U.S. patent application number 17/561265 was filed with the patent office on 2022-04-21 for battery capacity estimation method and battery capacity estimation system.
This patent application is currently assigned to TOYOTA JIDOSHA KABUSHIKI KAISHA. The applicant listed for this patent is TOYOTA JIDOSHA KABUSHIKI KAISHA. Invention is credited to Junta IZUMI, Masahiko MITSUI, Juni YASOSHIMA.
Application Number | 20220120818 17/561265 |
Document ID | / |
Family ID | 1000006052299 |
Filed Date | 2022-04-21 |
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United States Patent
Application |
20220120818 |
Kind Code |
A1 |
MITSUI; Masahiko ; et
al. |
April 21, 2022 |
BATTERY CAPACITY ESTIMATION METHOD AND BATTERY CAPACITY ESTIMATION
SYSTEM
Abstract
A battery capacity estimation system executes a charging and
discharging process (S1), an alternating current impedance
acquiring process (S2 and S3), and a battery capacity estimating
process (S4 to S6). The charging and discharging process involves
charging and discharging a target secondary battery. The
alternating current impedance acquiring process involves acquiring
a measurement result of an alternating current impedance of a
target secondary battery, by applying an alternating current signal
within a specific frequency range to the target secondary battery
after completion of the charging and discharging in the charging
and discharging process and before a predetermined maximum waiting
time elapses. The battery capacity estimating step involves
estimating a battery capacity of the target secondary battery based
on the measurement result of the alternating current impedance.
Inventors: |
MITSUI; Masahiko;
(Toyota-shi, JP) ; IZUMI; Junta; (Nagoya-shi,
JP) ; YASOSHIMA; Juni; (Toyota-shi, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
TOYOTA JIDOSHA KABUSHIKI KAISHA |
Toyota-shi |
|
JP |
|
|
Assignee: |
TOYOTA JIDOSHA KABUSHIKI
KAISHA
Toyota-shi
JP
|
Family ID: |
1000006052299 |
Appl. No.: |
17/561265 |
Filed: |
December 23, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
16722509 |
Dec 20, 2019 |
|
|
|
17561265 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01R 31/3648 20130101;
G01R 31/3828 20190101; G01R 31/389 20190101; H02J 7/00036
20200101 |
International
Class: |
G01R 31/3828 20190101
G01R031/3828; H02J 7/00 20060101 H02J007/00; G01R 31/389 20190101
G01R031/389; G01R 31/36 20200101 G01R031/36 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 26, 2018 |
JP |
2018-243294 |
Claims
1. A battery capacity estimation method comprising: a charging and
discharging step of charging and discharging a target secondary
battery; an alternating current impedance acquiring step of
acquiring a measurement result of an alternating current impedance
of the target secondary battery, by applying an alternating current
signal within a specific frequency range to the target secondary
battery after completion of the charging and discharging in the
charging and discharging step and before a predetermined maximum
waiting time elapses; and a battery capacity estimating step of
estimating a battery capacity of the target secondary battery based
on the measurement result of the alternating current impedance,
wherein: the battery capacity estimating step includes obtaining an
estimation result of the battery capacity of the target secondary
battery by inputting data based on a Nyquist plot of the target
secondary battery into a pre-trained neural network model; and the
pre-trained neural network model performs learning using a
plurality of training data including data based on Nyquist plots
indicating measurement results of alternating current impedance of
a plurality of secondary batteries and actual battery capacities of
the plurality of secondary batteries.
2. The battery capacity estimation method according to claim 1,
wherein the pre-trained neural network model is trained based on
the training data including data relating to the Nyquist plots of
the secondary batteries after completion of the charging and
discharging and before the maximum waiting time elapses.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a divisional application of U.S.
application Ser. No. 16/722,509, filed Dec. 20, 2019, which claims
priority from Japanese Patent Application No. 2018-243294 filed on
Dec. 26, 2018, which is incorporated by reference herein in its
entirety.
BACKGROUND
[0002] The present disclosure relates to a battery capacity
estimation method and a battery capacity estimation system that are
used to estimate the capacity of a secondary battery.
[0003] Secondary batteries are widely used as a portable power
source for various devices such as personal computers and mobile
terminals, and as a vehicle power source for various vehicles such
as electric vehicles (EVs), hybrid vehicles (HVs), and plug-in
hybrid vehicles (PHVs). The secondary batteries that have been used
onboard the vehicles may be removed and collected. It is desirable
that, if usable, the collected secondary batteries be reused.
However, secondary batteries can deteriorate over time and repeated
charging and discharging. The degree of deterioration in secondary
batteries is different from one battery to another. For this
reason, it is desirable that the performance of each of the
collected secondary batteries should be evaluated, and according to
the results of the evaluation, the policy on how to reuse each of
the secondary batteries should be determined. In addition, not only
in determining the policy on how to reuse collected secondary
batteries, it is desirable to evaluate the performance of a
secondary battery in many cases.
[0004] Various techniques have been proposed for evaluating
performance and characteristics of secondary batteries. For
example, JP 2003-317810 A proposes a method of evaluating battery
characteristics that involves determining the presence or absence
of micro-short circuit in a secondary battery, based on a reaction
resistance value of the secondary battery that is acquired by an
alternating current impedance measurement method.
SUMMARY
[0005] In estimating the capacity of a secondary battery using an
alternating current impedance measurement method, variations may
occur in the results of the estimation in some cases, even when the
same alternating current impedance measurement technique is used to
estimate the capacity of the same secondary battery. It is
desirable to further increase the accuracy of estimation of the
capacity.
[0006] In an embodiment of the present disclosure, a battery
capacity estimation method may include: a charging and discharging
step of charging and discharging a target secondary battery; an
alternating current impedance acquiring step of acquiring a
measurement result of an alternating current impedance of the
target secondary battery, by applying an alternating current signal
within a specific frequency range to the target secondary battery
after completion of the charging and discharging in the charging
and discharging step and before a predetermined maximum waiting
time elapses; and a battery capacity estimating step of estimating
a battery capacity of the target secondary battery based on the
measurement result of the alternating current impedance.
[0007] The inventors of the present application have discovered
that variations in the results of battery capacity estimation based
on alternating current impedance change according to elapsed time
after completion of charging and discharging of a secondary battery
until the alternating current impedance is measured. Specifically,
it has been found from the results of experiments conducted by the
inventors that variations in the estimation results become smaller
when the elapsed time after completion of the charging and
discharging until the alternating current impedance is measured is
shorter than the maximum waiting time that is determined according
to the conditions such as the type of the secondary battery. The
above-described battery capacity estimation method estimates
battery capacity based on the alternating current impedance that is
measured after completion of charging and discharging of the
secondary battery and before the maximum waiting time elapses. As a
result, the capacity of the target secondary battery is estimated
with higher accuracy.
[0008] It should be noted that the maximum waiting time varies
depending on conditions such as the type of the secondary battery,
whether or not the secondary battery is a battery pack, and the
structure of the battery pack if the secondary battery is a battery
pack. Accordingly, the maximum waiting time may be determined as
appropriate, for example, through experimentation, depending on the
conditions such as the type of the secondary battery and so
forth.
[0009] In a preferable embodiment of the battery capacity
estimation method disclosed herein, in the alternating current
impedance acquiring step, the measurement result of the alternating
current impedance of the target secondary battery is acquired
within a period in which an elapsed time after completion of the
charging and discharging in the charging and discharging step is
longer than a predetermined minimum waiting time and shorter than
the maximum waiting time.
[0010] The inventors of the present application has discovered that
variations in the results of battery capacity estimation become
even smaller when the elapsed time after completion of the charging
and discharging of the secondary battery until the alternating
current impedance is measured is longer than the minimum waiting
time and shorter than the maximum waiting time, the minimum waiting
time and the maximum waiting time being determined according to the
conditions such as the type of the secondary battery. Accordingly,
the battery capacity of the target secondary battery can be
estimated with higher accuracy by setting the elapsed time after
completion of the charging and discharging of the secondary battery
until the alternating current impedance is measured to be longer
than the minimum waiting time and shorter than the maximum waiting
time. It should be noted that, like the maximum waiting time, the
minimum waiting time may also be determined as appropriate
depending on the conditions such as the type of the secondary
battery and so forth.
[0011] In another embodiment of the battery capacity estimation
device disclosed herein, the battery capacity estimating step
involves obtaining an estimation result of the battery capacity of
the target secondary battery by inputting data based on a Nyquist
plot of the target secondary battery into a pre-trained neural
network model. The pre-trained neural network model performs
learning using a plurality of training data including data based on
a plurality of Nyquist plots indicating measurement results of
alternating current impedance of a plurality of secondary batteries
and actual battery capacities of the plurality of secondary
batteries.
[0012] In this case, it is possible to acquire the estimation
result of the battery capacity of the target secondary battery
easily and appropriately by properly training the neural network
model, without executing a complicated process (such as the process
of analyzing the measurement results of alternating current
impedance). It is also possible, however, to estimate the battery
capacity based on other algorithms without using the neural network
model.
[0013] In another embodiment of the battery capacity estimation
method disclosed herein, the pre-trained neural network model may
be trained based on the training data including data relating to
Nyquist plots of secondary batteries after completion of the
charging and discharging and before the maximum waiting time
elapses.
[0014] In this case, the time condition for obtaining the Nyquist
plots of the target secondary battery is in agreement with the time
condition for obtaining the Nyquist plots used for training the
neural network model. Therefore, the pre-trained neural network
model is optimized for estimating the battery capacity of the
target secondary battery based on the Nyquist plot obtained before
the elapse of the maximum waiting time. As a result, the accuracy
of estimation of the battery capacity is further improved.
[0015] When the alternating current impedance of the target
secondary battery is to be acquired in a period from the minimum
waiting time to the maximum waiting time after the charging and
discharging, the Nyquist plots used for training the neural network
model may also be obtained in the period from the minimum waiting
time to the maximum waiting time after the charging and
discharging. This serves to further optimize the pre-trained neural
network model.
[0016] In another embodiment of the present disclosure, a battery
capacity estimation system includes: a charging and discharging
process of charging and discharging a target secondary battery; an
alternating current impedance acquiring process of acquiring a
measurement result of an alternating current impedance of the
target secondary battery by applying an alternating current signal
within a specific frequency range to the target secondary battery
after completion of the charging and discharging process by the
charging and discharging process and before a predetermined maximum
waiting time elapses; and a battery capacity estimating process of
estimating a battery capacity of the target secondary battery based
on the measurement result of the alternating current impedance. In
this case, the capacity of the target secondary battery is
estimated with high accuracy, as with the battery capacity
estimation method.
[0017] In another embodiment of the battery capacity estimation
system disclosed herein, in the alternating current impedance
acquiring process, the measurement result of the alternating
current impedance of the target secondary battery is acquired
within a period in which an elapsed time after completion of the
charging and discharging by the charging and discharging process is
longer than a predetermined minimum waiting time and shorter than
the maximum waiting time. In this case, the capacity of the target
secondary battery is estimated with high accuracy, as with the
battery capacity estimation method.
[0018] In another embodiment of the present disclosure, a battery
capacity estimation system may include: a charging and discharging
device executing the charging and discharging process; a
measurement device executing the alternating current impedance
acquiring process; and a battery capacity estimation device
executing the battery capacity estimating process. In this case,
the foregoing processes are executed appropriately by the
respective devices. However, it is also possible to modify the
configuration of the battery capacity estimation system. For
example, the same one of the devices may execute a plurality of the
processes. For example, the measurement device may execute the
charging and discharging process and the alternating current
impedance acquiring process together. It is also possible that one
of the processes may be executed by a plurality of the devices that
cooperate with each other.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] FIG. 1 is a view illustrating an example of logistics of
secondary batteries, including collection, manufacturing, and sales
of secondary batteries according to the present embodiment.
[0020] FIG. 2 is a view illustrating a configuration of a battery
performance evaluation system 1.
[0021] FIG. 3 is a graph illustrating an example of a Nyquist plot
that shows the results of an alternating current impedance
measurement for a module M.
[0022] FIG. 4 is a graph illustrating an example of Nyquist plots
obtained when alternating current impedance was measured a
plurality of times while varying waiting time.
[0023] FIG. 5 is an enlarged view of the low frequency area of the
graph shown in FIG. 4.
[0024] FIG. 6 is a graph illustrating the estimation results of the
battery capacity of the module M when the elapsed time is varied
from 0 minutes to 120 minutes.
[0025] FIG. 7 is a graph illustrating the estimation results of the
battery capacity of the module M when the elapsed time is varied
from 120 minutes to 1440 minutes.
[0026] FIG. 8 is a schematic illustrative drawing for illustrating
learning of a neural network model in the present embodiment.
[0027] FIG. 9 is a flow-chart illustrating a battery capacity
estimating process according to the present embodiment.
DETAILED DESCRIPTION
[0028] Hereinbelow, embodiments of the present disclosure will be
described in detail with reference to the drawings. It should be
noted that the matters not specifically described in this
description but necessary to carry out the embodiments can be
understood as design variations by a skilled person based on the
prior art in the related field. The present invention may be
implemented based on the contents disclosed herein and the common
general technical knowledge in the related field. Throughout the
drawings, identical reference characters and descriptions are used
to designate like elements or features. It should be noted that
dimensional relationships in the drawings do not necessarily
reflect actual dimensional relationships.
[0029] The present embodiment describes an example of estimating
the capacity of a secondary battery that has been used onboard a
vehicle. Specifically, in the present embodiment, a secondary
battery that has been carried onboard a vehicles is collected, then
the capacity of the collected secondary battery is estimated, and
the policy on how to reuse the secondary battery is determined
according to the result of the estimation. It should be noted,
however, that at least part of the technology illustrated in the
present disclosure can be applied also to other situations (for
example, a situation in which the capacity of a secondary battery
used in a device other than a vehicle is to be estimated, or a
situation in which the capacity of a newly manufactured secondary
battery is to be estimated).
[0030] In the present embodiment, the battery pack carried onboard
a vehicle includes a plurality of modules. The plurality of modules
may be either connected in series or connected in parallel with
each other. Each of the modules includes a plurality of battery
cells (unit cells) connected in series.
[0031] In the present embodiment, a battery pack collected from a
vehicle is disassembled into modules, and thereafter, the capacity
of each of the modules is estimated module by module. As a result
of the capacity estimation, a module that is determined to be
reusable is reused (rebuilt) as a part of a newly manufactured
battery pack. However, depending on the structure of the battery
pack, it is also possible to estimate the capacity in the shape of
the battery pack without disassembling the battery pack.
Furthermore, it is also possible to estimate the capacity for each
of the battery cells that make up a module.
[0032] In the present embodiment, the secondary battery cell (each
of the battery cells that make up the module) is a nickel-metal
hydride battery. Specifically, the positive electrode includes
nickel hydroxide [Ni(OH).sub.2] containing a cobalt oxide additive.
The negative electrode includes a hydrogen-absorbing alloy
(MnNi.sub.5-based alloy, which is a nickel-based alloy). The
electrolyte solution includes potassium hydroxide (KOH). However,
this is merely an illustrative example of specific cell structure,
and it is possible to apply the technique illustrated in the
present disclosure to various other types of secondary
batteries.
Battery Logistics Model
[0033] With reference to FIG. 1, an embodiment of logistics of
secondary batteries, including collection, manufacturing
(recycling), and sales of secondary batteries according to the
present embodiment will be described. In the example shown in FIG.
1, a collection operator 101 collects used secondary batteries
(battery packs) 111, 121, and 131 from vehicles 110, 120, and 130.
Although only three vehicles 110, 120, and 130 are illustrated in
FIG. 1, secondary batteries are collected from a greater number of
vehicles in a real situation. The collection operator 101
disassembles the collected battery packs to obtain a plurality of
modules from each of the battery packs. In the example shown in
FIG. 1, an identification number is assigned to each of the
modules, and the information about each of the modules is managed
by a management server 108. The collection operator 101 transmits
the identification numbers of the modules obtained from the battery
packs to the management server 108 using a terminal device (not
shown).
[0034] An inspection operator 102 carries out performance
evaluation for each of the modules collected by the collection
operator 101. For example, the inspection operator 102 assesses at
least one electrical characteristic of each of the modules,
including the battery capacity (for example, fully charged
capacity), the resistance value, the open circuit voltage (OCV),
and the state of charge (SOC) of each of the modules. The
inspection operator 102 determines a plan for reusing the modules
based on the results of the evaluation. For example, the inspection
operator 102 separates the modules into reusable modules and
non-reusable modules based on the results of the evaluation, and
hands over the reusable modules to a performance recovery operator
103 and the non-reusable modules to a recycling operator 106. The
results of the performance evaluation for each of the modules is
transmitted to a management server 108 by a terminal (not shown) of
the inspection operator 102.
[0035] The performance recovery operator 103 carries out a process
for recovering the performance of the modules that have been
determined as reusable by the inspection operator 102. In an
example, the performance recovery operator 103 charges the modules
to an overcharged state so as to recover the fully charged capacity
of the modules. However, it is possible that the performance
recovery process by the performance recovery operator 103 may be
eliminated for the modules that have been evaluated to have low
performance degradation in the performance evaluation by the
inspection operator 102. The results of the performance recovery
for each of the modules is transmitted to the management server 108
by a terminal (not shown) of the performance recovery operator
103.
[0036] A manufacturer 104 manufactures battery packs using the
modules of which the performance recovery operator 103 have
recovered the performance. For example, the manufacturer 104 may
replace a module showing degraded performance in a battery pack of
a vehicle 109 with a module of which the performance recovery
operator 103 have recovered the performance, to manufacture
(rebuild) the battery pack of the vehicle 109.
[0037] A dealer 105 may sell the battery packs manufactured by the
manufacturer 104 as battery packs for vehicle use or as battery
packs for stationary use that may be usable in residential
settings. A recycling operator 106 disassembles the modules that
have been determined as non-reusable by the inspection operator
102, and recycles the modules for using them as a material for new
battery cells or the like.
[0038] Note that FIG. 1 depicts the collection operator 101, the
inspection operator 102, the performance collection operator 103,
the manufacturer 104, the dealer 105, and the recycling operator
106 as different business entities. However, the types of business
entities are not limited to the examples shown in FIG. 1. For
example, the inspection operator 102 and the performance recovery
operator 103 may be the same business entity. Also, the collection
operator 101 may be fulfilled by two separate operators, one that
collects battery packs and the other that disassembles the
collected battery packs. The base locations of the operators and
dealers are not limited to specific locations, and a plurality of
operators are based at either the same location or different
locations.
[0039] The following description is provided to illustrate an
example in which performance evaluation (particularly estimation of
battery capacity) is performed for a module M that may be contained
in battery packs 111, 112, and 113 collected from vehicles 110,
120, and 130, and according to the evaluation result, the policy on
how to reuse the module M is determined. That is, in the present
embodiment, the target secondary battery, the battery capacity of
which is to be estimated, is the module M. However, the target
secondary battery may be a single battery cell, or may be an entire
battery pack including a plurality of modules M.
Battery Capacity Estimation System
[0040] With reference to FIG. 2, a battery capacity estimation
system 1 according to the present embodiment will be described. In
the battery logistics model shown in FIG. 1, the battery capacity
estimation system 1 is provided for the inspection operator 102,
for example. The battery capacity estimation system 1 according to
the present embodiment includes a charging and discharging device
5, a measurement device 10, a memory storage device 20, a battery
capacity estimation device 30, and a display device 40. These
devices may be independent of each other, or two or more of these
devices may be combined into one device.
[0041] The charging and discharging device 5 performs charging and
discharging of a module M that is in a condition before the
alternating current impedance is measured. For example, the battery
capacity estimation system 1 causes the charging and discharging
device 5 to perform charging and discharging of the module M so as
to adjust the SOC of the module M to be in a desired range, and
thereafter, allows the measurement device 10 to conduct the
later-described measurement of alternating current impedance. The
charging and discharging process is executed by a charging and
discharging processing unit 6 provided for the charging and
discharging device 5. However, the charging and discharging of the
module M before the alternating current impedance measurement may
also be carried out by a device other than the charging and
discharging device 5 (for example, by the measurement device
10).
[0042] The measurement device 10 measures the alternating current
impedance of the module M, and outputs a Nyquist plot indicating
the measurement result to the battery capacity estimation device
30. More specifically, the measurement device 10 of the present
embodiment includes an oscillator 11, a potentiostat 12, a lock-in
amplifier 13, and a plotter 14.
[0043] The oscillator 11 outputs sine waves of the same phase to
the potentiostat 12 and the lock-in amplifier 13.
[0044] The potentiostat 12 generates an alternating current
application signal by superposing a predetermined direct current
voltage on an alternating current voltage (for example, a voltage
with an amplitude of about 10 mV) that is in the same phase as that
of the sine wave output from the oscillator 11, and applies the
generated application signal to the module M. The potentiostat 12
detects the electric current flowing through the module M, and
outputs the detection result of the electric current to the lock-in
amplifier 13 as a response signal from the module M. The
potentiostat 12 also outputs the application signal and the
response signal to the plotter 14.
[0045] The lock-in amplifier 13 compares the phase of the sine wave
that is output from the oscillator 11 and the phase of the response
signal that is output from the potentiostat 12, and outputs the
result of the comparison (i.e., the phase difference between the
sine wave and the response signal) to the plotter 14.
[0046] The plotter 14 plots the measurement results of the
alternating current impedance of the module M onto a complex plane
based on the signal from the potentiostat 12 (the signal indicating
the amplitude ratio of the application signal and the response
signal) and the signal from the lock-in amplifier 13 (the signal
indicating the phase difference between the application signal and
the response signal). More specifically, the frequency of the sine
wave that is output from the oscillator 11 is swept over a
predetermined frequency range, and the above-described processes by
the potentiostat 12 and the lock-in amplifier 13 are executed
repeatedly. Thereby, the measurement results of alternating current
impedance of the module M for various frequencies of the sine wave
are plotted on a complex plane. The generated plot is referred to
as a Nyquist plot (which may be also referred to as a Cole-Cole
plot). The Nyquist plot of the module M is output to the battery
capacity estimation device 30.
[0047] It should be noted that the configuration of the measurement
device 10 is not limited to that illustrated in FIG. 2. For
example, it is described that the potentiostat 12 of the present
embodiment applies an alternating current voltage to the module M
and detects the electric current passing through the module M while
applying the voltage. However, it is also possible that the
potentiostat 12 may detect a voltage response while applying an
alternating current to the module M. It is also possible that the
measurement device 10 may include a frequency response analyzer in
place of the lock-in amplifier 13.
[0048] Furthermore, it is also possible to modify the technique of
the alternating current impedance measurement. For example, it is
also possible that the measurement device 10 may generate an
application signal containing various frequency components within a
predetermined frequency range (either one of voltage signal or
current signal) and it may detect the response signal (the other
one of voltage signal or current signal) while applying the
application signal. It is also possible that the measurement device
10 may calculate an alternating current impedance for each of the
frequencies by performing a fast Fourier transform on each of the
application signal and the response signal.
[0049] The memory storage device 20 stores a pre-trained neural
network model that causes the battery capacity estimation device 30
to execute a process of estimating a battery capacity (a fully
charged capacity in the present embodiment) of the module M. In
response to a request from the battery capacity estimation device
30, the memory storage device 20 executes processes such as
learning of the neural network model, updating of learning results,
and outputting of the learning results to the battery capacity
estimation device 30.
[0050] The battery capacity estimation device 30 includes a
controller (for example, a CPU), a memory, and input/output ports.
Specifically, the battery capacity estimation device 30 is provided
with the functions of a capacity estimation unit 31 and a
reusability determination unit 32. Although the details will be
described later, the capacity estimation unit 31 estimates the
battery capacity (the fully charged capacity in the present
embodiment) of the module M based on the alternating current
impedance of the module M that has been measured by the measurement
device 10. This process is referred to as a "battery capacity
estimating process" in the present embodiment. The reusability
determination unit 32 determines how the module M is reused
according to the result of estimation of the battery capacity of
the module M. The reusability determination unit 32 may determine
whether or not the module M can be reused.
Nyquist Plot
[0051] With reference to FIG. 3, a Nyquist plot showing the results
of an alternating current impedance measurement for a module M will
be described. In FIG. 3, the horizontal axis represents the real
component (Z.sub.Re) of the alternating current impedance (complex
impedance) of the module M, and the vertical axis represents the
imaginary component (-Z.sub.Im) of the alternating current
impedance of the module M.
[0052] The measurement device 10 of the present embodiment applies
application signals with various frequencies in the range of 100
mHz to 1 kHz to the module M. Since the signals with various
frequencies are applied to the module M, the measurement results of
alternating current impedance of the module M, which correspond to
the frequencies of the signals, are plotted on a complex plane as
discrete values, as shown in FIG. 3. Specifically, application
signals with 52 different frequencies in the frequency range of 100
mHz to 1 kHz are used in the present embodiment. As a result, the
resulting Nyquist plot has a semi-circular portion, which is
obtained from application signals at high frequencies (from 1 Hz to
1 kHz in the example shown in FIG. 3), and a linear portion, which
is obtained from application signals at low frequencies (from 100
mHz to 1 Hz).
[0053] The battery capacity estimating process of the present
embodiment uses a neural network model in order to estimate the
battery capacity (the fully charged capacity in the present
embodiment) of the module M. Machine learning of the neural network
model is performed so that, when data based on a measurement result
of alternating current impedance are fed into the input layer of
the neural network model, a highly accurate estimation result of
battery capacity is output from the output layer of the neural
network model. The details of an example of the learning method of
the neural network model will be described later.
Relationship Between Elapsed Time from Completion of Charging and
Discharging to Alternating Current Impedance Measurement and
Measurement Result
[0054] With reference to FIGS. 4 to 7, the following describes the
relationship between the elapsed time after completion of charging
and discharging of the module M until the alternating current
impedance is measured and the measurement result of the alternating
current impedance. The inventors of the present application have
discovered that variations in the measurement results of
alternating current impedance change according to the elapsed time
after completion of charging and discharging of a secondary battery
(a module M in the present embodiment) until the alternating
current impedance is measured.
[0055] FIG. 4 is a graph illustrating an example of Nyquist plots
obtained from the respective measurement results when the
alternating current impedance of the same module M was measured a
plurality of times while the elapsed time was varied from 0 minutes
to 1,440 minutes. FIG. 5 is an enlarged view of the low frequency
area of the graph shown in FIG. 4. As illustrated in FIG. 4, it is
seen that the measurement results (the Nyquist plots in the present
embodiment) vary when the elapsed time is changed, even though the
measurement target module M is the same and the measurement method
of alternating current impedance is the same. More specifically,
variations in the measurement results change according to the
elapsed time. As illustrated in FIG. 5, when the frequency of the
application signal is in a low frequency range, the variations in
the measurement results are particularly greater. It is
demonstrated from the graph shown in FIG. 5 that the longer the
waiting time, the greater the variations in the measurement
results.
[0056] FIG. 6 is a graph illustrating the results of estimation of
the battery capacity of the module M while the elapsed time is
varied from 0 minutes to 120 minutes. FIG. 7 is a graph
illustrating the results of estimation of the battery capacity of
the module M while the elapsed time is varied from 120 minutes to
1,440 minutes. In the examples shown in FIGS. 6 and 7, all the
modules M used as the target of battery capacity estimation were
the same ones, and the alternating current impedances of the
modules M were measured by the same method. In addition, the
battery capacity estimation in the examples shown in FIGS. 6 and 7
is carried out by inputting data based on the measurement results
of the alternating current impedance of the module M (data of
Nyquist plots in the present embodiment) into the input layer of a
pre-trained neural network model. The details of this battery
capacity estimation method will be described later.
[0057] As illustrated in FIG. 6, with the module M of the present
embodiment, variations in the estimation results were greater when
the elapsed time was set to be less than or equal to 13 minutes
than when the elapsed time was set to be longer than 13 minutes.
Accordingly, when estimating the battery capacity of the module M
of the present embodiment, the minimum waiting time is set to 13
minutes in advance, and the elapsed time after completion of
charging and discharging until the alternating current impedance is
measured is set to be longer than the minimum waiting time, so that
the battery capacity can be estimated with higher accuracy.
[0058] In addition, as illustrated in FIG. 7, with the module M of
the present embodiment, variations in the estimation results were
greater when the elapsed time was set to be greater than or equal
to 120 minutes than when the elapsed time was set to be shorter
than 120 minutes. Accordingly, when estimating the battery capacity
of the module M of the present embodiment, the maximum waiting time
is set to 120 minutes in advance, and the elapsed time after
completion of charging and discharging until the alternating
current impedance is measured is set to be shorter than the maximum
waiting time, so that the battery capacity can be estimated with
higher accuracy.
[0059] Various causes are conceivable for the changes in the
measurement results of the alternating current impedance (FIGS. 4
and 5) and the changes in the results of the battery capacity
estimation based on the alternating current impedance (FIGS. 6 and
7). For example, it may be conceivable that, due to the
electrochemical polarization resulting from charging and
discharging of the secondary battery, variations in the results of
the measurement and the results of the estimation change according
to the elapsed time.
[0060] The foregoing experimental results demonstrate that the
battery capacity estimation device 30 according to the present
embodiment improves the accuracy of battery capacity estimation by
setting the elapsed time after completion of charging and
discharging of a module M to be longer than the minimum waiting
time (13 minutes) and shorter than the maximum waiting time (120
minutes).
[0061] The experimental results illustrated in FIGS. 4 to 7 were
obtained using a module M that includes six series-connected
battery cells, each of which is composed of the
previously-described nickel-metal hydride battery. Herein,
variations in the results of the alternating current impedance
measurement and variations in the results of the battery capacity
estimation, which change according to the elapsed time, become
different depending on conditions such as the type of the secondary
battery, whether or not the secondary battery is a battery pack or
a module, and the battery structure if the secondary battery is a
battery pack or a module. For this reason, the specific values of
the minimum waiting time and the maximum waiting time also vary
depending on the conditions such as the type of the secondary
battery and so forth. Therefore, the minimum waiting time and the
maximum waiting time may be changed as appropriate depending on the
conditions such as the type of the secondary battery and so
forth.
[0062] Moreover, in the present embodiment, the elapsed time after
completion of charging and discharging of the module M is set to be
longer than the minimum waiting time (13 minutes) and shorter than
the maximum waiting time (120 minutes). However, as illustrated in
FIGS. 6 and 7, the present embodiment shows that the variations in
the estimation results when the elapsed time is less than or equal
to the minimum waiting time are smaller than the variations in the
estimation results when the elapsed time is greater than or equal
to the maximum waiting time. Therefore, the battery capacity
estimation device 30 may not set the minimum waiting time and may
set the elapsed time after completion of charging and discharging
of the module M to be shorter than the maximum waiting time. Even
in this case, the accuracy of estimation of the battery capacity is
improved in comparison with the case where the elapsed time is not
at all taken into consideration.
Neural Network Learning
[0063] With reference to FIG. 8, learning of a neural network model
that is used for estimating the battery capacity of a module M will
be described. First, an example of the neural network model is
described. The neural network model in the present embodiment
includes, for example, an input layer x, a hidden layer y, and an
output layer z. The weighting between the input layer x and the
hidden layer y is denoted as W1, and the weighting between the
hidden layer y and the output layer z is denoted as W2. The neural
network model of the present embodiment is trained using training
data including input training data, which are the data relating to
the alternating current impedances of the modules M, and output
training data, which are the data relating to the actual battery
capacities of the modules M. The actual battery capacity of a
module M may be either an actual measurement value of battery
capacity of the module M or an estimate value that has been
estimated with high accuracy. For example, the actual measurement
value of the fully charged capacity of a module M can be obtained
by, for example, measuring the amount of charge required for
charging the module M from the fully discharged state to the fully
charged state.
[0064] The learning method of the neural network model in the
present embodiment will be described below. First, a charging and
discharging process is performed for a module M, the actual battery
capacity (the fully charged capacity in the present embodiment) of
which is known. The alternating current impedance of the module M
is measured within a period in which the elapsed time after
completion of the charging and discharging is longer than the
above-described minimum waiting time and shorter than the
above-described maximum waiting time. A Nyquist plot is obtained
from the measurement results of the alternating current impedance.
Thus, in the learning of the neural network model, the alternating
current impedance of the module M is measured during a period in
which the elapsed time after completion of the charging and
discharging is longer than the minimum waiting time and shorter
than the maximum waiting time. As a result, the time condition for
obtaining the Nyquist plots of the target secondary battery that is
the target of battery capacity estimation is in agreement with the
time condition for obtaining the Nyquist plots used for training
the neural network model. Therefore, the pre-trained neural network
model is optimized for estimating the battery capacity of the
target secondary battery based on the Nyquist plots obtained after
the elapse of the minimum waiting time and before the elapse of the
maximum waiting time. As a result, the accuracy of estimation of
the battery capacity is further improved.
[0065] Next, the data of learning image based on the obtained
Nyquist plot are generated as input training data. The learning
image of the present embodiment includes, for example, a region
including 47 vertical pixels and 78 horizontal pixels, a total of
3,666 pixels. In the learning image, each of all the 3,666 pixels
contains the information indicating whether or not it matches any
of the alternating current impedance measurement results (Nyquist
plots) at 52 different frequencies. Therefore, the effect of
learning is stronger than the case where merely the alternating
current impedance measurement results at 52 different frequencies
are used as the input training data. As a result, the accuracy of
estimation of the battery capacity is improved. It should be noted
that the input layer x of the neural network model includes 3,666
nodes, which correspond to the 3,666 pixels. In addition, it is
possible that the specific form of the learning image and the
later-described estimation image may be modified. For example,
instead of using the image of the Nyquist plot which contains a
plurality of plot points in itself, it is possible to use an image
containing a line or a region that is generated based on the
plurality of plot points as the learning image and the estimation
image. Alternatively, it is also possible to employ data other than
image data (for example, the data of the Nyquist plot per se) as
the data to be input into the neural network model.
[0066] Next, learning of the neural network model is conducted
using the data of the learning image obtained from a module M as
the input training data, and using the data of the actual battery
capacity of the same module M as the output training data. More
specifically, the present embodiment supplies learning image data
to the input layer x of the neural network model, and acquires a
battery capacity estimate value that is output from the output
layer z. The acquired battery capacity estimate value is compared
with the actual battery capacity, and the result of the comparison
is fed back as a training signal to the neural network model.
According to the training signal, the weightings W1 and W2 of the
neural network model are adjusted. As the above-described procedure
is repeated using a plurality of training data, the accuracy of
estimation of the battery capacity is improved correspondingly.
Battery Capacity Estimation Process
[0067] With reference to FIG. 9, a battery capacity estimating
process executed by the battery capacity estimation system 1 will
be described. The battery capacity estimating process of the
present embodiment is executed by a control unit (for example, a
controller, such as CPU, included in at least one of the charging
and discharging device 5, the measurement device 10, and the
battery capacity estimation device 30) included in the battery
capacity estimation system 1. For example, when a start instruction
for battery capacity estimation is input by an operating unit (not
shown), the control unit of the battery capacity estimation system
1 executes the battery capacity estimating process illustrated in
FIG. 9. Each of the steps of the battery capacity estimating
process is basically implemented by software processing by the
control unit. However, at least part of the process may be
implemented by hardware (e.g., electric circuit). Also, a plurality
of control units of a respective plurality of the devices may
cooperate to execute the battery capacity estimating process.
[0068] First, the control unit performs charging and discharging of
a module M (hereinafter referred to as a "target secondary
battery") that is the target of performance evaluation (S1). As
described previously, in the present embodiment, the charging and
discharging device 5 performs the charging and discharging of the
module M. Next, the control unit judges whether or not the elapsed
time after completion of the charging and discharging is within a
predetermined period (i.e., within a period that is longer than the
above-described minimum waiting time and shorter than the maximum
waiting time) (S2). If the elapsed time is not within the
predetermined period (S2: NO), the battery capacity estimation
system 1 enters a waiting state.
[0069] If the control unit judges that the elapsed time is within
the predetermined period (S2: YES), the control unit acquires a
measurement result of the alternating current impedance of the
target secondary battery (S3). As described previously, in the
present embodiment, the alternating current impedance of the target
secondary battery is measured by the measurement device 10. The
control unit generates an estimation image for estimating the
battery capacity from the Nyquist plot showing the measurement
results of the alternating current impedance of the target
secondary battery (S4). The technique for generating the estimation
image is similar to the above-described technique for generating
the learning image. The control unit inputs the generated
estimation image into the input layer x (see FIG. 8) of the
pre-trained neural network model (S5). The control unit acquires
the result of estimation of battery capacity, which is output from
the output layer z (S6). Based on the acquired result of estimation
of battery capacity, the control unit determines how to reuse the
target secondary battery (S7). Then, the process ends.
[0070] It should be noted that the process of charging and
discharging the target secondary battery at step S1 in FIG. 9 is an
example of the "charging and discharging step/process". The process
of acquiring an alternating current impedance at step S3 in FIG. 9
is an example of the "alternating current impedance acquiring
step/process". The process of estimating the battery capacity at
steps S4 to S6 in FIG. 9 is an example of the "battery capacity
estimating step/process".
[0071] Although various embodiments of the present disclosure have
been described in detail hereinabove, it should be understood that
the foregoing embodiments are merely exemplary and are not intended
to limit the scope of the claims. Various modifications and
alterations of the embodiments described hereinabove are within the
scope of the invention as defined by the appended claims.
* * * * *