U.S. patent application number 11/452007 was filed with the patent office on 2007-01-04 for apparatus and method for testing state of charge in battery.
Invention is credited to Il Cho, Do Yang Jung, Do Youn Kim.
Application Number | 20070005276 11/452007 |
Document ID | / |
Family ID | 37532491 |
Filed Date | 2007-01-04 |
United States Patent
Application |
20070005276 |
Kind Code |
A1 |
Cho; Il ; et al. |
January 4, 2007 |
Apparatus and method for testing state of charge in battery
Abstract
Disclosed is an apparatus and method for estimating a state of
charge (SOC) in a battery, in which the battery SOC is estimated
using a fusion type soft computing algorithm, thereby accurately
estimating the battery SOC in a high C-rate environment. The
apparatus includes a detector unit for detecting current, voltage
and temperature of a battery cell; and soft computing unit for
outputting a battery SOC estimation value of processing the
current, the voltage and the temperature detected by the detector
unit using a radial function based on a neural network algorithm.
Especially, the soft computing unit combines the neural network
algorithm with any one of a fuzzy algorithm, a genetic algorithm
(GA), a cellular automata (CA) algorithm, an immune system
algorithm, and a rough-set algorithm, and thereby adaptively
updates the parameters of the neural network algorithm.
Inventors: |
Cho; Il; (Seo-gu, KR)
; Kim; Do Youn; (Seo-gu, KR) ; Jung; Do Yang;
(Hwaseong-si, KR) |
Correspondence
Address: |
CANTOR COLBURN, LLP
55 GRIFFIN ROAD SOUTH
BLOOMFIELD
CT
06002
US
|
Family ID: |
37532491 |
Appl. No.: |
11/452007 |
Filed: |
June 13, 2006 |
Current U.S.
Class: |
702/60 |
Current CPC
Class: |
B60L 2260/46 20130101;
B60L 2260/48 20130101; B60L 58/21 20190201; G01R 31/374 20190101;
B60L 2240/549 20130101; B60L 2240/545 20130101; B60L 2240/547
20130101; G01R 31/3842 20190101; B60L 3/0046 20130101; Y02T 10/70
20130101; G01R 31/367 20190101; B60L 58/12 20190201; B60L 2260/44
20130101 |
Class at
Publication: |
702/060 |
International
Class: |
G01R 21/06 20060101
G01R021/06 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 13, 2005 |
KR |
10-2005-0050273 |
Claims
1. An apparatus for estimating a state of charge (SOC) in a
battery, the apparatus comprising: a detector unit for detecting
current, voltage and temperature of a battery cell; and a soft
computing unit for outputting a battery SOC estimation value of
processing the current, the voltage and the temperature detected by
the detector unit using a radial function based on a neural network
algorithm.
2. The apparatus according to claim 1, wherein the soft computing
unit: combines the neural network algorithm with any one of a fuzzy
algorithm, a genetic algorithm (GA), a cellular automata (CA)
algorithm, an immune system algorithm, and a rough-set algorithm,
all of which adaptively update parameters; and adaptively updates
the parameters of the neural network algorithm.
3. The apparatus according to claim 1, wherein the neural network
algorithm is updated based on a learning algorithm in which, when a
difference between the estimation value output from the soft
computing unit and a predetermined target value is outside of a
critical range, learning is made so as to follow the predetermined
target value.
4. The apparatus according to claim 3, wherein the target value is
a reference value obtained through a corresponding test on a
specific condition.
5. The apparatus according to claim 3, wherein the reference value
is a value of complementing an amp-hour (Ah) counting value and an
open circuit voltage (OCV) value, which are input from the
charger-discharger, to rated capacity of the battery.
6. The apparatus according to claim 3, wherein the learning
algorithm is any one of a back propagation learning algorithm, a
Kalman filter, a genetic algorithm, and a fuzzy learning
algorithm.
7. The apparatus according to claim 2, wherein the neural network
algorithm, which is combined with any one of a fuzzy algorithm, a
genetic algorithm (GA), a cellular automata (CA) algorithm, an
immune system algorithm, and a rough-set algorithm, is updated
based on a learning algorithm in which, when a difference between
the estimation value output from the soft computing unit and a
predetermined target value is outside of a critical range, learning
is made so as to follow the predetermined target value.
8. The apparatus according to claim 7, wherein the target value is
a reference value obtained through a corresponding test on a
specific condition.
9. The apparatus according to claim 8 using fusion type soft
computing, wherein the reference value is a value of complementing
an amp-hour (Ah) counting value and an open circuit voltage (OCV)
value, which are input from the charger-discharger, to rated
capacity of the battery.
10. The apparatus according to claim 7 using fusion type soft
computing, wherein the learning algorithm is any one of a back
propagation learning algorithm, a Kalman filter, the genetic
algorithm, and a fuzzy learning algorithm.
11. A method for estimating a state of charge (SOC) in a battery,
the method comprising the steps of: detecting current, voltage and
temperature of a battery cell; and outputting a battery SOC
estimation value of processing the current, voltage and temperature
detected by the detector unit using a radial function based on a
neural network algorithm.
12. The method according to claim 11, wherein the neural network
algorithm: is combined with any one of a fuzzy algorithm, a genetic
algorithm (GA), a cellular automata (CA) algorithm, an immune
system algorithm, and a rough-set algorithm, all of which
adaptively update parameters; and adaptively update the parameters
of the neural network algorithm.
13. The method according to claim 11, wherein the neural network
algorithm is updated based on a learning algorithm in which, when a
difference between the estimation value and a predetermined target
value is outside of a critical range, learning is made so as to
follow the predetermined target value.
14. The method according to claim 13, wherein the target value is a
reference value obtained through a corresponding test on a specific
condition.
15. The method according to claim 13, wherein the reference value
is a value of complementing an amp-hour (Ah) counting value and an
open circuit voltage (OCV) value, which are input from the
charger-discharger, to rated capacity of the battery.
16. The method according to claim 13, wherein the learning
algorithm is any one of a back propagation learning algorithm, a
Kalman filter, a genetic algorithm, and a fuzzy learning
algorithm.
17. The method according to claim 12, wherein the neural network
algorithm, which is combined with any one of a fuzzy algorithm, a
genetic algorithm (GA), a cellular automata (CA) algorithm, an
immune system algorithm, and a rough-set algorithm, is updated
based on a learning algorithm in which, when a difference between
the estimation value output from the soft computing unit and a
predetermined target value is outside of a critical range, learning
is made so as to follow the predetermined target value.
18. The method according to claim 17, wherein the target value is a
reference value obtained through a corresponding test on a specific
condition.
19. The method according to claim 18 using fusion type soft
computing, wherein the reference value is a value of complementing
an amp-hour (Ah) counting value and an open circuit voltage (OCV)
value, which are input from the charger-discharger, to rated
capacity of the battery.
20. The method according to claim 17 using fusion type soft
computing, wherein the learning algorithm is any one of a back
propagation learning algorithm, a Kalman filter, the genetic
algorithm, and a fuzzy learning algorithm.
Description
[0001] This application claims the benefit of the filing date of
Korean Patent Application No. 2005-50273, filed on Jun. 13, 2005,
in the Korean Intellectual Property Office, the disclosure of which
is incorporated herein in its entirety by reference.
TECHNICAL FIELD
[0002] The present invention relates to an apparatus and method for
estimating a state of charge (SOC) in a battery, and more
particularly to an apparatus and method for estimating an SOC in a
battery, using fusion type soft computing.
BACKGROUND ART
[0003] In general, a state of charge (SOC) in a battery has a
nonlinear characteristic. Hence, it is difficult to accurately
detect the battery SOC in practice. As a result, the detection of
the battery SOC depends on its estimation method.
[0004] Examples of a conventional estimation method of the battery
SOC include an amp-hour (Ah) counting method, an open circuit
voltage (OCV) measuring method, a battery impedance measuring
method, and so on.
[0005] The Ah counting method is for detecting the SOC by detecting
a real capacity of the battery. However, the Ah counting method is
greatly influenced by errors or precision of sensors detecting the
real capacity, thereby having a great error.
[0006] The OCV measuring method is for reading out open voltage of
the battery in an idle state, and estimating an SOC from the read
open voltage. This method has problems in that it can be used only
in the idle state, and it is greatly influenced by external factors
such as temperature.
[0007] The battery impedance measuring method is for estimating an
SOC of the battery from an impedance measurement value of the
battery. This method has a problem in that the precision of an
estimation value is lowered because it is greatly influenced by
temperature.
[0008] Mobile phones, laptop computers etc. used in a low C-rate
environment do not require accurate detection of the battery SOC in
view of their characteristics. In these products, the battery SOC
is readily estimated by the Ah counting method, the OCV measuring
method, or so on. Here, the term C-rate refers to magnitude of the
peak current that can be output in a moment.
[0009] However, in the case of hybrid electrical vehicles (HEVs),
electrical vehicles (EVs) etc. used in a high C-rate environment,
accurate information on the battery SOC is required like the fuel
gauge of an ordinary vehicle, while a degree of non-linearity of
the battery SOC is enhanced. Hence, the conventional methods for
estimating the battery SOC have difficulty in estimating the
battery SOC in these products.
DISCLOSURE OF THE INVENTION
[0010] It is an objective of the present invention to provide an
apparatus and method for estimating a state of charge (SOC) in a
battery, in which the battery SOC is estimated using a fusion type
soft computing algorithm, thereby accurately estimating the battery
SOC in a high C-rate environment.
[0011] According to an aspect of the present invention, there is
provided an apparatus for estimating a state of charge (SOC) in a
battery. The apparatus comprises a detector unit for detecting
current, voltage and temperature of a battery cell; and soft
computing unit for outputting a battery SOC estimation value of
processing the current, the voltage and the temperature detected by
the detector unit using a radial function based on a neural network
algorithm.
[0012] Further, the soft computing unit may combine the neural
network algorithm with any one of a fuzzy algorithm, a genetic
algorithm (GA), a cellular automata (CA) algorithm, an immune
system algorithm, and a rough-set algorithm, and thereby adaptively
update the parameters of the neural network algorithm.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 is a block diagram of an apparatus for estimating a
state of charge (SOC) in a battery in accordance with an embodiment
of the present invention.
[0014] FIG. 2 illustrates a construction of a fuzzy neural network
in the soft computing unit of FIG. 1.
[0015] FIG. 3 is a flowchart of a method for estimating an SOC in a
battery in accordance with an embodiment of the present
invention.
MODE FOR CARRYING OUT THE INVENTION
[0016] Reference will now be made in detail to the exemplary
embodiments of the present invention.
[0017] FIG. 1 is a block diagram of an apparatus for estimating a
state of charge (SOC) in a battery in accordance with an embodiment
of the present invention. Referring to FIG. 1, the SOC estimation
apparatus is comprised of a battery cell 10, a detector unit 11, a
soft computing unit 20, a charger-discharger 30, and a comparator
40.
[0018] The detector unit 11 includes a current detector 12, a
voltage detector 14, and a temperature detector 16. The current
detector 12 detects current i from the battery cell 10 at a point
of time k. The voltage detector 14 detects voltage v from the
battery cell 10 at a point of time k. The temperature detector 16
detects temperature T from the battery cell 10 at a point of time
k.
[0019] Soft computing is generically called a function approximator
made by engineeringly modeling brain information transfer,
reasoning, learning, genetic, and immune systems of a living thing,
and is widely used in control and identification fields throughout
the industry. Here, the identification refers to capturing
input/output characteristics of a system.
[0020] A soft computing algorithm is an algorithm capable of
performing identification and control of a specific system while
self-organizing parameters only with input/output information
although accurate information and model are not known.
[0021] However, the problem is that soft computing techniques each
involve different drawbacks. In other words, the battery SOC
estimation using any soft computing technique is relatively
accurate only in a specific environment, but not in another
environment.
[0022] In order to solve the above-mentioned problem and to make an
approximation of function more accurate, the soft computing unit 20
estimates the battery SOC, using the fusion type soft
computing.
[0023] A fusion type soft computing algorithm of which the soft
computing unit 20 makes use is an algorithm in which a plurality of
algorithms that can be self-organized by performing adaptive
parameter update are mutually combined in a fusion type, and is
subjected to bio-motive. Here, the bio-motive refers to use after
the model of biological information literacy.
[0024] More specifically, the soft computing algorithm of which the
soft computing unit 20 makes use is an algorithm combining a neural
network algorithm with any one of a fuzzy algorithm, a genetic
algorithm (GA), a cellular automata (CA) algorithm, an immune
system algorithm, and a rough-set algorithm. The immune system
algorithm is a modeling method in which an identification or
control point, and disturbance are set as an antibody and an
antigen respectively, and thereby any desired point can be
estimated even when any disturbance is added. The CA algorithm is a
method of modeling a complicated algorithm in a binary type string.
The rough-set algorithm is a method of modeling and applying
correlation of parameters in a numerical formula.
[0025] A neuro-fuzzy algorithm combining the neural network
algorithm to the fuzzy algorithm is a type of automatically
adjusting parameters by implementing a fuzzy reasoning system using
a neural network.
[0026] The neuro-fuzzy algorithm can automatically create the
expert rule base of a fuzzy theory by execution of a learning
algorithm.
[0027] Generally, persons who are well aware of a certain system
perform a work using fuzzy information rather than accurate
information. For example, a skilled welder who is well aware of a
welding system performs the welding well using fuzzy information,
for instance, that the welding is well done when a welding
temperature should be slightly increased at about this
position.
[0028] Creating the expert rule base of a fuzzy theory refers to a
process of obtaining a rule composed of an IF-THEN statement from
experts on a certain system in this manner.
[0029] In general, it is the most difficult work that obtains the
rule base in the fuzzy algorithm. Meanwhile, the neuro-fuzzy
algorithm has an advantage in that this rule base can be
automatically created by using learning capability of the neural
network.
[0030] Further, in view of the neural network, a size of the neural
network (i.e. a neuron number), selection of an activation
function, etc. have a great influence on entire performance. When
the fuzzy theory is used in this field, the performance of the
neural network can be optimized. To be specific, by setting the
neural network size to the number of rule bases, and using any one
of fuzzy functions as the activation function, the performance of
the neural network can be optimized.
[0031] In the neuro-fuzzy algorithm, the neural network models
hardware embodiment of a brain, and the fuzzy concept models human
thinking.
[0032] A neuro-GA algorithm combining the neural network algorithm
to the GA is an algorithm for performing identification of various
parameters required for learning by implementing a learning
algorithm of the neural network using the GA.
[0033] In addition to these algorithms, the soft computing unit 20
may make use of a neuro-CA algorithm combining the neural network
algorithm to the CA algorithm, a neuro-rough set algorithm
combining the neural network algorithm to the rough set algorithm,
and so on.
[0034] In the present embodiment, the soft computing unit 20
estimates the battery SOC using the neuro-fuzzy algorithm, wherein
the neuro-fuzzy algorithm is merely illustrative of the fusion type
soft computing algorithm. The soft computing unit 20 may estimate
the battery SOC using a fusion type soft computing algorithm other
than the neuro-fuzzy algorithm.
[0035] The soft computing unit 20 performs the neuro-fuzzy
algorithm based on current i, voltage v, and temperature T detected
by the detector unit 11, and a detecting time k, and outputs an
estimation value F of the battery SOC.
[0036] When receiving an algorithm update signal from the
comparator 40, the soft computing unit 20 performs a learning
algorithm on the neuro-fuzzy algorithm, thereby updating the soft
computing algorithm.
[0037] When the soft computing algorithm is updated, the soft
computing unit 20 performs the updated soft computing algorithm,
and outputs an updated estimation value F of the battery SOC.
[0038] The charger-discharger 30 supplies the battery cell 10 with
charge/discharge current.
[0039] The comparator 40 compares the estimation value F output by
the soft computing unit 20 with a predetermined target value
F.sub.T. When a difference between the output estimation value F
and the target value F.sub.T is beyond a critical range, the
comparator 40 outputs the algorithm update signal to the soft
computing unit 20.
[0040] Ideally, the target value F.sub.T is a value of the real
"genuine" battery SOC. However, because it is difficult to find the
value, a reference value obtained through a proper test under a
specific condition is used.
[0041] For example, the target value F.sub.T may be a value that
complements an amp-hour (Ah) counting value and an open circuit
voltage (OCV) value, which are input from the charger-discharger,
to rated capacity of the battery.
[0042] FIG. 2 illustrates a construction of a fuzzy neural network
in the soft computing unit 20 of FIG. 1. Referring to FIG. 2, the
fuzzy neural network is generally composed of an input layer, a
hidden layer, and an output layer.
[0043] If the number of basis functions is the same as the number
of fuzzy control rules, if the consequent of a fuzzy rule is a
constant, if an operator of the network is equal to a function of
the output layer, and if a membership function in the fuzzy rule is
the basis function of the same width (dispersion), a fuzzy system
is equivalent to a radial basis function network of FIG. 2. Here,
the radial basis function network is a concrete name of the neural
network, and is a kind of neural network.
[0044] The soft computing unit 20 executes the neuro-fuzzy
algorithm according to a structure of the fuzzy neural network. The
neuro-fuzzy algorithm is no other than the battery SOC estimation
algorithm. Final output for applying the battery SOC estimation
algorithm according to the fuzzy neural network in the soft
computing unit 20 has a form as expressed by Equation 1 below.
F=.PHI.(P,X)W Equation 1
[0045] where .PHI. is the fuzzy radial function, or the radial
basis function or the activation function in the neural network, P
is the parameter, X is the input, W is the weight to be updated
during learning.
[0046] Now, the following is to apply Equation 1 to the structure
of the fuzzy neural network of FIG. 2.
[0047] In FIG. 2, X=x.sub.d(k) is an input data vector input into
the structure of the fuzzy neural network. In the present
embodiment, x.sub.d(k)=(i, v, T, k). Here, i, v, and T are current,
voltage, and temperature data, which are detected from the battery
cell 10 at a point of time k by the detector unit 11 of FIG. 1.
[0048] In Equation 1, F, i.e. the final output is the product of
the radial function, .PHI.=Od(k), and W=wd(k).
[0049] Here, W is the coefficient denoting the connection strength
(weight). W is updated at every point of time k by a back
propagation (BP) learning algorithm to be described below. Thus,
the function is approximated to perform identification of a
non-linear function.
[0050] As a result of the comparator 40 of FIG. 1 comparing the
output value F and the target value F.sub.T of the fuzzy neural
network, when an error between the output value g.sub.o and the
target value g.sub.T is beyond a critical range (e.g. 3%), the
comparator 40 of FIG. 1 outputs the algorithm update signal to the
soft computing unit 20 of FIG. 1.
[0051] When the soft computing unit 20 of FIG. 1 receives the
algorithm update signal, the learning algorithm is executed in the
fuzzy neural network of FIG. 2. In the present embodiment, the
learning algorithm will be described focused on the BP learning
algorithm, but it is merely illustrative. For example, the learning
algorithm may include a Kalman filter, the genetic algorithm, a
fuzzy learning algorithm, or so on.
[0052] As for the BP learning algorithm, first, an error function
is defined as follows. E = 1 2 .times. ( F T .function. ( k ) - F
.function. ( k ) ) Equation .times. .times. 2 ##EQU1##
[0053] where F.sub.T(k) is the desired output, i.e. the target
value, and F(k) is the real output of the fuzzy neural network.
Thus, final weight update is expressed by Equation 3 below. W
.function. ( t + 1 ) = W .function. ( t ) + .eta. .function. ( -
.differential. E .differential. W ) Equation .times. .times. 3
##EQU2##
[0054] where .eta. is the learning rate.
[0055] In this manner, the neuro-fuzzy algorithm is updated by
repetitively executing the BP learning algorithm. More
specifically, a W value of the neuro-fuzzy algorithm is updated by
repetitively executing the BP learning algorithm.
[0056] The fuzzy neural network outputs a new output value F
determined by the updated W value to the comparator 40 again. This
process is repeated until the error between the output value F and
the target value F.sub.T of the fuzzy neural network falls within a
predetermined range.
[0057] When the error between the output value F and the target
value F.sub.T of the fuzzy neural network does not deviate from the
predetermined range, the comparator 40 of FIG. 1 does not transmit
the algorithm update signal. Thereby, the execution of the learning
algorithm on the fuzzy neural network is terminated. An estimation
value of the battery SOC is output using the final neuro-fuzzy
algorithm formula (i.e. Equation 1) obtained by the execution of
the learning algorithm.
[0058] FIG. 3 is a flowchart of a method for estimating an SOC in a
battery in accordance with an embodiment of the present invention.
Referring to FIG. 3, the detector unit 11 detects current i,
voltage v, and temperature T from the battery cell 10 at a point of
time k (S30).
[0059] The soft computing unit 20 performs the neuro-fuzzy
algorithm using data of the current i, voltage v, and temperature T
detected by the detector unit 11 and data of the time k, as input
data vectors, thereby outputting a provisional estimation value
g.sub.o(S32). In other words, the soft computing unit 20 performs
the neuro-fuzzy algorithm using x.sub.d(k)=(i,v,T,k), thereby
outputting a provisional estimation value F.
[0060] The comparator 40 compares the provisional estimation value
F with a target value F.sub.T, and checks whether or not the
compared error is inside of 3% (S34). In the present embodiment, a
critical range of the error is set to within 3%, but it is merely
illustrative. Accordingly, the critical range of the error may be
sufficiently varied by a designer. A final estimation value of the
battery SOC becomes higher as the critical range of the error
becomes narrower, and it becomes lower as the critical range of the
error becomes wider.
[0061] When the error between the provisional estimation value F
and the target value F.sub.T is outside of 3%, the soft computing
unit 20 performs the above-mentioned learning algorithm on the
neuro-fuzzy algorithm, thereby updating the neuro-fuzzy algorithm
(S36). Then, the soft computing unit 20 performs the updated soft
computing algorithm to calculate an updated provisional estimation
value F of the battery SOC (S32).
[0062] The comparator 40 compares the updated provisional
estimation value F with a target value F.sub.T, and checks whether
or not the compared error is inside of 3% (S34). When the error
between the provisional estimation value F and the target value
F.sub.T is outside of 3%, the soft computing unit 20 performs the
learning algorithm on the neuro-fuzzy algorithm again (S36), and
performs the updated neuro-fuzzy algorithm (S32).
[0063] In other words, the soft computing unit 20 performs the
learning algorithm and the updated neuro-fuzzy algorithm
repetitively, until the error between the provisional estimation
value F and the target value F.sub.T gets inside of 3%.
[0064] When the error between the provisional estimation value F
and the target value F.sub.T is inside of 3%, the soft computing
unit 20 does not perform the learning algorithm. As a result, a
final neuro-fuzzy algorithm formula (e.g. Equation 3) is
obtained.
[0065] The provisional estimation value F calculated by the final
neuro-fuzzy algorithm formula is determined as a fixed estimation
value F of the battery SOC (S38).
[0066] The present invention can implement a computer-readable
recording medium as a computer-readable code. The computer-readable
recording medium includes all types of recording media in which
computer-readable data is stored. Examples of the computer-readable
recording media include a read only memory (ROM), a random access
memory (RAM), a compact disk (CD)-ROM, a magnetic tape, a floppy
disk, an optical data storage device, and so on, and furthermore
what is embodied in the type of a carrier wave (e.g. transmitted
through Internet). Further, the computer-readable recording media
are distributed on a computer system connected through a network,
and allow the code that can be read by the computer in a
distributed way to be stored and executed.
INDUSTRIAL APPLICABILITY
[0067] As can be seen from the foregoing, according to the present
invention, the battery SOC can be dynamically estimated through the
fusion type soft computing algorithm and the learning algorithm.
Further, the battery SOC can be more accurately estimated using at
least data according to the various environments such as
temperature, C-rate, and so on.
[0068] Thus, according to the present invention, the battery SOC
can be accurately estimated in the high C-rate environment. Because
the fusion type soft computing algorithm is used for estimating the
battery SOC, it is possible to overcome a drawback that each single
soft computing algorithm is relatively accurate only in a specific
environment and is lowered in precision in another environment.
[0069] Especially, when the neuro-fuzzy algorithm is used as the
fusion type soft computing algorithm, the fuzzy logic is
implemented as the neural network. Thereby, it is possible to
automatically create the fuzzy rules through learning. Due to this
possibility, it is possible to exert excellent performance in
connection with initial weight setting stability and system
convergence, compared to the existing single neuro-fuzzy
algorithm.
[0070] The present invention can be more broadly utilized in a
field in which the estimation of the battery SOC requires higher
precision as in the field of hybrid electrical vehicles. Thus, the
present invention can be applied to a lithium ion polymer battery
(LiPB) for the hybrid electrical vehicle, as well as other
batteries.
[0071] While this invention has been described in connection with
what is presently considered to be the most practical and exemplary
embodiment, it is to be understood that the invention is not
limited to the disclosed embodiment and the drawings, but, on the
contrary, it is intended to cover various modifications and
variations within the spirit and scope of the appended claims.
* * * * *