U.S. patent application number 14/219293 was filed with the patent office on 2015-09-24 for trip partitioning based on driving pattern energy consumption.
This patent application is currently assigned to FORD GLOBAL TECHNOLOGIES, LLC. The applicant listed for this patent is FORD GLOBAL TECHNOLOGIES, LLC. Invention is credited to Fling TSENG, Qing WANG, Hai YU.
Application Number | 20150268053 14/219293 |
Document ID | / |
Family ID | 53938831 |
Filed Date | 2015-09-24 |
United States Patent
Application |
20150268053 |
Kind Code |
A1 |
YU; Hai ; et al. |
September 24, 2015 |
TRIP PARTITIONING BASED ON DRIVING PATTERN ENERGY CONSUMPTION
Abstract
A vehicle includes an electric machine configured to propel the
vehicle using battery power. The vehicle also includes a controller
programmed to operate the electric machine along a predetermined
route based on a superposition of a plurality of partitionings of
the route. Each of the partitionings is based on a different force
characteristic associated with the route and defines at least one
segment transition, where each segment transitions defines an end
of a previous segment and a beginning of a subsequent segment. The
predetermined route segments are such that no two adjacent segments
of the superposition are defined by a same force
characteristic.
Inventors: |
YU; Hai; (Canton, MI)
; TSENG; Fling; (Ann Arbor, MI) ; WANG; Qing;
(Canton, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
FORD GLOBAL TECHNOLOGIES, LLC |
Dearborn |
MI |
US |
|
|
Assignee: |
FORD GLOBAL TECHNOLOGIES,
LLC
Dearborn
MI
|
Family ID: |
53938831 |
Appl. No.: |
14/219293 |
Filed: |
March 19, 2014 |
Current U.S.
Class: |
701/22 |
Current CPC
Class: |
B60L 15/2045 20130101;
G01C 21/3469 20130101; Y02T 10/64 20130101; Y02T 10/645 20130101;
B60L 2240/10 20130101; Y02T 10/72 20130101; Y02T 90/161 20130101;
G01C 21/343 20130101; G01C 21/3492 20130101; Y02T 90/16 20130101;
Y02T 10/7283 20130101 |
International
Class: |
G01C 21/34 20060101
G01C021/34; B60L 15/20 20060101 B60L015/20 |
Claims
1. A vehicle comprising: an electric machine configured to propel
the vehicle using battery power; and a controller programmed to
operate the electric machine along a predetermined route based on a
superposition of a plurality of partitionings of upcoming portions
of the route, wherein each of the partitionings is based on a
different set of upcoming static and dynamic resistive force
characteristics associated with the route and defines at least one
segment transition, and wherein each of the segment transitions
defines an end of a previous segment and a beginning of a
subsequent segment such that no two adjacent segments of the
superposition are defined by a same set of upcoming static and
dynamic resistive force characteristics.
2. The vehicle of claim 1 wherein the static force characteristics
include static resistive force factors indicative of road
curvature, road gradient, road terrain type, forecasted stop
frequency, or route speed limits.
3. The vehicle of claim 2 wherein the controller is further
programmed to, while operating the electric machine along the
predetermined route, receive updated route information and modify
at least one of the static resistive force factors based on the
updated route information.
4. The vehicle of claim 1 wherein the dynamic force characteristics
include dynamic resistive force factors indicative of vehicle
acceleration, sudden stop frequency, weather conditions, or traffic
conditions.
5. The vehicle of claim 4 wherein the controller is further
programmed to periodically update at least one of the partitionings
based on changes in the dynamic resistive force factors.
6. A method of operating a vehicle comprising: partitioning a
predetermined route based on an upcoming static route
characteristic into a first series of segments each defined by a
first segment transition; receiving an upcoming dynamic route
characteristic from an external source; partitioning the
predetermined route based on the upcoming dynamic route
characteristic into a second series of segments each defined by a
second segment transition; superposing the first and second series
of segments to define the route such that no two adjacent route
segments are defined by a same route characteristic; and operating
an electric machine along the predetermined route based on
resistive forces associated with the superposed segments.
7. The method of claim 6 wherein the static route characteristics
include static force factors indicative of road curvature, road
gradient, road terrain type, forecasted stop frequency, or route
speed limits.
8. The method of claim 6 wherein the dynamic route characteristics
include dynamic force factors indicative of vehicle acceleration,
sudden stop frequency, weather conditions, or traffic
conditions.
9. The method of claim 6 further comprising receiving updated route
information from an off-vehicle processor while operating the
electric machine, and in response, updating at least one of the
superposed series of segments based on the updated route
information.
10. The method of claim 6 wherein the static route characteristic
comprises a vehicle speed pattern along the predetermined
route.
11. The method of claim 6 wherein the static route characteristic
comprises a stop frequency pattern along the predetermined
route.
12. A vehicle powertrain comprising: an electric machine powered by
a battery; and a controller programmed to partition a predetermined
route based on a static force characteristic, receive a dynamic
force characteristic from an external source during driving,
partition the predetermined route based on the dynamic force
characteristic, superpose the route partitions to define unique
route segments, and demand power output from the electric machine
according to an overall route force pattern defined by the
segments.
13. The vehicle powertrain of claim 12 further comprising an engine
configured to propel the vehicle, wherein the controller is further
programmed to allocate power output between the electric machine
and the engine.
14. The vehicle powertrain of claim 12 wherein the static force
characteristic includes static force factors indicative of road
curvature, road gradient, road terrain type, forecasted stop
frequency, or route speed limits.
15. The vehicle powertrain of claim 12 wherein the controller is
further programmed to, while operating the electric machine along
the predetermined route, receive updated route information from an
off-vehicle source, and modify at least one of the segments based
on the updated route information.
16. The vehicle powertrain of claim 12 wherein the dynamic force
characteristic includes dynamic force factors indicative of vehicle
acceleration, sudden stop frequency, weather conditions, or traffic
conditions.
Description
TECHNICAL FIELD
[0001] The present disclosure relates to vehicle powertrain control
systems.
BACKGROUND
[0002] Environmental and energy security concerns have caused
increasingly higher requirements on energy sustainability and
efficiency. Vehicle electrification is a technology that can
contribute to addressing environmental and energy issues. Electric
vehicles (EV) enabled by the power grid offer an efficient energy
source as compared to vehicles propelled solely by internal
combustion. However, technical challenges like EV range and
ownership costs should be overcome to facilitate large scale market
acceptance.
[0003] Range anxiety has been a limiting factor for EV
applications. The range of EV largely depends on battery capacity.
A full recharge of the EV battery often takes several hours using a
standard power outlet. In comparison, it takes only several minutes
to refill a gas tank. Potential trip abortion, or interruption,
caused by battery depletion are primary concerns of EV customers.
Success of EV applications may rely on the establishment of fast
battery charging and replacing facilities. Also, information
technology may provide EV customers with more accurate range
estimation, energy efficient routing, and recharge guidance.
SUMMARY
[0004] In at least one embodiment, a vehicle includes an electric
machine configured to propel the vehicle using battery power. The
vehicle also includes a controller programmed to operate the
electric machine along a predetermined route based on a
superposition of a plurality of partitionings of the route. Each of
the partitionings is based on a different force characteristic
associated with the route and defines at least one segment
transition, where each segment transitions defines an end of a
previous segment and a beginning of a subsequent segment. The
predetermined route segments are such that no two adjacent segments
of the superposition are defined by a same force
characteristic.
[0005] In at least one embodiment, a method of operating a vehicle
includes partitioning a predetermined route based on a first route
characteristic into a first series of segments each defined by a
first segment transition, and partitioning the predetermined route
based on a second route characteristic into a second series of
segments each defined by a second segment transition. The method
also includes superposing the first and second series of segments
to define the route such that no two adjacent route segments are
defined by a same route characteristic. The method further includes
operating an electric machine along the predetermined route based
on route characteristics associated with the superposed
segments.
[0006] In at least one embodiment, a vehicle powertrain includes an
electric machine powered by a battery. The vehicle further includes
a controller programmed to partition a predetermined route based on
a first force characteristic, and additionally partition the
predetermined route based on a second force characteristic. The
controller is further programmed to superpose the route partitions
to define segments such that no two adjacent segments are defined
by a same force characteristic, and demand power output from the
electric machine according to an overall route force pattern
defined by the segments.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a schematic view of a hybrid-electric vehicle.
[0008] FIG. 2 is a block diagram for drive energy.
[0009] FIG. 3 is a block diagram for static factor input to drive
energy.
[0010] FIG. 4 is a block diagram for dynamic factor input to drive
energy.
[0011] FIG. 5 is a system diagram of a powertrain management
system.
[0012] FIGS. 6A through 6E are corresponding spatial plots showing
a progression of pattern recognition.
[0013] FIG. 7 is a superposition of route partitions based on
different characteristics.
DETAILED DESCRIPTION
[0014] As required, detailed embodiments of the present invention
are disclosed herein; however, it is to be understood that the
disclosed embodiments are merely exemplary of the invention that
may be embodied in various and alternative forms. The figures are
not necessarily to scale; some features may be exaggerated or
minimized to show details of particular components. Therefore,
specific structural and functional details disclosed herein are not
to be interpreted as limiting, but merely as a representative basis
for teaching one skilled in the art to variously employ the present
invention.
[0015] FIG. 1 depicts an example of a plug in hybrid-electric
vehicle 100. A hybrid-electric powertrain 102 may comprise one or
more electric machines, or electric motors 104 mechanically
connected to a hybrid transmission 106. In addition, the hybrid
transmission 106 is mechanically connected to an engine 108. The
hybrid transmission 106 may also be mechanically connected to a
drive shaft 110 that drives wheels 112. The electric motor(s) 104
can provide vehicle propulsion when the engine 108 is turned on, as
well as when the engine is turned off. The electric motor(s) 104
can additionally provide vehicle deceleration by imparting a
resistive torque upon the drive shaft. The electric motor(s) 104
may also be configured as electric generators and provide fuel
economy benefits by recovering energy that would normally be lost
as heat in the friction braking system. The electric motor(s) 104
may also reduce pollutant emissions since the hybrid electric
vehicle 102 may be operated in an electric machine powertrain mode
under certain conditions.
[0016] The traction battery, or battery pack 114, stores energy
that can be used to power the electric motor(s) 104. A vehicle
battery pack 114 is capable of providing a high voltage DC output.
The battery pack 114 is electrically connected to a power
electronics module 116. The power electronics module 116 is
electrically connected to the electric motor(s) 104, and provides
the ability to bi-directionally transfer energy between the battery
pack 114 and the electric motor(s) 104. For example, the battery
pack 114 may provide a DC voltage while the electric motor(s) 104
may require a three-phase AC current to function. In this case, the
power electronics module 116 converts the DC voltage to a
three-phase AC current to be received by the electric motor(s) 104.
In a regenerative mode, the power electronics module 116 will
convert the three-phase AC current from the electric motor(s) 104
acting as generators to the DC voltage required by the battery pack
114. The methods described herein are equally applicable to a pure
electric vehicle or any other device using a battery pack.
[0017] In addition to providing battery power for propulsion, the
battery pack 114 may provide energy for other vehicle electrical
systems. A DC/DC converter module 118 is capable of converting the
high voltage DC output of the battery pack 114 to a low voltage DC
supply that is compatible with low voltage vehicle loads. Other
high voltage loads, such as compressors and electric heaters, may
be connected directly to the high-voltage bus from the battery pack
114. The low voltage systems may also be electrically connected to
a 12V battery 120. An all-electric vehicle may have a similar
architecture but without the engine 108.
[0018] The battery pack 114 may be recharged by an external power
source 126. The external power source 126 may provide AC or DC
power to the vehicle 102 by electrically connecting through a
charge port 124. The charge port 124 may be any type of port
configured to transfer power from the external power source 126 to
the vehicle 102. The charge port 124 may be electrically connected
to a power conversion module 122. The power conversion module may
condition the power from the external power source 126 to provide
the proper voltage and current levels to the battery pack 114. In
some applications, the external power source 126 may be configured
to provide the proper voltage and current levels to the battery
pack 114 such that the power conversion module 122 may not be
necessary. For example, the functions of the power conversion
module 122 may be contained in the external power source 126. The
vehicle powertrain including engine, transmission, electric motors,
electric generators and power electronics may be controlled by a
powertrain control module (PCM) 128.
[0019] In addition to illustrating a plug-in hybrid vehicle, FIG. 1
can be representative of a battery electric vehicle (BEV) if the
engine 108 is removed. Likewise, FIG. 1 can represent a traditional
hybrid electric vehicle (HEV) or a power-split hybrid electric
vehicle if the components 122, 124, and 126 that are related to
plug in charging are removed.
[0020] The present disclosure provides a method of accurately
estimating changes in energy consumption rate associated with
changes in transportation conditions. The energy consumed to
satisfy demand for vehicle propulsion is largely determined by
vehicle states such as speed and inertial acceleration. Variations
of the vehicle speed and acceleration are generally associated with
driving behavior. Reality dictates that driving behavior is
constrained by traffic and road conditions. However driving
behaviors do tend to exhibit a certain degree of similarity across
the same road sections, but may differ from section to section
along a given trip. As a result, methods capable of modeling
various driving behavior patterns generically can assist in
trip-oriented energy consumption analysis and planning.
[0021] Previewing vehicle speed information can be used for
constructing driving patterns. Acceleration may be implicitly
modeled from the vehicle speed combined with other factors that
either deterministically or statistically shape acceleration
expected profiles. The additional factors may also be used for
construction of separate patterns. The aggregate of all relevant
factors largely shapes how a vehicle behaves on road. A
predetermined trip can be partitioned in advance based on
attributes of these factors such transitions are recognized between
adjacent partitions. Each partitioned road section is distinguished
by a characteristic feature combination that will result in
distinctive drive energy demand property when experienced on road.
Data clustering methods are used to identify different zones along
the route. The driving patterns of the overall route are realized
by synthesizing all of the separately partitioned zones along the
route. Driving scenarios can be differentiated by objective
measures based on high impact factors such as road curvature,
elevation variation, speed regulation, etc. Generically modeled
driving patterns are used as initial candidate patterns to
construct a more application-oriented driving pattern once the host
and specific trip information available.
[0022] During driving, a majority of on-road vehicle energy
consumption comes from the energy required to propel the vehicle.
Other energy consumption sources include vehicle accessories and
supporting systems. The total electrical energy available for
vehicle propulsion over a predetermined route, E.sub.drv may be
characterized in a time domain as:
E.sub.drv=E.sub.batt-.intg..sub.0.sup.T.sup.SP.sub.accdt (1)
where E.sub.batt is the total electrical energy available from the
battery and T.sub.S is the time duration of vehicle operation
during the route. P.sub.acc is power provided to vehicle
accessories, plus other operational losses of the vehicle.
P.sub.acc may be integrated over the time duration T.sub.S to
represent the total energy discharged from the battery.
[0023] The total energy available for vehicle propulsion E.sub.drv
may also be characterized in a spatial domain over the distance of
the entire route.
E.sub.drv=.intg..sub.0.sup.SF.sub.drvds=.intg..sub.0.sup.SF.sub.whlds+.i-
ntg..sub.0.sup.SF.sub.accds (2)
where S is the total trip distance and s is the spatial domain
distance variable. F.sub.drv is the equivalent resistive drive
force needed for vehicle propulsion. This is the total force
reflected at the wheels to maintain or to change an operating state
of the vehicle. F.sub.drv may also be integrated over the trip
distance to obtain the energy associated with the total work
performed to propel the vehicle along the route. F.sub.whl is the
actual wheel traction force, and F.sub.acc is a dummy wheel force
term representing the vehicle accessory loads plus operational
energy losses. Each of the component forces may also be integrated
over the length of the route to obtain the respective contributions
to overall energy consumption.
[0024] To further decompose these factors, the driving patterns of
the present disclosure are indirectly constructed from several
contributive impact factors of F.sub.drv. Decomposing F.sub.drv
into smaller components allows for a more complete analysis of
driving patterns. The total force may be represented as:
F.sub.drv=F.sub.ine+F.sub.fbk+F.sub.drag+F.sub.rgl+F.sub.acc
(3)
where F.sub.ine=m{dot over (V)}.sub.x is the inertial resistive
force based on vehicle speed and vehicle mass. V.sub.x is vehicle
speed, and {dot over (V)}.sub.x is the rate of change of vehicle
speed, or acceleration. m is the total vehicle mass. F.sub.fbk is
the frictional brake resistive force at the wheels.
[0025] F.sub.drag is a lumped drag force including aerodynamic and
rolling resistances that can be approximated by:
F.sub.drag=k.sub.0+k.sub.1V.sub.x+k.sub.2V.sub.x.sup.2+k.sub.3.delta.
(4)
where .delta. is the road wheel steering angle. The parameter ki,
i=0, 1, 2, 3 are constants associated with vehicle and
environmental conditions. F.sub.rgl is a road gradient load
resistive force that may approximated by:
F.sub.rgl=mg*sin .alpha..sub.r (5)
where .alpha..sub.r is the road pitch angle.
[0026] Based on the above decomposition of F.sub.drv, a
representative model of the contributive force components and
impact factors may be derived for advance planning before direct
measurements are available. From the drive force decomposition,
particularly high impact factors like V.sub.x, {dot over
(V)}.sub.x, .delta. and .alpha..sub.r are identified, as they
contribute to several of the resistive forces. Certain individual
factors may be grouped by a particular related feature. For
example, the feature combination of V.sub.x and {dot over
(V)}.sub.x may be referred to as a "speed factor." Also, both
.delta. and .alpha..sub.r are derived from features related to road
geometry data and may be referred to a "curve factor" and a "hill
factor," respectively.
[0027] Drive patterns may be distinguished based on the above
identified feature factors. Each of the feature factors frequently
exhibit different properties when view in a different time domains.
Using particular time scales for data analysis of various factors
may facilitate the pattern processing and accuracy. To this end, at
least three time scales are utilized in aspects of this disclosure:
static, quasi-static, and dynamic time scales.
[0028] The static time scale may indicate feature behavior or
patterns that are stable over a relatively long period of time. The
feature factors best analyzed in the static time scale are
considered "static factors." For example, they may include road
geometry data such as road pitch angle, road curvature,
regulation-based stop frequency, speed limits, terrain types, etc.
Each of these factors typically remains stable over long durations.
Driving patterns identified from static factors are generic in that
they can typically be applied to different applications, different
vehicles, and driver configurations.
[0029] The quasi-static time scale is much like the static time
scale. However, factors that may be subject to very infrequent
change are analyzed in the quasi-static time scale. Such features
are still relatively stable over a certain period of time, and can
be considered "quasi-static factors." For example, a speed limit
change due to road construction, or a regulatory speed change due
to traffic accident are feature factor variations that may be
suitable for analysis in the quasi-static time domain. Feature
factor variation in the quasi-static time scale can be used to
update the static feature factors based on pattern recognition
results that may have become out of date, or obsolete. Since the
quasi-static feature information will be valid and stable for a
period of time and for a certain section of road, the updated
pattern recognition can also be applied generically to different
applications, different vehicles, and driver configurations.
[0030] The dynamic time scale captures route characteristics that
frequently or even continuously change over time. Commonly this
information is only valid for very short periods of time and
therefore the effective horizon for upcoming road section is
limited. Dynamic feature information is also often host-specific,
and may be limited to use for only certain applications. For
example, traffic information can be conducive for analysis in the
dynamic time scale and may be subject to up-to-the-minute updating.
In a spatial domain, traffic flow information is often reliable
only for hundreds of meters to several miles ahead of the subject
vehicle. Also weather conditions may also be considered as a
dynamic feature factor. Additionally, certain impact factors may be
further decomposed to include static and dynamic components.
[0031] By carrying out the time scale decomposition for the
pertinent feature factors, it enables pattern synthesis and
identification to be processed separately in different time scales.
As discussed above, a large amount of feature factors that are
highly impactful to a driving process are actually static. Thus,
driving patterns can be identified over these factors offline
relative to the vehicle to construct a map based spatial domain
pattern database. For example, off-vehicle cloud computing based
control and information sharing are possible, particularly for the
static factors. Offline preprocessing of the route data largely
minimizes the processing computation and communication resources
required at the vehicle for real time applications. Additionally,
the processed static data are generic such that the processing
results can be shared for all vehicles driving on and approaching a
common section of road. Once the particular host vehicle
information and the dynamic trip information are available,
advanced driving pattern updating needs only to be processed with
respect to the information for base driving patterns that has
changed from the static pattern database over the trip sections
applied.
[0032] Impact feature factors also vary in the spatial domain. They
exhibit different modes of variations through different sections of
road. For example, the road pitch angle .alpha..sub.r exhibits
large variation and magnitude in a hilly region but it is much more
stable along a section of freeway. Thus, trip sections through
hilly regions are separated from those along interstate highways
since the energy demand contributed by the road gradient load
resistive force F.sub.rgl will be significantly different across
each of these different regions. A second example is that the speed
profile in an urban area contains much more iteration of stop and
go cycles as compared to an interstate highway. As a result, it may
be beneficial to carry out the driving pattern analysis together
with the trip partitioning such that the pattern features in each
partitioned road section has distinguishable energy demand
properties. Through feature-based trip partitioning, the behavior
of a single feature within a trip section can be consistent and
monotonous. The energy demand over each section can be more
accurately evaluated since the stronger the differentiation among
patterns, the more precise the energy demand property can be
established through Dynamic Programming energy consumption analysis
or from vehicle test data.
[0033] Referring to FIG. 2, impact factor analysis is shown
schematically by a system block diagram illustrating high-level
information flow. Drive energy 202 is derived from the total drive
force 204 applied at the wheels, applied over the total route
distance 206. As described above, the total drive force 204
comprises several component forces caused by a number of different
sources. Also described above, static impact factors 208 and
dynamic impact factors 210 influence the total drive force 204
required at the vehicle wheels. Certain attributes of the subject
vehicle, or host factors 212, further contribute to the force
components that contribute to the total drive force 204.
[0034] FIG. 3 is also a block diagram, but depicts the static
impact factors and total drive force components in greater detail.
In the example, total drive force 204 at the wheels is represented
by five component forces. Vehicle inertia force 214, drag force 216
(including aerodynamic and rolling resistance), equivalent dummy
wheel force 218 representing power to vehicle accessories, road
gradient load force 220, and frictional brake force 222 each
contribute to the modeled total drive force 204 required at the
wheels. It is contemplated that additional resistive force
characteristics may be used to comprise the total drive force.
[0035] Similarly, the static impact factors 208 include several
resistive force factors that influence the required force at the
wheels. In the example of FIG. 3, a speed factor 224, a curve
factor 226, terrain type 228, altitude 230, hill factor 232, and
stop factor 234 each are static factors that contribute to the
force required to propel the vehicle. As discussed above, each of
the static impact factors is generally stable and may be treated as
generic so as to apply to different vehicles.
[0036] The speed factor 224 is largely based on vehicle speed V,
and serves as an input into the drag force 216 as well as wheel
force 218 representing power to vehicle accessories. For example,
known speed limits along the route may serve to influence the speed
factor 224. Speed profile information may be available from vehicle
driving history, or from transportation databases. Also,
information about route speed limits may be available from
navigation or map data.
[0037] The curve factor 226 is a resistive force factor largely
based on road curvature as indicated by the road wheel steering
angle 6, and contributes to the overall drag force 216. Geometric
map data regarding road shapes may be used to derive static road
curvature information along a route.
[0038] The terrain factor 228 according to terrain type also
influences overall drag 216. For example the road terrain type may
account for paved versus unpaved road surfaces, which can directly
contribute to rolling resistance. The altitude 230 may contribute
to the equivalent dummy wheel force 218 representing power required
to operate vehicle accessories.
[0039] The hill factor 232 is a resistive force factor largely
based on the road pitch angle .alpha..sub.r, and directly
contributes to the road gradient load force 220. Road elevation
data is available along a trip from map data or from host vehicle
driving records. The road pitch angle can be derived from the
geometric elevation data of roads.
[0040] The stopping characteristics of the route, or the stop
factor 234, contributes to the frictional brake force 222 required
at the wheels. The stop factor is a resistive force factor largely
based on the frequency and duration of known stop locations along
the route. Raw data for the stop factor includes the potential of a
stop in terms of percentage at known locations along the route.
Traffic lights, intersections, stop and yield signs all present
varying likelihoods of vehicle stoppage. The stop intensity may be
available from traffic data or from previous driving patterns.
[0041] By further reference to FIG. 3, there is also interaction
between the static factors whereby several factors influence other
factors. For example, the curve factor 226 which accounts for road
curvature, may also operate to influence the speed factor 224 and
subsequent speed traveled along the route. Similarly, the terrain
type 228 may allow for greater or lesser speed along certain
portion of the route. In this way the terrain type 228 also
influences the speed factor 224. Also, the stop factor 234 which
accounts for frequency and duration of stop events influences the
speed factor 224.
[0042] FIG. 4 depicts a similar relationship of information
transfer by way of a block diagram. The influence of both of the
dynamic impact factors 210 and host vehicle factors 212 upon the
total drive force 204 is shown in the diagram. The total drive
force 204 is the same as that shown in FIG. 3, and includes several
sources that influence the required force at the wheels. In the
example of FIG. 4, the dynamic impact factors 210 include a dynamic
speed factor 236, a weather factor 238, a curve factor 240, and a
stop factor 242. In at least one embodiment, the controller
monitors each of the dynamic impact factors, updates at least one
of the partitions based on changes in a dynamic resistive force
factor.
[0043] A dynamic speed factor 236 is influenced by the change in
vehicle speed {dot over (V)}.sub.x and affects the vehicle inertia
force 214, drag force 216, and the equivalent dummy wheel force 218
representing power required to operate vehicle accessories. The
dynamic speed factor 236 can be derived from vehicle acceleration
and deceleration along the route that deviates from the static
force pattern. Also, live traffic conditions may be transmitted to
the controller to update the dynamic speed factor 236.
[0044] The weather factor 238 is another dynamic impact factor that
affects the vehicle inertia force 214, drag force 216, and the
equivalent dummy wheel force 218 representing power required to
operate vehicle accessories. The weather factor 238 may include,
for example, high winds, snow, ice, rain, temperature, or other
conditions which may affect speed, drag, or accessory load.
[0045] The dynamic curve factor 240 includes the road wheel
steering angle .delta.. Compared to the static curve factor 226,
the dynamic curve factor 240 additionally accounts for driver
dynamic steering and curvature that deviates from the known static
road curvature.
[0046] The stop factor 242 is based on changes in stop intensity
that deviate from the predetermined static stop factor 234. For
example, in response to more or less frequent stops compared to the
static stop pattern, the controller may update the energy
consumption forecast.
[0047] There are also several host factors 212 that are particular
to the subject vehicle. The driver factor 244 may include a profile
assigned to a particular driver, including preferences and driving
habits of the driver. The driver factor 244 may therefore have an
influence on other dynamic impact factors. For example, each of the
speed factor 236, the curve factor 240, and the stop factor 242 are
influenced by the driver factor 244.
[0048] The vehicle configuration 246 is an additional host factor
particular to the vehicle. For example the vehicle may offer sport
modes, comfort modes, or economy modes that change the force
applied and the wheels influence the speed factor 236. Additional
forces may be attributed to the equivalent accessory force 218
representing accessary energy consumption and operational energy
losses. Also relevant to the vehicle configuration 246 is whether
the vehicle is engaged in four-wheel drive or two-wheel drive. The
number of wheels propelling the vehicle may further influence the
force applied at the vehicle wheels.
[0049] The mass factor 248 represents the mass of the vehicle, as
well as the driver. These masses operate as input to the inertial
resistive force 214 and the road load gradient force 220. There are
further interactions between the mass factor 248 and the dynamic
speed factor 236.
[0050] FIG. 5 is a system diagram depicting the relationship
between various data sources and data recipients of a driving
pattern recognition system according to an embodiment. As discussed
above, static impact factors may be calculated and processed
offline. A static transportation database 502 can provide certain
data concerning roads. These data may include at least information
regarding road curvature, elevation, speed limits, and surface
terrain type. The general static transportation information may be
transmitted to an offline processor 504 from the transportation
database 502.
[0051] Certain static information concerning roads may be
considered quasi-static in the sense that changes may occur from
time to time. A traffic and regulation information center 506 may
transmit quasi-static transportation information updates to the
offline processor 504. The quasi-static information may include for
example, speed limit changes or road closures related to
construction. Also, regulatory speed limit changes due to a traffic
accident that are stable, but deviate from the static speed factor
may be considered quasi-static.
[0052] The offline processor 504 includes a first subroutine 508
that utilizes the static and quasi-static road information to
partition road segments based on based on different force
characteristics associated with the route. The vehicle information
center 510 transmits user-selected route data to the offline
processor 504. The offline processor 504 further includes a second
subroutine 512 that uses information about the user-selected route
and the road partitions to generate an initial trip partition
specific to the user-selected route. The initial trip partition
based on static and quasi-static road information is in turn
transmitted to an online vehicle processor 514.
[0053] The online processor 514 includes a first subroutine 516 to
process dynamic feature information. The traffic and regulation
information center 506 transmits dynamic transportation information
concerning the roads to the online processor 514. The dynamic
information may change often and include information such as
traffic flow, accident locations, and weather conditions. In the
example of FIG. 5, updated route information may be periodically
provided to the vehicle from an external source.
[0054] The vehicle information center 510 also transmits the
user-selected route data to the online processor 514. The first
subroutine 516 may apply the dynamic transportation information
from the traffic and regulation information center 506 to the
user-selected route to determine dynamic features that are
particular to the user-selected route.
[0055] The online processor 514 also receives information regarding
host vehicle features from the vehicle information center 510. The
host vehicle feature information is specific to the subject vehicle
and includes at least overall powertrain efficiency and loss
factors, vehicle mass, and driver and passenger mass. The host
vehicle information may also include information regarding driving
patterns from previous trips along common route segments.
[0056] The online processor 514 further includes a second
subroutine 518 that considers the dynamic route information, the
host vehicle information, and the static trip partition information
to update the user-selected route. The second subroutine may update
the trip partitions for example by subdividing a particular route
partition based on dynamic road information, or previous host
vehicle driving patterns. Alternatively, the second subroutine may
combine sequential route partitions if the dynamic information
affects the route such that adjacent route partitions have similar
features.
[0057] A vehicle application 520 may use the refined route
information to plan energy usage over the route. Discuss the
vehicle applications 520 that may rely on the trip partitioned
routes to plan for energy consumption over the route.
[0058] FIGS. 6A through 6E are a series of spatial plots that show
a progression of the route partitioning for a generic feature
factor. It is envisioned that the general progression described in
reference to the series of plots may be independently applied to a
number of different features. The controller may be programmed to
separately partition the predetermined route for each of the
different force characteristics.
[0059] FIG. 6A depicts the static feature profile along the route.
As discussed above, static profiles of various impact factors may
be determined offline and distributed to different vehicles for
generic usage in route planning. The vertical axis 602 corresponds
to a magnitude of the static feature. The horizontal axis 604
corresponds to the location along the route between the origin and
destination. In the example of FIG. 6A, a step profile 606 is
illustrated that includes instant transitions in magnitude between
each of the initial partitions of the route. It should be
appreciated that different profiles may exist depending on the
route characteristics and the static feature being considered.
[0060] FIG. 6B depicts a quasi-static updating of the static
feature profile. In the example, the quasi-static update profile
608 reflects a change from the previous values along the route. The
quasi-static update profile 608 is general zero along the route,
where portion 610 reflects an increased magnitude of change of the
base static feature profile values. Region 612 of the static
feature step profile 606 is affected by the updated route
information.
[0061] FIG. 6C is an initial route partitioning based on the static
feature after consideration of any update. Based on differences in
magnitude of the generic feature, the route is partitioned. Each
partition corresponds to a predetermined magnitude level (or
range). In the example of FIG. 6C, eight partitions are established
using four different magnitude levels. It should be noted that
partition 6 is the particular portion affected by quasi-static
updating.
[0062] FIG. 6D shows dynamic updating of one partition of the route
based on a dynamic feature profile. Clustering methods may be used
to further subdivide the partition based on distinguishing the
feature behavior during the partitioning. Region 614 is updated in
the example based on the dynamic variation of the generic feature
during the corresponding partition.
[0063] FIG. 6E depicts a final series of partitions after the
several refinements discussed above. As can be seen from the plot,
what was previously the second partition of FIG. 6B is separated
into three sub-partitions based on the dynamic behavior of the
generic feature. In the final partitioning 616, there are ten
partitions along the route.
[0064] Based on the combination of the time scale decomposition
with spatial domain partitioning discussed above, an overall
driving resistive force pattern may be generated by systematically
organizing the results from all of the individual feature
identifications. The output of the progression of partitioning
applied to a single feature as shown in FIGS. 6A through 6E is
similarly applied to all of the relevant features. Each series of
partitions is superposed over one another to determine an overall
trip segmentation.
[0065] FIG. 7 shows an example superposition of multiple
partitionings of the route based on different force characteristics
associated with the route. Route 702 shows the overall trip between
the start and the destination. Several different series are
partitioned based on different force characteristics associated
with the route.
[0066] The speed-based partitioning 704 shows various speed zones
along the route. In the example of FIG. 7, there are three
different levels of speed considered. Low speed zones 706, medium
speed zones 708, and high speed zones 710 are generally used to
characterize the different speed limits that will be experienced
over the route. For example, residential speed limits 712, local
highway speed limits 714, and interstate highway speed limits 716
may be represented by each of the three speed zones.
[0067] A hill-based partitioning 718 shows various zones separated
based on the degree of flatness of each partition along the route.
Flat zones 720, moderately hilly zones 722, and extremely hilly
zones 724 may each correspond to respective ranges of road pitch
angle along the route. In the example of FIG. 7, a hill factor may
be used to distinguish each of the partitions.
[0068] A curvature-based partitioning 726 shows various zones
separated based on the curvature of the roads along the route.
Similar to the partitions of other factors discussed above,
separate zones correspond to ranges of curvature along the route.
Straight zones 728, and curved zones 730 are characterized by the
magnitude of the curve factor.
[0069] A stop-based partitioning 732 shows various zones based on
the frequency of expected stops along the route. High frequency
stop zones 734, and non-stop zones 736 may each correspond to
respective ranges of intensity of the forecasted stop frequency.
Known locations of stop signs and yield signs directly contribute
to a stop factor used to quantify the stop intensity.
[0070] Each of the series of partitions defines at least one
segment transition. The partitionings are superposed over one
another. An overall route force pattern 738 is derived based on the
alignment of the various locations of the segment transition of all
of the series of partitions. The segment transition location may
define the end of a previous segment and the beginning of a
subsequent segment. By dividing the route based on the locations of
the segment transition, no two adjacent segments of the overall
route force pattern are defined by the same combination of force
characteristics. In the example of FIG. 7, there are seven
different combinations of force characteristics, and eight separate
segments provided along the route. The number of levels of each
partitioning, as well as the particular variation of relative
factors may lead to higher segmentation of the overall route force
pattern.
[0071] It is contemplated that in certain instances the transition
locations may occur spatially close enough that a single transition
may accommodate multiple magnitude changes of the individual series
of partitionings. In the example of FIG. 7, the transition from the
fifth segment 748 to the sixth segment 750 may be located to
balance several different segment transitions of the individual
features. In contrast, the spatial location separation of the
magnitude changes between the second segment 742 and fourth segment
746 is great enough to cause an interleaved third segment 744
having different characteristics from the previous segment and the
subsequent segment.
[0072] The present disclosure provides representative control
strategies and/or logic that may be implemented using one or more
processing strategies such as event-driven, interrupt-driven,
multi-tasking, multi-threading, and the like. As such, various
steps or functions illustrated herein may be performed in the
sequence illustrated, in parallel, or in some cases omitted.
Although not always explicitly illustrated, one of ordinary skill
in the art will recognize that one or more of the illustrated steps
or functions may be repeatedly performed depending upon the
particular processing strategy being used. Similarly, the order of
processing is not necessarily required to achieve the features and
advantages described herein, but it is provided for ease of
illustration and description.
[0073] The control logic may be implemented primarily in software
executed by a microprocessor-based vehicle, engine, and/or
powertrain controller. Of course, the control logic may be
implemented in software, hardware, or a combination of software and
hardware in one or more controllers depending upon the particular
application. When implemented in software, the control logic may be
provided in one or more computer-readable storage devices or media
having stored data representing code or instructions executed by a
computer to control the vehicle or its subsystems. The
computer-readable storage devices or media may include one or more
of a number of known physical devices which utilize electric,
magnetic, and/or optical storage to keep executable instructions
and associated calibration information, operating variables, and
the like. Alternatively, the processes, methods, or algorithms can
be embodied in whole or in part using suitable hardware components,
such as Application Specific Integrated Circuits (ASICs),
Field-Programmable Gate Arrays (FPGAs), state machines, controllers
or other hardware components or devices, or a combination of
hardware, software and firmware components.
[0074] While exemplary embodiments are described above, it is not
intended that these embodiments describe all possible forms
encompassed by the claims. The words used in the specification are
words of description rather than limitation, and it is understood
that various changes can be made without departing from the spirit
and scope of the disclosure. As previously described, the features
of various embodiments can be combined to form further embodiments
of the invention that may not be explicitly described or
illustrated. While various embodiments could have been described as
providing advantages or being preferred over other embodiments or
prior art implementations with respect to one or more desired
characteristics, those of ordinary skill in the art recognize that
one or more features or characteristics can be compromised to
achieve desired overall system attributes, which depend on the
specific application and implementation. These attributes can
include, but are not limited to cost, strength, durability, life
cycle cost, marketability, appearance, packaging, size,
serviceability, weight, manufacturability, ease of assembly, etc.
As such, embodiments described as less desirable than other
embodiments or prior art implementations with respect to one or
more characteristics are not outside the scope of the disclosure
and can be desirable for particular applications.
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