FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)
“BATTERY MANAGEMENT DEVICE, BATTERY
MANAGEMENT METHOD, AND BATTERY PACK”
LG ENERGY SOLUTION, LTD., of Tower 1, 108, Yeouidaero, Yeongdeungpo-gu, Seoul 07335, Republic of Korea
The following specification particularly describes the invention and the manner in
which it is to be performed.
2
TECHNICAL FIELD
The present disclosure relates to technology that analyzes a correlation between
external variables and internal variables dependent on the chemical state inside a battery
5 cell.
The present application claims the benefit of Korean Patent Application No. 10-
2019-0095075 filed on August 05, 2019 with the Korean Intellectual Property Office, the
disclosure of which is incorporated herein by reference in its entirety.
10 BACKGROUND ART
Recently, there has been dramatically growing demand for portable electronic
products such as laptop computers, video cameras and mobile phones, and with the
extensive development of electric vehicles, accumulators for energy storage, robots and
satellites, many studies are being made on high performance batteries that can be
15 recharged repeatedly.
Currently, commercially available batteries include nickel-cadmium batteries,
nickel-hydrogen batteries, nickel-zinc batteries, lithium batteries and the like, and among
them, lithium batteries have little or no memory effect, and thus they are gaining more
attention than nickel-based batteries for their advantages that recharging can be done
20 whenever it is convenient, the self-discharge rate is very low and the energy density is
high.
A battery cell gradually degrades over time due to repeated charging and
discharging. As the battery cell degrades, the chemical state inside the battery cell also
3
changes. However, the internal variables (e.g., electrical conductivity of the positive
electrode active material) indicating the chemical state inside the battery cell cannot be
observed outside the battery cell.
Meanwhile, the chemical state inside the battery cell induces a change in external
5 variables (e.g., the temperature of the battery cell) that can be observed outside the battery
cell. Accordingly, there are attempts to estimate the internal variables based on the
external variables.
However, some of the internal variables and some of the external variables may
have a very low correlation. When the correlation between each internal variable and
10 each external variable is not taken into account, the interanl variables estimated using the
external variables may be greatly different from the actual chemical state inside the battery
cell.
DISCLOSURE
15 Technical Problem
The present disclosure is designed to solve the above-described problem, and
therefore the present disclosure is directed to providing a battery management apparatus, a
battery management method, and a battery pack that analyzes a correlation between
internal variables and external variables of a battery cell.
20 The present disclosure is further directed to providing a battery management
apparatus, a battery management method and a battery pack, in which only external
variables having at least a certain level of correlation with each internal variable can be
used in the modeling of a multilayer perceptron for estimation of each internal variable.
4
These and other objects and advantages of the present disclosure may be
understood by the following description and will be apparent from the embodiments of the
present disclosure. In addition, it will be readily understood that the objects and
advantages of the present disclosure may be realized by the means set forth in the
5 appended claims and a combination thereof.
Technical Solution
A battery management apparatus according to an aspect of the present disclosure
includes a memory unit configured to store first to mth input data sets, first to nth desired
data sets and first to nth 10 reference values, and a control unit operably coupled to the
memory unit. Each of m and n is a natural number of two or greater. The first to mth
input data sets are associated with first to mth external variables that are observable outside
the battery cell, respectively. Each of the first to mth input data sets includes a
predetermined number of input values. The first to nth desired data sets are associated
with first to nth internal variables, respectively, wherein the first to nth 15 internal variables
rely on a chemical state inside the battery cell and are not observable outside the battery
cell. Each of the first to nth desired data sets includes the predetermined number of target
values. The control unit is configured to set at least one of the first to mth external
variables as a valid external variable for each of the first to nth internal variables based on
the first to mth input datasets, the first to nth desired data sets and the first to nth 20 reference
values.
The memory unit may be configured to store a main multilayer perceptron that
defines a correspondence relationship between the first to mth external variables and the
5
first to nth internal variables. The control unit may be configured to obtain first to nth
output data sets from first to nth output nodes included in an output layer of the main
multilayer perceptron by providing the first to mth input data sets to first to mth input nodes
included in an input layer of the main multilayer perceptron. Each of the first to nth
5 output data sets may include the predetermined number of result values. The control unit
may be configured to determine first to nth error factors based on the first to nth output data
sets and the first to nth desired data sets. The control unit may be configured to determine
the first to nth reference values by comparing each of the first to nth error factors with a
threshold error factor.
The control unit may be configured to determine the j
th 10 error factor of the first to
n
th error factors to be equal to an error ratio of the j
th output data set of the first to nth
output data sets to the j
th desired data set of the first to nth desired data sets. j is a natural
number of n or smaller.
The control unit may be configured to set the j
th reference value of the first to nth
reference values to be equal to a first predetermined value when the jth 15 error factor is
smaller than the threshold error factor.
The control unit may be configured to set the jth reference value to be equal to a
second predetermined value when the jth error factor is equal to or greater than the
threshold error factor. The second predetermined value is smaller than the first
20 predetermined value.
The control unit may be configured to determine whether to set the ith external
variable of the first to mth external variables as a valid external variable for the jth internal
variable based on the ith input data set of the first to mth input data sets, the jth desired data
6
set of the first to nth desired data sets and the jth reference value of the first to nth reference
values. The control unit may be configured to learn a sub-multilayer perceptron
associated with the jth internal variable using the ith input data set as training data when the
i
th external variable is set as the effective external variable for the jth internal variable. i is
5 a natural number of m or smaller, and j is a natural number of n or smaller.
The control unit may be configured to determine a multiple correlation coefficient
between the ith input data set and the jth desired data set. The control unit may be
configured to set the ith external variable as the valid external variable for the jth internal
variable when an absolute value of the multiple correlation coefficient is greater than the
j
th 10 reference value.
The control unit may be configured to exclude the ith external variable from the
valid external variable for the jth internal variable when the absolute value of the multiple
correlation coefficient is equal to or less than the jth reference value.
A battery pack according to another aspect of the present disclosure includes the
15 battery management apparatus.
A battery management method according to still another aspect of the present
disclosure includes storing first to mth input data sets, first to nth desired data sets and first
to nth reference values, wherein each of m and n is a natural number of two or greater, the
first to mth input data sets are associated with the first to mth external variables that can be
observed outside a battery cell, respectively, each of the first to mth 20 input data sets includes
a predetermined number of input values, the first to nth desired data sets are associated
with the first to nth internal variables that cannot be observed outside the battery cell,
respectively, and each of the first to nth desired data sets includes the predetermined
7
number of target values, and setting at least one of the first to mth external variables as a
valid external variable for each of the first to nth internal variables based on the first to mth
input data sets, the first to nth desired data sets and the first to nth reference values.
The i
th external variable of the first to mth external variables may be set as the
valid external variable for the j
th internal variable of the first to nth 5 internal variables when
an absolute value of a multiple correlation coefficient between the ith input data set of the
first to mth input data sets and the jth desired data set of the first to nth desired data sets is
greater than the j
th reference value of the first to nth reference values. i is a natural
number of m or smaller, and j is a natural number of n or smaller.
10
Advantageous Effects
According to at least one of the embodiments of the present disclosure, it is
possible to analyze a correlation between internal variables and external variables of a
battery cell.
15 Further, according to at least one of the embodiments of the present disclosure,
only external variables having at least a certain level of correlation with each internal
variable may be used in the modeling of a multilayer perceptron for estimation of each
internal variable.
The effects of the present disclosure are not limited to the effects mentioned above,
20 and these and other effects will be clearly understood by those skilled in the art from the
appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
8
The accompanying drawings illustrate a preferred embodiment of the present
disclosure, and together with the detailed description of the present disclosure described
below, serve to provide a further understanding of the technical aspects of the present
disclosure, and thus the present disclosure should not be construed as being limited to the
5 drawings.
FIG. 1 is a diagram exemplarily showing a configuration of a battery pack
including a battery management apparatus according to an embodiment of the present
disclosure.
FIG. 2 is a diagram exemplarily showing a main multilayer perceptron used by the
10 battery management apparatus of FIG. 1.
FIG. 3 is a diagram exemplarily showing a sub-multilayer perceptron.
FIG. 4 is a flowchart exemplarily showing a method that may be performed by the
battery management apparatus of FIG. 1.
FIG. 5 is a flowchart exemplarily showing another method that may be performed
15 by the battery management apparatus of FIG. 1.
DETAILED DESCRIPTION
Hereinafter, the preferred embodiments of the present disclosure will be described
in detail with reference to the accompanying drawings. Prior to the description, it should
20 be understood that the terms or words used in the specification and the appended claims
should not be construed as being limited to general and dictionary meanings, but rather
interpreted based on the meanings and concepts corresponding to the technical aspects of
the present disclosure on the basis of the principle that the inventor is allowed to define the
9
terms appropriately for the best explanation.
Therefore, the embodiments described herein and illustrations shown in the
drawings are just a most preferred embodiment of the present disclosure, but not intended
to fully describe the technical aspects of the present disclosure, so it should be understood
5 that a variety of other equivalents and modifications could have been made thereto at the
time that the application was filed.
The terms including the ordinal number such as "first", "second" and the like, are
used to distinguish one element from another among various elements, but not intended to
limit the elements by the terms.
10 Unless the context clearly indicates otherwise, it will be understood that the term
"comprises" when used in this specification, specifies the presence of stated elements, but
does not preclude the presence or addition of one or more other elements. Additionally,
the term "control unit" as used herein refers to a processing unit of at least one function or
operation, and this may be implemented by either hardware or software or a combination
15 of hardware and software.
In addition, throughout the specification, it will be further understood that when
an element is referred to as being "connected to" another element, it can be directly
connected to the other element or intervening elements may be present.
FIG. 1 is a diagram exemplarily showing a configuration of a battery pack 1
20 including a battery management apparatus 100 according to an embodiment of the present
disclosure, FIG. 2 is a diagram exemplarily showing a main multilayer perceptron used by
the battery management apparatus 100 of FIG. 1, and FIG. 3 is a diagram exemplarily
showing a sub-multilayer perceptron.
10
Referring to FIG. 1, the battery pack 1 includes a battery cell 10, a switch 20, and
a battery management apparatus 100.
The battery pack 1 is mounted on an electricity-powered device such as an electric
vehicle to supply electrical energy required to operate the electricity-powered device.
5 The battery management apparatus 100 is provided to be electrically connected to the
positive terminal and the negative terminal of the battery cell 10.
The battery cell 10 may be a lithium ion cell. The battery cell 10 may include
any type that can be repeatedly charged and discharged, and is not limited to the lithium
ion cell.
10 The battery cell 10 may be electrically coupled to an external device through
power terminals (+, -) of the battery pack 1. The external device may be, for example, an
electrical load (e.g., a motor), a direct current (DC)-alternating current (AC) inverter and a
charger of the electric vehicle.
The switch 20 is installed on a current path connecting the positive terminal of the
15 battery cell 10 and the power terminal (+) or the negative terminal of the battery cell 10
and the power terminal (-). While the switch 20 is in an open operating state, charging
and discharging of the battery cell 10 is stopped. While the switch 20 is in a closed
operating state, charging and discharging of the battery cell 10 is possible.
The battery management apparatus 100 includes an interface unit 110, a memory
20 unit 120 and a control unit 130. The battery management apparatus 100 may further
include a sensing unit 140.
The sensing unit 140 includes a voltage sensor 141, a current sensor 142 and a
temperature sensor 143. The voltage sensor 141 is configured to measure the voltage
11
across the battery cell 10. The current sensor 142 is configured to measure the current
flowing through the battery cell 10. The temperature sensor 143 is configured to measure
the temperature of the battery cell 10. The sensing unit 140 may transmit sensing data
indicating the measured voltage, the measured current, and the measured temperature to
5 the control unit 130.
The interface unit 110 may be coupled to the external device to enable
communication between. The interface unit 110 is configured to support wired
communication or wireless communication between the control unit 130 and the external
device. The wired communication may be, for example, controller area network (CAN)
10 communication, and the wireless communication may be, for example, ZigBee or
Bluetooth communication. The interface unit 110 may include an output device such as a
display or a speaker to provide the results of each operation performed by the control unit
130 in a form that can be recognized by a user. The interface unit 110 may include an
input device such as a mouse and a keyboard to receive data from the user.
15 The memory unit 120 is operably coupled to at least one of the interface unit 110,
the control unit 130 or the sensing unit 140. The memory unit 120 may store the results
of each operation performed by the control unit 130. The memory unit 120 may include,
for example, at least one type of storage medium of flash memory type, hard disk type,
Solid State Disk (SSD) type, Silicon Disk Drive (SDD) type, multimedia card micro type,
20 random access memory (RAM), static random access memory (SRAM), read-only
memory (ROM), electrically erasable programmable read-only memory (EEPROM) or
programmable read-only memory (PROM).
The memory unit 120 is configured to store various data required to estimate the
12
chemical state inside the battery cell 10. Specifically, the memory unit 120 stores a first
number of input data sets and a second number of desired data sets. The memory unit
120 may further store the main multilayer perceptron. Hereinafter, it is assumed that the
first number is m, the second number is n, and each of m and n is a natural number of 2 or
greater. For example, the first to mth 5 input data sets and the first to nth desired data sets
transmitted from an external device are stored in the memory unit 120 through the
interface unit 110.
The first to mth input data sets are associated with first to mth external variables
that are observable outside the battery cell 10, respectively. That is, when a first index i
is a natural number of 1 to m (i.e., a natural number of m or smaller), the ith 10 input data set
is associated with the ith external variable. Each input data set includes a third number
(e.g., 10000) of input values. For example, the third number of values preset as the ith
external variable are included in the ith input data set. In the specification, Xi denotes the
i
th input data set, and Xi(k) denotes the kth input value of the ith input data set.
15 For example, when m = 16, the first to sixteenth external variables may be defined
as follows.
The first external variable may indicate the period of time from the time point
when a state of charge (SOC) of the battery cell 10 is equal to a first charge state (e.g.,
100%) to the time point when the voltage of the battery cell 10 reaches a first voltage (e.g.,
20 3.0 V) by a first test of discharge of the battery cell 10 with a current of a first current rate
(e.g., 1/3 C) at a first temperature.
The second external variable may indicate the voltage of the battery cell 10 at the
time point when a second test is performed for a first reference time (e.g., 0.1 sec), and in
13
the second test, the battery cell 10 is discharged with a current of a second current rate
(e.g., 200 A) at a second temperature from the time point when the SOC of the battery cell
10 is equal to a second charge state (e.g., 50%).
The third external variable may indicate the voltage of the battery cell 10 at the
5 time point when the second test is performed for a second reference time (e.g., 1.0 sec)
that is longer than the first reference time.
The fourth external variable may indicate the voltage of the battery cell 10 at the
time point when the second test is performed for a third reference time (e.g., 10.0 sec) that
is longer than the second reference time.
10 The fifth external variable may indicate the voltage of the battery cell 10 at the
time point when the second test is performed for a fourth reference time (e.g., 30.0 sec)
that is longer than the third reference time.
The sixth external variable may include the period of time from the time point
when the SOC of the battery cell 10 is equal to a third SOC (e.g., 100%) to the time point
15 when the voltage of the battery cell 10 reaches a second voltage (e.g., 3.8 V) by a third test
of discharge of the battery cell 10 with a current of a third current rate (e.g., 1.0 C) at a
third temperature..
The seventh external variable may indicate the period of time until the time point
when the voltage of the battery cell 10 reaches a third voltage (e.g., 3.5 V) that is lower
20 than the second voltage by the third test.
The eighth external variable may represent the time taken for the voltage of the
battery cell 10 to reduce from the second voltage to the third voltage by the third test.
The ninth external variable may indicate the voltage of the battery cell 10 at the
14
time point when a fourth test of discharge of the battery cell 10 with a current of a fourth
current rate (e.g., 1.0 C) at a fourth temperature is performed for a fifth reference time (e.g.,
0.1 sec) from the time point when the SOC of the battery cell 10 is equal to a fourth charge
state (e.g., 10 %).
5 The tenth external variable may indicate the voltage of the battery cell 10 at the
time point when the fourth test is performed for a sixth reference time (e.g., 1.0 sec) that is
longer than the fifth reference time.
The eleventh external variable may indicate the voltage of the battery cell 10 at
the time point when the fourth test is performed for a seventh reference time (e.g., 10.0
10 sec) that is longer than the sixth reference time.
The twelfth external variable may indicate the voltage of the battery cell 10 at the
time point when the fourth test is performed for an eighth reference time (e.g., 30.0 sec)
that is longer than the seventh reference time.
The thirteenth external variable may indicate the voltage of the battery cell 10 at
15 the time point when the fourth test is performed for a ninth reference time (e.g., 100.0 sec)
that is longer than the eighth reference time.
The fourteenth external variable may indicate a ratio between a voltage change of
the battery cell 10 during a first period (e.g., 0.0 to 0.1 sec) and a voltage change of the
battery cell 10 during a second period (e.g., 20.0 to 20.1 sec) by a fifth test of charge of the
20 battery cell 10 with a current of a fifth current rate (e.g., 1.0 C) at a fifth temperature from
the time when the SOC of the battery cell 10 is equal to a fifth state (e.g., 10%).
The fifteenth external variable may indicate the voltage of the first peak on a
differential capacity curve of the battery cell 10 obtained by a sixth test of charge of the
15
battery cell 10 with a current of a sixth current rate (e.g., 0.04 C) at a sixth temperature
from the time point when the SOC of the battery cell 10 is equal to a sixth SOC (e.g., 0%).
When V, dV and dQ are the voltage of the battery cell 10, a voltage change of the battery
cell 10, and a capacity change of the battery cell 10, respectively, the differential capacity
5 curve shows a correspondence relationship between V and dQ/dV. The differential
capacity curve may be referred to as 'V-dQ/dV curve'. The first peak may be a peak
having the smallest V among a plurality of peaks on the differential capacity curve.
The sixteenth external variable may indicate a difference between the voltage of
the first peak and a reference voltage. The reference voltage is the voltage of the first
10 peak of the differential capacity curve obtained when the battery cell 10 is at Beginning Of
Life (BOL), and may be preset.
The first to nth desired data sets are associated with the first to nth internal
variables that are not observable outside the battery cell 10, respectively. That is, when a
second index j is a natural number of 1 to n (i.e., a natural number of n or smaller), the jth
desired data set is associated with the jth 15 internal variable. Each internal variable
indicates the chemical state inside the battery cell 10. Each desired data set includes the
third number of target values. For example, the third number of values preset as the jth
internal variable are included in the jth desired data set. In the specification, Yj denotes
the jth desired data set, and Yj(k) denotes the kth target value of the jth desired data set.
Y1(k) to Yn(k) are expected values of the first to nth 20 internal variables when the
battery cell 10 has a specific degradation state. X1(k) to Xm(k) are expected values of the
first to mth external variables induced by Y1(k) to Yn(k) when the battery cell 10 has the
specific degradation state.
16
The degradation state of the battery cell 10 changes depending on the environment
in which the battery cell 10 is used, and each degradation state may be defined by a
combination of the first to nth internal variables. When a ≠ b, X1(a) to Xm(a) and Y1(a) to
Yn(a) are associated with 'a' degradation state, and X1(b) to Xm(b) and Y1(b) to Yn(b) are
5 associated with 'b' degradation state that is different from the 'a' degradation state of the
battery cell 10.
For example, when n = 14, the first to fourteenth internal variables may be defined
as follows.
The first internal variable may be the electrical conductivity of the positive
10 electrode of the battery cell 10.
The second internal variable may be the ionic diffusivity of the positive electrode
active material of the battery cell 10.
The third internal variable may be the rate constant of the exchange current
density of the positive electrode active material of the battery cell 10.
15 The fourth internal variable may be the ionic diffusivity of the negative electrode
active material of the battery cell 10.
The fifth internal variable may be the rate constant of the exchange current density
of the negative electrode active material of the battery cell 10.
The sixth internal variable may be the tortuosity of the negative electrode of the
20 battery cell 10. The tortuosity is the ratio of the actual distance traveled by an ion from
one point to the other relative to the straight line distance between the two points.
The seventh internal variable may be the porosity of the negative electrode of the
battery cell 10.
17
The eighth internal variable may be the ion concentration of the electrolyte of the
battery cell 10.
The ninth internal variable may be a scale factor that multiplies the initial ionic
conductivity of the electrolyte of the battery cell 10. The initial ionic conductivity may
5 be a preset value indicating the ionic conductivity of the electrolyte of the battery cell 10
when the battery cell 10 is at BOL.
The tenth internal variable may be a scale factor that multiplies the initial ionic
diffusivity of the electrolyte of the battery cell 10. The initial ionic diffusivity may be a
preset value indicating the ionic diffusivity of the electrolyte of the battery cell 10 when
10 the battery cell 10 is at BOL.
The eleventh internal variable may be the cation transference number of the
electrolyte of the battery cell 10. The transference number indicates the fractional
contribution of cations (e.g., Li+) to the electrical conductivity of the electrolyte.
The twelfth internal variable may be the Loss of Lithium Inventory (LLI) of the
15 battery cell 10. The LLI indicates how much lithium in the battery cell 10 has decreased
compared to BOL.
The thirteenth internal variable may be the Loss of Active Material (LAM) of the
positive electrode of the battery cell 10. The LAM of the positive electrode indicates
how much the positive electrode active material of the battery has decreased compared to
20 the BOL.
The fourteenth internal variable may be the LAM of the negative electrode of the
battery cell 10. The LAM of the negative electrode indicates how much the negative
electrode active material of the battery has decreased compared to BOL.
18
The first to mth input data sets and the first to nth desired data sets may be preset
from the simulation results of different battery cells having the same specifications as the
battery cell 10.
The control unit 130 is operably coupled to at least one of the interface unit 110,
5 the memory unit 120 or the sensing unit 140. The control unit 130 may be implemented
in hardware using at least one of application specific integrated circuits (ASICs), digital
signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic
devices (PLDs), field programmable gate arrays (FPGAs), microprocessors or electrical
units for performing other functions.
The control unit 130 determines first to nth 10 reference values required for the
modeling of the first to n-th sub-multilayer perceptrons using the main multilayer
perceptron.
Referring to FIG. 2, the main multilayer perceptron 200 includes an input layer
201, a predetermined number of intermediate layers 202 and an output layer 203. In the
15 main multilayer perceptron 200, the number of nodes (also referred to as 'neurons')
included in each layer, connections between nodes and functions of each node included in
each intermediate layer may be preset. Weights for each connection may be set using
predetermined training data.
The input layer 201 includes first to mth input nodes I1 to Im. The first to mth
input nodes I1 to Im are associated with the first to mth 20 external variables, respectively.
Each input value included in the ith input data set Xi is provided to the ith
input node Ii.
The output layer 203 includes first to nth output nodes O1 to On. The first to nth
output nodes O1 to On are associated with the first to nth internal variables, respectively.
19
When the kth input value of each of the first to mth input data sets X1 to Xm is input
to the first to mth
input nodes I1 to Im, respectively, the k
th result value of each of the first to
n
th output data sets Z1 to Zn is output from the first to nth output nodes O1 to On,
respectively.
The control unit 130 may generate first to nth 5 output data sets Z1 to Zn, each
having the third number of result values by repeating the process of inputting m input
values (e.g., X1(k) to Xm(k)) of the same order in the first to mth input data sets X1 to Xm to
the first to mth input nodes I1 to Im, respectively. In the specification, Zj refers to the j
th
output data set, and Zj(k) refers to the kth result value of the j
th output data set. In the first
to nth 10 output data sets, n result values Z1(k) to Zn(k) may be arranged in the same order.
The control unit 130 may determine first to nth error factors based on the first to nth
output data sets and the first to nth desired data sets. That is, the control unit 130 may
determine the j
th error factor by comparing the j
th output data set with the j
th desired data
set. Specifically, the control unit 130 may determine the jth error factor using the
15 following Equation 1.

In Equation 1, u is the third number and Ferror_j is the j
th error factor. That is, the
j
th error factor may be determined to be equal to an error ratio of the j
th output data set to
the j
th 20 desired data set.
The control unit 130 may determine the first to nth reference values by comparing
the first to nth error factors with a threshold error factor, respectively. The threshold error
20
factor may be a preset, for example, 3%. Specifically, when the j
th error factor is smaller
than the threshold error factor, the control unit 130 may determine the j
th reference value to
be equal to a first predetermined value. On the contrary, when the j
th error factor is equal
to or greater than the threshold error factor, the control unit 130 may determine the j
th
5 reference value to be equal to a second predetermined value. The second predetermined
value (e.g., 0.25) may be smaller than the first predetermined value (e.g., 0.50). The
process using the main multilayer perceptron 200 may be completed by determining the
first to nth reference values. Alternatively, each of the first to nth reference values may be
preset to the first predetermined value or the second predetermined value. In this case,
10 the process using the main multilayer perceptron 200 may be omitted.
The control unit 130 performs a process for determining the first to nth submultilayer perceptrons.
The control unit 130 determines a multiple correlation coefficient between each of
the first to nth desired data sets and each of the first to mth input data sets. The multiple
correlation coefficient between the ith input data set and the jth 15 desired data set may be
determined from the following Equation 2.

In Equation 2, N is the third number, and ri,j is the multiple correlation coefficient
between the ith input data set and the jth 20 desired data set. m multiple correlation
coefficients may be determined for each desired data set, and a total of m×n multiple
21
correlation coefficients may be determined.
The absolute value of the multiple correlation coefficient ri,j being greater than the
j
th reference value indicates a high correlation between the ith external variable and the jth
internal variable. When the absolute value of the multiple correlation coefficient ri,j is
greater than the jth reference value, the control unit 130 may determine to use the ith 5 input
data set for the modeling of the jth sub-multilayer perceptron. When the absolute value of
the multiple correlation coefficient ri,j is greater than the jth reference value, the control unit
130 may set the ith external variable as a valid external variable for the jth internal variable.
On the contrary, the absolute value of the multiple correlation coefficient ri,j being
equal to or less than the jth reference value indicates a low correlation between the ith 10
external variable and the jth internal variable. When the absolute value of the multiple
correlation coefficient ri,j is equal to or less than the jth reference value, the control unit 130
may determine not to use the ith input data set for the modeling of the jth sub-multilayer
perceptron. That is, the control unit 130 may exclude the ith external variable from a
valid external variable for the jth 15 internal variable.
The control unit 130 may generate the first to nth sub-multilayer perceptrons
associated with the first to nth internal variables, respectively. That is, the jth internal
variable and the jth sub-multilayer perceptron are associated with each other. The jth submultilayer perceptron is used to estimate the value of the jth internal variable.
Referring to FIG. 3, the jth 20 sub-multilayer perceptron 300j includes an input layer
301j, a predetermined number of intermediate layers 302j and an output layer 303j. The
function of each node included in each intermediate layer 302j may be preset. The output
layer 303j has a single output node associated with the j
th internal variable.
22
The control unit 130 may generate the same number of input nodes of the input
layer 301j of the jth sub-multilayer perceptron as the number of valid external variables sets
for the jth internal variable. FIG. 3 shows that each of the first, third, and fifth external
variables is set as a valid external variable for the j
th internal variable.
The input layer 301j of the jth 5 sub-multilayer perceptron has three input nodes Ij1 to
Ij3. The first, third and fifth input data sets associated with each of the three external
variables are provided as training data to each of the three input nodes Ij1 to Ij3, and the jth
sub-multilayer perceptron 300j is learned through comparison between the result values of
the data set Wj obtained from the output layer of the jth sub-multilayer perceptron and
target values of the jth 10 desired data set.
After the learning of the jth sub-multilayer perceptron 300j is completed, the
control unit 130 may measure the voltage, the current and the temperature of the battery
cell 10 using the sensing unit 140. The control unit 130 may determine values of three
external variables based on the sensing data from the sensing unit 140. Subsequently, the
15 control unit 130 inputs the values of the three external variables to the three input nodes Ij1
to Ij3 of the jth sub-multilayer perceptron 300j, respectively, so that the result value may be
obtained in the output layer 303j of the jth sub-multilayer perceptron 300j. The
corresponding result value is an estimated value of the j
th internal variable associated with
the degradation state corresponding to the values of the three external variables. When
the estimated value of the jth internal variable is outside of a predetermined jth 20 safety range,
the control unit 130 may control the switch 20 into the open operating state to protect the
battery cell 10.
FIG. 4 is a flowchart exemplarily showing a method that may be performed by the
23
battery management apparatus 100 of FIG. 1.
Referring to FIGS. 1 to 4, in step S410, the control unit 130 stores first to mth
input data sets, first to nth
desired data sets and a main multilayer perceptron 200 in the
memory unit 120.
In step S420, the control unit 130 obtains first to nth 5 output data sets from the first
to mth input data sets using the main multilayer perceptron 200.
In step S422, the control unit 130 sets a second index j to 1.
In step S430, the control unit 130 determines a j
th error factor based on the j
th
output data set and the j
th desired data set.
In step S440, the control unit 130 determines whether the j
th 10 error factor is smaller
than a threshold error factor. When a value of the step S440 is "YES", step S450 is
performed. When the value of the step S440 is "NO", step S460 is performed.
In step S450, the control unit 130 sets a j
th reference value to be equal to a first
predetermined value.
In step S460, the control unit 130 sets the jth 15 reference value to be equal to a
second predetermined value.
In step S470, the control unit 130 determines whether the second index j is less
than a second number n. When a value of the step S470 is "YES", step S480 is
performed.
20 In step S480, the control unit 130 increases the second index j by 1. After the
step S480 is completed, the process returns to the step S430.
The first to nth reference values may be determined in a sequential order through
the method of FIG. 4. After the first to nth reference values are determined through the
24
method of FIG. 4, the control unit 130 may perform the method of FIG. 5.
Alternatively, when the first to nth reference values are preset and stored in the
memory unit 120 as described above, the control unit 130 may perform the method of FIG.
5 without performing the method of FIG. 4.
5 FIG. 5 is a flowchart exemplarily showing another method that may be performed
by the battery management apparatus 100 of FIG. 1.
Referring to FIGS. 1 to 5, in step S510, the control unit 130 sets each of a first
index i and a second index j to 1.
In step S520, the control unit 130 determines a multiple correlation coefficient
between an i
th input data set and a j
th 10 desired data set.
In step S530, the control unit 130 determines whether the absolute value of the
multiple correlation coefficient is greater than a j
th reference value. When a value of the
step S530 is "YES", step S540 is performed. When the value of the step S530 is "NO",
step S550 is performed.
In step S540, the control unit 130 sets an i
th 15 external variable as a valid external
variable for a j
th internal variable.
In step S550, the control unit 130 determines whether the first index i is less than a
first number m. When a value of the step S550 is "YES", step S560 is performed.
When the value of the step S550 is "NO", step S570 is performed.
20 In step S560, the control unit 130 increases the first index i by 1. After the step
S560 is completed, the process returns to the step S520.
In step S570, the control unit 130 determines whether the second index j is less
than a second number n. When a value of the step S570 is "YES", step S580 is
25
performed.
In step S580, the control unit 130 sets the first index i to 1, and increases the
second index j by 1. After the step S580 is completed, the process returns to the step
S520.
5 The value of the step S550 being "NO" indicates that the setting of the valid
external variable for the j
th internal variable is completed. Each time the value of the step
S550 is “NO”, the control unit 130 may generate a jth sub-multilayer perceptron to be used
to estimate the value of the jth internal variable.
The embodiments of the present disclosure described hereinabove are not
10 implemented only through the apparatus and method, and may be implemented through
programs that perform functions corresponding to the configurations of the embodiments
of the present disclosure or recording media having the programs recorded thereon, and
such implementation may be easily achieved by those skilled in the art from the disclosure
of the embodiments previously described.
15 While the present disclosure has been hereinabove described with regard to a
limited number of embodiments and drawings, the present disclosure is not limited thereto
and it is obvious to those skilled in the art that various modifications and changes may be
made thereto within the technical aspects of the present disclosure and the equivalent
scope of the appended claims.
20 Additionally, as many substitutions, modifications and changes may be made to
the present disclosure described hereinabove by those skilled in the art without departing
from the technical aspects of the present disclosure, the present disclosure is not limited by
the above-described embodiments and the accompanying drawings, and some or all of the
26
embodiments may be selectively combined to allow various modifications.
27
WHAT IS CLAIMED IS:
1. A battery management apparatus, comprising:
a memory unit configured to store first to mth input data sets, first to nth desired
data sets and first to n
th 5 reference values, wherein each of m and n is a natural number of
two or greater; and
a control unit operably coupled to the memory unit,
wherein the first to mth input data sets are associated with first to mth external
variables that are observable outside the battery cell, respectively,
each of the first to mth 10 input data sets includes a predetermined number of input
values,
the first to n
th desired data sets are associated with first to n
th internal variables,
respectively, wherein the first to n
th internal variables rely on a chemical state inside the
battery cell and are not observable outside the battery cell,
each of the first to n
th 15 desired data sets includes the predetermined number of
target values, and
the control unit is configured to set at least one of the first to mth external variables
as a valid external variable for each of the first to n
th internal variables based on the first to
mth input datasets, the first to n
th desired data sets and the first to n
th reference values.
20
2. The battery management apparatus according to claim 1, wherein the
memory unit is configured to store a main multilayer perceptron that defines a
correspondence relationship between the first to mth external variables and the first to nth
28
internal variables, and
the control unit is configured to:
obtain first to nth output data sets from first to nth output nodes included in an
output layer of the main multilayer perceptron by providing the first to mth input data sets
to first to mth 5 input nodes included in an input layer of the main multilayer perceptron,
each of the first to nth output data sets including the predetermined number of result values,
determine first to nth error factors based on the first to nth output data sets and the
first to nth desired data sets, and
determine the first to nth reference values by comparing each of the first to nth
10 error factors with a threshold error factor.
3. The battery management apparatus according to claim 2, wherein the
control unit is configured to determine the j
th error factor of the first to nth error factors to
be equal to an error ratio of the j
th output data set of the first to nth output data sets to the j
th
desired data set of the first to nth 15 desired data sets, and
wherein j is a natural number of n or smaller.
4. The battery management apparatus according to claim 3, wherein the
control unit is configured to set the j
th reference value of the first to nth reference values to
be equal to a first predetermined value when the jth 20 error factor is smaller than the
threshold error factor.
5. The battery management apparatus according to claim 4, wherein the
29
control unit is configured to set the jth reference value to be equal to a second
predetermined value when the jth error factor is equal to or greater than the threshold error
factor, and
wherein the second predetermined value is smaller than the first predetermined
5 value.
6. The battery management apparatus according to claim 1, wherein the
control unit is configured to:
determine whether to set the ith external variable of the first to mth external
variables as a valid external variable for the jth internal variable based on the ith 10 input data
set of the first to mth input data sets, the jth desired data set of the first to nth desired data
sets and the jth reference value of the first to nth reference values, and
learn a sub-multilayer perceptron associated with the jth internal variable using the
i
th input data set as training data when the ith external variable is set as the effective
external variable for the jth 15 internal variable, and
wherein i is a natural number of m or smaller, and j is a natural number of n or
smaller.
7. The battery management apparatus according to claim 6, wherein the
20 control unit is configured to:
determine a multiple correlation coefficient between the ith input data set and the
j
th desired data set, and
set the ith external variable as the valid external variable for the jth internal variable
30
when an absolute value of the multiple correlation coefficient is greater than the jth
reference value.
8. The battery management apparatus according to claim 7, wherein the
control unit is configured to exclude the ith 5 external variable from the valid external
variable for the j
th internal variable when the absolute value of the multiple correlation
coefficient is equal to or less than the jth reference value.
9. A battery pack comprising the battery management apparatus according to
10 any one of claims 1 to 8.
10. A battery management method, comprising:
storing first to mth input data sets, first to nth desired data sets and first to nth
reference values, wherein each of m and n is a natural number of two or greater, the first to
mth input data sets are associated with the first to mth 15 external variables that are observable
outside a battery cell, respectively, each of the first to mth input data sets includes a
predetermined number of input values, the first to nth desired data sets are associated with
the first to nth internal variables that are not observable outside the battery cell,
respectively, and each of the first to nth desired data sets includes the predetermined
20 number of target values; and
setting at least one of the first to mth external variables as a valid external variable
for each of the first to nth internal variables based on the first to mth input data sets, the first
to nth desired data sets and the first to nth reference values.

11. The battery management method according to claim 10, wherein the i
th
external variable of the first to mth external variables is set as the valid external variable for
the j
th internal variable of the first to nth internal variables when an absolute value of a
multiple correlation coefficient between the ith input data set of the first to mth 5 input data
sets and the jth desired data set of the first to nth desired data sets is greater than the j
th
reference value of the first to nth reference values, and
wherein i is a natural number of m or smaller, and j is a natural number of n or
smaller.