U.S. patent application number 16/960375 was filed with the patent office on 2020-11-12 for device detecting abnormality of secondary battery, abnormality detection method, and program.
The applicant listed for this patent is SEMICONDUCTOR ENERGY LABORATORY CO., LTD.. Invention is credited to Kei TAKAHASHI, Kouhei TOYOTAKA.
Application Number | 20200355749 16/960375 |
Document ID | / |
Family ID | 1000005020191 |
Filed Date | 2020-11-12 |
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United States Patent
Application |
20200355749 |
Kind Code |
A1 |
TAKAHASHI; Kei ; et
al. |
November 12, 2020 |
DEVICE DETECTING ABNORMALITY OF SECONDARY BATTERY, ABNORMALITY
DETECTION METHOD, AND PROGRAM
Abstract
A secondary battery control system that conducts abnormality
detection while predicting other parameters (internal resistance,
SOC, and the like) with high accuracy is provided. A difference
between an observation value (voltage) at a certain point in time
and a voltage that is estimated using a prior-state variable is
sensed. A threshold voltage is set in advance, and from the voltage
difference that is sensed, a sudden abnormality, specifically a
micro-short circuit or the like is detected. Furthermore, it is
preferable that detection be performed by using a neural network to
learn data on voltage difference in a time series and determine
abnormality or normality.
Inventors: |
TAKAHASHI; Kei; (Isehara,
Kanagawa, JP) ; TOYOTAKA; Kouhei; (Atsugi, Kanagawa,
JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SEMICONDUCTOR ENERGY LABORATORY CO., LTD. |
Atsugi-shi, Kanagawa-ken |
|
JP |
|
|
Family ID: |
1000005020191 |
Appl. No.: |
16/960375 |
Filed: |
December 25, 2018 |
PCT Filed: |
December 25, 2018 |
PCT NO: |
PCT/IB2018/060597 |
371 Date: |
July 7, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01R 31/367 20190101;
H01M 10/0525 20130101; G01R 31/3842 20190101; H01M 10/48 20130101;
G01R 19/16576 20130101 |
International
Class: |
G01R 31/3842 20060101
G01R031/3842; H01M 10/0525 20060101 H01M010/0525; G01R 31/367
20060101 G01R031/367; G01R 19/165 20060101 G01R019/165 |
Foreign Application Data
Date |
Code |
Application Number |
Jan 11, 2018 |
JP |
2018-002680 |
Feb 2, 2018 |
JP |
2018-017091 |
Claims
1. An abnormality detection device of a secondary battery
comprising: a first sensing circuit configured to sense a voltage
value of the secondary battery to be a first observation value; a
second sensing means sensing circuit configured to sense a current
value of the secondary battery to be a second observation value; a
calculation unit calculating an estimated voltage value range using
a regression model, and a determination unit finding a difference
between the voltage value of the first observation value and the
estimated voltage value obtained from a previous time and
determining the secondary battery has an abnormality when the
difference exceeds a certain threshold value range.
2. The abnormality detection device of a secondary battery
according to claim 1, wherein the regression model is a Kalman
filter on the basis of a state equation.
3. The abnormality detection device of a secondary battery
according to claim 1, wherein the determination unit comprises one
or a plurality of comparators.
4. The abnormality detection device of a secondary battery
according to claim 1, further comprising a neural network structure
portion inputting the difference between the voltage value of the
first observation value and the estimated voltage value obtained
from a previous time.
5. The abnormality detection device of a secondary battery
according to claim 1, wherein the secondary battery is a
lithium-ion secondary battery.
6. The abnormality detection device of a secondary battery
according to of claim 1, wherein the secondary battery is an
all-solid-state battery.
7. An abnormality detection method determining whether a secondary
battery has an abnormality, comprising: a prior-estimation
prediction step outputting an estimated voltage value using a
regression model, and a filtering step calculating a post-state
estimation value and a post error covariance matrix.
8. The abnormality detection method according to claim 7, wherein
the regression model is a Kalman filter on the basis of a state
equation.
9. A non-transitory computer readable medium storing a program, the
program being for making a computer which comprises: a calculation
unit calculating an estimated voltage value using a regression
model, and a determination unit finding a difference between a
voltage value of the observation value and the estimated voltage
value obtained from a previous time determining a secondary battery
has an abnormality when the difference exceeds a certain threshold
value range.
10. The non-transitory computer readable medium storing the program
according to claim 9, wherein the regression model is a Kalman
filter on the basis of a state equation.
11. A state estimation method of a secondary battery estimating a
charging state of the secondary battery, wherein data on an
observation value is obtained from the secondary battery, wherein a
prior-state estimation value is calculated using a regression
model, wherein a forecast error voltage Vd which is a difference
between the observation value and the prior-state estimation value
is calculated, wherein whether data is noise is determined on the
basis of whether or not data of the forecast error voltage Vd
exceeds a threshold value set in advance, wherein instead of data
that is determined as noise, a mean value of k data before
abnormality sensing is input to the regression model after which
correction is performed, and wherein abnormality detection is
continued even after noise sensing.
12. The state estimation method of a secondary battery according to
claim 11, wherein the regression model is a Kalman filter on the
basis of a state equation.
13. The state estimation method of a secondary battery according to
claim 11, wherein noise is generated when a micro-short circuit of
the secondary battery occurs.
14. A charging state estimation device of a secondary battery
estimating a charging state of the secondary battery, comprising: a
first sensing circuit configured to sense a voltage value of the
secondary battery that is to be a first observation value, a
calculation unit calculating an estimated voltage value using a
regression model, and a determination unit finding a difference
between the voltage value of the first observation value and the
estimated voltage value obtained from a previous time and
determining the secondary battery has an abnormality when the
difference exceeds a certain threshold value range, wherein the
determination unit comprises one or a plurality of comparators, a
multiplexer, and a delay circuit.
15. The charging state estimation device of a secondary battery
according to claim 14, wherein the regression model is a Kalman
filter on the basis of a state equation.
16. The charging state estimation device of a secondary battery
according to claim 14, wherein the determination unit comprises one
or a plurality of comparators.
17. The charging state estimation device of a secondary battery
according to claim 14, further comprising a second sensing circuit
configured to sense a current value of the secondary battery to be
a second observation value.
Description
TECHNICAL FIELD
[0001] One embodiment of the present invention relates to an
object, a method, or a manufacturing method. Alternatively, the
present invention relates to a process, a machine, manufacture, or
a composition (a composition of matter). One embodiment of the
present invention relates to a manufacturing method of a
semiconductor device, a display device, a light-emitting device, a
power storage device, a lighting device, or an electronic device.
In addition, one embodiment of the present invention relates to a
method of controlling charge of a power storage device, a method of
estimating the state of a power storage device, and a method of
detecting abnormality of a power storage device. In particular, one
embodiment of the present invention relates to a charge system of a
power storage device, a state estimation system of a power storage
device, and an abnormality detection system of a power storage
device.
[0002] Note that in this specification, a power storage device
refers to every element and device having a function of storing
power. For example, the power storage device includes a storage
battery (also referred to as secondary battery) such as a
lithium-ion secondary battery, a lithium-ion capacitor, a nickel
hydrogen battery, an all-solid-state battery, and an electric
double layer capacitor.
[0003] One embodiment of the present invention relates to an
abnormality detection system of a power storage device and a state
estimation device of a power storage device using a neural network.
One embodiment of the present invention relates to vehicles using a
neural network. One embodiment of the present invention relates to
an electronic device using a neural network. One embodiment of the
present invention is not limited to vehicles, and is related to a
state estimation system and an abnormality detection system that
can be applied to a power storage device for storing electric power
obtained from power generation facilities such as a solar power
generation panel provided in a structure body.
BACKGROUND ART
[0004] In recent years, a variety of power storage devices such as
lithium-ion secondary batteries, lithium-ion capacitors, and air
batteries have been actively developed. In particular, demand for
lithium-ion secondary batteries with high energy density have
rapidly grown with the development of the semiconductor industry
for portable information terminals such as mobile phones,
smartphones, tablets, or laptop computers; game machines; portable
music players; digital cameras; medical equipment; next-generation
clean energy vehicles such as hybrid electric vehicles (HEVs),
electric vehicles (EVs), and plug-in hybrid electric vehicles
(PHEVs); electric bikes; or the like, and lithium-ion secondary
batteries have become essential as rechargeable energy supply
sources for the modem information society.
[0005] Electric vehicles are vehicles in which only an electric
motor is used for a driving portion, and there are also hybrid
vehicles having both an internal-combustion engine such as an
engine and an electric motor. A plurality of secondary batteries
used in vehicles are provided as a battery pack, and a plurality of
the battery packs are provided on the lower portion of a
vehicle.
[0006] The secondary battery used in an electric vehicle, a hybrid
electric vehicle or an electric bike degrades due to the number of
charging, depth of discharge, charge current, charging environment
(temperature change), or the like. The degradation also depends on
the usage of the user; and charging temperatures, frequency of
quick charging, charging amount from regenerative braking, charging
timing with a regenerative brake, and the like might be related to
the degradation. An abnormality such as a short circuit may occur
in the secondary battery used in an electric vehicle or a hybrid
electric vehicle due to degradation over time or the like.
[0007] Having a high reliability is desirable for the secondary
battery used for an electric vehicle, a hybrid electric vehicle, or
an electric bike because it is assumed to be used for a long
time.
[0008] In the design capacity (DC) of a lithium-ion secondary
battery, the proportion of the remaining capacity (RC) in the full
charge capacity (FCC), that is, the state of charge (SOC), is not
set to use all of the design capacity from 0% to 100%, and a margin
of 5% (or 10%), not 0%, is provided to prevent overdischarge. In
addition, to prevent overcharge, a margin of approximately 5% (or
10%), not 100%, is provided; accordingly, a design capacity within
a range of 5% to 95% (or within a range of 10% to 90%) is said to
be used. In practice, a design capacity of 5% to 95% (or within a
range of 10% to 90%) is used by setting the voltage range of an
upper limit voltage V.sub.max and a lower limit voltage V.sub.min
using a BMS (Battery Management System) connected to a secondary
battery.
[0009] Degradation in a secondary battery occurs due to usage,
change over time, or change in temperature. The secondary battery
is managed by accurately determining the state of the inside of the
secondary battery, particularly the SOC (state of charge). By
accurately determining the SOC, the voltage range of the upper
limit voltage V.sub.max and the lower limit voltage V.sub.min can
be widened. Conventionally, the SOC is estimated by a coulomb
counting method.
[0010] Patent Document 1 shows an example where a neural network is
used for calculation of the remaining capacity of a secondary
battery.
REFERENCE
Patent Document
[0011] [Patent Document 1] United States Published Patent
Application No. 2006/0181245
SUMMARY OF THE INVENTION
Problems to be Solved by the Invention
[0012] An object is to secure safety by sensing abnormality in a
secondary battery, for example, sensing a phenomenon that lowers
the safety of the secondary battery early and warning users or
changing the operating conditions of the secondary battery.
[0013] In a conventional abnormality detection of a secondary
battery, when the secondary battery degrades and errors occur,
corrections are required; however, corrections by feedback are not
made and are insufficient, hence the accuracy is low. An object is
to increase the accuracy of abnormality detection of a secondary
battery.
[0014] In the case where a large noise is generated in a secondary
battery, when monitoring the internal resistance, SOC, and the like
of the secondary battery, an error of the numerical value of the
SOC to be estimated later occurs due to the noise data that is
input. An object is to provide a control system of a secondary
battery that ideally predicts other parameters (internal
resistance, SOC, and the like) with high accuracy while performing
abnormality detection.
Means For Solving the Problems
[0015] In a lithium-ion battery, only parameters of a current, a
voltage, and a temperature can he measured, and it is difficult to
measure the internal resistance and the SOC (state of charge)
directly. Therefore, arithmetic processing using a regression model
(regression method) such as a regression analysis, a Kalman filter,
and a multiple regression analysis is performed to estimate the
internal resistance and the SOC.
[0016] A Kalman filter is a kind of infinite impulse response
filter. A multiple regression analysis is a multivariate analysis
and uses a plurality of independent variables in a regression
analysis. Examples of the multiple regression analysis include a
least-squares method. The regression analysis requires a large
number of observation values of time series, whereas the Kalman
filter has an advantage of being able to obtain an optimal
correction coefficient successively as long as there is an
accumulation of data to some extent. Moreover, the Kalman filter
can be applied to transient time series.
[0017] As a method of estimating the internal resistance and the
SOC of the secondary battery, a non-linear Kalman filter
(specifically an unscented Kalman filter (also referred to as UKF))
can be used. In addition, an extended Kalman filter (also referred
to as EKF) can be used.
[0018] Estimating the internal resistance and the SOC of the
secondary battery using a Kalman filter is known, and it is
difficult to sense sudden abnormalities, specifically a micro-short
circuit and the like, using only this method. When estimating the
internal resistance and the SOC of the secondary battery, a
post-state estimation value is treated as an output; however, in
this invention, a state estimation value is not directly used, and
sudden abnormalities can be sensed by using a difference between an
observation value and a prior-state estimation value.
[0019] In order to achieve the above objects, in the abnormality
detection device, the abnormality detection system, and the
abnormality detection method of a secondary battery disclosed in
this specification, the following means is used.
[0020] Using a Kalman filter, a difference between an observation
value (voltage) at a certain point in time and a voltage that is
estimated using a prior-state variable is sensed. A threshold
voltage is set in advance, and a sudden abnormality, specifically a
micro-short circuit, is sensed using the sensed voltage difference.
An abnormality in a secondary battery can be detected early by
sensing a micro-short circuit or the like.
[0021] A micro-short circuit refers to a minute short circuit in a
secondary battery and a phenomenon in which a short circuit of a
positive electrode and a negative electrode of the secondary
battery does not make charging and discharging impossible, and a
small amount of short-circuit current flows through a minute short
circuit portion. A cause of a micro-short circuit is a plurality of
charging and discharging; an uneven distribution of positive
electrode active materials leads to local concentration of current
in part of the positive electrode and the negative electrode; and
then part of a separator stops functioning or a by-product is
generated by a side reaction, which is thought to generate a micro
short-circuit.
[0022] A thinner separator to make a secondary battery smaller and
quick electric power supply at a high voltage are desired for an
ideal secondary battery, both of which have configurations that
allow a micro-short circuit to occur in a secondary battery easily.
Moreover, repetition of micro-short circuits may cause abnormal
heating in a secondary battery, and may lead to serious accidents
such as a fire.
[0023] Thus, an abnormality detection system, a secondary battery
control system, or a secondary battery charging system is
configured for early sensing and preventing serious accidents from
happening in the case where a micro-short circuit occurs. A
micro-short circuit is an abnormality particular to a secondary
battery, and thus far, a method or a system of paying attention to
the micro-short circuit and sensing the micro-short circuit has not
existed. The present inventors build a system that finds and
calculates a value that changes greatly when a micro-short occurs.
In addition, correction by feedback is provided using an AI
(Artificial Intelligence) system (neural network) to perform
sensing of an abnormality in a secondary battery.
[0024] A measurement model for performing sensing of an abnormality
in a secondary battery is described below. Here, the model is a
model of an abnormality detection system of a secondary battery,
and output from the system is determined by calculation according
to the steps for inputting to the system that have been established
in advance or performing a simulation. A method (for example, a
neural network, a hidden Markov model, a polynomial function
approximation, or the like) for determining an optimal output with
respect to an input of the system by means such as regression and
learning is used as a model. These models are examples, and are not
limited.
[0025] In a prior-estimate prediction step, a model and an input
value are used, and in a post-estimate step (also referred to as a
filtering step), an observation value is used.
x(k+1)=Ax(k)+bu(k)+bv(k) Equation 1
[0026] The above equation is a state e ration. that expresses the
transition of the system.
[0027] The relationship between an observation value y(k) and x(k)
in a point in tune (time k) is represented by the following.
y(k)=c.sup.Tx(k)+w(k) Equation 2
[0028] c.sup.T is an observation model that has a function of
linear mapping a state space into an observation space. w(k)
represents an observation noise. The above equation is an
observation equation.
[0029] The state equation and the observation equation are
collectively called state space.
[0030] The prior-state estimation value (on the left) can be
expressed by the following equation.
{circumflex over (x)}.sup.-(k)=A{circumflex over (x)}(k-1)+bu(k-1)
Equation 3
[0031] Note that k is an integer of 0, 1, 2, or the like, and k
represents time. u(k) is an input signal and is a combined current
value in the case of a secondary battery, and x(k) expresses a
state variable.
[0032] In addition, a prior error covariance (P.sup.-(k) on the
left represents an inverse matrix of a covariance matrix) can be
expressed by the following equation.
P.sup.-(k)=AP(k-1)A.sup.T+.sigma..sub..nu..sup.2bb.sup.T Equation
4
[0033] In the prior-estimate prediction step, the prior-state
estimation value and a prior covariance matrix of a state are
calculated in accordance with the state equation. A prior-state
estimation value and a prior covariance matrix at time k+1 are
calculated in accordance with a post-state estimation value and a
post covariance matrix of a state at time k.
[0034] An estimation value and an actual measurement of the voltage
(the observation value) are compared, and a Kalman gain which is a
weight coefficient of a difference is calculated using a Kalman
filter, after which the estimation value is corrected. The Kalman
gain g(k) used in the filtering step can be expressed by the
following equation.
g ( k ) = P - ( k ) c c T P - ( k ) c + .sigma. .omega. 2 [
Equation 5 ] ##EQU00001##
[0035] A post-state estimation value (on the left) used in the
filtering step can be expressed by the following equation.
{circumflex over (x)}(k)={circumflex over
(x)}.sup.-(k)+g(k)(y(k)-c.sup.T{circumflex over (x)}.sup.-(k))
Equation 6
[0036] A post error covariance matrix P(k) used in the filtering
step can be expressed by the following equation.
P(k)=(I-g(k)c.sup.T)P.sup.-(k).sub..nu. Equation 7
[0037] With the above measurement model of sensing an abnormality
that occur in a secondary battery, the value obtained from the
equation below, that is, a difference (voltage difference) between
an observation value (voltage) at a certain point in time and a
voltage that is estimated using a prior-state variable is
monitored, and abnormality is sensed by regarding a large change in
behavior of the value as an occurrence of abnormality such as a
micro-short circuit.
y(k)-c.sup.T{circumflex over (x)}.sup.-(k) Equation 8
[0038] In the case where a comparator or the like outputs a signal
when the value of voltage difference obtained from the equation
above exceeds a certain threshold value and an abnormality is
sensed, a signal for displaying that notifies the outside with an
abnormality signal is output to a display or a signal for warning
with a buzzer or the like is output to a speaker. In this
specification, the terms "sensing" and "detection" are used
separately. "Detection" refers to a case where an abnormality data
is sensed, and when the abnormality data is correct, notification
to the outside, that is, a signal is output to other circuits.
"Sensing" is defined as only picking up abnormality data including
noise (abnormality data that is incorrect). Therefore, "sensing" is
part of but does not equate to "detection", and "detection" at
least includes notifying (outputting a signal) to other
circuits.
[0039] When a charging state is switched to a discharging state, or
when a discharging state is switched to a charging state, the
voltage difference changes greatly and noise is generated. The
generation of this noise does not lead to an abnormality in a
secondary battery, and thus a plurality of comparators can be
provided to remove this noise.
[0040] A structure disclosed in this specification is an
abnormality detection device including a first sensing means that
senses a voltage value of a secondary battery that is to be a first
observation value; a second sensing means that senses a current
value of the secondary battery that is to be a second observation
value; a calculation unit that calculates a prior-state estimation
value (estimated voltage value) using a Kalman filter on the basis
of a state equation; and a determination unit that finds a
difference between the voltage value of the first observation value
and the estimated voltage value obtained from a previous time and
determines that the secondary battery has an abnormality
(micro-short circuit) when the difference exceeds a certain
threshold value range.
[0041] In the above structure, the determination unit includes one
or a plurality of comparators. By using a plurality of comparators,
noise can be removed and errors in abnormality sensing can be
reduced.
[0042] It is preferable that detection be performed by learning
data on voltage difference in a time series using a neural network
and determining abnormality or normality. The above structure
includes a neural network structure portion that inputs a
difference between the voltage value of the first observation value
and the estimated voltage value obtained from the previous
time.
[0043] Another structure disclosed in this specification is an
abnormality detection method determining whether a secondary
battery has an abnormality, and includes a prior-estimate
prediction step outputting an estimated voltage value using a
Kalman filter on the basis of a state equation and a filtering step
calculating a post-state estimation value and a post error
covariance matrix.
[0044] Another structure disclosed in this specification is a
program that makes a computer function as a calculation unit that
calculates a prior-state estimation value (estimated voltage value)
using a Kalman filter on the basis of a state equation and a
determination unit that finds a difference between a voltage value
of the observation value and the estimated voltage value obtained
from a previous time and determines that the secondary battery has
an abnormality when the difference exceeds a certain threshold
value range.
[0045] An abnormality detection system of a secondary battery can
be formed using the above abnormality detection device, the above
method, and a computer that executes the above program. Examples of
the computer includes a control device of an electric vehicle, a
smartphone, and a laptop personal computer, and the computer
includes a control unit, a memory unit, and an input/output unit.
The control unit includes a CPU (or an MPU or an MCU (Micro
Controller Unit)) or the like. The control unit can also use a GPU
(Graphics Processing Unit). A chip in which a CPU and a GPU are
integrated is sometimes called an APU (Accelerated Processing
Unit), and this APU chip can also be used. An IC with an AI system
(also referred to as an inference chip) can be used. The IC with an
AI system is referred to as a circuit performing neural network
calculation (a microprocessor) in some cases.
[0046] The memory unit includes a RAM, a ROM, an HDD, or the like.
The input/output unit includes an operation portion, a display
portion, a communication portion, and the like. The program is not
limited to being stored in the memory unit of a computer, and may
be stored in a storage medium that the computer can read and the
computer reads and executes the program. Examples of a storage
medium that the computer can read include a disc such as a CD-ROM,
a magnetic tape, a USB memory, and a flash memory. Furthermore, the
above program may be stored in a device that is connected to a
connection line such as the Internet, a LAN (Local Area Network),
or a wireless LAN, and the computer may read the program from the
connection line and executes the program.
[0047] Another structure disclosed in this specification is an
abnormality detection system that detects a micro-short circuit.
The abnormality detection system includes a first sensing means
that senses a voltage value of a secondary battery that is to be a
first observation value; a second sensing means that senses a
current value of the secondary battery that is to be a second
observation value; a calculation unit that calculates a prior-state
estimation value (estimated voltage value) using a Kalman filter on
the basis of a state equation; and a determination unit that finds
a difference between the voltage value of the first observation
value and the estimated voltage value obtained from a previous time
and determines that the secondary battery has an abnormality
(micro-short circuit) when the difference exceeds a certain
threshold value range. The abnormality detection system detects
data derived from the micro-short circuit.
[0048] The abnormality detection system of a secondary battery
disclosed in this specification always or periodically monitors the
secondary battery. The sampling cycle (and the calculation cycle)
can be set as appropriate. The abnormality detection system of a
secondary battery disclosed in this specification can also be
called a secondary battery monitoring system. Furthermore, when
sensing of an abnormality in a secondary battery such as an
external surface temperature or an external deformity using a
temperature sensor, a camera, a gas sensor, or the like are
included in the abnormality detection system for a secondary
battery, an abnormality can be sensed more certainly.
[0049] A forecast error determined as an abnormality is not input
to a Kalman filter and a forecast error determined as a normality
is input instead. The accuracy of the estimation can be increased
by calculating the internal resistance and the SOC of a secondary
battery without using an abnormal value.
[0050] Another structure disclosed in this specification is a state
estimation method of a secondary battery that estimates a charging
state of a secondary battery. In the state estimation method of a
secondary battery, data on an observation value is obtained from
the secondary battery; a prior-state estimation value is calculated
using a regression model; a forecast error voltage Vd which is a
difference between the observation value and the prior-state
estimation value is calculated; whether data is noise is determined
on the basis of whether or not data of the forecast error voltage
Vd exceeds a threshold value set in advance; instead of data that
is determined as noise, a mean value of k data before abnormality
sensing is input to the regression model after which correction is
performed; and abnormality detection is continued even after noise
sensing.
[0051] A micro-short circuit problem occurs during charging. For
example, in the case where only one battery is employed, current is
controlled by a charger; thus the perceived current value does not
change during a micro-short circuit, and a change in voltage is
observed. However, in the case of parallel batteries, the change in
voltage becomes small and sensing becomes difficult. Moreover, this
change in voltage is within the range of upper and lower limit
voltages of battery use, and hence a special detecting mechanism is
required. Furthermore, regarding current, in parallel batteries,
the internal resistance decreases when a micro-short circuit
occurs; hence the amount of current that flows into a healthy
battery becomes relatively small and a large amount of current
flows into an abnormal battery, which is dangerous. However, it is
difficult to detect an abnormality because a controlled value of
current is maintained in the whole battery pack. In the case of a
structure of a typical battery pack, it is common to monitor the
voltage of each set of series; however, monitoring the current of
all the batteries is difficult in terms of costs and the complexity
of the wirings.
[0052] As shown in the flow chart in FIG. 14, when a value is found
to be small when compared with a signal REF using a comparator,
that is, when the value obtained in Equation 8<REF, an
abnormality such as a micro-short circuit is deemed to have
occurred, and after sensing this abnormality, forecast error data
is created; for example, the mean of normal forecast errors of
previous one to four steps is put into the Kalman filter. The SOC
can be accurately found even after detecting abnormality. An
advantage of the Kalman filter is that the remaining capacity can
be forecast with high accuracy, and even if the initial remaining
capacity is unknown, the remaining capacity can be forecast.
[0053] Hitherto, there is a problem where an error in estimation
value occurs before or after a micro-short circuit occurs, and it
varies from the actual capacity value. The accuracy of the
estimation results can be increased by removing data derived from
the occurrence of a micro-short circuit and inputting a normal
value.
[0054] Thus, data that is the basis of the abnormality detection is
not used for prediction after the abnormality detection, whereby a
secondary battery can be used until a micro-short circuit occurs
again after the abnormality detection.
[0055] A prediction method for predicting the charging state of a
secondary battery is described below. After abnormality sensing in
a secondary battery is carried out, the steps for prediction
continue to be repeatedly carried out. In the prediction, a method
(for example, a neural network, a hidden Markov model, a polynomial
function approximation, or the like) for determining an optimal
output with respect to a system input by means such as regression
and learning is used. To perform learning, it is preferable to use
a large amount of data and analysis for learning; hence the
learning may be conducted at a site such as a workstation or an
appliance server, and in that case one or more servers are used and
data accumulation and analysis are performed automatically or
semi-automatically in coordination with an operator. In the case
where storage and analysis of a large amount of data have finished
and results have been obtained, by integrating the results into a
system, specifically a program or a memory such as an IC chip,
abnormality sensing and prediction of a charging state can be
conducted without using a server.
[0056] In the case where power is wirelessly supplied to charge a
secondary battery, the abnormality detection system of a secondary
battery disclosed in this specification can be used. Methods for
wirelessly transmitting power of several watts or more in a
distance of several tens of centimeters or less include an
electromagnetic induction method and a magnetic resonance method.
The electromagnetic induction method includes a Qi (dice) standard.
The magnetic resonance method includes a WiPower standard. Power
from a power-transmitting device is received by a receiving coil,
and an abnormality detection device is provided between the
receiving coil and the secondary battery. In the case where the
abnormality detection device detects an abnormality, instruction of
stopping power from the power-transmitting device using a
connection function wireless LAN or Blue tooth (registered
trademark)) is performed.
[0057] An embodiment described below in this specification includes
use of a dedicated computer or a general-purpose computer including
a variety of kinds of computer hardware or software. A
computer-readable recording medium can be used and mounted on the
embodiment described below in this specification. The recording
medium may include a RAM, a ROM, an optical disk, a magnetic disk,
or any other storage media that can be accessed by a computer.
Algorithms, components, flows, programs, and the like presented as
examples in an embodiment described below in this specification can
be implemented in software or implemented in a combination of
hardware and software.
Effect of the Invention
[0058] By monitoring the value of the above Equation 8 (voltage
difference), an easy and highly accurate abnormality sensing of a
secondary battery is made possible. Furthermore, performing
abnormality sensing of a secondary battery with input of correction
by feedback using a neural network enables abnormality sensing of a
secondary battery with a higher accuracy.
[0059] Moreover, not only abnormality detection of one secondary
battery but also abnormality detection of a plurality of secondary
batteries connected in series can be performed.
[0060] A secondary battery is not limited to a lithium-ion
secondary battery using an electrolyte solution, and an
all-solid-state battery using a solid electrolyte, a sodium-ion
secondary battery, a potassium-ion secondary battery, and the like
can be used. A potassium-ion secondary battery has a weak ability
in attracting solvents compared with lithium or sodium, and ion can
move freely in the electrolyte solution. When the kind or size of
the secondary battery is changed, the threshold value is set as
appropriate in accordance with the secondary battery. Since a
micro-short circuit can also occur in an all solid-state battery,
the abnormality detection system disclosed in this specification is
useful.
[0061] The abnormality detection system disclosed in this
specification is useful because it can be mounted on an IC chip or
the like, and can be incorporated in part of a system of a vehicle.
The abnormality detection system can be an IC chip in which other
functional circuits (Random Access Memory (RAM), GPU (Graphics
Processing Unit), PMU (Power Management Unit), and the like) are
integrated.
[0062] The abnormality detection system disclosed in this
specification can shorten the timing of sensing and can achieve
abnormality sensing in real time. Furthermore, abnormality sensing
can be achieved regardless of the state of the secondary battery
such as charging or discharging.
[0063] Moreover, a secondary battery control system that detects an
abnormality in a secondary battery in real time, removes noise used
for abnormality detection, and predicts other parameters (internal
resistance, SOC, and the like) with high accuracy can be
achieved.
BRIEF DESCRIPTION OF THE DRAWINGS
[0064] FIG. 1 An equivalent circuit model illustrating one
embodiment of the present invention.
[0065] FIG. 2 FIG. 2(A) is a functional block diagram illustrating
one embodiment of the present invention, and FIG. 2(B) is a diagram
illustrating the relationship between voltage difference and
time.
[0066] FIG. 3 Functional block diagrams illustrating one embodiment
of the present invention.
[0067] FIG. 4 Functional block diagrams illustrating one embodiment
of the present invention.
[0068] FIG. 5 Functional block diagrams illustrating one embodiment
of the present invention.
[0069] FIG. 6 Functional block diagrams illustrating one embodiment
of the present invention.
[0070] FIG. 7 A functional block diagram illustrating one
embodiment of the present invention.
[0071] FIG. 8 A graph showing a simulation result using a
measurement model illustrating one embodiment of the present
invention.
[0072] FIG. 9 A graph showing a simulation result using a
measurement model illustrating one embodiment of the present
invention.
[0073] FIG. 10 A block diagram of an electric vehicle and a
perspective view of a secondary battery illustrating one embodiment
of the present invention.
[0074] FIG. 11 Diagrams illustrating examples of a moving body.
[0075] FIG. 12 Perspective diagrams illustrating an example of a
secondary battery.
[0076] FIG. 13 Cross-sectional views and a perspective view
illustrating an example of a secondary battery.
[0077] FIG. 14 A flow chart illustrating one embodiment of the
present invention.
[0078] FIG. 15 Diagrams showing an equivalent circuit model
illustrating one embodiment of the present invention.
[0079] FIG. 16 A system diagram illustrating one embodiment of the
present invention.
[0080] FIG. 17 A system diagram illustrating one embodiment of the
present invention.
[0081] FIG. 18 A flow chart showing one embodiment of the present
invention.
MODE FOR CARRYING OUT THE INVENTION
[0082] Hereinafter, embodiments of the present invention will be
described in detail using the drawings. Note that the present
invention is not limited to the description below, and it is easily
understood by those skilled in the art that modes and details of
the present invention can be modified in various ways. In addition,
the present invention should not be construed as being limited to
the description in the following embodiments.
(Embodiment 1)
[0083] FIG. 1 is an example of an equivalent circuit model of a
battery (also referred to as a battery model) used in abnormality
sensing. R0 shown in FIG. 1 represents a series resistance
component, and R1, R2, R3, and R4 represent resistance. C2, C3, and
C4 represent capacity.
[0084] A numerical simulation of the battery model is conducted by
applying a pulse signal at regular time intervals to a micro-short
circuit model 400 in FIG. 1 to generate a pseudo-micro-short
circuit. In this embodiment, the numerical simulation is used for
describing; however, in actuality, the voltage of a battery with
abnormality is monitored.
[0085] OCV shown in FIG. 1 is an abbreviation for Open Circuit
Voltage, and it is a voltage difference between a positive
electrode and a negative electrode when a sufficient time has
passed after a battery is separated from an external circuit, and
the electrochemical reaction inside the battery becomes a state of
equilibrium.
[0086] FIG. 2(A) is a diagram illustrating an example of a
functional block, and can be referred to as an estimation
logic.
[0087] In FIG. 2(A), a delay circuit 402 is a circuit for using a
state estimation value at time k in an estimation at time (k+1). In
FIG. 2(A), A and b represent matrices that can be obtained from the
model. C.sup.T represents an observed coefficient vector. Z.sup.-1
represents a delay circuit.
[0088] In FIG. 2(A), the portion shown by a reference numeral 401
is a portion denoting a difference (voltage difference) between an
observation value (voltage) and a voltage that is estimated using a
prior-state variable. When the voltage difference changes steeply,
a micro-short circuit can be regarded as having occurred, thus
whether the battery is abnormal is determined by inputting the
value of voltage difference to a comparator 403 and comparing the
voltage difference with the threshold value that is provided by a
reference voltage (REF) in the comparator 403. The lower value in a
comparison between the two values input to the comparator 403 is
represented by an output LO. REF may be changed more than once
within a period of one step and comparison at multiple levels may
be performed.
[0089] FIG. 2(B) illustrates a graph of the relationship between a
terminal voltage of a secondary battery, a voltage difference, and
time. The horizontal axis represents time and the vertical axis
represents voltage. Since the change in the value of the terminal
voltage is not very significant, It is difficult to identify the
timing at which a micro-short circuit occurs. On the other hand, as
shown in FIG. 2(B), a steep change is shown in the voltage
difference (a voltage difference between an observation value and a
prior-state estimation value) at the center, and it matches the
timing at which a micro-short circuit occurs. Therefore, the timing
at which a micro-short circuit occurs can be determined by
monitoring the voltage difference.
[0090] Furthermore, an SOC estimation value can also be output by
using an estimation logic illustrated in FIG. 2(A) that is made
partly common by employing the same input data. The SOC estimation
value can be output without increasing the size of the circuit.
(Embodiment 2)
[0091] In this embodiment, a structure that further uses a neural
network (NN) to increase accuracy is described below.
[0092] FIG. 3 is a diagram illustrating an example of a functional
block. The difference from Embodiment 1 is that FIG. 3 includes a
neural network unit 404. NN in FIG. 3(A) denotes a neural network
unit, and its output is LO(k).
[0093] In FIG. 3(A), the portion denoted by the reference numeral
401 is a portion denoting a difference (voltage difference) between
an observation value (voltage) and a voltage that is estimated
using a prior-state variable, and the value of this voltage
difference is input to the neural network unit 404 to determine
whether there is an abnormality by comparing with learning data.
The accuracy can be further increased by accumulating data input to
the neural network unit 404 and making the data part of the
learning data.
[0094] FIG. 3(B) is one of the other variations. In the functional
block illustrated in FIG. 3(B), the value of voltage difference is
input to both the comparator and the neural network (NN), and OR of
the output from both of them is selected. In addition, AND may be
selected instead of OR.
[0095] FIG. 4(A) is one of the other variations. In the functional
block illustrated in FIG. 4(A), the output of the comparator is
input to the neural network (NN), and abnormality is determined
using the value that is output.
[0096] FIG. 4(B) is one of the other variations. In the functional
block illustrated in FIG. 4(B), the output of the comparator is
input to the neural network (NN), and abnormality is determined
using the value derived from selecting AND of the comparator and
the neural network (NN).
[0097] FIG. 5(A) is one of the other variations. In the functional
block illustrated in FIG. 5(A), abnormality is sensed using data
that has passed through a Kalman gain g(k).
(Embodiment 3)
[0098] In this embodiment, a structure in which accuracy is
increased by further using an additional comparator is described
below.
[0099] The relationship of the elapsed time and the value of
voltage difference was investigated, and it was found that there is
an error different from the error when a micro-short circuit
occurs. The present inventors have found that an error occurs when
switching between charge and discharge in a secondary battery. As
illustrated in FIG. 6(A), the terminal current changes at the
timing of discharge.
[0100] During the switching between charge and discharge, an
overshoot on the positive side is not observed. In contrast, as
illustrated in FIG. 6(B), when a micro-short circuit occurs,
overshoots occur both on the positive side and the negative side.
The overshoot on the positive side is due to corrections of
forecast error being reflected in the next step. The overshoot
occurs on the negative side first, after which the overshoot occurs
on the positive side. Due to this difference, in the case where an
overshoot occurs only on the positive side, the case is deemed as
an error and it is determined that a micro-short circuit has not
occurred.
[0101] Two comparators are provided as shown in FIG. 5(B); an
overshoot value on the negative side (REF1) is input to Comparator
1, an overshoot value on the positive side (REF2) is input to
Comparator 2, and an AND calculation of an output of the comparator
in a previous one step and an output of the comparator in the
present is performed using a delay circuit.
[0102] With such a structure, noise in situations such as switching
between charge and discharge is removed, and the accuracy of
abnormality sensing of micro-short circuits can be increased.
[0103] This embodiment can be freely combined with any of the other
embodiments.
(Embodiment 4)
[0104] In this embodiment, an example in which the present
invention is applied to an electric vehicle (EV) is described using
FIG. 10
[0105] FIG. 10(A) illustrates an example of a block diagram of an
electric vehicle.
[0106] In an electric vehicle, a first battery 301 as a secondary
battery for main driving and a second battery 311 which supplies
electric power to an inverter 312 starting a motor 304 are
provided. In this embodiment, an abnormality-monitor unit 300
driven by power supply from the second battery 311 monitors a
plurality of secondary batteries constituting the first battery 301
collectively.
[0107] The first battery 301 supplies electric power to in-vehicle
parts for 42 V (for a high-voltage system) and the second battery
311 supplies electric power to in-vehicle parts for 14 V (for a
low-voltage system). Lead batteries are usually used for the second
battery 311 due to cost advantage. Lead batteries have
disadvantages compared with lithium-ion secondary batteries in that
they have a larger amount of self-discharge and are more likely to
degrade due to a phenomenon called sulfation. An advantage of using
a lithium-ion secondary battery for the second battery 311 is
eliminating the need for maintenance however, if it is used over a
long time, for example three years or longer, abnormalities that
cannot be determined at the time of manufacturing the battery may
occur. In particular, when the second battery 311 that starts the
inverter becomes inoperative, the motor cannot be started even when
the first battery 301 has remaining capacity; thus, in order to
prevent this, in the case where the second battery 311 is a lead
storage battery, the second battery is supplied with power from the
first battery to constantly maintain a fully-charged state.
[0108] In this embodiment, an example in which a lithium-ion
secondary battery is used for both the first battery 301 and the
second battery 311 is described. A lead battery or an
all-solid-state battery can be used for the second battery 311.
[0109] An example of a cylindrical secondary battery is described
with reference to FIG. 12(A) and FIG. 12(B). A cylindrical
secondary battery 600 includes, as illustrated in FIG. 12(A), a
positive electrode cap (battery lid) 601 on the top surface and a
battery can (outer can) 602 on the side and bottom surfaces. The
positive electrode cap and the battery can (outer can) 602 are
insulated by a gasket (insulating packing) 610.
[0110] FIG. 12(B) illustrates a schematic cross-sectional view of
the cylindrical secondary battery. Inside the battery can 602
having a hollow cylindrical shape, a battery element in which a
belt-like positive electrode 604 and a belt-like negative electrode
606 are wound with a separator 605 located therebetween is
provided. Although not illustrated, the battery element is wound
around a center pin. One end of the battery can 602 is closed and
the other end thereof is opened. For the battery can 602, a metal
having corrosion resistance to an electrolyte solution, such as
nickel, aluminum, or titanium, an alloy of such a metal, or an
alloy of such a metal and another metal (e.g., stainless steel or
the like) can be used. The battery can 602 is preferably covered
with nickel, aluminum, or the like to prevent corrosion due to an
electrolyte solution. Inside the battery can 602, the battery
element in which the positive electrode, the negative electrode,
and the separator are wound is interposed between a pair of
insulating plates 608 and 609 that face each other. Furthermore, a
nonaqueous electrolyte solution (not illustrated) is injected
inside the battery can 602 provided with the battery element. The
secondary battery is composed of a positive electrode containing an
active material such as lithium cobalt oxide (LiCoO.sub.2) or
lithium iron phosphate (LiFePO.sub.4), a negative electrode
composed of a carbon material such as graphite capable of occluding
and releasing lithium ions, a nonaqueous electrolytic solution in
which an electrolyte composed of a lithium salt such as LiBF.sub.4
or LiPF.sub.6 is dissolved in an organic solvent such as ethylene
carbonate or diethyl carbonate, and the like.
[0111] Since the positive electrode and the negative electrode of
the cylindrical secondary battery are wound, active materials are
preferably formed on both sides of the current collectors. A
positive electrode terminal (positive electrode current collector
lead) 603 is connected to the positive electrode 604, and a
negative electrode terminal (negative electrode current collector
lead) 607 is connected to the negative electrode 606. For both the
positive electrode terminal 603 and the negative electrode terminal
607, a metal material such as aluminum can be used. The positive
electrode terminal 603 and the negative electrode terminal 607 are
resistance-welded to a safety valve mechanism 612 and the bottom of
the battery can 602, respectively. The safety valve mechanism 612
is electrically connected to the positive electrode cap 601 through
a PTC (Positive Temperature Coefficient) element 611. The safety
valve mechanism 612 cuts off the electrical connection between the
positive electrode cap 601 and the positive electrode 604 when the
increased internal pressure of the battery exceeds a predetermined
threshold value. In addition, the PTC element 611 is a thermally
sensitive resistor whose resistance increases in the case where
temperature rises, and limits the amount of current by increasing
the resistance to prevent abnormal heat generation. Barium titanate
(BaTiO.sub.3)-based semiconductor ceramic or the like can be used
for the PTC element.
[0112] A lithium-ion secondary battery using an electrolyte
solution includes a positive electrode, a negative electrode, a
separator, an electrolyte solution, and an exterior body. Note that
in a lithium-ion secondary battery, the anode (positive electrode)
and the cathode (negative electrode) are interchanged in charging
and discharging, and the oxidation reaction and the reduction
reaction are interchanged; thus, an electrode with a high reaction
potential is called the positive electrode and an electrode with a
low reaction potential is called the negative electrode. For this
reason, in this specification, the positive electrode is referred
to as a "positive electrode" or a "+ electrode (plus electrode)"
and the negative electrode is referred to as a "negative electrode"
or a "- electrode (minus electrode)" in any of the case where
charging is performed, the case where discharging is performed, the
case where a reverse pulse current is made to flow, and the case
where charging current is made to flow. The use of terms such as
anode (positive electrode) and cathode (negative electrode) related
to oxidation reaction and reduction reaction might cause confusion
because the anode and the cathode are reversed in charging and in
discharging. Thus, the terms anode (positive electrode) and cathode
(negative electrode) are not used in this specification. If the
term anode (positive electrode) or cathode (negative electrode) is
used, it should be clearly mentioned whether the anode or the
cathode is charging or discharging, and corresponds to which of the
positive electrode (plus electrode) or the negative electrode
(minus electrode).
[0113] A charger is connected to two terminals illustrated in FIG.
12(C), and a storage battery 1400 is charged. In FIG. 12(C), 1406
denotes an electrolyte solution and 1408 denotes a separator. As
the charge of the storage battery 1400 proceeds, a potential
difference between electrodes increases. The positive direction in
FIG. 12(C) is the direction which a current flows from a terminal
outside the storage battery 1400 to a positive electrode 1402; from
the positive electrode 1402 to a negative electrode 1404 in the
storage battery 1400; and from the negative electrode to a terminal
outside the storage battery 1400. In other words, the direction in
which a charge current flows is regarded as the direction of a
current.
[0114] In this embodiment, an example of a lithium-ion secondary
battery is shown; however, it is not limited to a lithium-ion
secondary battery and a material including an element A, an element
X, and oxygen can be used as a positive electrode material for the
secondary battery. The element A is preferably one or more selected
from the Group 1 elements and the Group 2 elements. As a Group 1
element, for example, an alkali metal such as lithium, sodium, or
potassium can be used. As a Group 2 element, for example, calcium,
beryllium, magnesium, or the like can be used. As the element X,
for example, one or more selected from metal elements, silicon, and
phosphorus can be used. The element X is preferably one or more
selected from cobalt, nickel, manganese, iron, and vanadium.
Typical examples include lithium-cobalt composite oxide
(LiCoO.sub.2) and lithium iron phosphate (LiFePO.sub.4).
[0115] The negative electrode includes a negative electrode active
material layer and a negative electrode current collector. In
addition, the negative electrode active material layer may contain
a conductive additive and a binder.
[0116] For the negative electrode active material, an element that
enables charge-discharge reaction by alloying and dealloying
reactions with lithium can be used. For example, a material
containing at least one of silicon, tin, gallium, aluminum,
germanium, lead, antimony, bismuth, silver, zinc, cadmium, indium,
and the like can be used. Such elements have higher capacity than
carbon, and silicon in particular has a high theoretical capacity
of 4200 mAh/g.
[0117] In addition, the secondary battery preferably includes a
separator. As the separator, for example, a fiber containing
cellulose such as paper; nonwoven fabric; a glass fiber; ceramics;
a synthetic fiber using nylon (polyamide), vinylon (polyvinyl
alcohol-based fiber), polyester, acrylic, polyolefin, or
polyurethane; or the like can be used.
[0118] Regenerative energy generated by rolling of tires 316 is
transmitted to a motor 304 through a gear 305 and a motor
controller 303 and a battery controller 302 charges the second
battery 311 or the first battery 301.
[0119] The first battery 301 is mainly used for driving the motor
304 and supplies electric power to in-vehicle parts for 42 V (such
as an electric power steering 307, a heater 308, and a defogger
309) through a DC-DC circuit 306. Even in the case where there is a
rear motor for the rear wheels, the first battery 301 is used to
drive the rear motor.
[0120] The second battery 311 supplies electric power to car parts
for 14V (such as an audio 313, a power window 314, and lamps 315)
through a DC-DC circuit 310.
[0121] The first battery 301 is composed of a module set including
a plurality of secondary batteries. For example, a cylindrical
secondary battery 600 illustrated in FIG. 12(A) is used. As
illustrated in FIG. 10(B), the cylindrical secondary battery 600
may be interposed between a conductive plate 613 and a conductive
plate 614 to form a module. In FIG. 10(B), switches are not
illustrated between the secondary batteries. A plurality of
secondary batteries 600 may be connected in parallel, connected in
series, or connected in series after connecting in parallel. By
forming a module including the plurality of secondary batteries
600, large power can be extracted.
[0122] In order to cut off electric power from the plurality of
secondary batteries, the secondary batteries in the vehicle include
a service plug or a circuit breaker which can cut off a high
voltage without the use of equipment; these are provided in the
first battery 301. For example, in the case where 48 battery
modules which each include two to ten cells are connected directly,
a service plug or a circuit breaker is placed between the 24th
module and the 25th module.
[0123] FIG. 11 illustrates examples of vehicles each using the
abnormality detection system of a secondary battery of one
embodiment of the present invention. A secondary battery 8024 of an
automobile 8400 illustrated in FIG. 11(A) not only drives an
electric motor 8406 but also can supply electric power to a
light-emitting device such as a headlight 8401 or a room light (not
illustrated). For the secondary battery 8024 in the automobile
8400, the cylindrical secondary batteries 600 illustrated in FIG.
10(B) that are interposed between the conductive plate 613 and the
conductive plate 614 to form a module can be used.
[0124] An automobile 8500 illustrated in FIG. 11(B) can be charged
when the secondary battery included in the automobile 8500 is
supplied with electric power from external charging equipment by a
plug-in system, a contactless power feeding system, or the like.
FIG. 11(B) illustrates a state in which the secondary battery 8024
incorporated in the automobile 8500 is charged with a ground-based
charging apparatus 8021 through a cable 8022. Charging may be
performed as appropriate by a given method such as CHAdeMO
(registered trademark) or Combined Charging System as a charging
method, the standard of a connector, or the like. The charging
apparatus 8021 may be a charging station provided in a commercial
facility or a power source in a house. For example, with a plug-in
technique, the secondary battery 8024 incorporated in the
automobile 8500 can be charged by power supply from the outside.
Charging can be performed by converting AC power into DC power
through a converter such as an AC-DC converter.
[0125] Furthermore, although not illustrated, a power-receiving
device can be incorporated in the vehicle, and the vehicle can be
charged by being supplied with power from an above-ground power
transmitting device in a contactless manner. In the case of this
contactless power feeding system, by incorporating a
power-transmitting device in a road or an exterior wall, charging
is not limited to while the vehicle is stopped but also can be
performed while the vehicle is running. In addition, this
contactless power feeding system may be utilized to transmit and
receive power between vehicles. Furthermore, a solar cell may be
provided in the exterior of the vehicle to charge the secondary
battery while the vehicle is stopped or while the vehicle is
running. For supply of power in such a contactless manner, an
electromagnetic induction method or a magnetic resonance method can
be used.
[0126] FIG. 11(C) is an example of a motorcycle using the secondary
battery of one embodiment of the present invention. A scooter 8600
illustrated in FIG. 11(C) includes a secondary battery 8602, side
mirrors 8601, and a direction indicator light 8603. The secondary
battery 8602 can supply electricity to the direction indicator
light 8603.
[0127] Furthermore, in the scooter 8600 illustrated in FIG. 11(C),
the secondary battery 8602 can be held in an under-seat storage
8604. The secondary battery 8602 can be stored in the under-seat
storage 8604 even when the under-seat storage 8604 is small.
[0128] For the secondary battery 8602, an all-solid-state battery
can be used. The secondary battery 8602 is composed of a plurality
of laminated secondary batteries. FIG. 13(D) illustrates an example
of a laminated secondary battery that uses an all-solid-state
battery.
[0129] A laminated secondary battery 500 illustrated in FIG. 13(D)
includes a positive electrode lead electrode 510 and a negative
electrode lead electrode 511.
[0130] A procedure for manufacturing the laminated secondary
battery will be briefly described. First, a positive electrode and
a negative electrode are prepared. The positive electrode includes
a positive electrode current collector, and a positive electrode
active material layer is formed on a surface of the positive
electrode current collector. In addition, the positive electrode
includes a region where the positive electrode current collector is
partly exposed (hereinafter, such a region is referred to as a tab
region). The negative electrode includes a negative electrode
current collector, and a negative electrode active material layer
is formed on a surface of the negative electrode current collector.
In addition, the negative electrode includes a region where the
negative electrode current collector is partly exposed, that is, a
tab region.
[0131] Then, the negative electrode, a solid electrolyte layer, and
the positive electrode are stacked. An example of using five sets
of negative electrodes and four sets of positive electrodes is
described here. Next, the tab regions of the positive electrodes
are bonded to each other, and the positive electrode lead electrode
510 is bonded to the tab region of the positive electrode on the
outermost surface. Ultrasonic welding or the like may be used for
the bonding, for example. In a similar manner, the tab regions of
the negative electrodes are bonded to each other, and the tab
region of the negative electrode on the outermost surface is bonded
to the negative electrode lead electrode 511.
[0132] Then, the negative electrode, the solid electrolyte layer,
and the positive electrode are provided over an exterior body. The
solid electrolyte layer may be a material layer containing a solid
component with lithium-ion conductivity (e.g., ceramic or the
like). As the solid electrolyte layer, for example, a sheet is
formed using slurry made from ceramic powder or glass powder. Note
that ceramic is defined as a metal or non-metal material of an
inorganic compound such as an oxide, a carbide, a nitride, and a
boride. Glass is defined as an amorphous material having a glass
transition phenomenon, and microcrystalline glass may be called
ceramic glass. Ceramic glass, which has crystallinity, can be
identified by X-ray diffraction. For the solid electrolyte, an
oxide solid electrolyte, a sulfide solid electrolyte, and the like
can be used. Each of the positive electrode active material layer
and the negative electrode active material layer contains a solid
electrolyte and may include a conductive additive. The conductive
additive can be any material with electron conductivity such as a
carbon material and a metal material.
[0133] For an oxide solid electrolyte used as a positive electrode
active material, Li.sub.3PO.sub.4, Li.sub.3BO.sub.3,
Li.sub.4SiO.sub.4, Li.sub.4GeO.sub.4, LiNbO.sub.3, LiVO.sub.2,
LiTiO.sub.3, LiZrO.sub.3, and the like can be used. Composite
compounds thereof such as Li.sub.3BO.sub.3--Li.sub.4SiO.sub.4 can
also be used. At least part of the surface of the solid electrolyte
may be covered with a coat layer with a thickness of 1 nm or more
to 20 nm or less, and as a material of the coat layer, an oxide
with lithium-ion conductivity is used.
[0134] Examples of an oxide solid electrolyte used as a negative
electrode active material include Nb.sub.2O.sub.5,
Li.sub.4Ti.sub.5O.sub.12, and SiO. In this specification and the
like, SiO refers to, for example, silicon monoxide. Note that SiO
may refer to a material with a higher silicon content than
SiO.sub.2, and may be expressed as SiO.sub.x. Here, x preferably
has an approximate value of 1. For example, x is preferably more
than or equal to 0.2 and less than or equal to 1.5, further
preferably more than or equal to 0.3 and less than or equal to
1.2.
[0135] Examples of a sulfide solid electrolyte used as a positive
electrode active material include a material containing Li and S,
such as Li.sub.7P.sub.3S.sub.11, Li.sub.2S--SiS.sub.2, and
Li.sub.2S--P.sub.2S.sub.5.
[0136] Next, the exterior body is bent. Then, the outer portions of
the exterior body are bonded. For the exterior body, a laminated
film in which metal foil and an organic resin film are stacked such
as aluminum foil or stainless steel foil is used; and for the
bonding, thermocompression is used, for example. In this manner,
the laminated secondary battery 500 illustrated in FIG. 13(D) can
be manufactured. Although an example in which one laminated film is
used for the bonding is described, two laminated films may be
stacked and sealed with the outer edges thereof attached to each
other.
[0137] FIG. 13(A) is a conceptual view of a solid-state battery
including a solid electrolyte layer 83 between a positive electrode
81 and a negative electrode 82. The solid-state battery include a
thin-film-type all-solid-state battery and a bulk-type
all-solid-state battery. The thin-film-type all-solid-state battery
is obtained by stacking thin films, and the bulk-type
all-solid-state battery is obtained by depositing microscopic
particles.
[0138] FIG. 13(B) illustrates an example of the bulk-type
all-solid-state battery including a particle-state positive
electrode active material 87 in the vicinity of the positive
electrode 81 and a particle-state negative electrode active
material 88 in the vicinity of the negative electrode 82; and the
solid-state electrolyte layer 83 is positioned to fill the gaps in
between. More than one kind of particles is filled between the
positive electrode 81 and the negative electrode 82 with pressure
pressing so that gaps are eliminated.
[0139] FIG. 13(C) illustrates an example of a thin-film-type
all-solid-state battery. Films of the thin-film-type
all-solid-state battery are deposited by a gas phase method (a
vacuum evaporation method, a thermal spraying method, a pulsed
laser deposition method, an ion plating method, a cold spray
method, an aerosol deposition method, and a sputtering method).
FIG. 13(C) illustrates an example of fabricating a lithium-ion
storage battery in a manner that wiring electrodes 85 and 86 are
formed on a substrate 84, the positive electrode 81 is formed on
the wiring electrode 85, the solid electrolyte layer 83 is formed
on the positive electrode 81, the negative electrode 82 is formed
on the solid electrolyte layer 83 and the wiring electrode 86.
Examples of the substrate 84 include a ceramic substrate, a glass
substrate, a plastic substrate, and a metal substrate.
[0140] This embodiment can be combined with the description of the
other embodiments as appropriate.
(Embodiment 5)
[0141] An example of a method for estimating the SOC of a secondary
battery is illustrated in FIG. 14. FIG. 14 is a flow chart, and
after an abnormality such as a micro-short circuit is detected,
forecast error data is created; for example, the mean of normal
forecast errors of previous one to four steps is put into the
Kalman filter. The SOC can be accurately found even after sensing
abnormality.
[0142] When the value of voltage difference obtained from the
equation above exceeds a certain threshold value, a comparator or
the like outputs a signal, and an abnormality is detected. An
abnormality is determined by performing comparison with a voltage
signal REF which is a threshold value that is input to the
comparator. Data on the timing at which the abnormality is detected
is not used in the estimation later, and instead, the mean value of
the previous steps is input to an estimation algorithm. When the
value of voltage difference obtained from the above Equation 8
falls below the voltage signal REF, it is replaced by the mean
value of the previous steps. Therefore, when the value of voltage
difference obtained from the above Equation 8 falls below the
voltage signal REF that is input to the comparator, the voltage
difference is not put into the Kalman filter loop. Instead, a mean
value is input to the estimation algorithm, whereby estimation of
SOC or the like can be performed with high accuracy even when an
abnormality occurs. When data on the timing at which an abnormality
such as a micro-short circuit is detected is not used, and instead,
the mean value of the previous steps is input to an estimation
algorithm, the value of voltage difference obtained in the above
Equation 8 approximates to data in the case where a micro-short
circuit does not occur.
[0143] FIG. 16 illustrates a specific system diagram for executing
the flow chart shown in FIG. 14. In FIG. 16, a charging state
estimation device of a secondary battery includes at least the
comparator 403, a delay circuit, an AND circuit 405, and a
multiplexer 407. A clock signal CLK is input to the AND circuit. A
reference signal REF is input to the comparator 403. FIG. 16 is an
example and is not particularly limited. The charging state
estimation device of a secondary battery that estimates the
charging state of the secondary battery includes a sensing means
that senses a voltage value of the secondary battery that is to be
a first observation value; a calculation unit that calculates an
estimated voltage value using a regression model; and a
determination unit that finds a difference between the voltage
value of the first observation value and the estimated voltage
value obtained from a previous time and determines that the
secondary battery has an abnormality when the difference exceeds a
certain threshold value range. The determination unit includes one
or a plurality of comparators, a multiplexer, and a delay circuit.
MUX in FIG. 16 denotes a multiplexer. The charging state estimation
device of a secondary battery may further include a second sensing
means that senses a current value of the secondary battery that is
to be a second observation value. In FIG. 16, an FIR (Finite
Impulse Response) filter is used. FIG. 17 illustrates an example of
a different system diagram. In FIG. 17, an IIR (Infinite Impulse
Response) filter is used. In FIG. 17, N represents infinite time
which is a value that is sufficiently large in time k.
[0144] Even when data of abnormality that is sensed is not input to
the Kalman filter loop, by accurately calculating and reflecting
the current that is lost due to a micro-short circuit during
abnormality sensing, the value of SOC can be more accurate. FIG. 18
illustrates a flow chart. As shown in FIG. 18, when a value that is
small compared with the signal REF in a comparator is output as LO,
that is, the value obtained in Equation 8<REF, an abnormality
such as a micro-short circuit is deemed to have occurred, and this
abnormality is detected
[0145] The data of abnormality that is sensed is a forecast error
voltage, and I.sub.micro, which is a current when a micro-short
circuit occurs, is obtained using a state equation. This is
described below using an equivalent circuit model illustrated in
FIG. 15(A) and FIG. 15(B). In FIG. 15(A) and FIG. 15(B), OCV
denotes a potential difference during discharge, and V.sub.0,
V.sub.1, V.sub.2, and V.sub.3 denote the voltage at each point.
x(k)=[SOC(k)V.sub.1(k)V.sub.2(k)V.sub.3(k)R.sub.0(k)].sup.T
Equation 9
[0146] The above equation expresses the state variable x(k) in a
circuit in FIG. 15(A). FIG. 15(A) illustrates an equivalent circuit
model corresponding to the state before a micro-short circuit
occurs.
[0147] In addition, u(k) denotes a current I.sub.BAT(k). u(k) is an
input signal, and in the case of a secondary battery, u(k) is a
current value.
b = [ T S FCC T S C 1 T S C 2 T S C 3 0 ] T [ Equation 10 ]
##EQU00002##
[0148] b is a constant that composes a state equation, and T.sub.S
is a sampling period.
x ( k + 1 ) = [ 1 0 0 0 0 0 1 - T S C 1 R 1 0 0 0 0 0 1 - T S C 2 R
2 0 0 0 0 0 1 - T S C 3 R 3 0 0 0 0 0 0 ] x ( k ) + [ T S FCC T S C
1 T S C 2 T S C 3 0 ] u ( k ) [ Equation 11 ] ##EQU00003##
[0149] The above equation is a state equation of a Kalman filter.
Note that the resistances R.sub.1, R.sub.2, and R.sub.3, capacities
C.sub.1, C.sub.2, and C.sub.3, and a full-charge capacity FCC in
the above equation may be expanded and be included in the state
variable x(k).
[0150] Next, a state when a micro-short circuit occurs is regarded
as an equivalent circuit model illustrated in FIG. 15(B), and
calculation procedures are described below.
V.sub.0(k+1)=R.sub.0(k).times.I.sub.0 Equation 12
[0151] The equation above is a relational equation in the case
where the time when a micro-short circuit occurs is represented by
k+1. In addition, the current at each point can be expressed by the
following.
I.sub.1=I.sub.2=I.sub.3=I.sub.BAT(k) Equation 13
[0152] The voltage V.sub.1 applied to the resistance R.sub.1 and
the capacity C.sub.1 is expressed by the following equation.
V 1 ( k + 1 ) = ( 1 - T S C 1 R 1 ) .times. V 1 ( k ) + I BAT ( k )
C 1 [ Equation 14 ] ##EQU00004##
[0153] The voltage V.sub.2 applied to the resistance R.sub.2 and
the capacity C.sub.2 is expressed by the following equation.
V 2 ( k + 1 ) = ( 1 - T S C 2 R 2 ) .times. V 2 ( k ) + I BAT ( k )
C 2 [ Equation 15 ] ##EQU00005##
[0154] The voltage V.sub.3 applied to the resistance R.sub.3 and
the capacity C.sub.3 is expressed by the following equation.
V 3 ( k + 1 ) = ( 1 - T S C 3 R 3 ) .times. V 3 ( k ) + I BAT ( k )
C 3 [ Equation 16 ] ##EQU00006##
[0155] In the case where in the above equations, a numerical value
shown below is significantly smaller than 1 or accuracy is not
substantially required, the value shown below may be 1.
1 - T S C N R N [ Equation 17 ] ##EQU00007##
[0156] In the case where the above equation is 1, the amount of
calculation can be reduced.
I micro = I 1 - I 0 = I BAT ( k ) - V 0 ( k + 1 ) R 0 ( k ) = I BAT
( k ) - V IN - { V 1 ( k + 1 ) + V 2 ( k + 1 ) + V 3 ( k + 1 ) + V
ocv ( k ) } R 0 ( k ) [ Equation 18 ] ##EQU00008##
[0157] A current when a micro-short circuit occurs (I.sub.micro)
can be found by calculating the above equation. As shown in the
above equation, the current when a micro-short circuit occurs is
calculated by using a voltage V.sub.IN including R.sub.0, which is
estimated in the previous one step, OCV and the forecast error
voltage, and an observation value of the current I.sub.BAT.
R.sub.0(k) is a covariance of the forecast error
SOC ( k ' ) = SOC ( k ) - T S .times. I micro FCC [ Equation 19 ]
##EQU00009##
[0158] SOC(k) in the above equation corresponds to SOC data inside
the Kalman filter at the prior-estimate prediction step. The
current when a micro-short circuit occurs can be reflected in the
SOC inside the Kalman filter by replacing the value on the left
side in the equation above with the SOC data inside the Kalman
filter just before the filtering step.
[0159] For the estimation processing that performs calculation of
the SOC, in transferring a program that can execute the above
equation to a microcomputer or a microprocessor, the SOC can be
calculated.
[0160] This embodiment can be combined with the other embodiments
as appropriate.
EXAMPLE 1
[0161] FIG. 7 illustrates an example of a functional block that
performs calculation of the Kalman filter. Except for not having a
comparator, FIG. 7 is the same as Embodiment 1. A value of voltage
difference denoted by a reference numeral 401 in FIG. 7 is
important in the case where a micro-short circuit occurs, and
abnormality sensing of a secondary battery is performed by
monitoring this value.
[0162] A simulation is conducted using data where a pseudo-current
that generates a micro-short circuit periodically is created.
[0163] FIG. 8 shows the simulation results; the horizontal axis
represents time and the vertical axis represents voltage
difference, specifically a difference (voltage difference) between
an observation value (voltage) and a voltage that is estimated
using a prior-state variable.
[0164] In FIG. 8, data in which overshoots are observed
periodically on the positive side and on the negative side is data
with a micro-short circuit. Data in which an overshoot is observed
only on the negative side is data without a micro-short circuit,
and is a comparative example. The point where an overshoot is
observed only on the negative side corresponds to the point where a
voltage changes when switching from charge to discharge. In the
comparative example in FIG. 8, the point where overshoots are
observed only on the negative side is -0.0213 V, hence when a value
larger than this value, for example, when -0.03 V is set as the
threshold value, the value can be regarded as an error of margin
and be prevented from being sensed as an abnormality.
[0165] Note that in FIG. 9, data in FIG. 8 is edited, and the
vertical axis is split into left and right for easy viewing. Data
used for verification in FIG. 8 is data in which waveforms of a
micro-short circuit are input regularly, and although in FIG. 8 the
micro-short circuit is generated 13 times, in an actual secondary
battery, micro-short circuits occur randomly, and the sizes of the
peaks change depending on the state of usage (charge or discharge)
of the secondary battery. In any case, when a micro-short circuit
is sensed, the degradation of the secondary battery is accelerated
or the secondary battery becomes unusable; hence, notifying users
of a warning of sensing is useful. Although the cause of the
occurrence of a micro-short circuit has not been identified yet,
there is a theory that this is due to entry of metal powder at
manufacture, and even though an abnormality is not sensed right
after manufacture, it is possible that in repeating charge and
discharge the abnormal portion (conductive portion) is formed and
grown, leading to the occurrence of the micro-short circuit. When a
secondary battery in which a micro-short circuit has occurred is
charged and discharged, degradation progresses rapidly, and the
secondary battery may suddenly become unusable. Thus, the method of
the present invention capable of sensing a micro-short circuit is
useful.
[0166] In a Kalman filter, input values of a battery are input to a
battery equivalent circuit model and output values are compared;
when there is a difference, a Kalman gain is applied to the
difference and feedback is given, after which the battery
equivalent circuit model is corrected so that the error becomes
minimum. This is repeated in succession.
[0167] Note that the Kalman filter is a system in which data is
matched in succession, and the errors near the beginning in FIG. 8
and FIG. 9 can be ignored.
[0168] In the value of voltage difference denoted by the reference
numeral 401 in FIG. 7, -0.0631 V is set as the minimum value and
+0.0324 V is set as the maximum value. In addition, a peak point
that is close to 0 in a point at which a micro-short circuit is
generated is -0.0386 V on the negative side and +0.0186 V on the
positive side. Hence, in order to sense all micro-short circuits,
the sensing may be performed using a comparator or the like with
the threshold value set at -0.0386 V on the negative side and
+0.0186 V on the positive side. Since these values vary depending
on the secondary battery that is used, simulation is performed as
appropriate in advance using the characteristics data of the
secondary battery that is used, and the threshold value may be
determined on the basis of the results.
[0169] Note that the simulation results in FIG. 8 and FIG. 9 are
obtained by using LTspice (Simulation program with integrated
circuit emphasis), which is a circuit simulator provided by Analog
Devices.
REFERENCE NUMERALS
[0170] 1 comparator, 2 comparator, 81: positive electrode, 82:
negative electrode, 83: solid electrolyte layer, 84: substrate, 85:
wiring electrode, 86: wiring electrode, 87: positive electrode
active material, 88: negative electrode active material, 300:
abnormality-monitor unit, 301: battery, 302: battery controller,
303: motor controller, 304: motor, 305: gear, 306: DC-DC circuit,
307: electric power steering, 308: heater, 309: defogger, 310:
DC-DC circuit, 311: battery, 312: inverter, 313: audio, 314: power
window, 315: lamps, 316: tire, 400: micro-short circuit model, 401:
reference numeral, 402: delay circuit, 403: comparator, 404: neural
network unit, 405: AND circuit, 407: multiplexer, 600: secondary
battery, 601: positive electrode cap, 602: battery can, 603:
positive electrode terminal, 604: positive electrode, 605:
separator, 606: negative electrode, 607: negative electrode
terminal, 608: insulating plate, 609: insulating plate, 611: PTC
element, 612: safety valve mechanism, 613: conductive plate, 614:
conductive plate, 1400: storage battery, 1402: positive electrode,
1404: negative electrode, 8021: charging apparatus, 8022: cable,
8024: secondary battery, 8400: automobile, 8401: headlight, 8406:
electric motor, 8500: automobile, 8600: scooter, 8601: side mirror,
8602: secondary battery, 8603: direction indicator light, 8604:
under-seat storage
* * * * *