link coefficients
1106 are adjusted automatically by using the learning function of
the neural network, it will not be essential for the engineers to
construct a complicated control logic such that the desired output
can be obtained only at specific input condition. Hence, engineer's
process can be eliminated substantially.
183. Since the complicated process does not needed, a capacitance
of a memory (ROM) of a computer, which constitutes an
air-conditioning electric control device, can be decreased.
184. Furthermore, the temporary target blow-out temperatures TAOBi
are calculated via the neural network 1100 shown in FIG. 3. The
amounts of radiation corrections TAOSi are calculated via the
neural network 1200 shown in FIG. 5. The final target blow-out
temperatures TAOi are calculated by the equation (1) based on the
TAOBi and TAOi. Therefore, since the radiation amount signal Tsi
can be eliminated from the input signals, the number of input
signals to the neural network 1100 can be decreased. Then, the
number of intermediate layers can be decreased, and the layered
structure of the neural network 1100 can be simplified.
185. Now, it is acceptable to combine above-mentioned two neural
networks 1100, 1200 to one neural network-to calculate the final
target blow-out temperature TAOi directly by the combined neural
network, in place with the independent two neural networks 1100,
1200.
186. Here, the setpoint temperatures (Tset(Dr), Tset(Pa)) of each
the first and the second air-conditioning zones, which are input to
the first and the second target blow-out temperature calculating
portions, require at least one of the setpoint temperatures Tset
(Dr) or Tset(Pa) of each air-conditioning zones relative to each
calculating portions. It is acceptable for this device to input a
temperature difference .DELTA.Tseti between the setpoint
temperatures Tset(Dr) and Tset(Pa) in addition to the selected the
setpoint temperature, or to input both of the setpoint temperatures
Tset(Dr) and Tset(Pa).
187. In other words, at least one of the setpoint temperatures
Tset(Dr) or Tset(Pa) of each air-conditioning zones relative to
each calculating portions is input. And further a signal, of which
reflects an influence from the setpoint temperature of the opposite
side air-conditioning zone, is input to calculate the target
blow-out temperature.
188. The first embodiment can be also modified as follows.
189. (1) The neural network 1100 inputs the .DELTA.Tseti, which is
temperature difference between the setpoint temperatures Tset(Dr)
and Tset(Pa), as one of input signals when the temporary target
blow-out temperatures TAOBi are calculated. However, when the Dr
side temporary target blow-out temperature TAOB(Dr) is calculated,
it is acceptable to input the setpoint temperatures Tset(Dr) and
Tset(Pa) in stead of the .DELTA.Tseti. Also, when the Pa side
temporary target blow-out temperature TAOB (Pa) is calculated, it
is acceptable to input the setpoint temperatures Tset(Pa) and
Tset(Dr) in stead of the .DELTA.Tseti. This way can also control
both temperatures of the area surrounding passengers in the Dr side
and Pa side independently and accurately.
190. (2) In the first embodiment, the blow-out modes of each the Dr
side air passage 1007 and the Pa side air passage 1008 are
determined independently each other. However, it is acceptable to
determine the blow-out modes of each the Dr side air passage 1007
and the Pa side air passage 1008 to identical blow-out mode. In
this case, a TAOX, which is an average of the first and the second
target blow-out temperatures TAO(Dr) and TAO(Pa), and a TsX, which
is an average of the radiation amount TsDr to the Dr side
air-conditioning zone and the radiation amount TsPa to the Pa side
air-conditioning zone, are input as the input to the neural network
1300 to calculate the blow-out port mode signal TMODE.
191. (3) In the first embodiment, the blow-out port mode and air
amount (blower voltage level) are calculated via the neural
networks 1300, 1400. However, it is acceptable to calculate the
blow-out port mode and air amount (blower voltage level) by
conventional calculation based on the TAOi without neural
networks.
192. (4) The learning method of the neural network can be
replaceable with Semi Newton method or the like instead of the
above-mentioned back propagation method.
193. (5) Several of calculations for air-conditioning control are
executed via the neural networks 1100-1400. However, it is
replaceable with fuzzy control system instead of the neural
networks 1100-1400.
194. That is, in the fuzzy system, both a member ship function and
a fuzzy rule table are changed. By this changing, the desired
outputs at the other input condition are maintained even if the
output at the specific input condition is changed, and the output
change at the specific input condition does not influence the
outputs at the other input conditions. Therefore, it can also
control both temperatures of the area surrounding passengers in the
Dr side and Pa side (shown in FIGS. 11A, 11B) independently and
accurately as well as the air-conditioning device using
above-mentioned neural networks.
195. Here, the air-conditioning device using above-mentioned neural
networks is superior to the air-conditioning device using the fuzzy
in the point of view of total process of the engineers.
196. (6) In the first embodiment, the air-mixing doors 1009, 1010,
which are the temperature adjuster to adjust the blow-out air
temperature to each Dr side air-conditioning zone and the Pa side
air-conditioning zone in a passenger component independently, are
arranged in the Dr side air passage 1007 and the Pa side air
passage 1008 independently. However, it is replaceable with the
following structure. That is, the heater cores 1005 are
independently arranged in the Dr side air passage 1007 and the Pa
side air passage 1008. A Dr side hot water valve is arranged to
control amount of hot water flowing into the heater core 1005 of
the Dr side air passage 1007 or control a temperature on the hot
water. A Pa side hot water valve is arranged to control amount of
hot water flowing into the heater core 1005 of the Pa side air
passage 1008 or control a temperature on the hot water. Then, the
blow-out air temperature to each Dr side air-conditioning zone and
the Pa side air-conditioning zone in a passenger component are
adjusted independently by the two hot water valves.
197. (7) The present invention can be adaptable to another
air-conditioning device, which has plurality of air-conditioning
zone, and is needed to control each of temperature of blow-out air
to each zones independently.
198. (8) The neural networks 1100-1400 are replaceable with
recurrent type neural networks.
199. (Second Embodiment)
200. A second embodiment of the present invention will be described
hereinafter with reference to FIGS. 16-24 and 26-29. FIG. 16 is a
diagram illustrating the constitution of whole system of an
air-conditioning device for automobiles. In FIG. 16, an internal
air/external air change-over door 2001 is arranged at the most
upstream side of air flow in the air-conditioning device for
automobiles. One of an external air and an internal air is
selectively introduced into an air duct 2002 by moving the door
2001.
201. The air duct 2002 constitutes an air passage of the
air-conditioning device. A blower 2003, an evaporator 2004 and a
heater core 2005 are arranged from upstream side to downstream side
in the air duct 2002. The evaporator 2004 is a cooling heat
exchanger for cooling an air by absorbing an evaporating latent
heat of a refrigerant in a refrigerating cycle from air. The heater
core 2005 is a heating heat exchanger for heating air with heat
from a hot water (engine coolant) from a vehicle engine (not
shown).
202. An air-mixing door 2006 as a temperature adjuster is arranged
at the upstream of the heater core 2005. The air-mixing door 2006
adjusts a ratio of two air amount, wherein an amount of heated air
passed through the heater core 2005 and an amount of cooled air
by-passed the heater core 2005. The adjusting the ratio of heated
air and cooled air can adjusts the blow-out air temperature to a
passenger component of the automobile.
203. At the most downstream side of the air duct 2, foot blowout
ports 2008a, 2008b, face blow-out ports 2009a-2009d, and a
defroster blow-out port 2010 are provided. Here, the foot blowout
ports 2008a, 2008b are provided to blow the conditioned air onto
the feet of the passengers. The face blow-out ports 2009a-2009d are
provided at each of center portion and side portion in the
passenger component to blow the conditioned air to the upper half
of the body of the passengers. The defroster blow-out port 2010 is
provided to blow the conditioned air to a windshield.
204. At the most downstream side of the air duct 2002, blow-out
port change-over doors 2011-2013 are arranged to selectively
open/close the blow-out ports 2008a, 2008b and 2009a-2009d. Each of
predetermined blow-out modes, such a FACE mode (FACE), a BI-LEVEL
mode (B/L mode), a FOOT mode (FOOT), and a defroster mode or the
like can be set by changing the open/close condition of the doors
2011-2013.
205. Here, a control system for controlling the air-conditioning
device will be explained. The internal air/external air changing
door 2001, the air-mixing door 2006, and the blow-out port
change-over doors are driven by servomotors 2014-2018. The
servomotors 2014-2018 are controlled by outputs of an
air-conditioning electric control device 2019 (hereinafter, called
"ECU 2019"). A motor 3a of the blower 2003 is also controlled by
the output of the ECU 2019 via a motor control circuit (motor
applied voltage control circuit) 2020.
206. An air amount blown by the blower 2003 is adjusted by the
motor control circuit 2020 by way of changing a rotation speed of
the motor with an applied voltage to the motor. The ECU 2019
includes a microcomputer and its peripheral circuits.
207. A temperature setter 2021 is provided to set a setpoint
temperature Tset of the passenger component, which is input to the
ECU 2019. The temperature setter 2021 is arranged an
air-conditioning control panel 2027, and is manually controlled by
users.
208. As temperature data detectors, the following sensors are
provided. That is, an internal air sensor 2022 is provided to
detect an internal air temperature Tr. An external air sensor 2023
is provided to detect an external air temperature Tam. A radiation
sensor 2024 is provided to detect an amount of (solar) radiation Ts
to the passenger component. An evaporator temperature sensor 2025
is provided to detect a cooling temperature Te (blow-out air
temperature) of the evaporator 2004. A water temperature sensor
2026 is provided to detect a water temperature Tw of hot water
flowing into the heater core 2005.
209. Control functions processed by the microcomputer in the ECU
2019 is generally divided as shown in FIG. 17. The ECU 2019
includes a temporary target temperature calculating portion 2028, a
radiation amount correction calculating portion 2029, a target
temperature calculating portion 2030, an air-mixing door opening
degree calculating portion 2031, a blow-out port mode calculating
portion 2032, an air amount calculating portion 2033, and an air
amount selecting portion 2034.
210. Here, the temporary target temperature calculating portion
2028 calculates a temporary target blow-out temperature, and
includes a neural network 2100 as shown in FIG. 20. The temporary
target temperature calculating portion 2028 inputs signals
including the setpoint temperature Tset, the internal air
temperature Tr and the external air temperature Tam, and calculates
the temporary target blow-out temperature TAOB based on the input
signals via the neural network 2200.
211. The radiation amount correction calculating portion 2029
calculates a radiation amount correction TAOS, and includes a
neural network 2300 as shown in FIG. 21. The radiation amount
correction calculating portion 2029 inputs signals including the
setpoint temperature Tset, the internal air temperature Tr, the
external air temperature Tam and the radiation amount Ts (output
signal of the radiation sensor 2024). Then the radiation amount
correction calculating portion 2029 calculates the radiation amount
correction TAOS based on the input signals via the neural network
2300.
212. The target temperature calculating portion 2030 calculates the
final target blow-out temperature TAO based on the output signals
from the temporary target temperature calculating portion 2028 and
the radiation amount correction calculating portion 2029.
213. The air-mixing door opening degree calculating portion 2031
calculates an air-mixing door opening degree SW based on the final
target blow-out temperature TAO from the target temperature
calculating portion 2030.
214. The blow-out port mode calculating portion 2032 calculates a
blow-out port mode TMODE, and includes a neural network 2400 as
shown in FIG. 22. The blow-out port mode calculating portion 2032
inputs the final target blow-out temperature TAO from the target
temperature calculating portion 2030 and the detected information,
which is the circumstance factor to influence a temperature
sensitively of the passenger such as the radiation amount Ts, the
external air temperature Tam and the water temperature Tw. Then the
blow-out port mode calculating portion 2032 calculates the blow-out
port mode TMODE via the neural network 2400.
215. The air amount calculating portion 2033 includes a neural
network 2100 as shown in FIG. 18. The air amount calculating
portion 2033 inputs signals including a temperature difference TD
between the setpoint temperature Tset and the internal air
temperature Tr (i.e., Tr-Tset), the radiation amount Ts and the
external air temperature Tam. Then the air amount calculating
portion 2033 calculates a blower voltage, which decides an air
amount via the neural network 2100. In detail, the air amount
calculating portion 2033 calculates a blower voltage TBLO.sub.1 for
the FACE, B/L modes, and a blower voltage TBLO.sub.2 for the FOOT
mode, independently.
216. The air amount selecting portion 2034 selects one of the
blower voltage TBLO, and the blower voltage TBLO.sub.2 based on the
blow-out port mode, and output the selected one as a blower voltage
TBLO.
217. The constitutions of the neural networks 2100-2400 will be
explained. Since the constitutions of the neural networks 2100-2400
are substantially equal, these constitutions are explained based on
the neural network 2100 with reference to FIGS. 18, 19A, 19B.
218. As shown in FIG. 18, the neural network 2100 includes an input
layer 2101, a first intermediate layer 2102, a second intermediate
layer 2103, and output layer 2104. Each of the input layer 2101,
the first and the second intermediate layers 2102, 2103 has a
plurality of neurons 2105, and output layer has one neuron 2105.
Each of neurons 2105 in the input layer 2101 is connected to each
of neurons 2105 in the first intermediate layer 2102 with a certain
link coefficient (synapse weight) 2106. Similarly, each of neurons
2105 in the first intermediate layer 2102 is connected to each of
neurons 2105 in the second intermediate layer 2103 with a certain
link coefficient 2106. Each of neurons 2105 in the second
intermediate layer 2103 is connected to the neuron 2105 in the
output layer 2104 with a certain link coefficient 2106. These
neurons 2105 are formed by memories in the ECU 2019 in an actual
circuit, and the neural network 2100 is formed by combined
memories.
219. The neural network 2100, which is a network having a layered
structure, has a Back Propagation Learning Function. This learning
function can automatically correct the link coefficients 2106
between each neurons among the input layer 2101, the first and
second intermediate layers 2102, 2103, and output layer, so as to
adjust its output to a desired value (teacher signal) related to
input signals such as TD, Ts, Tam.
220. When the teacher signal is changed, the link coefficients 2106
are adjusted by repeatedly "learning" so as to correct the output
to be the changed teacher signal related to the input signals. In
other words, based on a large number of desired data (teacher
signals), a correlation function (link coefficients 2106) is
automatically generated. The teacher signals are determined to
desired values obtained by an experience or the like (desired
output for the input signals).
221. In the layered structure neural network 2100, there is no
connection among each of the neurons 2105 in an identical layer,
but the neurons 2105 in each layer are connected to only neurons
2105 in forward and backward layers. The clink coefficients 2106
between each neuron 2105 in each layer represent a weight of
connection (magnitude). The larger the weight of connection, the
larger an amplitude of signal, which is output from each neuron
2105 and propagated to neurons 2105 in backward layer.
222. Here, the input signals (TD, Ts, Tam) are normalized to 0-1
value before inputting to the neural network 2100 by a normalize
portion 2107. The output signal from the neural network 2100 is
transformed in reverse procedure of the normalize portion 2107 by
output transform portions 2108a, 2108b. For example, actual
detected internal temperature Tr detected by the internal air
sensor is normally in a range from 0.degree. C. to 50.degree.C.
This detected value is assigned to a certain value in a range from
0 to 1 in the normalize portion 2107, and is output to the input
layer 2101 in the neural network 2100. Since the output signal from
the output layer 2104 is a value in the range from 0 to 1, this
output signal is transformed to the actual value corresponding to
the sensor signal in the reverse procedure by using a predetermined
transformation map in the output transform portions 2108a,
2108b.
223. Here, the output layer 2104 of the neural network 2100 for the
air amount calculation includes two output neurons 2105a, 2105b.
The neural network 2100 outputs the blower voltage TBLO, for the
FACE, B/L modes and the blower voltage TBLO.sub.2 for the FOOT
mode, independently, via the two output transform portions 2108a,
2108b.
224. The environment conditions, of which the air-conditioning
device for vehicle faces, are variable, as described above.
Therefore, the number of the teacher signals, which are desired
values related to the various environment conditions, will be
enormously.
225. Therefore, in a designing process of the ECU 2019, the
learning is executed by a high-speed calculator including a neural
network, which is the same as the neural network 2100, before being
mounted to the vehicle. The high-speed calculator calculates the
link coefficients 2106 so that the output of the neural network is
to be desired teacher signals for various input signals. Then, the
calculated coefficients 2106 are memorized in a memory portion
(ROM) corresponding to each neural network 2100, 2200, 2300, 2400
in each of the calculating portions of the ECU 2019 shown in FIG.
17.
226. Therefore, when the ECU 2019 is mounted on the vehicle, the
link coefficients 2106 among the neurons 2105 in each layer of the
neural networks 2100-2400 are set to predetermined value.
227. After being mounted on the vehicle, the neural networks
2100-2400 in each layer calculate an output for input signals as
shown in FIG. 19A. Specifically, in each of the neurons 2105, each
of the input signals O.sub.l-O.sub.n is multiplied by each of the
corresponding coefficients 2106 (W.sub.l-W.sub.n), and multiplied
value are adapted to a so-called sigmoid function as shown FIG.
19B. The calculated results of the sigmoid function are output to
the neurons 2105 in backward layer. These calculations are repeated
between each layer to decide a final output.
228. Since the neural network 2100 in each calculating portion of
mounted ECU 2019 is previously set so that the desired value for
the various input signals O.sub.l-O.sub.n can be obtained, learning
for correction of the link coefficients does not need.
229. The neural networks 2100 as shown in FIG. 18 of the air amount
calculating portion 2033 calculates and outputs the desired value
as the blower voltage levels TBLO.sub.1, TBLO.sub.2, in relation to
the changes of input signals (TD, Ts, Tam).
230. The neural network 2200 of the temporary target temperature
calculating portion 2028 outputs the desired teacher signals as the
temporary target blow-out temperature TAOB in relation to the
change of the input signals (Tseti, Tr, Tam).
231. The neural network 2300 of the radiation correction
calculating portion 2029 outputs the desired teacher signals as the
radiation amount correction TAOS in relation to the change of the
input signals (Tset, Tr, Tam, Ts).
232. The neural networks 2400 as shown in FIG. 22 of the blowout
port mode calculating portion 2032 calculates and outputs the
desired value as the blow-out port mode signal TMODE in relation to
the changes of input signals (TAO, Ts, Tam, Tw).
233. In the target temperature calculating portion 2030, the final
target blow-out temperature TAO is calculated by the following
equation:
TAO=TAOB-TAOS (3)
234. In the air-mixing door opening degree calculating portion
2031, the air-mixing door opening degree SW is calculated based on
the TAO from the target temperature calculating portion 2030, the
temperature Te of the evaporator 2004, and the water temperature Tw
of the heater core 2005, by using the following equation:
Sw(%)=(TAO-Te) / (Tw-Te).times.100 (4)
235. The control flow according to the preferred embodiment will be
explained with reference to a flow chart shown in FIG. 23. This
control routine starts from a control switch signal, which starts
the air-conditioning device.
236. At step 2510, the air-conditioning ECU 19 is reset. At step
2520, the ECU 2019 inputs the output signals from each sensor such
as the internal air temperature Tr, the external air temperature
Tam, and the signals from the temperature setter 2021.
237. At step 2530, the temporary target blow-out temperature TAOB
is calculated via the neural network 2200, shown in FIG. 20. The
step 2530 corresponds to the temporary target temperature
calculating portion 2028 in FIG. 17.
238. At step 2540, the radiation amount correction TAOS is
calculated via the neural network 300, shown in FIG. 21. The step
2540 corresponds to the radiation amount correction calculating
portion 2029 in FIG. 17.
239. At step 2550, the final target blow-out temperature TAO is
calculated by the equation (3). The step 2550 corresponds to the
second target temperature calculating portion 2030 in FIG. 17.
240. At step 2560, the air-mixing door opening degree SW is
calculated by the equation (4). The step 2560 corresponds to the
air-mixing door opening degree calculating portion 2031 in FIG.
17.
241. At step 2570, the blow-out port mode signal TMODE is
calculated via the neural network 2400, shown in FIG. 22. The step
2570 corresponds to the blow-out port mode calculating portion 2032
in FIG. 17. Here, outputs in the blow-out port mode signal TMODE
have characteristics that this output increases in proportion to an
increasing of the target blow-out temperature TAO. One of a face
mode (FACE), a bi-level mode (B/L), and a foot mode (FOOT), which
are shown in FIG. 24, is determined based on the output TMODE from
the neural network 2400.
242. At step 2580, the blower voltage TBLO.sub.1 for the FACE, B/L
modes and the blower voltage TBLO.sub.2 for the FOOT mode are
calculated, independently, via the neural network 2100, shown in
FIG. 18. The step 2580 corresponds to the air amount calculating
portion 2033 in FIG. 17.
243. At step 2590, one of the blower voltage TBLO.sub.1 and the
blower voltage TBLO.sub.2 is selected based on the blow-out port
mode TMODE calculated in the step 2570. The step 2590 corresponds
to the air amount selecting portion 2034 in FIG. 17.
244. At steps 2600-2620, each of actuators such as motors 2014-2018
and the motor applied voltage control circuit 2020 as shown in FIG.
16 are controlled so as to correspond to the above-mentioned
calculated values.
245. FIG. 25 shows the relation between the blower voltage level
for deciding the air amount and the temperature difference TD (=
Tr-Tset), as described the above. The temperature difference TD of
the abscissa can be replaced with the internal air temperature Tr.
When the internal air temperature is used as the abscissa, the
right side of the abscissa represents a high temperature side, and
the left side of the abscissa represents a low temperature
side.
246. An operation when the FOOT mode is selected in a heating
operation in winter will be explained. At just after the heating
starts, the blower 2003 starts with the blower voltage =E.sub.1, as
shown in FIG. 25. After the internal air temperature Tr rises and
reaches T.sub.1, the blower voltage decreases from E.sub. 1 in
proportion to temperature rising. After the internal air
temperature reaches T.sub.2, and approaches the setpoint
temperature Tset, the operation is set to a normal operation area
A, and the blower voltage is set to the minimum voltage
E.sub.2.
247. An operation when the FACE mode is selected in a cooling
operation in summer will be explained. At just after the cooling
starts, the blower 2003 starts with the blower voltage=the maximum
voltage E.sub.3 (E.sub.3>E.sub.1). After the internal air
temperature Tr falls and reaches T.sub.3, the blower voltage
decreases from E.sub.3 in proportion to temperature falling. After
the internal air temperature reaches T.sub.4, and approaches the
setpoint temperature Tset, the operation is set to a normal
operation area A, and the blower voltage is set to voltage
E.sub.4.
248. Here, the voltage E.sub.4 is set to high comparing to the
voltage E.sub.2, which is the FOOT mode voltage, so that the air
amount increases in proportion to the radiation amount. The air
amount can be increased in proportion to the voltage difference
.DELTA.E between the voltage E.sub.4 and voltage E.sub.2, and can
be increased a cooling feeling of the passenger during the
radiation in the FACE mode.
249. In FIG. 25, a blower voltage characteristic of the FACE mode
has a voltage change of E.sub.3.fwdarw.E.sub.4.fwdarw.E.sub.1, and
one of them is determined by the blower voltage level TBLO.sub.1. A
blower voltage characteristic of the B/L mode using in intermediate
season such as spring or autumn is the same as that of FACE mode so
that the cooling feeling of the passenger can be increased during
the radiation.
250. The voltage difference .DELTA.E between the voltage E.sub.4
and voltage E.sub.2 continuously change in proportion to the amount
on radiation Ts. Therefore, when the FACE mode or the B/L mode and
the radiation amount Ts=0, the blower voltage decreases to E.sub.2
level and then set to the same voltage as that of the FOOT mode at
the normal operation area A.
251. A blower voltage characteristic of the FOOT mode has a voltage
change of E.sub.1.fwdarw.E.sub.2.fwdarw.E.sub.3 independent of the
radiation amount, and one of them is determined by the blower
voltage level TBLO.sub.2.
252. Here, during the normal operation area A, if there is the
radiation, when the blow-out port mode is changed among the FACE
mode, B/L mode and the FOOT mode, the blower voltage may be changed
step by step. However, in this embodiment, the blower voltage level
TBLO.sub.1 for the FACE mode or B/L mode and the blower voltage
level TBLO.sub.2 for the FOOT mode are always calculated via the
neural network 2100, independently. Hence, the blower voltage level
TBLO.sub.1 for the FACE mode or B/L mode can be changed
continuously in proportion to the radiation amount Ts.
253. Therefore, since it is not needed to change the output step by
step during the calculation of the blower voltage level TBLO.sub.1,
the learning for calculating the link coefficient 2106 of the
neural network 2100 can be simplified.
254. Furthermore, in this embodiment, the temperature difference TD
(=Tr-Tset) between the setpoint temperature Tset and the internal
air temperature Tr is calculated, and then the calculated value is
input to the neural network 2100. Therefore, the number of input to
the neural network 2100 can be decreased compared to an input way
inputting both of the Tset and the Tr.
255. Here, it may need to input both of the setpoint temperature
Tset and the internal air temperature Tr to detect whether the
operation area is in a transition area, which is just after the
air-conditioning device starts (the internal air temperature is
changing to the setpoint temperature), or the normal operation
area. However, in this embodiment, this condition (the operation
area is in the normal operation area) can be detected from the fact
that the temperature difference TD between the internal air
temperature Tr and the setpoint temperature Tset reaches
substantially zero. Therefore, this embodiment can easily detect
whether the transition area or the normal operation by inputting
the temperature difference TD.
256. Since the number of input is decreased, the number of the
layer in the neural network can be also decreased, the link
coefficient can be decreased by about 25%, and the total learning
time for calculating the link coefficient 2106 can be decreased
largely.
257. It is acceptable to input both the setpoint temperature Tset
and the internal air temperature Tr replaced with the temperature
difference TD to the neural network 2100, if an increasing of the
number of the input would be less than allowable condition.
258. The two blower voltage levels TBLO.sub.1 and TBLO.sub.2 can be
calculated by using independent two neural networks 2100 replaced
with the calculation by using one neural network 2100. However, it
is preferable to use one neural network 2100 as this embodiment,
because using one neural network 2100 does not need further process
to set two neural network independently or further memories.
259. According to an investigation on an air amount control at the
transition area just after the air-conditioning device starts in
summer (so-called cool down), the following facts is found. That
is, at the point the blower voltage decreases from E.sub.3
(TD=.sub.3) in FIG. 25, an air-conditioning feeling for the
passenger can be further improved by changing the blower voltage
level based on a heat load condition at the start timing of the
air-conditioning device. This mechanism will be explained
hereinafter with reference to FIGS. 26-29.
260. FIGS. 26-29 show a characteristic of a target blower voltage
(air amount) based on an average of the air amount, of which a
plurality of monitor people feels comfort. In FIGS. 26-29, the
ordinate represents the blower voltage (V), the abscissa represents
the temperature difference TD (.degree.C.).
261. A result shown in FIG. 26 is measured under the following heat
load condition. That is, at the start timing of the
air-conditioning device, the external air temperature Tam is
20.degree. C., the radiation amount Ts is 1000 W/m.sup.2, initial
internal air temperature Tr is 50.degree.C., and the setpoint
temperature Tset is 25.degree.C. An air-conditioning load due to
both of the external air temperature Tam and the radiation amount
Ts is largest in this condition among conditions shown in FIGS.
26-29.
262. As shown in FIG. 26, it takes long time to reduce the
temperature difference TD of which just after air-condition starts,
when the air-conditioning load at the start timing of the
air-conditioning device is large. Therefore, the blower voltage
(air amount) is kept its maximum value (e.g., 13.3 V) for a long
time. In this situation, cooled air from the face blow-out ports
2009a-2009d is kept blowing to the passenger's face strongly for a
while, when the FACE mode is selected. Then, the passenger may not
feel comfort due to an excessive cooled air.
263. Therefore, in the case when the air-conditioning load at the
start timing of the air-conditioning device is large, a decreasing
point of TD at which starts to decrease the blower voltage is set
large so that the blower voltage starts to decrease early. That is,
the blower voltage (amount on air) starts to decrease at the point
the TD=12.degree.C.
264. A result shown in FIG. 27 is measured under the following heat
load condition. That is, at the start timing of the
air-conditioning device, the external air temperature Tam is
20.degree.C., the radiation amount Ts is 500 W/m.sup.2, initial
internal air temperature Tr is 40.degree.C., and the setpoint
temperature Tset is 25.degree.C. An air-conditioning load due to
both of the external air temperature Tam and the radiation amount
Ts is in intermediate. In this characteristic, the blower voltage
(amount on air) starts to decrease at the point the
TD=9.5.degree.C.
265. A result shown in FIG. 28 is measured under the following heat
load condition. That is, at the start timing of the
air-conditioning device, the external air temperature Tam is
10.degree.C., the radiation amount Ts is 1000 W/m.sup.2, initial
internal air temperature Tr is 40.degree.C., and the setpoint
temperature Tset is 25.degree.C. An air-conditioning load due to
both of the external air temperature Tam and the radiation amount
TS is also in intermediate. In this characteristic, the blower
voltage (amount on air) starts to decrease at the point the
TD=10.degree.C.
266. A result shown in FIG. 29 is measured under the following heat
load condition. That is, at the start timing of the
air-conditioning device, the external air temperature Tam is
10.degree.C., the radiation amount Ts is 500 W/m.sup.2, initial
internal air temperature Tr is 40.degree.C., and the setpoint
temperature Tset is 25.degree.C. An air-conditioning load due to
both of the external air temperature Tam and the radiation amount
Ts is smallest. In this characteristic, the blower voltage (amount
on air) starts to decrease at the point the TD=7.5.degree.C.
267. As described above, the decreasing point of TD during the cool
down is set gradually small in proportion to decreasing the
air-conditioning load at the start timing of the air-conditioning
device. Then, it can determine a suitable period that the blower
voltage is set at the maximum value during the cool down, and a
feeling of the cooling can be improved.
268. Here, in FIGS. 26-29, the air amount is based on the
temperature difference TD. However, one of the target blow-out
temperature TAO, the internal air temperature Tr and so on can be
used to determined to the air amount replaced with the temperature
difference TD. That is, both the temperature difference TD and the
target blow-out temperature Tr change in proportion to the change
of the internal air temperature Tr during the cool down. Therefore,
another data can be replaceable with the temperature difference TD
as long as it changes in proportion to the change of the internal
air temperature.
269. Further, in FIGS. 26-29, the characteristic of the air amount
during the cool down in the FACE mode is explained. However, the
passenger may manually switch the blow-out port mode to B/L mode
during a high heat load condition in summer. Therefore, these
characteristics are adaptable to the high heat load condition in
the B/L mode.
270. Here, the neural network has the learning function, which
adjusts the link coefficients 2106 (synapse weights) between each
neurons in each layers in the neural network automatically to
correct its output to be desired data. Therefore, the output at a
specific input condition can be adjusted, by changing the teacher
signal at the specific input condition and then adjusting the link
coefficients 2106 (synapse weights) automatically in advance.
271. Furthermore, since the neural network adjusts its whole link
coefficients 2106 so that the desired outputs at the other input
condition are maintained even if the output at the specific input
condition is changed. Therefore, the output change at the specific
input condition does not influence the outputs at the other input
conditions. Consequently, desired characteristic of the air amount
can be achieved easily for a wide range of input change due to a
change of automobile circumstance condition.
272. (Third Embodiment)
273. In this embodiment, the present invention is adopted to an
air-conditioning device for automobiles, which is capable of
controlling independently between a driver seat (Dr) side
air-conditioning zone and a passenger seat (Pa) side
air-conditioning zone in a passenger component.
274. In an air-duct 2002, a partitioning wall 2002a is arranged
from the heater core 2005 portion to its downstream in the air duct
2002 to divide it into a Dr side air passage 2002b and a Pa side
air passage 2002c.
275. A Dr side air-mixing door 2061 is arranged at the upstream of
the heater core 2005 in the Dr side air passage 2002b. The Dr side
air-mixing door 2061 adjusts a ratio of two air amount in the Dr
side air passage 2002b, wherein an amount of heated air passed
through the heater core 2005 and an amount of cooled air by-passed
the heater core 2005. A Pa side air-mixing door 2062 is arranged at
the upstream of the heater core 2005 in the Pa side air passage
2002c, and adjusts a ratio of two air amount in the Pa side air
passage 2002c. Here, the two air amount are an amount of heated air
passed through the heater core 2005 and an amount of cooled air
by-passed the heater core 2005.
276. At the most downstream side of the Dr side air passage 2002b,
a foot blow-out port 2008a, face blow-out ports 2009a, 2009b are
provided. At the most downstream side of the Pa side air passage
2002c, a foot blow-out port 2008b, face blow-out ports 2009c, 2009d
are provided. Blowout port change-over doors 2011a, 2012a are
arranged to selectively open/close the Dr side foot blow-out port
2008a and the face blow-out ports 2009a, 2009b. Blowout port
change-over doors 2011b, 2012b are arranged to selectively
open/close the Pa side foot blow-out port 2008b and the face
blow-out ports 2009c, 2009d. A defroster blow-out port 2010 and its
blow-out is port change-over door 13 are provided in the same way
as the second embodiment.
277. The Dr side air-mixing door 2061 and the Pa side air-mixing
door 2062 are driven by independent servomotors 2015 and 2015a. The
Dr side blow-out port change-over doors 2011a, 2012a and the Pa
side blow-out port change-over doors 2011b, 2012b are driven by
independent servomotors 2160 and 2170. Each of predetermined
blow-out mode, such as a FACE mode (FACE), a BI-LEVEL mode (B/L
mode), and a FOOT mode (FOOT) or the like can be set with
respective to each ports 2002c, 2002c, independently, by changing
the open/close condition of the blow-out port change-over doors
2011a, 2012a, 2011b and 2012b.
278. A Dr side temperature setter (first temperature setter) 2021a
is provided to set a setpoint temperature Tset(Dr) of the Dr side
air-conditioning zone relative to the Dr side air passage 2002b,
and output the setpoint temperature Tset (Dr) to the ECU 2019. A Pa
side temperature setter (second temperature setter) 2021b is
provided to set a setpoint temperature Tset(Pa) of the Pa side
air-conditioning zone relative to the Pa side air passage 2002c,
and output the setpoint temperature Tset(Pa) to the ECU 2019. Both
temperature setters 2021b, 2021c are provided independently each
other.
279. A Dr side radiation sensor 2024a is arranged to detect a
radiation amount TsDr to the Dr side air-conditioning zone, and a
Pa side radiation sensor 2024b is arranged to detect a radiation
amount TsPa to the Pa side air-conditioning zone.
280. As temperature data detectors, an internal air sensor 2022 to
is arranged detect an internal air temperature Tr. An external air
sensor 2023 is arranged to detect an external air temperature Tam.
An evaporator temperature sensor 2025 is arranged to detect a
cooling temperature (blow-out air temperature) of the evaporator
2004. A water temperature sensor 2026 is arranged to detect a
temperature Tw of hot water flowing into the heater core 2005.
281. Control functions processed by the microcomputer in the ECU
2019 are executed independently between the Dr side and the Pa side
as shown in FIG. 31. In this figure, subscript "a" after symbol
means Dr side functions, and "b" after symbol means Pa side
functions.
282. In this embodiment, a blow-out port mode signal TMODE common
to both of the Dr side and the Pa side, are calculated by a
blow-out port mode calculating portion 2032 so that the blow-out
port modes of the Dr side and the Pa side are set to be equal.
283. A blower voltage TBLO, which decides an air amount, calculated
as follows. As to the Dr side, a blower voltage level
TBLO(Dr).sub.1 in the FACE mode or B/L mode and a blower voltage
level TBLO(Dr).sub.2 in the FOOT mode are calculated by an air
amount calculating portion 2033a. As to the Pa side, a blower
voltage level TBLO(Pa).sub.1 in the FACE mode or B/L mode and a
blower voltage level TBLO(Pa).sub.2 in the FOOT mode are calculated
by an air amount calculating portion 2033b. Then, an air amount
selecting portion 2034a selects one of the blower voltage
TBLO(Dr).sub.1 and the blower voltage TBLO(Dr).sub.2 based on the
blow-out port mode, and output it as a blower voltage TBLO(Dr). An
air amount selecting portion 2034b selects one of the blower
voltage TBLO(Pa).sub.1 and the blower voltage TBLO(Pa).sub.2 based
on the blow-out port mode, and output it as a blower voltage TBLO
(Pa). Finally, an average of the blower voltage TBLO(Dr) and the
blower voltage TBLO(Pa) are calculated by an air amount deciding
portion 2035. The averaged value is output as a final blower
voltage TBLO.
284. As shown in FIG. 32, a neural network 2100, which constitutes
each the Dr side air amount calculating portion 2033a and the Pa
side air amount calculating portion 2033b, inputs temperature
differences TDi of each the Dr side and the Pa side, and radiation
amount Tsi of each the Dr side and the Pa side.
285. As shown in FIG. 33, a neural network 2200, which constitutes
each a Dr side temporary target temperature calculating portion
2028a and a Pa side temporary target temperature calculating
portion 2028b, inputs setpoint temperatures Tset(Dr) and Tset(Pa)
of each the Dr side and the Pa side, and further inputs a
difference (.DELTA.Tset) between the setpoint temperatures Tset(Dr)
and Tset(Pa). By inputting the difference (.DELTA.Tset), it can
restrict a temperature interference between the first
air-conditioning zone and in the second air-conditioning zone.
286. As shown in FIG. 34, a neural network 2300, which constitutes
each a Dr side radiation amount correction calculating portion
2029a and a Pa side radiation amount correction calculating portion
2029b, inputs setpoint temperatures Tset(Dr) and Tset(Pa) of each
the Dr side and the Pa side, and further input the Dr side
radiation amount TsDr and the Pa side radiation amount TsPa.
287. As shown in FIG. 35, a neural network 2400, which constitutes
a blow-out port mode calculating portion 2032, inputs an average
TAOX, which is an averaged value of a Dr side target blow-out
temperature TAO (Dr) and a Pa side target blow-out temperature TAO
(Pa), and further input an average TsX, which is an averaged value
of the Dr side radiation amount TsDr and the Pa side radiation
amount TsPa. Then, the neural network 2400 calculates the blow-out
port mode signal TMODE.
288. Here, in this embodiment, it is acceptable to control the Dr
side blow-out port mode and the Pa side blow-out port mode
independently. In this case, the blow-out port mode calculating
portion 2032 is divided into plurality of blow-out port mode
calculating portions including a Dr side blow-out port mode
calculating portion 2032a and Pa side blow-out port mode
calculating portion 2032b. As shown in FIG. 36, the neural network
2400, which is constitutes each the blow-out port mode calculating
portions 2032a, 2032b, inputs the Dr side target blow-out
temperature TAO(Dr) or the Pa side target blow-out temperature
TAO(Pa), and further inputs the Dr side radiation amount TsDr or
the Pa side radiation amount TsPa. Then, each neural network 2400
calculates a Dr side blowout port mode signal TMODE (Dr) and a Pa
side blow-out port mode signal TMODE(Pa), independently.
289. Then, the air amount selecting portions 2034a, 2034b, shown in
FIG. 31, select the blower voltage TBLO (Dr) and the blower voltage
TBLO(Dr) based on each the Dr side blow-out port or the Pa side
blow-out port mode. Finally, an average of the blower voltage
TBLO(Dr) and the blower voltage TBLO(Pa) are calculated by an air
amount deciding portion 2035. The averaged value is output as a
final blower voltage TBLO.
290. The second and third embodiments can be also modified as
follows.
291. (1) In the above-described embodiment, the temporary target
blow-out temperature TAOBi, the radiation amount correction TAOSi
and the blow-out port mode TMODEi are calculated via the neural
networks 2200, 2300, 2400. However, it is acceptable to calculate
one of the temporary target blow-out temperature TAOBi, the
radiation amount correction TAOSi and the air amount by
conventional calculation without neural networks.
292. (2) The learning method of the neural network can be
replaceable with Semi Newton method or the like instead of the
above-mentioned back propagation method.
293. (3) The air-mixing doors 2006, 2061, 2062 are arranged to
adjust the mix ratio of cooled air and heated air as the
temperature adjuster. However, it is replaceable with a hot water
valve to control an amount of hot water flowing into the heater
core 2005 or a temperature of the hot water.
294. (4) In the third embodiment, the average of the blower voltage
TBLO(Dr) and the blower voltage TBLO(Pa) are calculated by the air
amount deciding portion 2035. The averaged value is output as the
final blower voltage TBLO. However, it is acceptable to calculate
the final blower voltage TBLO by putting an adequate weight to each
the blower voltage TBLO(Dr) and the blower voltage TBLO(Pa) and
adding or subtracting them.
295. (5) In the third embodiment, the blower 3 can be arranged in
each Dr side and Pa side independently. Further, an air provider
can be arranged in each the Dr side air passage 2002b and the Pa
side air passage 2002c to adjust each air amount, independently, so
that each Dr side air amount and Pa side air amount can be
controlled, independently.
296. (6) The temperature difference TD can be obtained by using
just Tr and Tset to calculate (Tr-Tset), or using another factor in
addition to Tr and Tset.
297. (7) As the temperature setter 2021, 2021a, 2021b for setting
the temperature of the air-conditioning zones, it can be used an
analog display in which indicates the temperature without digital
figures, e.g., the temperature is indicated by colors.
298. (8) To adjust the air amount, the blower 3 is controlled by
the voltage level applied thereto to control the its rotation
speed. However, it is acceptable to control the rotation speed by a
pulse wave modulation (PWM) method, which changes a duty ratio of a
pulse voltage applied to the blower motor 3a.
299. (9) The present invention can be adaptable to another
air-conditioning device, which has plurality of air-conditioning
zone, and is needed to control each of temperature of blow-out air
to each zones independently.
300. (10) The neural networks 2100-2400 are replaceable with
recurrent type neural networks.
301. (Fourth Embodiment)
302. A fourth embodiment of the present invention will be described
hereinafter with reference to FIGS. 37-45 and 47C-50. FIG. 37 is a
diagram illustrating the constitution of whole system of an
air-conditioning device for automobiles. In FIG. 37, an internal
air/external air change-over door 3001 is arranged at the most
upstream side of air flow in the air-conditioning device for
automobiles. One of an external air and an internal air is
selectively introduced into an air duct 3002 by moving the door
3001.
303. The air duct 3002 constitutes an air passage of the
air-conditioning device. A blower 3003, an evaporator 3004 and a
heater core 3005 are arranged from upstream side to downstream side
in the air duct 3002. The evaporator 3004 is a cooling heat
exchanger for cooling an air by absorbing an evaporating latent
heat of a refrigerant in a refrigerating cycle from the air. The
heater core 3005 is a heating heat exchanger for heating air with
heat from a hot water (engine coolant) from a vehicle engine (not
shown).
304. An air-mixing door 3006 as a temperature adjuster is arranged
at the upstream of the heater core 3005. The air-mixing door 3006
adjusts a ratio of two air amount, wherein an amount of heated air
passed through the heater core 3005 and an amount of cooled air
by-passed the heater core 3005. The adjusting the ratio of heated
air and cooled air can adjusts the blow-out air temperature to a
passenger component of the automobile.
305. At the most downstream side of the air duct 3002, foot
blow-out ports 3008a, 3008b, face blow-out ports 3009a-3009d, and a
defroster blow-out port 3010 are provided. Here, the foot blow-out
ports 3008a, 3008b are provided to blow the conditioned air onto
the feet of the passengers. The face blow-out ports 3009a-3009d are
provided at each of center portion and side portion in the
passenger component to blow the conditioned air to the upper half
of the body of the passengers. The defroster blow-out port 3010 is
provided to blow the conditioned air to a windshield.
306. At the most downstream side of the air duct 3002, blow-out
port change-over doors 3011-3013 are arranged to selectively
open/close the blow-out ports 3008a, 3008b and 3009a-3009d. Each of
predetermined blow-out modes, such a FACE mode (FACE), a BI-LEVEL
mode (B/L), a FOOT mode (FOOT), and a defroster mode or the like
can be set by changing the open/close condition of the doors
3011-3013.
307. Here, a control system for controlling the air-conditioning
device will be explained. The internal air/external air changing
door 3001, the air-mixing door 3006, and the blow-out port
change-over doors are driven by servomotors 3014-3018. The
servomotors 3014-3018 are controlled by outputs of an
air-conditioning electric control device 3019 (hereinafter, called
"ECU 3019"). A motor 3a of the blower 3003 is also controlled by
the output of the ECU 3019 via a motor control circuit (motor
applied voltage control circuit) 3020.
308. An air amount blown by the blower 3003 is adjusted by the
motor control circuit 3020 by way of changing a rotation speed of
the motor with an applied voltage to the motor. The ECU 3019
includes a microcomputer and its peripheral circuits.
309. A temperature setter 3021 is provided to set a setpoint
temperature Tset of the passenger component, which is input to the
ECU 3019. The temperature setter 3021 is arranged an
air-conditioning control panel, and is manually controlled by
users.
310. As temperature data detectors, the following sensors are
provided. That is, an internal air sensor 3022 is provided to
detect an internal air temperature Tr. An external air sensor 3023
is provided to detect an external air temperature Tam. A radiation
sensor 3024 is provided to detect a radiation amount Ts to the
passenger component. An evaporator temperature sensor 3025 is
provided to detect a cooling temperature Te (blow-out air
temperature) of the evaporator 3004. A water temperature sensor
3026 is provided to detect a water temperature Tw of hot water
flowing into the heater core 3005.
311. Control functions processed by the microcomputer in the ECU
3019 is generally divided as shown in FIG. 38. The ECU 3019
includes a temporary target temperature calculating portion 3027, a
radiation amount correction calculating portion 3028, a target
temperature calculating portion 3029, an air-mixing door opening
degree calculating portion 3030, a blow-out port mode calculating
portion 3031, and an air amount calculating portion 3032.
312. Here, the temporary target temperature calculating portion
3027 calculates a temporary target blow-out temperature, and
includes a neural network 3200 as shown in FIG. 41. The temporary
target temperature calculating portion 3027 inputs signals
including the setpoint temperature Tset, the internal air
temperature Tr and the external air temperature Tam, and calculates
the temporary target blow-out temperature TAOB based on the input
signals via the neural network 3200.
313. The radiation amount correction calculating portion 3028
calculates a radiation amount correction TAOS, and includes a
neural network 3300 as shown in FIG. 42. The radiation amount
correction calculating portion 3028 inputs signals including the
setpoint temperature Tset, the internal air temperature Tr, the
external air temperature Tam and the radiation amount Ts (output
signal of the radiation sensor 3024). Then the, radiation amount
correction calculating portion 3028 calculates the radiation amount
correction TAOS based on the input signals via the neural network
3300.
314. The target temperature calculating portion 3029 calculates the
final target blow-out temperature TAO based on the output signals
from the temporary target temperature calculating portion 3027 and
the radiation amount correction calculating portion 3028.
315. The air-mixing door opening degree calculating portion 3030
calculates an air-mixing door opening degree SW based on the final
target blow-out temperature TAO from the target temperature
calculating portion 3029.
316. The blow-out port mode calculating portion 3031 calculates a
blow-out port mode TMODE, and includes a neural network 3100 as
shown in FIG. 39. The blow-out port mode calculating portion 3031
inputs the final target blow-out temperature TAO from the target
temperature calculating portion 3029 and the detected information,
which is the environment factor to influence a temperature
sensitively of the passenger such as the radiation amount Ts, the
external air temperature Tam and the water temperature Tw. Then the
blow-out port mode calculating portion 3031 calculates the blow-out
port mode TMODE via the neural network 3100.
317. The air amount calculating portion 3032 includes a neural
network 3400 as shown in FIG. 43. The air amount calculating
portion 3032 inputs signals including the setpoint temperature
Tset, the internal air temperature Tr, the external air temperature
Tam and the radiation amount. Then the air amount calculating
portion 3032 calculates a blower voltage, which decides an air
amount via the neural network 3400.
318. The constitutions of the neural networks 3100-3400 will be
explained. Since the constitutions of the neural networks 3100-3400
are substantially same, these constitutions is explained based on
the neural network 3100 with reference to FIGS. 39, 40A, 40B.
319. As shown in FIG. 39, the neural network 3100 includes an input
layer 3101, a first intermediate layer 3102, a second intermediate
layer 3103, and output layer 3104. Each of the input layer 3101,
the first and the second intermediate layers 3102, 3103 has a
plurality of neurons 3105, and output layer has one neuron 3105.
Each of neurons 3105 in the input layer 3101 is connected to each
of neurons 3105 in the first intermediate layer 3102 with a certain
link coefficient 3106 (synapse weight). Similarly, each of neurons
3105 in the first intermediate layer 3102 is connected to each of
neurons 3105 in the second intermediate layer 3103 with a certain
link coefficient 3106. Each of neurons 3105 in the second
intermediate layer 3103 is connected to the neuron 3105 in the
output layer 3104 with a certain link coefficient 3106. These
neurons 3105 are formed by memories in the ECU 3019 in an actual
circuit, and the neural network 3100 is formed by combined
memories.
320. The neural network 3100, which is a network having a layered
structure, has a Back Propagation Learning Function. The Back
Propagation Learning Function can automatically correct the link
coefficients 3106 between each neurons among the input layer 3101,
the first and second intermediate layers 3102, 3103, and output
layer, so as to adjust its output to a desired value (teacher
signal) related to input signals such as TD, Ts, Tam, Tw.
321. When the teacher signal is changed, the link coefficients 3106
are adjusted by repeatedly "learning" so as to correct the output
to be the changed teacher signal related to the input signals. In
other words, based on a large number of desired data (teacher
signals), a correlation function (link coefficients 3106) is
automatically generated. The teacher signals are determined to
desired values obtained by an experience or the like (desired
output for the input signals).
322. In the layered structure neural network 3100, there is no
connection among each of the neurons 3105 in an identical layer,
but the neurons 3105 in each layer are connected to only neurons
3105 in forward and backward layers. The clink coefficients 3106
between each neuron 3105 in each layer represent a weight of
connection (magnitude). The larger the weight of connection, the
larger an amplitude of signal, which is output from each neuron
3105 and propagated to neurons 3105 in backward layer.
323. Here, the input signals (TD, Ts, Tam, Tw) are normalized to
0-1 value before inputting to the neural network 3100 by a
normalize portion 3107. The output signal from the neural network
3100 is transformed in reverse procedure of the normalize portion
3107 by an output transform portion 3108. For example, actual
detected internal temperature Tr detected by the internal air
sensor is normally in a range from 0.degree. C. to 50.degree.C.
This detected value is assigned to a certain value in a range from
0 to 1 in the normalize portion 3107, and is output to the input
layer 3101 in the neural network 3100. Since the output signal from
the output layer 3104 is a value in the range from 0 to 1, this
output signal is transformed to the actual value corresponding to
the sensor signal in the reverse procedure by using a predetermined
transformation map in the output transform portion 3108. Here,
since the neural network 3100 in FIG. 39 outputs the blow-out port
signal TMODE, it does not need to reverse transform the output from
the output layer 3104. Therefore, this neural network 3100 does not
have the output transform portion 3108.
324. The environment conditions, of which the air-conditioning
device for vehicle faces, are variable, as described above.
Therefore, the number of the teacher signals, which are desired
values related to the various environment conditions, will be
enormously.
325. Therefore, in a designing process of the ECU 3019, the
learning is executed by a high-speed calculator including a neural
network, which is the same as the neural network 3100, before
mounted to the vehicle. The high-speed calculator calculates the
link coefficients 3106 so that the output of the neural network is
to be desired teacher signals for various input signals. Then, the
calculated coefficients 3106 are memorized in a memory portion
(ROM) corresponding to each neural network 3100, 3200, 3300, 3400
in each of the calculating portions of the ECU 3019 shown in FIG.
38.
326. Therefore, when the ECU 3019 is mounted on the vehicle, the
link coefficients 3106 among the neurons 3105 in each layer of the
neural networks 3100-3400 are set to predetermined value.
327. After mounted on the vehicle, the neural networks 3100-3400 in
each layer calculate an output for input signals as shown in FIG.
40A. Specifically, in each of the neurons 3105, each of the input
signals O.sub.l-O.sub.n is multiplied by each of the corresponding
coefficients 3106 (W.sub.l- W.sub.n), and multiplied value are
adapted to a so-called sigmoid function as shown FIG. 40B. The
calculated results of the sigmoid function are output to the
neurons 3105 in backward layer. These calculations are repeated
between each layer to decide a final output.
328. Since the neural network 3100 in each calculating portion of
mounted ECU 3019 is previously set so that the desired value for
the various input signals O.sub.l-O.sub.n can be obtained, learning
for correction of the link coefficients does not needed.
329. Therefore, the neural networks 3100 as shown in FIG. 39 of the
blow-out port mode calculating portion 3031 calculates and outputs
the desired value as the blow-out port mode signal TMODE (=0-1)
related to the changes of input signals (TAO, Ts, Tam, Tw).
330. The neural network 3200 of the temporary target temperature
calculating portion 3027 outputs the desired teacher signals as the
temporary target blow-out temperature TAOB in relation to the
change of the input signals (Tset, Tr, Tam).
331. The neural network 3300 of the radiation correction
calculating portion 3028 outputs the desired teacher signals as the
radiation amount correction TAOS in relation to the change of the
input signals (Tset, Tr, Tam, Ts).
332. The neural networks 3100 of the air amount calculating portion
3032 calculates and outputs the desired value as the blower voltage
level TBLO in relation to the changes of input signals (Tset, Tr,
Tam, Ts).
333. In the target temperature calculating portion 3029, the final
target blow-out temperature TAO is calculated by the following
equation:
TAO=TAOB-TAOS (5 )
334. In the air-mixing door opening degree calculating portion
3030, the air-mixing door opening degree SW is calculated based on
the TAO from the target temperature calculating portion 3029, the
temperature Te of the evaporator 3004, and the water temperature Tw
of the heater core 3005, by using the following equation:
Sw(%)=(TAO-Te)/(Tw-Te).times.100 (6)
335. The control flow according to the preferred embodiment will be
explained with reference to a flow chart shown in FIG. 44. This
control routine starts from a control switch signal, which starts
the air-conditioning device.
336. At step 3510, the air-conditioning ECU 19 is reset. At step
3520, the ECU 19 inputs the output signals from each sensor such as
the internal air temperature Tr, the external air temperature Tam,
and the signals from the temperature setter 3021.
337. At step 3530, the temporary target blow-out temperature TAOB
is calculated via the neural network 3200, shown in FIG. 41. The
step 3530 corresponds to the temporary target temperature
calculating portion 3027 in FIG. 38.
338. At step 3540, the radiation amount correction TAOS is
calculated via the neural network 2300, shown in FIG. 42. The step
3540 corresponds to the radiation amount correction calculating
portion 3028 in FIG. 38.
339. At step 3550, the final target blow-out temperature TAO is
calculated by the equation (5). The step 3550 corresponds to the
second target temperature calculating portion 3030 in FIG. 38.
340. At step 3560, the air-mixing door opening degree SW is
calculated by the equation (6). The step 3560 corresponds to the
air-mixing door opening degree calculating portion 3030 in FIG.
38.
341. At step 3570, the blow-out port mode signal TMODE is
calculated via the neural network 3100, shown in FIG. 39. The step
3570 corresponds to the blow-out port mode calculating portion 3031
in FIG. 38. Here, outputs in the blow-out port mode signal TMODE
have characteristics that this output increases in proportion to an
increasing of the target blow-out temperature TAO. One of a face
mode (FACE), a bi-level mode (B/L), and a foot mode (FOOT), which
are shown in FIG. 45, is determined based on the output TMODE from
the neural network 3300.
342. At step 3580, the blower voltage TBLO is calculated via the
neural network 3400, shown in FIG. 44. The step 3580 corresponds to
the air amount calculating portion 3032 in FIG. 38.
343. At steps 3590-3610, each of actuators such as motors 3014-3018
and the motor applied voltage control circuit 3020 as shown in FIG.
37 are controlled so as to correspond to the above-mentioned
calculated values.
344. The procedure of calculation of the blow-out port mode signal
TMODE via the neural network 3100 will be explained in detail. In
FIG. 48, which is a diagram of characteristic illustrating a
relationship between inputs 1-4 and the output (teacher signal),
the inputs 1-4 and the output correspond to TAO, Ts, Tam, Tw and
TMODE. When the input conditions are as follows, that is
TAO=48.degree.C., Ts=1000W/m.sup.2, Tam=0.degree.C.,
Tw=80.degree.C. (i.e., there is the radiation Ts and the external
air temperature Tam is low), and output is 0.95, the blow-out port
mode TMODE is set the FOOT mode as shown in 47A. In this case, the
passenger may feel hot to the head portion.
345. Then, the output signal (teacher signal) is changed from 0.95
to 0.5 at above described input conditions.
346. Here, the neural network has the learning function, which
adjusts the link coefficients (synapse weights) 3106 between each
neurons in each layers in the neural network automatically to
correct its output to be desired data (teacher signal). Therefore,
the output at a specific input condition can be adjusted, by
changing the teacher signal at the specific input condition and
then adjusting the link coefficients (synapse weights) 3106
automatically in advance.
347. By changing the output (TMODE=0.95-0.5), it enable to set the
blow-out port mode to the B/L mode so as to blow-out the cooled air
to the upper body of the passenger, and enable to reduce the hot
feeling due to the radiation to improve the air-conditioning
feeling of the passenger.
348. Furthermore, since the neural network adjusts its whole link
coefficients so that the desired outputs at the other input
condition are maintained even if the output at the specific input
condition is changed. Therefore, the output change at the specific
input condition does not influence the outputs at the other input
conditions. Hence, in no radiation condition, it enable to set the
blow-out port mode to the FOOT mode so as to blow-out the heated
air to the foot area of the passenger, and enable to improve the
air-conditioning feeling of the passenger.
349. Furthermore, since the correction of the link coefficients
3106 are adjusted automatically by using the learning function of
the neural network, it will not be essential for the engineers to
construct a complicated control logic such that the desired output
can be obtained only at specific input condition. Hence, engineer's
process can be eliminated substantially.
350. Since the complicated process does not needed, a capacitance
of a memory (ROM) of a computer, which constitutes an
air-conditioning electric control device, can be decreased.
351. Here, when the B/L mode is selected in the radiation condition
at low temperature as shown in FIG. 47B, it is desirable to set an
upper limitation (A/M limiter) for the air-mixing door opening
degree as shown in FIG. 47C.
352. That is, as shown in FIG. 47C, in a B/L mode extension area d
in which TAO is "a" or more, it is desirable to restrict a blowing
of the heated air from the face blow-out ports 3009a-3009d. In
order to meet this desire, an actual blow-out temperature to the
passenger component is restricted to a predetermined temperature by
setting the upper limitation of the air-mixing door opening degree.
Here, in this figure, a line "b" represents the face blow-out
temperature and a line "c" represents the foot blow-out
temperature.
353. Next, the switching control of the blow-out port mode in the
following condition will be explained. That is, the condition that
the air-conditioning device starts just after the vehicle engine
starts at a low external temperature in winter will be explained.
After the engine starts, the temperature of an engine coolant (the
temperature of the hot water to the heater core 3005) rises. Then,
the temperature of the blow-out air of the-heater core 3005 rises.
Further, the temperature of blow-out air to the passenger component
rises. Here, the voltage applied to the motor of the blower is
adjusted so that the air amount to the passenger component
increases in proportion to the rising of the hot water
temperature.
354. Here, when the blow-out port mode to the passenger component
is maintained to the FOOT mode, it may take long time to warm up
the upper body of the passenger because the body of the passenger
is warmed up from his/her lower body. Hence, the passenger may not
feel comfort.
355. Therefore, it is desirable to set the blow-out port mode to
the FOOT mode initially, then set it to the B/L mode in proportion
to the rising of the hot water temperature. By setting to the B/L
mode, it enables to blow-out air to the upper body of the passenger
from the face blow-out port so as to warm up the upper body early.
Here, when the air-conditioning device starts at low external
temperature, the control of the air-conditioning device is set to a
maximum heating. That is, the air-mixing door opening degree SW is
set to its maximum degree (100%) so that an air passage to the
heater core 3005 is set to be full opened.
356. FIG. 49 shows a result of an experience to investigate a
condition that the passenger feels the B/L mode comfort when the
air-conditioning device starts at low external temperature. In this
experience was conducted on monitor people. According to the
experience, the following results were obtained. As shown in this
figure, there are certain ranges of the hot water temperature in
which the passenger feels comfort (comfort range) in the B/L mode
for each the external air temperatures. The comfort ranges of the
B/L mode (ranges between a line .DELTA.-.DELTA. and a line
.quadrature.-.quadrature. in figure) are related to the external
air temperature and the hot water temperature. Specifically, the
comfort ranges rise in proportion to falling of the external air
temperatures. Here, ranges upper a line .times.-.times. are that
the passenger feels hot, and ranges lower a line
.largecircle.-.largecircle. are that the passenger feels cold. Each
the range between the line .times.-.times. and the line
.DELTA.-.DELTA., and ranges between the line
.quadrature.-.quadrature. and the line .largecircle.-.largecircle.
are intermediate ranges that the passenger can not determine
whether comfort or not.
357. Here, the temperature of air actually blown to the passenger
component is lowered to around 90% of the hot water due to a heat
transfer efficient between the hot water of the heater core 3005
and air.
358. Based on the experience, this embodiment provides the
following control for the relationship between the inputs 1-4 of
the neural network 3100 and the output TMODE. That is, as shown in
FIG. 50, when the inputs 1-4 are TAO=80.degree.C., Ts=0 W/m.sup.2,
Tam=-10.degree.C., and when the Tw is within a range of
53.degree.C. to 62.degree.C., the output is changed from 0.95 to
0.5. Therefore, it enables to change the blow-out port mode from
the FOOT mode to the B/L mode. Consequently, it enables to warm up
the upper body of the passenger early within the range of Tw so as
to improve the air-conditioning feeling and a driving safety.
359. In the other water temperature range, since the blow-out port
mode can be set to the FOOT mode by maintaining the output 0.95, it
can prevent over-cooling to the foot area in the B/L mode when the
water temperature is low. Also, it can prevent over-heating to the
upper body in the B/L mode when the water temperature is high.
360. (Fifth Embodiment)
361. In the fourth embodiment, in the start timing at the low
external temperature, the blow-out port mode is set to the B/L mode
when the hot water temperature is within the certain range.
However, when the internal air temperature Ts is already risen up
due to radiation or the like, it is desirable to change the
blow-out port mode from the B/L mode to the FOOT mode early so as
to prevent occurring the over-heating to the upper body in the B/L
mode.
362. Therefore, in this embodiment, the neural network 3100 further
inputs the internal air temperature Tr as its input as shown in
FIG. 51. A changing timing, at which the blow-out port mode is
changed from the B/L mode to the FOOT mode, is changed to early
timing when the internal air temperature rises over a predetermined
temperature. Hence, it can restrain occurring the over-heating to
the upper body in the B/L mode in advance. That is, even if the
external air temperature is equal, when the internal air
temperature Tr rises over the predetermined temperature, a
threshold value (hot water temperature), at which the blow-out port
mode is changed from the B/L mode to the FOOT mode, is changed to
low temperature side.
363. (Sixth Embodiment)
364. The neural network 3100 of the fifth embodiment input the
internal air temperature Tr as an additional input. In this
embodiment, the neural network 3100 inputs a skin temperature
signal Th, which is output from a skin temperature detecting sensor
(not shown) for detecting a skin temperature of the passenger,
replace with the internal air temperature as shown in FIG. 52.
365. According to this embodiment, when the skin temperature Th of
the passenger rises over a predetermined temperature due to the
radiation or the like, a threshold value (hot water temperature),
at which the blow-out port mode is changed from the B/L mode to the
FOOT mode, is changed to low temperature side. Hence, it can
restrain occurring the over-heating to the upper body in the B/L
mode in advance.
366. (Seventh Embodiment)
367. The seventh embodiment is modification of the fourth
embodiment. In this embodiment, the neural network 3100 is equal to
that shown in FIG. 39, which inputs the external air temperature
Tam as an additional input. In this embodiment, a changing point of
the blow-out port mode is changed with respect to the external air
temperature Tam.
368. When the TAO is in a high temperature side (heating area), the
changing point (between the B/L mode and the FOOT mode) at the low
external air temperature (Tam=-10.degree.C.) is set low than that
at the high external air temperature (Tam=10.degree.C.) with
respect to the TAO. Hence, it can enlarge a FOOT mode area so as to
perform the foot heating positively. Consequently, in can improve
the heating feeling in the low external air temperature.
369. When the TAO is in a low temperature side (cooling area), the
changing point (between the B/L mode and the FACE mode) at the high
external air temperature (Tam=30.degree.C.) is set high than that
at the low external air temperature (Tam=10.degree.C.) with respect
to the TAO. Hence, it can enlarge a FACE mode area so as to perform
the face cooling positively. Consequently, in can improve the
cooling feeling in the high external air temperature.
370. (Eighth Embodiment)
371. In this embodiment, the present invention is adopted to an
air-conditioning device for automobiles, which is capable of
controlling independently between a driver seat (Dr) side
air-conditioning zone and a passenger seat (Pa) side
air-conditioning zone in a passenger component.
372. In an air-duct 3002, a partitioning wall 3002a is arranged
from the heater core 3005 portion to its downstream in the air duct
3002 to divide it into a Dr side air passage 3002b and a Pa side
air passage 3002c.
373. A Dr side air-mixing door 3061 is arranged at the upstream of
the heater core 3005 in the Dr side air passage 3002b. The Dr side
air-mixing door 3061 adjusts a ratio of two air amount in the Dr
side air passage 3002b, wherein an amount of heated air passed
through the heater core 3005 and an amount of cooled air by-passed
the heater core 3005. A Pa side air-mixing door 3062 is arranged at
the upstream of the heater core 3005 in the Pa side air passage
3002c, and adjusts a ratio of two air amount in the Pa side air
passage 3002c. Here, the two air amount are an amount of heated air
passed through the heater core 3005 and an amount of cooled air
by-passed the heater core 3005.
374. At the most downstream side of the Dr side air passage 3002b,
a foot blow-out port 3008a, face blow-out ports 3009a, 3009b are
provided. At the most downstream side of the Pa side air passage
3002c, a foot blow-out port 3008b, face blow-out ports 3009c, 3009d
are provided. Blowout port change-over doors 3011a, 3012a are
arranged to selectively open/close the Dr side foot blow-out port
3008a and the face blow-out ports 3009a, 3009b. Blowout port
change-over doors 3011b, 3012b are arranged to selectively
open/close the Pa side foot blow-out port 3008b and the face
blow-out ports 3009c, 3009d. A defroster blow-out port 3010 and its
blow-out port change-over door 13 are provided in the same way as
the second embodiment.
375. The Dr side air-mixing door 3061 and the Pa side air-mixing
door 3062 are driven by independent servomotors 3015 and 3015a. The
Dr side blow-out port change-over doors 3011a, 3012a and the Pa
side blow-out port change-over doors 3011b, 3012b are driven by
independent servomotors 3160 and 3170. Each of predetermined
blow-out mode, such as a FACE mode (FACE), a BI-LEVEL mode (B/L
mode), and a FOOT mode (FOOT) or the like can be set with
respective to each ports 3002c, 3002c, independently, by changing
the open/close condition of the blow-out port change-over doors
3011a, 3012a, 3011b and 3012b.
376. A Dr side temperature setter (first temperature setter) 3021a
is provided to set a setpoint temperature Tset(Dr) of the Dr side
air-conditioning zone relative to the Dr side air passage 3002b,
and output the setpoint temperature Tset (Dr) to the ECU 3019. A Pa
side temperature setter (second temperature setter) 3021b is
provided to set a setpoint temperature Tset(Pa) of the Pa side
air-conditioning zone relative to the Pa side air passage 3002c,
and output the setpoint temperature Tset(Pa) to the ECU 3019. Both
temperature setters 3021b, 3021c are provided independently each
other.
377. A Dr side radiation sensor 3024a is arranged to detect a
radiation amount TsDr to the Dr side air-conditioning zone, and a
Pa side radiation sensor 3024b is arranged to detect a radiation
amount TsPa to the Pa side air-conditioning zone.
378. As temperature data detectors, an internal air sensor 3022 to
is arranged detect an internal air temperature Tr. An external air
sensor 3023 is arranged to detect an external air temperature Tam.
An evaporator temperature sensor 3025 is arranged to detect a
cooling temperature (blow-out air temperature) of the evaporator
3004. A water temperature sensor 3026 is arranged to detect a
temperature Tw of hot water flowing into the heater core 3005.
379. Control functions processed by the microcomputer in the ECU
3019 are executed independently between the Dr side and the Pa side
as shown in FIG. 31. In this figure, subscript "a" after symbol
means Dr side functions, and "b" after symbol means Pa side
functions.
380. A blower voltage TBLO, which decides an air amount, calculated
as follows. A Dr side blower voltage level TBLO(Dr) and a Pa side
blower voltage level TBLO(Pa) are calculated by an air amount
calculating portions 3032a, 3032b. Then, an average of the blower
voltage TBLO(Dr) and the blower voltage TBLO(Pa) are calculated by
an air amount calculating portion 3032c The averaged value is
output as a final blower voltage TBLO.
381. Blowout port mode calculating portions 3031a, 3031b are
provided to calculate a Dr side blow-out port-mode signal TMODE(Dr)
and a Pa side blow-out port mode signal TMODE(Pa)
independently.
382. A neural network 3100, which constitutes each the Dr side
blow-out port mode calculating portion 3031a and the Pa side
blow-out port mode calculating portion 3031b, inputs one of the Dr
side target blow-out temperature TAO(Dr) and the Pa side target
blow-out temperature TAO(Pa), and further inputs one of the Dr side
radiation amount TsDr and the Pa side radiation amount TsPa. Then,
one of the Dr side blow-out port mode signal TMODE(Dr) and the Pa
side blow-out port mode signal TMODE(Pa) is calculated
independently.
383. A neural network 3200, which constitutes each a Dr side
temporary target temperature calculating portion 3027a and a Pa
side temporary target temperature calculating portion 3027b, inputs
one of setpoint temperatures Tset(Dr) and Tset(Pa) of each the Dr
side and the Pa side, and further inputs a difference (.DELTA.Tset)
between the setpoint temperatures Tset(Dr) and Tset(Pa). By
inputting the difference (.DELTA.Tset) it can restrain a
temperature interference between the first air-conditioning zone
and in the second air-conditioning zone.
384. Here, in this embodiment, it is acceptable to control both the
Dr side blow-out port mode and the Pa side blow-out port mode in
the same way. That is, it does not need to control independently.
In that case, as shown in FIG. 56, a neural network 3100 inputs an
average TAOX, which is an averaged value of a Dr side target
blow-out temperature TAO (Dr) and a Pa side target blow-out
temperature TAO (Pa), and further input an average TsX, which is an
averaged value of the Dr side radiation amount TsDr and the Pa side
radiation amount TsPa. Then, the neural network 3100 calculates the
blow-out port mode signal TMODE.
385. The fourth to eighth embodiments can be also modified as
follows.
386. (1) In the above-described embodiment, the target blowout
temperature TAOi, the air amount (blower voltage level) are
calculated via the neural networks 3200, 3300, 3400. However, it is
acceptable to calculate one of them by conventional calculation
without neural networks.
387. (2) The air-mixing doors 3006, 3061, 3062 are arranged to
adjust the mix ratio of cooled air and heated air as the
temperature adjuster. However, it is replaceable with a hot water
valve to control an amount of hot water flowing into the heater
core 3005 or a temperature of the hot water.
388. (3) In the eighth embodiment, the neural network 3100 inputs
the average value of the Dr side target blow-out temperature
TAO(Dr) and the Pa side target blow-out temperature TAO(Pa).
However, it is acceptable to change weights of one of the Dr side
target blow-out temperature TAO(Dr) and the Pa side target blow-out
temperature TAO (Pa). That is, it acceptable for neural network
3100 to input a changed target blow-out temperature TAO'. For
example, TAO'=TAO(Dr).times.80(%)+TAO(Pa).times.20%
389. (4) As the temperature setter 3021, 3021a, 3021b for setting
the temperature of the air-conditioning zones, it can be used an
analog display in which indicates the temperature without digital
figures, e.g., the temperature is indicated by colors.
390. (5) The present invention can be adaptable to another
air-conditioning device, which has plurality of air-conditioning
zone, and is needed to control each of temperature of blow-out air
to each zones independently.
391. (6) The neural networks 3100-3400 are replaceable with
recurrent type neural networks.
392. (Ninth Embodiment)
393. A fourth embodiment of the present invention will be described
hereinafter with reference to FIGS. 57-64. FIG. 57 is a diagram
illustrating the constitution of whole system of an
air-conditioning device for automobiles.
394. In FIG. 57, an internal air/external air change-over door 4001
is arranged at the most upstream side of air flow in the
air-conditioning device for automobiles. One of an external air and
an internal air is selectively introduced into an air duct 4002 by
moving the door 4001.
395. The air duct 4002 constitutes an air passage of the
air-conditioning device. A blower 4003, an evaporator 4004 and a
heater core 4005 are arranged from upstream side to downstream side
in the air duct 4002. The evaporator 4004 is a cooling heat
exchanger for cooling an air by absorbing an evaporating latent
heat of a refrigerant in a refrigerating cycle from the air. The
heat core 4005 is a heating heat exchanger for heating air with
heat from a hot water (engine coolant) from a vehicle engine (not
shown).
396. An air-mixing door 4006 as a temperature adjuster is arranged
at the upstream of the heater core 4005. The air-mixing door 4006
adjusts a ratio of two air amount, wherein an amount of heated air
passed through the heater core 4005 and an amount of cooled air
by-passed the heater core 4005. The adjusting the ratio of heated
air and cooled air can adjusts the blow-out air temperature to a
passenger component of the automobile.
397. At the most downstream side of the air duct 4002, foot
blow-out ports 4008a, 4008b, face blow-out ports 4009a-4009d, and a
defroster blow-out port 4010 are provided. Here, the foot blow-out
ports 4008a, 4008b are provided to blow the conditioned air onto
the feet of the passengers. The face blow-out ports 4009a-4009d are
provided at each of center portion and side portion in the
passenger component to blow the conditioned air to the upper half
of the body of the passengers. The defroster blow-out port 4010 is
provided to blow the conditioned air to a windshield.
398. At the most downstream side of the air duct 4002, blow-out
port change-over doors 4011-4013 are arranged to selectively
open/close the blow-out ports 4008a, 4008b and 4009a-4009d. Each of
predetermined blow-out modes, such a FACE mode (FACE), a BI-LEVEL
mode (B/L mode), a FOOT mode (FOOT), and a defroster mode or the
like can be set by changing the open/close condition of the doors
4011-4013.
399. Here, a control system for controlling the air-conditioning
device will be explained. The internal air/external air changing
door 4001, the air-mixing door 4006, and the blow-out port
change-over doors are driven by servomotors 4014-4018. The
servomotors 4014-4018 are controlled by outputs of an
air-conditioning electric control device 4019 (hereinafter, called
"ECU 4019"). A motor 4003a of the blower 4003 is also controlled by
the output of the ECU 4019 via a motor control circuit 4020 (motor
applied voltage control circuit).
400. An air amount blown by the blower 4003 is adjusted by the
motor control circuit 4020 by way of changing a rotation speed of
the motor with an applied voltage to the motor. The ECU 4019
includes a microcomputer and its peripheral circuits.
401. A temperature setter 4021 is provided to an air-conditioning
control panel 27 to set a setpoint temperature Tset of the
passenger component, which is input to the ECU 4019. The
temperature setter 4021 is arranged an air-conditioning control
panel, and is manually controlled by users.
402. As temperature data detectors, the following sensors are
provided. That is, an internal air sensor 4022 is provided to
detect an internal air temperature Tr. An external air sensor 4023
is provided to detect an external air temperature Tam. A radiation
sensor 4024 is provided to detect a radiation amount Ts to the
passenger component. An evaporator temperature sensor 4025 is
provided to detect a cooling temperature Te (blow-out air
temperature) of the evaporator 4004. A water temperature sensor
4026 is provided to detect a water temperature Tw of hot water
flowing into the heater core 4005.
403. Control functions processed by the microcomputer in the ECU
4019 is generally divided as follows. The ECU 4019 includes a
temporary target temperature calculating portion 4028, a radiation
correction coefficient calculating portion 4029, a radiation amount
correction calculation portion 4030, a target temperature
calculating portion 4031, an air amount calculating portion 4032, a
suction port mode calculating portion 4033, an blow-out port
calculating portion 4034, and an air-mixing door opening degree
calculating portion 4035.
404. The temporary target temperature calculating portion 4028
calculates a temporary target blow-out temperature. The temporary
target temperature calculating portion 4028 inputs signals
including the setpoint temperature Tset, the internal air
temperature Tr and the external air temperature Tam, and calculates
the temporary target blow-out temperature TAOB based on the input
signals by an equation (7) described after.
405. The radiation correction coefficient calculating portion 4029
calculates a radiation correction coefficient Fs, and includes a
neural network 4100 as shown in FIG. 58. The radiation correction
coefficient calculating portion 4029 inputs signals including a
temperature difference TD between the setpoint temperature Tset and
the internal air temperature Tr, and the external air temperature
Tam. Then, the radiation correction coefficient calculating portion
4029 calculates the radiation correction coefficient Fs via the
neural network 4100. Then, the radiation amount correction
calculating portion 4030 calculates the radiation amount correction
TAOS based on the radiation correction coefficient Fs and the
amount of (solar) radiation Ts by using an equation described
after.
406. The target temperature calculating portion 4031 calculates the
final target blow-out temperature TAO based on the output signals
from the temporary target temperature calculating portion 4028 and
the radiation amount correction calculating portion 4030.
407. The air amount calculating portion 4032 inputs the target
blow-out temperature TAO and calculates a blower voltage for
deciding an mount of air from the blower based on a characteristic
of FIG. 61 described after.
408. The suction port calculating portion 4033 inputs the target
blow-out temperature TAO and calculates a suction port mode of
internal and external air based on a characteristic of FIG. 62
described after.
409. The blow-out port calculating portion 4034 inputs the target
blow-out temperature TAO and calculates a blow-out port mode of
internal and external air based on a characteristic of FIG. 63
described after.
410. The air-mixing door opening degree calculating portion 4035
inputs the target blow-out temperature TAO and calculates an
air-mixing door opening degree SW by using an equation (10)
described after.
411. The constitutions of the neural networks 4100, which
constitutes the radiation correction coefficient portion 29, will
be explained with reference to FIG. 58.
412. As shown in FIG. 58, the neural network 4100 includes an input
layer 4101, a first intermediate layer 4102, a second intermediate
layer 4103, and output layer 4104. Each of the input layer 4101,
the first and the second intermediate layers 4102, 4103 has a
plurality of neurons 4105, and output layer has one neuron 4105.
Each of neurons 4105 in the input layer 4101 is connected to each
of neurons 4105 in the first intermediate layer 4102 with a certain
link coefficient 4106 (synapse weight). Similarly, each of neurons
4105 in the first intermediate layer 4102 is connected to each of
neurons 4105 in the second intermediate layer 4103 with a certain
link coefficient 4106. Each of neurons 3405 in the second
intermediate layer 4103 is connected to the neuron 4105 in the
output layer 4104 with a certain link coefficient 4106. These
neurons 4105 are formed by memories in the ECU 4019 in an actual
circuit, and the neural network 4100 is formed by combined
memories.
413. The neural network 4100, which is a network having a layered
structure, has a Back Propagation Learning Function. The Back
Propagation Learning Function can automatically correct the link
coefficients 4106 between each neurons among the input layer 4101,
the first and second intermediate layers 4102, 4103, and output
layer, so as to adjust its output to a desired value (teacher
signal) related to input signals such as TD or Tam.
414. When the teacher signal is changed, the link coefficients 4106
are adjusted by repeatedly "learning" so as to correct the output
to be the changed teacher signal related to the input signals. In
other words, based on a large number of desired data (teacher
signals), a correlation function (link coefficients 4106) is
automatically generated. The teacher signals are determined to
desired values obtained by an experience or the like (desired
output for the input signals).
415. In the layered structure neural network 4100, there is no
connection among each of the neurons 4105 in an identical layer,
but the neurons 4105 in each layer are connected to only neurons
4105 in forward and backward layers. The clink coefficients 4106
between each neuron 4105 in each layer represent a weight of
connection (magnitude). The larger the weight of connection, the
larger an amplitude of signal, which is output from each neuron
4105 and propagated to neurons 4105 in backward layer.
416. Here, the input signals (TD, Tam) are normalized to 0-1 value
before inputting to the neural network 4100 by a normalize portion
4107. The output signal from the neural network 4100 is transformed
in reverse procedure of the normalize portion 4107 by an output
transform portion 4108. For example, actual detected internal
temperature Tr detected by the internal air sensor is normally in a
range from -30.degree. C. to 50.degree.C. This detected value is
assigned to a certain value in a range from 0 to 1 in the normalize
portion 4107, and is output to the input layer 4101 in the neural
network 4100. Since the output signal from the output layer 4104 is
a value in the range from 0 to 1, this output signal is transformed
to the actual value corresponding to the sensor signal in the
reverse procedure by using a predetermined transformation map in
the output transform portion 4108.
417. The environment conditions, of which the air-conditioning
device for vehicle faces, are variable, as described above.
Therefore, the number of the teacher signals, which are desired
values related to the various environment conditions, will be
enormously.
418. Therefore, in a designing process of the ECU 4019, the
learning is executed by a high-speed calculator including a neural
network, which is the same as the neural network 4100, before
mounted to the vehicle. The high-speed calculator calculates the
link coefficients 4106 so that the output of the neural network is
to be desired teacher signals for various input signals. Then, the
calculated coefficients 4106 are memorized in a memory portion
(ROM) corresponding to the neural network 4100 in the radiation
correction coefficient calculating portion 4029 of the ECU 4019
shown in FIG. 57.
419. Therefore, when the ECU 4019 is mounted on the vehicle, the
link coefficients 4106 among the neurons 4105 in each layer are set
to predetermined value.
420. After mounted on the vehicle, the neural network 4100 in ECU
4019 calculate an output for input signals as shown in FIG. 59A.
Specifically, in each of the neurons 4105, each of the input
signals O.sub.l-O.sub.n is multiplied by each of the corresponding
coefficients 4106 (W.sub.l-W.sub.n), and multiplied value are
adapted to a so-called sigmoid function as shown FIG. 59B. The
calculated results of the sigmoid function are output to the
neurons 4105 in backward layer. These calculations are repeated
between each layer to decide a final output.
421. Since the neural network 4100 in each calculating portion of
mounted ECU 4019 is previously set so that the desired value for
the various input signals O.sub.l-O.sub.n can be obtained, learning
for correction of the link coefficients does not needed.
422. Therefore, the neural networks 4100 of the radiation
correction coefficient calculating portion 4029, as shown in FIG.
57, calculates and outputs the desired value as the radiation
correction coefficient Fs in relation to the changes of input
signals (TD, Tam).
423. The control flow according to the preferred embodiment will be
explained with reference to a flow chart shown in FIG. 60. This
control routine starts when the ECU 4019 is powered by turning on
an ignition switch (not shown) of the Vehicle.
424. At step 4510, a memory or the like in the ECU 4019 are
initialized. At step 4520, the ECU 4019 inputs signals from control
switches including the temperature setter 4021 of the
air-conditioning control panel 4027 and so on.
425. At step 4530, the ECU 4019 inputs sensor signals from the
sensors (4022-4026).
426. At step 4540, the temporary target blow-out temperature TAOB
is calculated by eliminating a radiation term in the following
equation (7). The step 4540 corresponds to the temporary target
temperature calculating portion 4028 in FIG. 57.
TAOB=Kset.times.Tset-Kr.times.Tr-Kam.times.Tam+C (7)
427. Here, Tset is the setpoint temperature, Tr is the internal air
temperature, Tam is the external air temperature, Kset is a
temperature set gain, Kr is an internal air temperature gain, Kam
is an external air temperature gain, and C is an correction
constant value.
428. At step 4550, the ECU 4019 inputs external air temperature and
the temperature difference TD between the setpoint temperature
Tset, and calculates the radiation correction coefficient Fs. The
step 4550 corresponds to the radiation correction coefficient
calculating portion 4029 in FIG. 57.
429. At step 4560, the radiation amount correction TAOS is
calculated by multiplying the radiation correction coefficient Fs,
the radiation Ts from the radiation sensor 4024, and a radiation
correction proportional gain Ks. The step 4560 corresponds to the
radiation amount correction calculating portion 4030 in FIG.
57.
TAOS=Ks.times.Fs.times.Ts (8)
430. Here, the radiation amount correction TAOS can be adjusted by
adjusting the radiation correction proportional gain Ks in the
equation (8). Therefore, when the radiation amount TAOS needs to be
adjusted based on an air-conditioning feeling result of actual
running test, the radiation amount correction TAOS can be adjusted
by the radiation correction proportional gain Ks without changing
the radiation correction coefficient Fs. Therefore, it does not
need to have the neural network learn again to adjust the radiation
amount correction TAOS.
431. At step 4570, the target blow-out temperature TAO is
calculated based on the temporary target blow-out temperature TAOB
And the radiation amount correction TAOS by using the following
equation (9). The step 4570 corresponds to the target blow-out
temperature calculating portion 4031 in FIG. 57.
TAO=TAOB-TAOS (9)
432. At step 4580, the blower voltage is calculated based on the
target blow-out temperature TAO with reference to a relationship
(map) shown in FIG. 61. The step 4580 corresponds to the air amount
calculating portion 4032 in FIG. 57.
433. At step 4590, the suction port modes of each the internal air,
the external air, and the half internal air are calculated based on
the target blow-out temperature TAO with reference to a
relationship (map) shown in FIG. 62. The step 4590 corresponds to
the suction port mode calculating portion 4033 in FIG. 57.
434. At step 4600, the blow-out port mode including FACE, B/L, FOOT
mode is calculated based on the target blow-out temperature TAO
with reference to a relationship (map) shown in FIG. 63. The step
4600 corresponds to the blow-out port mode calculating portion 4034
in FIG. 57.
435. Here, the relationships (maps) of FIGS. 61-63 are memorized
previously in memories in the ECU 4019.
436. At step 4610, the air-mixing door opening degree SW is
calculated based on the target blow-out temperature TAO, the
temperature Te of the evaporator 4004, and a hot water temperature
Tw of the heater core 4005 by using the following equation (10).
The step 4610 corresponds to the air-mixing door opening degree
calculating portion 4035 in FIG. 57.
SW(%)=(TAO-Te)/(Tw-Te).times.100 (10)
437. At step 4620, the ECU 4019 outputs the values calculated at
the steps 4580-4610 as control signals to each of actuators such as
motors 4014-4018 and the motor applied voltage control circuit 4020
as shown in FIG. 57. Then, at step 4630, the ECU 4019 wait for
passing a certain time "t". After the time "t" is over, it returns
to the step 4520.
438. In this embodiment, the radiation correction coefficient Fs is
calculated via the neural network 4100, and the radiation amount
correction TAOS is calculated by multiplying the radiation
correction coefficient Fs, radiation signal Ts, and radiation
correction proportional gain Ks. A merit of calculation using the
above-mentioned procedure will be explained hereinafter.
439. FIG. 64 shows a calculating procedure of the radiation
correction coefficient Fs in a start timing of heating in winter.
The abscissa represents a temperature difference TD (=Tr-Tset)
between the internal air temperature Tr in the passenger component
and the setpoint temperature Tset. This difference TD is in a minus
because the internal air temperature Tr is lower than the setpoint
temperature Tset.
440. When the external air temperature Tam is rather high (ex.
10.degree.C.), the radiation correction is started from the point
TD=-a. That is, the radiation correction efficient Fs starts to
rise from 0 from the point TD=-a (at a point the internal air
temperature is lower than the setpoint temperature by temperature
"a" ). When the external air temperature Tam is rather low (ex.
-10.degree.C.), the radiation correction is started from the point
TD=-b. That is, the radiation correction efficient Fs starts to
rise from 0 from the point TD= -b (at a point the internal air
temperature is lower than the setpoint temperature by temperature
"b").
441. In this way, the start point of radiation correction is
delayed as the external air temperature is low. That is, the
radiation correction is delayed until the internal air temperature
reaches the setpoint temperature substantially. This delaying can
keep the target blow-out temperature high in the start timing of
heating in winter. That is, even if the temperature difference is
equal value, the warm-up time can be shortened by decreasing the
radiation amount correction so as to keep the TAO high when the
external air temperature is low.
442. (Tenth Embodiment)
443. FIGS. 65A, 65B correspond to FIG. 64, and show a
characteristic of a radiation correction according to the tenth
embodiment. FIG. 65A is substantially equal to FIG. 64. In FIG.
65A, KFs represents a maximum value of the radiation correction
coefficient Fs. FIG. 65B shows that the maximum value KFs of the
radiation correction coefficient Fs is further changed in
proportion to the external air temperature Tam.
444. As shown in FIG. 65A, the maximum value KFs is set in normal
operation region of heating mode, when the temperature difference
TD reaches zero as the internal air temperature reaches the
setpoint temperature Tset in a start timing of heating in winter.
Here, since an angle of the sun is rather small in winter, the
solar radiation is likely to be radiated to upper body of the
passenger. Then, the passenger may feel hot due to the
radiation.
445. Therefore, in this embodiment, during the normal operation,
the maximum value KFs is increased from 1.0 to 1.2 as the external
air temperature Tam falls from 20.degree.C. so as to increase the
radiation amount correction. Hence, the TAO can be set to low
temperature so as to set the blow-out port mode to B/L mode to blow
cooled air from a face blow-out port. Then, the air-conditioning
feeling can be improved.
446. (Eleventh Embodiment)
447. The radiation correction coefficient Fs according to the
eleventh embodiment is shown in FIG. 66. As shown in FIG. 67, the
neural network 4100 of the radiation correction coefficient
calculating portion 4029 inputs the radiation amount Ts in addition
to the temperature difference TD and the external air temperature
Tam.
448. In this embodiment, the start point of the radiation
correction is delayed as the external air temperature Tam is low.
Furthermore, when there is little radiation amount, the start point
of the radiation correction is further delayed. Since this
embodiment decides the radiation correction efficient Fs based on
both the external air temperature Ts and the radiation amount Ts,
the radiation amount correction can be calculated more accurately.
Then, the warm-up time in the start timing of heating in winter can
be shortened.
449. In this embodiment, it is acceptable that the maximum value
KFs is increase as the external air temperature Tam falls so as to
increase the radiation amount correction like the characteristic
shown in FIG. 65B. Then, the air-conditioning feeling in the normal
operation can be improved.
450. FIG. 68 is a diagram illustrating a relation between inputs
1-3 and an output (teacher signal) Fs of the neural network 4100.
As shown in FIG. 68, when the temperature difference TD as the
input 1 is equal (5.degree.C., 0.degree.C.), and the radiation
amount Ts as the input 3 is equal (500 W/m.sup.2), and the external
air temperature is 10.degree.C., the maximum value KFs is increased
from 1.0 to 1.2 so as to obtain a desired radiation correction
coefficient Fs and the desired radiation amount correction
TAOS.
451. Here, the neural network has the learning function, which
adjusts the link coefficients (synapse weights) 4106 between each
neurons in each layers in the neural network automatically to
correct its output to be desired data (teacher signal). Therefore,
the output at a specific input condition can be adjusted, by
changing the teacher signal at the specific input condition and
then adjusting the link coefficients 4106 automatically in
advance.
452. By above-mentioned changing of output (change of the Fs from
1.0 to 1.2), the heat feeling of the passenger (user) due to the
radiation to the his/her upper bodies can be reduced so as to
improve the air-conditioning feeling of the passenger.
453. Furthermore, since the neural network adjusts its whole link
coefficients 4106 so that the desired outputs (teacher signal) at
the other input condition are maintained even if the output at the
specific input condition is changed. Therefore, the output change
at the specific input condition does not influence the outputs at
the other input conditions.
454. Furthermore, since the correction of the link coefficients
4106 are adjusted automatically by using the learning function of
the neural network, it will not be essential for the engineers to
construct a complicated control logic such that the desired output
can be obtained only at specific input condition. Hence, engineer's
process can be eliminated substantially.
455. Since the complicated process does not needed, a capacitance
of a memory (ROM) of the ECU 4019, which constitutes an
air-conditioning electric control device, can be decreased.
456. Furthermore, in this embodiment, the temperature difference TD
(=Tr-Tset) between the setpoint temperature Tset and the internal
air temperature Tr is calculated, and then the calculated value is
input to the neural network 4100. Therefore, the number of input to
the neural network 4100 can be decreased compared to an input way
inputting both of the Tset and the Tr.
457. Here, it may need to input both of the setpoint temperature
Tset and the internal air temperature Tr to detect whether the
operation area is in a transition area, which is just after the
air-conditioning device starts (the internal air temperature is
changing to the setpoint temperature), or the normal operation
area. However, in this embodiment, this condition (the operation
area is in the normal operation area) can be detected from the fact
that the temperature difference TD between the internal air
temperature Tr and the setpoint temperature Tset reaches
substantially zero. Therefore, this embodiment can easily detect
whether the transition area or the normal operation by inputting
the temperature difference TD.
458. Since the number of input is decreased, the number of the
layer in the neural network can be also decreased, the link
coefficient can be decreased by about 25%, and the total learning
time for calculating the link coefficient 4106 can be decreased
largely.
459. It is acceptable to input both the setpoint temperature Tset
and the internal air temperature Tr replaced with the temperature
difference TD to the neural network 4100, if an increasing of the
number of the input would be less than allowable condition.
460. (Twelfth Embodiment)
461. The twelfth embodiment will be explained with reference to
FIG. 69. In this embodiment, the radiation correction coefficient
calculating portion 4029 shown in FIG. 57 and the radiation amount
correction calculating portion 4030 are combined, and the combined
calculating portion is formed by one neural network 4200. That is,
the neural network 4200 inputs signals including the temperature
difference TD, the external air temperature Tam and the radiation
amount Ts, and outputs the radiation amount correction TAOS
directly.
462. (Thirteenth Embodiment)
463. In this embodiment, as shown in FIG. 70, the present invention
is adopted to an air-conditioning device for automobiles, which is
capable of controlling independently between a driver seat (Dr)
side air-conditioning zone and a passenger seat (Pa) side
air-conditioning zone in a passenger component.
464. In an air-duct 4002, a partitioning wall 4002a is arranged
from the heater core 4005 portion to its downstream in the air duct
4002 to divide it into a Dr side air passage 4002b and a Pa side
air passage 4002c.
465. A Dr side air-mixing door 4061 is arranged at the upstream of
the heater core 4005 in the Dr side air passage 4002b. The Dr side
air-mixing door 4061 adjusts a ratio of two air amount in the Dr
side air passage 4002b, wherein an amount of heated air passed
through the heater core 4005 and an amount of cooled air by-passed
the heater core 4005. A Pa side air-mixing door 4062 is arranged at
the upstream of the heater core 4005 in the Pa side air passage
4002c, and adjusts a ratio of two air amount in the Pa side air
passage 4002c. Here, the two air amount are an amount of heated air
passed through the heater core 4005 and an amount of cooled air
by-passed the heater core 4005.
466. At the most downstream side of the Dr side air passage 4002b,
a foot blow-out port 4008a, face blow-out ports 4009a, 4009b are
provided. At the most downstream side of the Pa side air passage
4002c, a foot blow-out port 4008b, face blow-out ports 4009c, 4009d
are provided. Blowout port change-over doors 4011a, 4012a are
arranged to selectively open/close the Dr side foot blow-out port
4008a and the face blow-out ports 4009a, 4009b. Blowout port
change-over doors 4011b, 4012b are arranged to selectively
open/close the Pa side foot blow-out port 4008b and the face
blow-out ports 4009c, 4009d. A defroster blow-out port 4010 and its
blow-out port change-over door 13 are provided in the same way as
the ninth embodiment.
467. The Dr side air-mixing door 4061 and the Pa side air-mixing
door 4062 are driven by independent servomotors 4015 and 4015a. The
Dr side blow-out port change-over doors 4011a, 4012a and the Pa
side blow-out port change-over doors 4011b, 4012b are driven by
independent servomotors 4160 and 4170. Each of predetermined
blow-out mode, such as a FACE mode (FACE), a BI-LEVEL mode (B/L
mode), and a FOOT mode (FOOT) or the like can be set with
respective to each ports 4002b, 4002c, independently, by changing
the open/close condition of the blow-out port change-over doors
4011a, 4012a, 4011b and 4012b.
468. A Dr side temperature setter (first temperature setter) 4021a
is provided to set a setpoint temperature Tset(Dr) of the Dr side
air-conditioning zone relative to the Dr side air passage 4002b,
and output the setpoint temperature Tset(Dr) to the ECU 4019. A Pa
side temperature setter (second temperature setter) 4021b is
provided to set a setpoint temperature Tset(Pa) of the Pa side
air-conditioning zone relative to the Pa side air passage 4002c,
and output the setpoint temperature Tset(Pa) to the ECU 4019. Both
temperature setters 4021b, 4021c are provided independently each
other.
469. A Dr side radiation sensor 4024a is arranged to detect a
radiation amount TsDr to the Dr side air-conditioning zone, and a
Pa side radiation sensor 4024b is arranged to detect a radiation
amount TsPa to the Pa side air-conditioning zone.
470. As temperature data detectors, an internal air sensor 4022 is
arranged detect an internal air temperature Tr. An external air
sensor 4023 is arranged to detect an external air temperature Tam.
An evaporator temperature sensor 4025 is arranged to detect a
cooling temperature (blow-out air temperature) of the evaporator
4004. A water temperature sensor 4026 is arranged to detect a
temperature Tw of hot water flowing into the heater core 4005.
471. Control functions processed by the microcomputer in the ECU
4019 are executed independently between the Dr side and the Pa
side. The control functions include Dr side and Pa side temporary
target temperature calculating portions 4028, Dr side and Pa side
radiation correction coefficient calculating portions 4029, Dr side
and Pa side radiation amount correction calculating portions 4030,
Dr side and Pa side target blow-out temperature calculating
portions 4031, Dr side and Pa side blow-out port mode calculating
portions 4034, Dr side and Pa side air-mixing door opening degree
calculating portions and so on.
472. Therefore, one of the neural networks 4100, which constitute
each Dr side radiation correction coefficient calculating portion
4029, inputs signals including the external air temperature Tam, Dr
side temperature difference TDDr (=Tset(Dr)-TrDr), and Dr side
radiation amount TsDr, and calculates Dr side radiation correction
coefficient FsDr. Similarly, another of the neural networks 4100,
which constitute each Pa side radiation correction coefficient
calculating portion 4029, inputs signals including the external air
temperature Tam, Pa side temperature difference TDPa
(=Tset(Pa)-TrPa), and Pa side radiation amount TsPa, and calculates
Pa side radiation correction coefficient FsPa.
473. Here, in this embodiment, it is acceptable to control both the
Dr side blow-out port mode and the Pa side blow-out port mode in
the same way. That is, it does not need to control independently.
In that case, the blow-out port mode signal TMODE is calculated
based on an average TAOX, which is an averaged value of a Dr side
target blow-out temperature TAO(Dr) and a Pa side target blow-out
temperature TAO(Pa). Similarly, the Dr side suction port mode and
the Pa side suction port mode are controlled in the same way by
using the same calculating procedure.
474. The ninth to thirteenth embodiments can be also modified as
follows.
475. (1) In the above-described embodiment, in addition to the
radiation correction coefficient calculating portion 4029, it is
acceptable to form the other calculating portions by neural
networks. Here, the other calculating portions include such as the
temporary target temperature calculating portion 4028, the target
blow-out temperature calculating portion 4031, the air amount
calculating portion 4032, the suction port mode calculating portion
4034.
476. (2) The learning method of the neural network can be
replaceable with Semi Newton method or the like instead of the
above-mentioned back propagation method.
477. (3) The temperature difference TD can be obtained by using
just Tr and Tset to calculate (Tr-Tset), or using another factor in
addition to Tr and Tset.
478. (4) The air-mixing doors 4006, 4061, 4062 are arranged to
adjust the mix ratio of cooled air and heated air as the
temperature adjuster. However, it is replaceable with a hot water
valve to control an amount of hot water flowing into the heater
core 4005 or a temperature of the hot water.
479. (5) As the temperature setter 4021, 4021a, 4021b for setting
the temperature of the air-conditioning zones, it can be used an
analog display in which indicates the temperature without digital
figures, e.g., the temperature is indicated by colors.
480. (6) The present invention can be adaptable to another
air-conditioning device, which has plurality of air-conditioning
zone, and is needed to control each of temperature of blow-out air
to each zones independently.
481. (7) The neural networks 3100-3400 are replaceable with
recurrent type neural networks.
* * * * *