FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)
“BATTERY CAPACITY MEASURING DEVICE AND
METHOD, AND BATTERY CONTROL SYSTEM
COMPRISING BATTERY CAPACITY MEASURING
DEVICE”
LG CHEM, LTD., of 128, Yeoui-daero, Yeongdeungpo-gu, Seoul
07336, Republic of Korea
The following specification particularly describes the invention and the manner in which
it is to be performed.
2
【DESCRIPTION】
【Disclosure Title】
BATTERY CAPACITY MEASURING DEVICE AND METHOD, AND BATTERY
CONTROL SYSTEM COMPRISING BATTERY CAPACITY MEASURING DEVICE
5 【Technical Field】
This application claims priority to and the benefits of Korean Patent Application No. 10-
2020-0151880, filed with the Korean Intellectual Property Office on November 13, 2020, the
entire contents of which are incorporated herein by reference.
The present application relates to a device and method for measuring the capacity of a
10 battery.
The present application relates to a battery management system device including the
device for measuring the capacity of a battery.
The present application relates to a mobile apparatus including the battery management
system device.
15 The present application relates to a computer program stored in a recording medium for
executing a method for measuring the capacity of a battery.
【Background Art】
The demand for secondary batteries in electric vehicles, mobile devices, etc. is rapidly
expanding, and the requirements for condition diagnosis and quality stability of the secondary
20 batteries are increasing.
A pass decision is made when the capacity of a secondary battery satisfies the rated
capacity, which is a predetermined criterion, by sampling a certain number of batteries from a
battery tray produced in the cycle charge/discharge process and continuously charging and
discharging the sampled batteries.
25 The capacity of such a secondary battery is expressed in the form of a dispersion having a
certain level or more of deviation within a range of upper and lower limits of the rated capacity,
and deviations occur in the results depending on battery manufacturing method and manufacturing
conditions and measurement conditions of temperature, humidity, charging rate, and discharging
rate to be measured.
3
Although the deviations occur depending on such measurement conditions, the capacity
value to be measured during charging or discharging is often measured under conditions different
from the operating conditions specified in the battery specification.
Further, even if batteries are determined to be good products as a result of the capacity
5 measurement, various situations may occur thereafter while actually operating or using (charging
and discharging) a module or pack composed of a plurality of batteries corresponding to the good
products.
That is, even when the module or pack is actually operated or used, continuous battery
capacity measurement and condition diagnosis are required.
10 However, deviations occur in the results depending on measurement conditions such as
temperature, humidity, charging rate, and discharging rate to be measured even during capacity
measurement and condition diagnosis of batteries used in the module or pack as in the production
process.
Therefore, in a production process of batteries and a process of actually operating a
15 module or pack composed of a plurality of batteries, there is a need for a method capable of
determining the quality of the batteries and diagnosing the battery condition by monitoring the state
of the batteries in real time, correcting the deviation between capacity measurement values caused
by the difference in the measurement conditions, and then measuring the capacity of secondary
batteries, and a device capable of implementing the corresponding method.
20 Korean Patent Application Publication No. 10-2004-0051195
【Disclosure】
【Technical Problem】
An object of the present application is to provide a device and method capable of
measuring the capacity of a battery by correcting the influence of battery usage conditions.
25 【Technical Solution】
An embodiment of the present disclosure provides a device for measuring the capacity of
a battery, the device including: a learning data input unit for receiving capacity factor learning data
of the battery in a charging and discharging process performed for a specific time of a battery
selected as a learning target; a measurement data input unit for receiving capacity factor
30 measurement data of the battery selected in a charging and discharging process performed for a
4
specific time of a battery selected as a prediction target; a data learning unit for deriving the
capacity distribution of the battery from the capacity factor learning data of the battery input to the
learning data input unit, and respectively performing a plurality of different machine learnings for
each battery capacity range of the capacity distribution of the battery derived from the learning
5 data; and an output unit for calculating battery capacity prediction data from the input capacity
factor measurement data of the battery, and outputting the battery capacity prediction data
respectively calculated for each battery capacity range of the battery capacity distribution derived
from the learning data through the results of the plurality of different machine learnings.
Another embodiment of the present disclosure provides a method for measuring the
10 capacity of a battery, the method comprising the steps of: inputting capacity factor learning data of
the battery in a charging and discharging process performed for a specific time of a battery selected
as a learning target; deriving a capacity distribution of the battery from the input capacity factor
learning data of the battery; respectively performing a plurality of different machine learnings for
each battery capacity range of the capacity distribution of the battery derived from the learning
15 data; inputting capacity factor measurement data of the battery selected in the charging and
discharging process performed for a specific time of a battery selected as a prediction target;
calculating battery capacity prediction data from the input capacity factor measurement data of the
battery; and outputting battery capacity prediction data respectively calculated for each battery
capacity range of the battery capacity distribution derived from the learning datathrough the results
20 of the plurality of machine learnings.
An embodiment of the present disclosure provides a battery management system device
including the above-described device for measuring the capacity of a battery.
An embodiment of the present disclosure relates to a mobile apparatus including the
battery management system device.
25 Finally, an embodiment of the present disclosure relates to a computer program stored in
a recording medium for executing the method for measuring the capacity of a battery.
【Advantageous Effects】
The device and method for measuring the capacity of a battery according to an
embodiment of the present application can improve the accuracy for measurement of the battery
30 capacity and can reduce the battery manufacturing and capacity measurement costs in the process
5
by correcting the influence of the battery usage conditions.
The device and method for measuring the capacity of a battery according to an
embodiment of the present application can improve the accuracy of diagnosing the state of the
battery and predicting the lifespan of the battery by improving the accuracy for measurement of the
5 battery capacity.
The device and method for measuring the capacity of a battery according to an
embodiment of the present application can improve the efficiency of quality control of battery
products and the efficiency of the battery activation process by providing a cost-effective and
accurate capacity measurement method and improving the accuracy for measurement of the
10 battery capacity.
When a plurality of batteries in the form of a module, a pack, and a tray are mounted on
electric vehicles, mobile devices, etc. and used as power sources, the device and method for
measuring the capacity of a battery according to an embodiment of the present application can
improve the accuracy of the measured capacity of individual batteries when performing capacity
15 matching and cell balancing, and can improve the lifespan of the batteries in the form of a module,
a pack, and a tray as a result.
【Description of Drawings】
FIG. 1 is a diagram showing a process of applying a device and method for measuring the
capacity of a battery of the present application.
20 FIG. 2 is a diagram schematically showing the configuration of a device for measuring the
capacity of a battery of the present application.
FIGS. 3 and 4 are respectively a capacity distribution comparison diagram and a box plot
which show derivation results according to Example and Comparative Example.
【 Best Mode for Carrying Out the Invention】
25 Hereinafter, the present disclosure will be described in detail so that those with ordinary
skill in the art will easily be able to implement the present disclosure. However, the present
disclosure may be embodied in various different forms and is not limited only to the configuration
described herein.
6
In the present specification, if a prescribed part “includes” a prescribed element, this
means that another element can be further included instead of excluding other elements unless any
particularly opposite description exists.
In the present specification, the meaning of 'at least one' means one or more and all or
5 less, for example, the meaning of 'at least one of A, B, and C' refers to including all of the case
where there is one such as A, B, or C, the case where there are two such as A and B, A and C, and
B and C, and the case where there are three (all) such as A to C.
That is, in the present specification, "learning data" refersto data for machine learning.
Further, in the present specification, "measurement data" refersto data to be input in order
10 to calculate "prediction data", and the prediction data refers to data to be output as a result of
reflecting machine learning on the input measurement data.
Further, in the present specification, "capacity factor learning data" is learning data for
making accurate capacity measurement results using machine learning, and refersto data including
battery charge voltage, battery discharge voltage, battery charge current, battery discharge current,
15 battery charge capacity, battery discharge capacity, battery impedance, battery temperature, etc.
measured, collected, and stored in the state of charging, discharging, and resting the battery by
corresponding to the capacity measurement values for the rated capacity of individual batteries.
However, the capacity factor learning data is not limited to including the factors as described
above, and all factors that affect the battery capacity and can be measured and collected may be
20 included.
Further, in the present specification, "capacity factor measurement data" is measurement
data for making an accurate capacity measurement result using machine learning, and refers to data
including battery charge voltage, battery discharge voltage, battery charge current, battery
discharge current, battery charge capacity, battery discharge capacity, battery impedance, battery
25 temperature, etc. measured, collected, and stored in the state of charging, discharging, and resting
the battery. The capacity measurement data may also include the factors as described above, does
not limit the capacity factor measurement data, and may include all factors that affect the battery
capacity and can be measured and collected.
An embodiment of the present disclosure provides a device for measuring the capacity of
30 a battery, the device including: a learning data input unit for receiving capacity factor learning data
7
of the battery in a charging and discharging process performed for a specific time of a battery
selected as a learning target; a measurement data input unit for receiving capacity factor
measurement data of the battery selected in a charging and discharging process performed for a
specific time of a battery selected as a prediction target; a data learning unit for deriving the
5 capacity distribution of the battery from the capacity factor learning data of the battery input to the
learning data input unit, and respectively performing a plurality of different machine learnings for
each battery capacity range of the capacity distribution of the battery derived from the learning
data; and an output unit for calculating battery capacity prediction data from the input capacity
factor measurement data of the battery, and outputting the battery capacity prediction data
10 respectively calculated for each battery capacity range of the battery capacity distribution derived
from the learning data through the results of the plurality of different machine learnings.
The capacity distribution of the battery is obtained as follows. After dividing the entire
capacity range by constant or varying intervals between sections, the number of batteries having
the corresponding capacity for each section is measured and displayed in the form of a bar chart.
15 In an embodiment of the present disclosure, the battery selected as the learning target and
the battery selected as the prediction target may refer to individual batteries which are each
independently disposed on a module, a pack, and a tray.
Here, machine learning is a field of artificial intelligence, and refers to a technology in
which a computer program improves information processing ability through learning using data
20 and processing experience, or a technology related thereto. The technology related to machine
learning is widely known in the technical field to which the present disclosure pertains. That is, a
detailed description of a specific learning algorithm for machine learning will be omitted.
In the present specification, "respectively performing a plurality of different machine
learningsfor each battery capacity range of the capacity distribution of the battery derived from the
25 learning data" means that the battery capacity range is designated as a certain standard in the
capacity distribution of the battery, and different machine learnings are each carried out depending
on the range.
In an embodiment of the present disclosure, the battery may be a secondary battery, but
the present disclosure is not limited thereto.
8
In the present specification, the term "specific time" refers to a time during which a
charging and discharging process of an arbitrarily determined battery is performed. For example,
when the charging and discharging process of a battery is to be performed for 1 hour, the specific
time means 1 hour.
5 In an embodiment of the present disclosure, the capacity factor learning data of the
battery of the device for measuring the capacity of a battery may include battery charge capacity
and battery discharge capacity which are measured during charging, discharging, and resting of the
battery by corresponding to the capacity measurement value for the rated capacity of an individual
battery selected as the learning target, and may further include one or more of battery charge
10 voltage, battery discharge voltage, battery open circuit voltage (OCV), battery charge current,
battery discharge current, battery impedance, and battery temperature, but the present disclosure is
not limited thereto, and any factor that may affect the capacity of the battery may be included
therein.
In an embodiment of the present disclosure, the capacity factor learning data of the
15 battery of the device for measuring the capacity of a battery may include battery charge voltage,
battery discharge voltage, battery open circuit voltage (OCV), battery charge current, battery
discharge current, battery charge capacity, battery discharge capacity, battery impedance, and
battery temperature which are measured during charging, discharging, and resting of the battery by
corresponding to the capacity measurement value for the rated capacity of an individual battery
20 selected as the learning target.
In an embodiment of the present disclosure, the capacity factor measurement data of the
battery of the device for measuring the capacity of a battery may include one or more of battery
charge voltage, battery discharge voltage, battery open circuit voltage (OCV), battery charge
current, battery discharge current, battery charge capacity, battery discharge capacity, battery
25 impedance, and battery temperature which are measured during charging, discharging, and resting
of the battery selected as the prediction target, but the present disclosure is not limited thereto.
In an embodiment of the present disclosure, the capacity factor measurement data of the
battery of the device for measuring the capacity of a battery may include battery charge voltage,
battery discharge voltage, battery open circuit voltage (OCV), battery charge current, battery
30 discharge current, battery charge capacity, battery discharge capacity, battery impedance, and
9
battery temperature which are measured during charging, discharging, and resting of the battery
selected as the prediction target.
That is, the capacity measurement value for the rated capacity may correspond to a
dependent variable value, and the independent variable for estimating the dependent variable value
5 may be said to be a capacity factor.
In the present specification, different machine learning models mean different machine
learning algorithms, for example, decision tree and support vector machine (SVM). However,
even if the same decision tree algorithms are used, when the properties (hyperparameters)
representing the decision tree structure, such as the depth of the tree, the number of leaf nodes, etc.,
10 are different, or when the structures of the deep neural networks, for example, input layer, hidden
layer, output layer, weight for each node, etc., are different from each other, they are considered to
be different machine learning models.
In an embodiment of the present disclosure, the plurality of different machine learnings of
the data learning unit may be performed by selecting respective different regression model
15 algorithms, but the type of machine learning is not limited thereto.
More specifically, in an embodiment of the present disclosure, the regression model
algorithms may be one or more selected from decision tree, support vector machine (SVM),
random forest, partial least square regression, quantile regression, gradient boosting machine, deep
neural networks, and generalized linear/nonlinear regression, but the present disclosure is not
20 limited thereto.
Since the technology related to the machine learning is widely known in the technical
field to which the present disclosure pertains, a detailed description of the specific learning
algorithm will be omitted.
In the present specification, the correlation of the capacity (Y) of the battery with the
25 capacity factors (X1, X2,..., Xn) is derived in the form of equations or rules through machine
learning. The capacity factors mean values including voltage, current, capacity, impedance,
temperature, etc. that are measured, collected, and stored during charging, discharging, and resting
of the battery affecting the capacity of the battery.
Specifically, an embodiment of the derived battery capacity (Y) obtained through
30 machine learning may be expressed by the equation Y = f(X1, X2, ..., Xn). Here, f(X1, X2,...,Xn)
10
means a function form of the capacity factors (X1, X2,.., Xn), and includes combinations of all
mathematical functions that derive a value equal to or approximately equal to the capacity (Y) of
the battery. Here, the combination of the mathematical functions of the capacity factors (X1, X2, ...,
Xn) that most accurately predict the capacity (Y) of the battery is obtained as a result of machine
5 learning for the data in which the capacity and capacity factors of the battery are measured,
collected, and stored. That is, a mathematical function combination of capacity factors that
minimizes the deviation from an actual value of the capacity (Y) is obtained in the process of
performing machine learning.
As another embodiment, a result obtained through machine learning may be expressed as
10 an IF-THEN rule. Here, the IF-THEN rule means that if the capacity factors satisfy a plurality of
certain specific conditions IF{(X1, X2, ..., Xn)}, the capacity (Y) of the battery has a certain specific
value or a value in a specific range (THEN Y = yi). Here, the plurality of specific conditions
IF{(X1, X2, ..., Xn)} mean a set of cases that each individual capacity factors (X1, X2, ..., Xn) or a
function combination composed of several capacity factors has a specific value or a value in a
15 specific range. The plurality of specific conditions IF{(X1, X2, …, Xn)} representing a specific
value or a value in a specific range of each individual capacity factors or a function combination
composed of several capacity factors may have a hierarchical relational structure between the
conditions. That is, some specific conditions may be applied after some other specific conditions
are applied first.
20 Further, when learning data measured, collected, and stored with respect to battery
capacity and capacity factors by applying a plurality of machine learning algorithms, for some
machine learning methods, learning may be performed without dividing the entire data, or learning
may be performed by dividing the entire data into several parts.
When learning is performed by dividing the entire data into several parts, learning may be
25 performed by dividing the entire data into a training dataset for deriving a mathematical
correlational formula or IF-THEN rule from the machine learning algorithm and a test dataset for
evaluating the corresponding mathematical correlationalformula or IF-THEN rule.
Specifically, a method of statistically summing the estimated capacity values derived by
applying a plurality of battery capacity prediction models to obtain an average value and
30 determining the average value as the final battery capacity when predicting capacity from newly
11
input capacity factor data after primarily dividing the entire learning data composed of battery
capacity and capacity factorsfor machine learning into several parts, and then performing learning
with respect to each of a plurality of machine learning algorithms using each divided dataset,
thereby creating the same number of battery capacity prediction models asthe learning algorithms
5 may be applied to a partial or full capacity range of the corresponding battery.
Further, the capacity values of batteries disposed on a module, pack or tray, or the
capacity values of batteries designed to have the same rated capacity and manufactured under the
same manufacturing conditions indicate a distribution having variance, standard deviation, upper
and lower limits. A plurality of machine learning models are applied symmetrically or
10 asymmetrically based on the center of the capacity distribution (mean value or median value).
For example, as shown in FIG. 1, the first to third machine learning models may be
equally applied according to the capacity interval of an integer multiple or a real number multiple
of the standard deviation from the center (mean value or median value) of the capacity distribution
toward a side where the capacity value decreases and a side where the capacity value increases, the
15 first to third machine learning models may be applied identically, or different machine learning
models may be applied toward the side where the capacity value decreases and the side where the
capacity value increases.
Specifically, first, after learning data and creating capacity prediction models using a
plurality of machine learning algorithms for a plurality of total capacity intervals disposed on a
20 module, pack, or tray, the accuracy and error reduction rate of the predicted capacity value for each
machine learning algorithm are evaluated in the data learning process to determine the priority
among the prediction models by such a method that the prediction model with the highest
performance (i.e., accuracy and error reduction rate) for each capacity interval is selected as the
optimal machine learning model in the corresponding capacity interval.
25 Next, the capacity value of each battery for each prediction model is predicted using
capacity factor measurement data newly measured and collected from individual batteries disposed
on a module, pack, or tray and a capacity prediction model derived from a plurality of machine
learnings. Thereafter, the capacity value returned by the machine learning prediction model of the
previously predetermined priority is determined as the final capacity value of the corresponding
30 battery according to the capacity interval to which the predicted value corresponds.
12
As an example of this, as shown in FIG. 1, first, the capacity prediction value derived by
applying the first machine learning model is allocated as the capacity value of the corresponding
battery in the capacity interval of a times the standard deviation or less from the center (mean value
or median value) of the preferred capacity distribution toward the side where the capacity value
5 decreases and the side where the capacity value increases. Further, as shown in FIG. 1, the
capacity prediction value derived by applying the second machine learning model is allocated as
the capacity value of the corresponding battery in the capacity interval of more than a times the
standard deviation and equal to or less than b times the standard deviation from the center (mean
value or median value) of the capacity distribution outside the capacity range of the battery derived
10 by the first machine learning model toward the side where the capacity value decreases and the
side where the capacity value increases.
Similarly, as shown in FIG. 1, learning may be performed by such a method that the
capacity prediction value derived by applying the third machine learning model is allocated as the
capacity value of the corresponding battery with respect to the outer capacity ranges of the battery
15 capacity range respectively derived by the first and second machine learning models and the
capacity interval of more than b times the standard deviation and equal to or less than c times the
standard deviation from the center (mean value or median value) of the capacity distribution
toward the side where the capacity value decreases and the side where the capacity value increases.
In an embodiment shown in FIG. 1, random forest, gradient boosting machine, and
20 quantile regression may be applied as first to third machine learning methods respectively, but this
exemplifies algorithms of the first to third machine learning methods. In addition, learning may be
performed in the same manner by applying other machine learning algorithms.
The definitions of the symbols shown in FIG. 1 are as follows.
Di = Input Data
25 Do = Output Data
σ = Capacity standard deviation, a, b, c = integer or real number
Daσ = Capacity value data in the range (aσ) of a times the standard deviation from the
center (Mean or Median) of the capacity distribution
Dbσ = Capacity value data in the range (bσ) of b times the standard deviation from the
30 center (Mean or Median) of the capacity distribution
13
Dcσ = Capacity value data in the range (cσ) of c times the standard deviation from the
center (Mean or Median) of the capacity distribution
That is, the combination of machine learning algorithms that show the best performance
(accuracy and error reduction rate) for the capacity prediction value is different depending on
5 various conditions such as the type of batteries, the manufacturing method of the batteries, the
configuration of batteries disposed on a module, pack, or tray, operating conditions, etc., and is not
limited only to the cases presented in the above examples.
The application criterion of a predicted capacity value obtained from the machine
learning model follows the priority and capacity interval of the machine learning model
10 determined in advance in the learning process. As an example, coefficient of determination (R
squared, R2
), mean absolute error (MAE), root mean square error (RMSE), mean absolute
percentage error (MAPE), etc. are calculated for each capacity interval to determine the range of
the capacity interval having an optimized value. For example, an integer multiple or real multiple
of the standard deviation of the capacity range in which the value of the coefficient of
15 determination becomes the maximum, or the mean absolute error, the root mean square error, or
the mean absolute percentage error becomes the minimum, that is, the capacity distribution is
obtained, and machine learning algorithms that exhibit the highest accuracy and error reduction
rate in each capacity interval are preferentially applied.
A capacity interval in which the capacity value predicted from the machine learning
20 model deviates from upper and lower limits of the rated capacity is diagnosed as the occurrence of
defects in the battery.
In the present specification, the "input unit" is an interface for receiving various types of
necessary data. Specifically, in the present specification, the input unit may be divided into a
learning data input unit for receiving learning data and a measurement data input unit for receiving
25 measurement data. More specifically, the "input unit" is an interface for measuring or collecting
capacity factors measured or collected under rated capacity conditions, and transferring the
measured or collected capacity factor measurement data to a reference value storage unit or a data
learning unit. A method in which the learning unit receives and transfers data is not particularly
limited.
14
In the present specification, the "data learning unit" is an interface for performing
machine learning using the learning data input to the learning data input unit.
In the present specification, the "output unit" is an interface for calculating prediction data
by reflecting the results of machine learning. A method in which the output unit calculates data is
5 not particularly limited.
In an embodiment of the present disclosure, the device for measuring the capacity of a
battery may further include: a reference value storage unit for storing data measured under a rated
capacity condition of the battery; and a capacity state diagnosis unit for comparing the output
battery capacity prediction data and results of the data measured under the rated capacity condition
10 of the battery to determine the reliability of the prediction data, diagnose the capacity and state of
the battery, and control the battery process depending on the diagnosis result.
In the present specification, the "reference value storage unit" is an interface for storing
the capacity factor measurement data measured or collected under the rated capacity condition,
calculating a capacity reference value using the capacity factor measurement data, and transferring
15 the capacity reference value to the capacity state diagnosis unit. A method in which the reference
value storage unit stores data and transfers a specific value is not particularly limited.
All data input, transferred, or calculated to the interface of the measuring device
according to the present disclosure may be integrally managed. Here, being integrally managed
may include, for example, all actions such as managing all data input, transferred, or calculated to
20 the interface of the measuring device according to the present disclosure by a specific main
computer or server, calculating a new value from the managed data, or inputting it as data again to
the input unit.
In the present specification, the "capacity state diagnosis unit" is an interface for
controlling the process of the battery depending on the diagnosis result by comparing the capacity
25 reference value received from the reference value storage unit and the capacity prediction value
derived from the data learning unit, thereby determining the reliability of the prediction data and
diagnosing the capacity and state of the battery.
Such a structure is as shown in FIG. 2 below, and may additionally have a necessary
interface other than that shown in FIG. 2below such as an output unit.
15
In an embodiment of the present disclosure, the device for measuring the capacity of a
battery may use a box plot to compare the output battery capacity prediction data and the actual
capacity data result of the battery.
In an embodiment of the present disclosure, determining the reliability of the battery
5 capacity prediction data in the device for measuring the capacity of a battery may be using actual
standard capacity distribution of the battery stored in the reference value storage unit, coefficient of
determination (R squared, R2
), mean absolute error (MAE), root mean square error (RMSE), or
mean absolute percentage error (MAPE), but the present disclosure is not limited thereto.
Since the technique related to the method of comparing the data and determining the
10 reliability is widely known in the art to which the present disclosure pertains, a detailed description
thereof will be omitted.
In an embodiment of the present disclosure, the battery capacity range of the capacity
distribution of the battery in the device for measuring the capacity of a battery may be determined
as an integer multiple or a real number multiple of the standard deviation (σ) based on the center of
15 the capacity distribution.
The center of the capacity distribution may mean an average (Mean) or a center value
(Median) of the capacity distribution.
In an embodiment of the present disclosure, the battery capacity range of the capacity
distribution of the battery in the device for measuring the capacity of a battery may be determined
20 as an integer multiple or a real number multiple of the standard deviation (σ) based on the average
(Mean) or the center value (Median) of the capacity distribution.
For example, in the present embodiment, the accuracy of the prediction capacity may
become the maximum when using the ensemble regression method in the capacity interval that
becomes 1.5 times the standard deviation from the center of the capacity distribution, and using the
25 quantile regression method in the capacity interval that is more than 1.5 times the standard
deviation.
In an embodiment of the present disclosure, there may be provided a battery management
system (BMS) including the device for measuring the capacity of a battery according to the present
disclosure. In other words, in an embodiment of the present disclosure, the device for measuring
30 the capacity of a battery may be used in a battery management system (BMS) device.
16
In an embodiment of the present disclosure, there may be provided a battery management
system device including the capacity measuring device including: a learning data input unit; a
measurement data input unit; a data learning unit; and an output unit.
In the present specification, a “battery management system (BMS) device” refers to all
5 types of interfaces including a battery managementsystem.
In an embodiment of the present disclosure, there may be provided a mobile apparatus
including the management system device according to the present application.
In the present specification, the term “mobile apparatus” refers to an apparatus which
may be moved by itself or may be easily transported by a user, and examples of the mobile
10 apparatus may include an electric vehicle, a mobile device, etc.
In an embodiment of the present disclosure, there may be provided a battery management
system device in which at least one of: the learning data input unit; the measurement data input
unit; the data learning unit; the reference value storage unit; and the capacity state diagnosis unit is
remotely controlled.
15 In an embodiment of the present disclosure, there may be provided a battery management
system device in which two or more of: the learning data input unit; the measurement data input
unit; the data learning unit; the reference value storage unit; and the capacity state diagnosis unit are
remotely controlled.
In an embodiment of the present disclosure, there may be provided a battery management
20 system device in which all of: the learning data input unit; the measurement data input unit; the
data learning unit; the reference value storage unit; and the capacity state diagnosis unit are
remotely controlled.
In the present specification, the meaning of the “being remotely controlled” means that
interfaces of the input unit, the learning unit, the output unit, etc. are located outside the battery
25 management system device to perform their functions while transmitting or receiving data and
signals between the interfaces through communication. For example, a method of the remote
control may include a method of managing performance of their functions while transmitting or
receiving data and signals between interfaces through communication by placing some of the
interfaces in a cloud server, but the present disclosure is not limited thereto, and any method
17
capable of performing their functions outside the battery management system device may be
applied to the configuration of the present disclosure.
When some or all interfaces of the device are remotely controlled, it is possible to reduce
the weight of the battery management system device so that it is easily applied to a mobile
5 apparatus, and a specific main computer or cloud server is used so that it is easy to integrally
manage data, etc. generated in the process of using the device.
Further, when some interfaces of the device are remotely controlled, the cost associated
with the computer hardware installed in the mobile apparatus may be reduced by lowering
specifications required for memory for data storage, computation, information processing, etc. in
10 relation to a computer hardware (H/W) and enabling the configuration to be simplified.
Further, in an embodiment of the present disclosure, at least one of: the learning data
input unit; the measurement data input unit; the data learning unit; the output unit; the reference
value storage unit; and the capacity state diagnosis unit of the battery management system device
may be embedded in a mobile apparatus.
15 Further, in an embodiment of the present disclosure, two or more of: the learning data
input unit; the measurement data input unit; the data learning unit; the output unit; the reference
value storage unit; and the capacity state diagnosis unit of the battery management system device
may be embedded in a mobile apparatus.
Further, in an embodiment of the present disclosure, all of: the learning data input unit;
20 the measurement data input unit; the data learning unit; the output unit; the reference value storage
unit; and the capacity state diagnosis unit of the battery management system device may be
embedded in a mobile apparatus.
In an embodiment of the present disclosure, there may be provided a mobile apparatus in
which at least one of: the learning data input unit; the measurement data input unit; the data
25 learning unit; the output unit; the reference value storage unit; and the capacity state diagnosis unit
is embedded in the mobile apparatus.
In an embodiment of the present disclosure, there may be provided a mobile apparatus in
which two or more of: the learning data input unit; the measurement data input unit; the data
learning unit; the output unit; the reference value storage unit; and the capacity state diagnosis unit
30 of the battery management system device are bedded in the mobile apparatus.
18
In an embodiment of the present disclosure, there may be provided a mobile apparatus in
which all of: the learning data input unit; the measurement data input unit; the data learning unit;
the reference value storage unit; and the capacity state diagnosis unit are embedded in the mobile
apparatus.
5 In the present specification, the term “being embedded in the mobile apparatus” means
that an interface of the above-mentioned input unit, learning unit, output unit, or the like
correspondsto one of the components of the mobile apparatus.
When the device is partially or entirely embedded in the mobile apparatus, there is an
advantage in that safety problems due to communication problems do not occur.
10 In an embodiment of the present disclosure, the device for measuring the capacity of a
battery, which composes the battery management system device may further include the abovedescribed reference value storage unit; and capacity state diagnosis unit, and the reference value
storage unit and the capacity state diagnosis unit may be partially embedded in a remote control or
mobile apparatus. If there are additional interfaces available, each of the interfaces may be
15 embedded in a remote control or mobile apparatus.
More specifically, the battery management system refers to a system which performs
capacity matching and cell balancing, controls charging or discharging of the battery, and controls
and manages the overall state of the battery such as the remaining amount of the battery, battery
failure, etc. when the battery management system is mounted on an electric vehicle, a mobile
20 device, etc. and used as a power source. The battery management system (BMS) may be applied
to one or more batteries. That is, it is generally applied to a plurality of batteries, but may be
applied to one battery, and the battery management system may be individually applied to each
battery.
Data generated by the battery management system device may also be integrally
25 managed as described above.
When the device for measuring the capacity of a battery according to the present
disclosure is applied to a battery management system device, the accuracy of the measurement of
the battery capacity can be improved so that the accuracy of diagnosing the state of the battery and
predicting the lifespan of the battery can be improved accordingly. That is, as one or more
30 batteries are mounted on an electric vehicle, a mobile device, etc. and used as power sources, the
19
batteries may be managed more accurately and efficiently by the battery management system
when performing an overall battery managementsuch as capacity matching, cell balancing, etc.
In an embodiment of the present disclosure, there is provided a method for measuring the
capacity of a battery, the method comprising the steps of: inputting capacity factor learning data of
5 the battery in a charging and discharging process performed for a specific time of a battery selected
as a learning target; deriving a capacity distribution of the battery from the input capacity factor
learning data of the battery; respectively performing a plurality of different machine learnings for
each battery capacity range of the capacity distribution of the battery derived from the learning
data; inputting capacity factor measurement data of the battery selected in the charging and
10 discharging process performed for a specific time of a battery selected as a prediction target;
calculating battery capacity prediction data from the input capacity factor measurement data of the
battery; and outputting battery capacity prediction data respectively calculated for each battery
capacity range of the battery capacity distribution derived from the learning data through the results
of the plurality of machine learnings.
15 In an embodiment of the present disclosure, the capacity factor learning data of the
battery of the method for measuring the capacity of a battery may include battery charge capacity
and battery discharge capacity which are measured during charging, discharging, and resting of the
battery by corresponding to the capacity measurement value for the rated capacity of an individual
battery selected as the learning target, and may further include one or more of battery charge
20 voltage, battery discharge voltage, battery open circuit voltage (OCV), battery charge current,
battery discharge current, battery impedance, and battery temperature, but the present disclosure is
not limited thereto, and any factor that may affect the capacity of the battery may be included
therein.
In an embodiment of the present disclosure, the capacity factor learning data of the
25 battery of the method for measuring the capacity of a battery may include battery charge voltage,
battery discharge voltage, battery open circuit voltage (OCV), battery charge current, battery
discharge current, battery charge capacity, battery discharge capacity, battery impedance, and
battery temperature which are measured during charging, discharging, and resting of the battery by
corresponding to the capacity measurement value for the rated capacity of an individual battery
30 selected as the learning target.
20
In an embodiment of the present disclosure, the capacity factor measurement data of the
battery of the method for measuring the capacity of a battery may include one or more of battery
charge voltage, battery discharge voltage, battery open circuit voltage (OCV), battery charge
current, battery discharge current, battery charge capacity, battery discharge capacity, battery
5 impedance, and battery temperature which are measured during charging, discharging, and resting
of the battery selected as the prediction target, but the present disclosure is notlimited thereto.
In an embodiment of the present disclosure, the capacity factor measurement data of the
battery of the method for measuring the capacity of a battery may include battery charge voltage,
battery discharge voltage, battery open circuit voltage (OCV), battery charge current, battery
10 discharge current, battery charge capacity, battery discharge capacity, battery impedance, and
battery temperature which are measured during charging, discharging, and resting of the battery
selected as the prediction target.
In an embodiment of the present disclosure, the step of performing machine learning on
the input learning data may be performed by selecting one or more of decision tree, support vector
15 machine (SVM), random forest, partial least square regression, quantile regression, gradient
boosting machine, deep neural networks, and generalized linear/nonlinear regression, but the
present disclosure is not limited thereto. For example, in the present embodiment, the accuracy of
the prediction capacity may become the maximum when using the ensemble regression method in
the capacity interval that becomes 1.5 times the standard deviation from the center of the capacity
20 distribution, and using the quantile regression method in the capacity interval that is more than 1.5
times the standard deviation.
In an embodiment of the present disclosure, the method for measuring the capacity of a
battery may further comprise the steps of: storing the actual capacity data of the battery; and
comparing the output battery capacity prediction data and the actual capacity data result of the
25 battery to determine the reliability of the battery capacity prediction data.
In an embodiment of the present disclosure, comparing the output battery capacity
prediction data and the actual capacity data result of the battery in the method for measuring the
capacity of a batterymay be using a box plot, but the present disclosure is not limited thereto.
In an embodiment of the present disclosure, the step of determining the reliability of the
30 battery capacity prediction data may be using actual standard capacity distribution of the battery
21
stored in the reference value storage unit, coefficient of determination (R squared, R2
), mean
absolute error (MAE), root mean square error (RMSE), or mean absolute percentage error
(MAPE), but the present disclosure is not limited thereto.
In an embodiment of the present disclosure, the battery capacity range of the capacity
5 distribution of the battery in the method for measuring the capacity of a battery may be determined
as an integer multiple or a real number multiple of the standard deviation (σ) based on the center of
the capacity distribution.
The center of the capacity distribution may mean an average (Mean) or a center value
(Median) of the capacity distribution.
10 In an embodiment of the present disclosure, the battery capacity range of the capacity
distribution of the battery in the method for measuring the capacity of a battery may be determined
as an integer multiple or a real number multiple of the standard deviation (σ) based on the average
(Mean) or the center value (Median) of the capacity distribution.
In an embodiment of the present disclosure, the plurality of machine learnings may be
15 connected in parallel.
In an embodiment of the present disclosure, the method for measuring the capacity of a
battery may be a method used in the battery management system (BMS). That is, in an
embodiment of the present disclosure, the battery management system may perform the function
of the battery management system described above by using the method for measuring the
20 capacity of a battery according to the present disclosure.
In this case also as described above, as one or more batteries are mounted on an electric
vehicle, a mobile device, etc. and used as power sources, the batteries may be managed more
accurately and efficiently by the battery management system when performing an overall battery
management such as capacity matching, cell balancing, etc.
25 In the present specification, the description applied to the device for measuring the
capacity of a battery according to the embodiment of the present disclosure may also be applied to
the method for measuring the capacity of a battery according to the embodiment of the present
disclosure.
The device and method for measuring the capacity of a battery according to the present
30 application apply a method capable of maximizing the accuracy among a plurality of machine
22
learning methods to capacity factor learning data of the battery for each capacity intervalso that the
accuracy and precision of battery capacity prediction can be improved. Through this, the
efficiency of battery state diagnosis and quality control can be improved, and process optimization
and production efficiency can be increased ultimately.
5 When a plurality of batteries in the form of a module, a pack, and a tray are mounted on
an electric vehicle, a mobile device, etc. and used as power sources, the device and method for
measuring the capacity of a battery according to the embodiment of the present application can
improve the accuracy of the measured capacity of individual batteries when performing capacity
matching and cell balancing, and as a result, the lifespan of the batteries in the form of the module,
10 pack, and tray can be improved.
An embodiment of the present disclosure provides a computer program stored in a
recording medium for executing the method for measuring the capacity of a battery according to
the present disclosure. The aforementioned description of the method for measuring the capacity
of a battery may be applied in the same manner except that each step of the method for measuring
15 the capacity of a battery is stored in a recording medium in the form of a computer program.
【 Mode for Carrying Out the Invention】
The process of the method for measuring the capacity of a battery will be described in
more detail as follows.

20 After disposing one or more batteries to be learned on a module, pack, and tray, and
measuring and collecting battery capacity factors such as charge voltages, discharge voltages, open
circuit voltages(OCV), charge currents, discharge currents, charge capacities, discharge capacities,
impedances, and temperatures of the disposed batteries while performing the charging and
discharging process of the batteries by corresponding to the capacity measurement value for the
25 rated capacity, the values were stored in a storage medium as capacity factor learning data.
Thereafter, the capacity distribution was derived from the measured and collected
capacity factor learning data, machine learning was performed using the first to third machine
learning models for each battery capacity range of the capacity distribution derived from the
learning data, and three battery capacity prediction models were derived through this.
23
Specifically, the capacity prediction value derived by applying the first machine learning
model was allocated as the capacity value of the corresponding battery within the range of 1.5
times the standard deviation based on the center of the capacity distribution, and the capacity
prediction value derived by applying the second machine learning model was allocated as the
5 capacity value of the corresponding battery with respect to the capacity range of the battery derived
by the first machine learning model, that is, the case that is outside the range of 1.5 times the
standard deviation based on the center of the capacity distribution. More specifically, the capacity
range of the battery to which the second machine learning model is applied means a range of 1.5 to
2 times the standard deviation.
10 Similarly, when the capacity of the battery derived by applying the first and second
machine learning models was out of the range, that is, when it was deviated twice the standard
deviation, the capacity prediction value derived by applying the third machine learning model was
allocated asthe capacity value of the corresponding battery.
More specifically, the random forest (first machine learning model) was applied within
15 the range of 1.5 times the standard deviation based on the center of the capacity distribution, and
the gradient boosting machine (second machine learning model) algorithm was applied to the
range of 1.5 to 2 times the standard deviation based on the center of the capacity distribution.
Finally, the quantile regression (third machine learning model) algorithm was applied
with respect to the range that was deviated two times the standard deviation based on the center of
20 the capacity distribution.
Further, two ensemble methods of a bagging algorithm and a boosting algorithm were
used in this process.
At this time, the accuracy became the maximum when the bagging and boosting
algorithms were applied from the center of the dispersion to the capacity interval of 1.5 to 2 times
25 the standard deviation, and the accuracy became the maximum when applying the quantile
regression method in the outer capacity interval thereof.
Thereafter, after disposing one or more batteries whose capacities were to be predicted on
a module, pack, and tray, and measuring and collecting battery capacity factors such as charge
voltages, discharge voltages, open circuit voltages (OCV), charge currents, discharge currents,
30 charge capacities, discharge capacities, impedances, and temperatures of the disposed batteries
24
while performing the charging and discharging process of the batteries, the values were stored in a
storage medium as capacity factor measurement data.
Thereafter, battery capacity prediction data was calculated by applying the three derived
battery capacity measurement models to the capacity factor measurement data.
5 Next, the battery capacity distribution was derived to predict the battery capacity by
outputting the calculated battery capacity prediction data for each capacity range of the capacity
distribution derived from the capacity factor learning data.

Further, battery capacity prediction data was derived (comparative example) in the same
10 manner except that the linear regression equation was applied as a single machine learning
algorithm.
The derivation results according to Example and Comparative Example are shown
through the capacity distribution comparison diagram of FIG. 3 and the box plot of FIG. 4.
The meanings of (a) to (d) in FIGS. 3 and 4 are as follows.
15 (a) rated capacity
(b) capacity calculated by a single machine learning (ML) method
(c) capacity calculated by a plurality of machine learning (ML) methods (Example of the
present disclosure)
(d) capacity calculated by the linear regression equation
20 It could be visually confirmed from the results of FIGS. 3 and 4 that the difference
between the measurement data and the prediction data was not large and the accuracy was
excellent in the case of the device and method for measuring the capacity of a battery according to
the embodiment of the present application.
Finally, the actual capacity data of the battery was compared with the output battery
25 capacity prediction data to determine the reliability of the battery capacity prediction data by
deriving R2
.
Further, the battery capacity prediction data was derived to determine the reliability
thereof by deriving R2
in the same manner except that a single machine learning algorithm was
applied.
25
As a result, it was confirmed that R
2 was improved by 20% or more in the device and
method for measuring the capacity of a battery according to the embodiment of the present
application compared to the case where the battery capacity was measured in the same manner
except that a single machine learning algorithm was applied.
5
26
【CLAIMS】
【Claim 1】
A device for measuring the capacity of a battery, the device including:
a learning data input unit for receiving capacity factor learning data of the battery
5 measured in a charging and discharging process performed for a specific time of an individual
battery selected as a learning target;
a measurement data input unit for receiving capacity factor measurement data of the
battery selected in a charging and discharging process performed for a specific time of a battery
selected as a prediction target;
10 a data learning unit for deriving the capacity distribution of the battery from the capacity
factor learning data of the battery input to the learning data input unit, and respectively performing
a plurality of different machine learnings for each battery capacity range of the capacity
distribution of the battery derived from the learning data; and
an output unit for calculating capacity prediction data of the battery selected as the
15 prediction target through the results of the plurality of different machine learnings from the input
capacity factor measurement data of the battery, and outputting the battery capacity prediction data
respectively calculated for each battery capacity range of the battery capacity distribution derived
from the learning data.
【Claim 2】
20 The device of claim 1, wherein the capacity factor learning data of the battery includes
battery charge capacity and battery discharge capacity which are measured during charging,
discharging, and resting of the battery by corresponding to the capacity measurement value for the
rated capacity of an individual battery selected as the learning target, and further includes one or
more of battery charge voltage, battery discharge voltage, battery open circuit voltage (OCV),
25 battery charge current, battery discharge current, battery impedance, and battery temperature.
【Claim 3】
The device of claim 1, wherein the capacity factor measurement data of the battery
includes one or more of battery charge voltage, battery discharge voltage, battery open circuit
voltage (OCV), battery charge current, battery discharge current, battery charge capacity, battery
27
discharge capacity, battery impedance, and battery temperature which are measured during
charging, discharging, and resting of the battery selected as the prediction target.
【Claim 4】
The device of claim 1, wherein the plurality of different machine learnings of the data
5 learning unit are performed by selecting respective different regression model algorithms.
【Claim 5】
The device of claim 4, wherein the regression model algorithms are one or more selected
from decision tree, support vector machine (SVM), random forest, partial least square regression,
quantile regression, gradient boosting machine, deep neural networks, and generalized
10 linear/nonlinear regression.
【Claim 6】
The device of claim 1, further including:
a reference value storage unit for storing data measured under a rated capacity condition
of the battery; and
15 a capacity state diagnosis unit for comparing the output battery capacity prediction data
and results of the data measured under the rated capacity condition of the battery to determine the
reliability of the battery capacity prediction data, diagnose the capacity and state of the battery, and
control the battery process depending on the diagnosis result.
【Claim 7】
20 The device of claim 6, wherein determining the reliability of the battery capacity
prediction data is using the capacity distribution measured under the rated capacity condition of the
battery stored in the reference value storage unit, coefficient of determination (R squared, R2
),
mean absolute error (MAE), root mean square error (RMSE), or mean absolute percentage error
(MAPE).
25 【Claim 8】
The device of claim 1, wherein the battery capacity range of the capacity distribution of
the battery is determined by an integer multiple or a real number multiple of the standard deviation
(σ) based on the average (Mean) or the center value (Median) of the capacity distribution.
【Claim 9】
30 A method for measuring the capacity of a battery, the method comprising the steps of:
28
inputting capacity factor learning data of the battery in a charging and discharging process
performed for a specific time of a battery selected as a learning target;
deriving a capacity distribution of the from the input capacity factor learning data of the
battery;
5 respectively performing a plurality of different machine learnings for each battery
capacity range of the capacity distribution of the battery derived from the learning data;
inputting capacity factor measurement data of the battery selected in the charging and
discharging process performed for a specific time of a battery selected as a prediction target;
calculating capacity prediction data of the battery selected as the prediction target through
10 the results of the plurality of machine learnings from the input capacity factor measurement data of
the battery; and
outputting battery capacity prediction data respectively calculated for each battery
capacity range of the battery capacity distribution derived from the learning data.
【Claim 10】
15 The method of claim 9, wherein the capacity factor learning data of the battery includes
battery charge capacity and battery discharge capacity which are measured during charging,
discharging, and resting of the battery by corresponding to the capacity measurement value for the
rated capacity of an individual battery selected as the learning target, and further includes one or
more of battery charge voltage, battery discharge voltage, battery open circuit voltage (OCV),
20 battery charge current, battery discharge current, battery impedance, and battery temperature.
【Claim 11】
The method of claim 9, wherein the capacity factor measurement data of the battery
includes one or more of battery charge voltage, battery discharge voltage, battery open circuit
voltage (OCV), battery charge current, battery discharge current, battery charge capacity, battery
25 discharge capacity, battery impedance, and battery temperature which are measured during
charging, discharging, and resting of the battery selected as the prediction target.
【Claim 12】
The method of claim 9, wherein the step of performing a plurality of different machine
learnings on the input learning data is performed by selecting respective different regression model
30 algorithms.
29
【Claim 13】
The method of claim 12, wherein the different regression model algorithms are one or
more selected from decision tree, support vector machine (SVM), random forest, partial least
square regression, quantile regression, gradient boosting machine, deep neural networks, and
5 generalized linear/nonlinear regression.
【Claim 14】
The method of claim 9, further comprising the steps of:
storing and updating the battery capacity data measured under a rated capacity condition of
the battery; and
10 comparing the output battery capacity prediction data and results of the battery capacity
data measured under the rated capacity condition of the battery to determine the reliability of the
battery capacity prediction data.
【Claim 15】
The method of claim 14, wherein the step of determining the reliability of the battery
15 capacity prediction data is using capacity distribution measured under the rated capacity condition
of the battery stored in the reference value storage unit, coefficient of determination (R squared,
R
2
), mean absolute error (MAE), root mean square error (RMSE), or mean absolute percentage
error (MAPE).
【Claim 16】
20 The method of claim 9, wherein the battery capacity range of the capacity distribution of
the battery is determined by an integer multiple or a real number multiple of the standard deviation
(σ) based on the average (Mean) or the center value (Median) of the capacity distribution.
【Claim 17】
A battery management system (BMS) device including the device for measuring the
25 capacity of a battery, according to any one of claims 1 to 8.
【Claim 18】
The battery management system device of claim 17, wherein at least one of: the learning
data input unit; the measurement data input unit; the data learning unit; the output unit; the
reference value storage unit; and the capacity state diagnosis unit isremotely controlled.
30 【Claim 19】
30
A mobile apparatus including the battery management system device according to claim
17.
【Claim 20】
The mobile apparatus of claim 19, wherein at least one of: the learning data input unit; the
5 measurement data input unit; the data learning unit; the output unit; the reference value storage
unit; and the capacity state diagnosis unit of the battery management system device is embedded in
the mobile apparatus.
【Claim 21】
A computer program stored in a recording medium for executing the method for
10 measuring the capacity of a battery, according to any one of claims 9 to 16.