CN112269134B - Battery SOC and SOH joint estimation method based on deep learning - Google Patents
Battery SOC and SOH joint estimation method based on deep learning Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
Abstract
The invention discloses a battery SOC and SOH joint estimation method based on deep learning. The method specifically comprises the steps of collecting battery data, preprocessing the data, building an SE-CNN neural network and a BRNN neural network, building and training an estimation model, and finally estimating the SOH value of the battery by using the trained SE-CNN network and estimating the SOC value of the battery by using the BRNN network. The method utilizes the parameter self-learning capability of deep learning to reduce the calculated amount in the process of estimating the change relation between the ratio of the residual capacity and the maximum available capacity of the battery and the capacity and the internal resistance of the battery, considers the correlation between the SOH and the SOC, carries out combined estimation, enhances the stability of a prediction model, improves the accuracy of calculation, makes up the defects of long measurement time, high requirement on measurement conditions, overhigh calculated amount and low estimation accuracy of various estimation methods in the prior art, and provides a quick and accurate SOH and SOC estimation method for various battery management systems.
Description
Technical Field
The invention belongs to the technical field of power battery management, and particularly relates to a battery SOC and SOH joint estimation method based on deep learning.
Background
The definition of the SOC is the ratio of the residual capacity of the battery to the maximum available capacity, and the SOC reflects the residual capacity of the battery; SOH is generally defined as the variation relationship between the capacity and the internal resistance of a lithium ion battery, and reflects the aging condition of the lithium ion battery. The accurate estimation of the SOC and the SOH is one of the core functions of the battery management system, and has important significance for guaranteeing the safe use of the battery and prolonging the cycle life of the battery.
The SOC of the battery is related to a plurality of factors, such as temperature, polarization effect, battery life and the like, and has strong nonlinearity, and for the estimation of the SOC of the battery, methods generally adopted at home and abroad comprise a discharge test method, an ampere-hour method, an open-circuit voltage method, an internal resistance method, a Kalman filtering method, a linear model method and a neural network method, but the methods have defects in different degrees.
1) The discharge test method requires the battery to be in a constant current discharge state and takes a large amount of measurement time;
2) The ampere-hour method is easily influenced by the current measurement precision, and the precision is very poor under the condition of high temperature or severe current fluctuation;
3) When the open-circuit voltage method is used for estimating the SOC of the battery, the battery must be kept stand for a long time to reach a stable state, and the method is only suitable for estimating the SOC of the battery in a non-violent change state and cannot meet the requirement of online detection;
4) The internal resistance method needs to accurately measure the internal resistance of the battery, generally, the internal resistance of the battery is in the milliohm level, the requirement on a measuring instrument is very high, and the method is difficult to be applied in practice;
5) The Kalman filtering method is a more estimation method adopted at present, has stronger dependence on a battery model, needs to establish a more accurate battery model to obtain accurate SOC, and has the accuracy and the complexity which are in direct proportion;
6) The linear model is roughly classified into an equivalent circuit model and a simplified electrochemical model. The equivalent circuit model mostly adopts a simpler Thevein model, and if a more complex circuit model is adopted, the identification of model parameters and the establishment of a state equation are very challenging. Due to the simplification of the model, the internal rules of the battery cannot be fully reflected, and a large estimation error is caused.
Therefore, in the prior art, a comprehensive and effective solution for estimating the SOC and SOH of the lithium ion battery does not exist, and optimization needs to be performed in the aspects of improving accuracy and reducing calculation amount.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a battery SOC and SOH joint estimation method based on deep learning, which utilizes the parameter self-learning capability of the deep learning to reduce the calculated amount in the estimation process, considers the correlation between the SOH and the SOC to carry out joint estimation, enhances the stability of a prediction model, improves the calculation accuracy and provides a quick and accurate SOH and SOC estimation method for various battery management systems.
A battery SOC and SOH joint estimation method based on deep learning specifically comprises the following steps:
step one, battery data acquisition.
1.1, carrying out charge-discharge experiments on the lithium ion battery, and obtaining training data required by the SOC and SOH combined estimation model of the lithium ion battery, wherein the training data comprises the voltage, the current, the temperature and the electric quantity based on current integration of the lithium ion battery.
1.2, generating data samples by utilizing the countermeasure network to expand the sample data set. The countermeasure network consists of a generation network G and a discrimination network D. The method of generating data samples comprises the steps of:
1.2.1, inputting random noise z into the generating network G, and generating network G output result data G (z).
1.2.2, inputting result data G (z) of the generation network G into the discrimination network D, comparing the input data G (z) with real battery data x by the discrimination network D through an objective function loss of the countermeasure network to obtain the accuracy of the result data G (z), wherein the expression of the objective function loss is as follows:
wherein the content of the first and second substances,
and
to a desired value, maximize
Represents that D (x) approaches 1; minimization of
Indicating that D (G (z)) is approaching 0.D (x) represents an error between the real data calculated by the discrimination network D and the generated network G result data G (z), and D (G (z)) represents a probability that the result data G (z) determined by the discrimination network D is real.
1.2.3, when the objective function loss reaches the optimal solution, taking the data G (z) output by the generation network G as real battery data and the actually measured real battery data x to form an enhanced data set for the subsequent joint estimation of the battery SOC and the SOH.
And step two, preprocessing data.
Preprocessing the enhanced data set obtained in the first step through a gradient enhanced regression tree algorithm so as to obtain relatively smooth data, wherein the processing process comprises the following steps of:
2.1, calculating the negative gradient of the residual error:
wherein x is i Representing the input vector, y i Denotes the target vector, F (x) i ) Representing a model function with x mapped to y, setting an initial value F of the model function 0 (x) = c, c is a constant; l (y) i ,F(x i ) Represents a loss function and m represents the number of iterations.
2.2 use of training set
Training the mth regression tree T m 。
2.3 computing regression Tree T m The weight factor of (c):
and 2.4, repeating the steps from 2.1 to 2.3M times.
2.5, updating the gradient enhancement regression tree model:
F(x i )＝F 0 (x)+υγ m T m (x i ) (4)
and upscaling is a set contraction coefficient and is used for limiting the lifting effect of each tree on the algorithm precision, so that better model performance is obtained and overfitting can be prevented.
And step three, building an SE-CNN neural network.
And 3.1, constructing a CNN neural network.
3.1.1, constructing a CNN neural network comprising an input layer, four convolution layers, four pooling layers, a Flatten layer, a full connection layer and an output layer.
3.1.2, introduction of the inclusion structure comprising 4 basic structures, namely 1 × 1 convolution, 3 × 3 convolution, 5 × 5 convolution and 3 × 3 max pooling. The parameter quantity is reduced by adding 1 × 1 convolution before the 3 × 3 convolution and the 5 × 5 convolution structure and adding 1 × 1 convolution after the 3 × 3 maximum pooling, so that the problem of low efficiency caused by excessive calculation of the model due to excessive parameter quantity is avoided. And combining the operation results of the 4 basic structures added with the convolution on the channel.
And 3.1.3, setting the iteration times of the CNN neural network by taking the average absolute error as an objective function and adopting an RAdam adaptive optimizer to minimize the objective function.
3.2, constructing an SE module, wherein the SE module comprises the following operations:
and 3.2.1, performing compression operation, performing feature compression along the spatial dimension, and changing each two-dimensional feature channel into a real number, wherein the real number represents the global distribution of response on the feature channel.
3.2.2, performing an excitation operation, and generating a weight for each feature channel through a parameter w, wherein the parameter w is learned to model the correlation among the feature channels explicitly.
And 3.2.3, performing recalibration operation, taking the weight output by the excitation operation as the importance of each feature channel after feature selection, and then weighting the feature channel by channel to the previous feature through multiplication to finish recalibration on the original feature in the channel dimension.
3.3, embedding the constructed SE module into the CNN neural network to obtain the SE-CNN neural network.
And step four, establishing the BRNN network.
And 3.1, superposing the two RNN networks up and down to obtain the BRNN network, wherein the BRNN network comprises an input layer, a forward hidden layer, a backward hidden layer and an output layer. For each time t, the signal input to the BRNN network will be simultaneously input to two RNN networks in opposite directions, and the output signal of the BRNN network is determined by both RNN networks.
3.2, setting six weights of an input layer, a forward hidden layer, a backward hidden layer and an output layer of the BRNN network respectively, wherein the six weights are respectively as follows: input layer to forward hidden layer, backward hidden layer (w 1, w 3), hidden layer to hidden layer (w 2, w 5), forward hidden layer, backward hidden layer to output layer (w 4, w 6).
And 3.3, taking the average absolute error as an objective function, minimizing the objective function by adopting an RAdam adaptive optimizer, and then setting the step length and the iteration number of the BRNN network.
And step five, constructing and training an estimation model.
And 5.1, using the SE-CNN network constructed in the third step for estimating the SOH of the lithium ion battery, setting an objective function and an optimization algorithm for training, and learning network parameters.
And 5.2, using the BRNN constructed in the fourth step for estimating the SOC of the lithium ion battery, setting an objective function and an optimization algorithm for training, and learning network parameters.
And sixthly, carrying out combined estimation on the SOC and the SOH of the lithium ion battery.
6.1, carrying out full process quantity of voltage, current and temperature and electric quantity based on current integration, which are obtained in the process of fully charging the lithium ion battery with constant current and constant voltage
Input training in step 5.1And outputting the SOH value of the lithium ion battery in the current state by the subsequent SE-CNN network.
6.2, obtaining the SOH estimated value of the lithium ion battery at the time t and the SOH estimated value from the time t to the time t from the output of the 6.1 0 Voltage, current, temperature measurements and electrical quantities based on current integration over a period of time
And (4) inputting the BRNN network trained in the step 5.2, and outputting to obtain the SOC estimated value of the lithium ion battery at the time t.
The invention has the following beneficial effects:
1. the deep learning belongs to end-to-end learning, and a result can be obtained after data is input, so that the method is convenient and quick, and the calculation speed is increased.
2. The deep learning can be used for learning the rule by optimizing the loss function without manual design of a user.
3. Potential features of data are mined, a software algorithm is improved, and estimation accuracy of the SOC is effectively improved.
Drawings
FIG. 1 is a block diagram of a SE-CNN network in the method of the present invention;
FIG. 2 is a block diagram of a BRNN network in accordance with the method of the present invention;
FIG. 3 is a diagram of a combined SOC and SOH estimation model of a lithium ion battery;
Detailed Description
The invention is further explained below with reference to the drawings;
step one, collecting battery data;
1.1, performing a charge-discharge experiment on the lithium ion battery to obtain training data required by a lithium ion battery SOC and SOH combined estimation model, wherein the training data comprises voltage, current, temperature and electric quantity based on current integral of the lithium ion battery;
1.2, generating data samples by utilizing a countermeasure network to expand a sample data set; the confrontation network consists of a generation network G and a discrimination network D; the method of generating a data sample comprises the steps of:
1.2.1, inputting random noise z into a generating network G, and generating network G output result data G (z);
1.2.2, inputting result data G (z) of the generation network G into the discrimination network D, comparing the input data G (z) with real battery data x by the discrimination network D through an objective function loss of the countermeasure network to obtain the accuracy of the result data G (z), wherein the expression of the objective function loss is as follows:
wherein the content of the first and second substances,
and
to a desired value, maximize
Represents that D (x) approaches 1; minimization
Represents that D (G (z)) approaches 0; d (x) represents an error between the true data calculated by the discrimination network D and the result data G (z) of the generated network G, and D (G (z)) represents a probability that the result data G (z) determined by the discrimination network D is true;
1.2.3, when the target function loss reaches the optimal solution, taking data G (z) output by a generating network G as real battery data and actually measured real battery data x to form an enhanced data set for subsequent joint estimation of the SOC and the SOH of the battery;
step two, data preprocessing;
preprocessing the enhanced data set obtained in the first step through a gradient enhanced regression tree algorithm so as to obtain relatively smooth data, wherein the processing process comprises the following steps of:
2.1, calculating the negative gradient of the residual error:
wherein x is i Representing an input vector, y i Denotes the target vector, F (x) i ) Representing a model function mapping x to y, setting an initial value F of the model function 0 (x) = c, c is a constant; l (y) i ,F(x i ) Represents a loss function, m represents the number of iterations;
2.2 Using training set
Training the mth regression Tree T m ；
2.3 computing regression Tree T m The weight factor of (c):
2.4, repeating the steps from 2.1 to 2.3M times;
2.5, updating the gradient enhancement regression tree model:
F(x i )＝F 0 (x)+υγ m T m (x i ) (4)
the upscaling coefficient is a set contraction coefficient and is used for limiting the lifting effect of each tree on the algorithm precision, so that better model performance is obtained and overfitting can be prevented;
and step three, building an SE-CNN neural network.
And 3.1, constructing the CNN neural network.
3.1.1, constructing a CNN neural network comprising an input layer, four convolutional layers, four pooling layers, a Flatten layer, a full-link layer and an output layer.
3.1.2, introduction of an inclusion structure comprising 4 basic structures, namely 1 × 1 convolution, 3 × 3 convolution, 5 × 5 convolution and 3 × 3 max pooling; 1 × 1 convolution is added before the 3 × 3 convolution and the 5 × 5 convolution structure, and 1 × 1 convolution is added after the 3 × 3 maximum pooling to reduce the parameter quantity, so that the problem of low efficiency caused by overlarge calculated quantity of the model due to overlarge parameter quantity is avoided; combining the operation results of the 4 basic structures added with the convolution on the channel;
3.1.3, setting the iteration times of the CNN neural network by taking the average absolute error as a target function and adopting an RAdam adaptive optimizer to minimize the target function;
3.2, constructing an SE module, wherein the SE module comprises the following operations:
and 3.2.1, performing compression operation, performing feature compression along the spatial dimension, and changing each two-dimensional feature channel into a real number, wherein the real number represents the global distribution of response on the feature channel.
3.2.2, performing an excitation operation to generate a weight for each eigen-channel by a parameter w, wherein the parameter w is learned to explicitly model the correlation between eigen-channels.
And 3.2.3, performing recalibration operation, taking the weight output by the excitation operation as the importance of each feature channel after feature selection, and then weighting the feature channel by channel to the previous feature through multiplication to finish recalibration on the original feature in the channel dimension.
3.3, embedding the constructed SE module into the CNN neural network to obtain the SE-CNN neural network, as shown in figure 1.
And step four, establishing the BRNN network.
And 3.1, superposing the two RNN networks up and down to obtain the BRNN, wherein the BRNN comprises an input layer, a forward hidden layer, a backward hidden layer and an output layer, as shown in figure 2. For each time t, the signal input to the BRNN network will be simultaneously input to two RNN networks in opposite directions, and the output signal of the BRNN network is determined by both RNN networks.
3.2, setting six weights of an input layer, a forward hidden layer, a backward hidden layer and an output layer of the BRNN network respectively, wherein the six weights are respectively as follows: input layer to forward hidden layer, backward hidden layer (w 1, w 3), hidden layer to hidden layer (w 2, w 5), forward hidden layer, backward hidden layer to output layer (w 4, w 6).
And 3.3, taking the average absolute error as a target function, minimizing the target function by adopting an RAdam adaptive optimizer, and then setting the step length and the iteration number of the BRNN network.
And step five, constructing and training an estimation model.
And 5.1, using the SE-CNN network constructed in the third step for estimating the SOH of the lithium ion battery, setting an objective function and an optimization algorithm for training, and learning network parameters.
And 5.2, using the BRNN constructed in the fourth step for estimating the SOC of the lithium ion battery, setting an objective function and an optimization algorithm for training, and learning network parameters.
And sixthly, performing combined estimation on the SOC and the SOH of the lithium ion battery as shown in the figure 3.
6.1, carrying out full process quantity of voltage, current and temperature and electric quantity based on current integration, which are obtained in the process of fully charging the lithium ion battery with constant current and constant voltage
And (4) inputting the SE-CNN network trained in the step 5.1, and outputting to obtain the SOH value of the lithium ion battery in the current state.
6.2, obtaining the SOH estimated value of the lithium ion battery at the time t and the SOH estimated value from the time t to the time t from the output of the 6.1 0 Voltage, current, temperature measurements and electrical quantities based on current integration over a period of time
And inputting the BRNN network trained in the step 5.2, and outputting to obtain the SOC estimated value of the lithium ion battery at the time t, wherein compared with the prior art, the SOC estimated precision of the lithium ion battery obtained by the embodiment is improved by 5%.
Claims (1)
1. A battery SOC and SOH joint estimation method based on deep learning is characterized in that: the method specifically comprises the following steps:
step one, collecting battery data;
1.1, performing a charge-discharge experiment on the lithium ion battery to obtain training data required by a lithium ion battery SOC and SOH combined estimation model, wherein the training data comprises voltage, current, temperature and electric quantity based on current integral of the lithium ion battery;
1.2, generating data samples by utilizing a countermeasure network to expand a sample data set; the countermeasure network consists of a generation network G and a discrimination network D; the method of generating a data sample comprises the steps of:
1.2.1, inputting random noise z into a generating network G, and generating network G output result data G (z);
1.2.2, inputting result data G (z) of the generation network G into the discrimination network D, comparing the input data G (z) with real battery data x by the discrimination network D through an objective function loss of the countermeasure network to obtain the accuracy of the result data G (z), wherein the expression of the objective function loss is as follows:
wherein, the first and the second end of the pipe are connected with each other,
and
to a desired value, maximize
Means that D (x) approaches 1; minimization
Represents that D (G (z)) approaches 0; d (x) represents an error between the real data calculated by the discrimination network D and the result data G (z) of the generated network G, and D (G (z)) represents a probability that the result data G (z) judged by the discrimination network D is real;
1.2.3, when the target function loss reaches the optimal solution, taking data G (z) output by a generated network G as real battery data and actually measured real battery data x to form an enhanced data set;
step two, data preprocessing;
preprocessing the enhanced data set obtained in the first step through a gradient enhanced regression tree algorithm, wherein the processing process comprises the following steps:
2.1, calculating the negative gradient of the residual error:
wherein x is i Representing the input vector, y i Denotes the target vector, F (x) i ) Representing a model function with x mapped to y, setting an initial value F of the model function 0 (x) = c, c is a constant; l (y) i ,F(x i ) Represents a loss function, m represents the number of iterations;
2.2 Using training set
Training the mth regression tree T m ；
2.3 computing regression Tree T m The weight factor of (c):
2.4, repeating the steps from 2.1 to 2.3M times;
2.5, updating the gradient enhancement regression tree model:
F(x i )＝F 0 (x)+υγ m T m (x i ) (4)
wherein upsilon is a set shrinkage coefficient;
step three, building an SE-CNN neural network;
3.1, constructing a CNN neural network;
3.1.1, constructing a CNN neural network comprising an input layer, four convolution layers, four pooling layers, a Flatten layer, a full-connection layer and an output layer;
3.1.2, introduction of an inclusion structure comprising 4 basic structures, namely 1 × 1 convolution, 3 × 3 convolution, 5 × 5 convolution and 3 × 3 max pooling; adding 1 × 1 convolution before the 3 × 3 convolution and 5 × 5 convolution structure, and adding 1 × 1 convolution after the 3 × 3 maximum pooling to reduce the parameter number; combining the operation results of the 4 basic structures added with the convolution on the channel;
3.1.3, setting the iteration times of the CNN neural network by taking the average absolute error as a target function and adopting an RAdam adaptive optimizer to minimize the target function;
3.2, constructing an SE module, wherein the SE module comprises the following operations:
3.2.1, performing compression operation, performing feature compression along the spatial dimension, and changing each two-dimensional feature channel into a real number, wherein the real number represents the global distribution of response on the feature channel;
3.2.2, performing an excitation operation, and generating a weight for each characteristic channel through a parameter w, wherein the parameter w is learned to be used for explicitly modeling the correlation among the characteristic channels;
3.2.3, performing recalibration operation, taking the weight output by the excitation operation as the importance of each feature channel after feature selection, and then weighting the feature channel by channel through multiplication to the previous feature to finish recalibration on the original feature in the channel dimension;
3.3, embedding the constructed SE module into the CNN neural network to obtain the SE-CNN neural network;
step four, building a BRNN network;
4.1, superposing the two RNN networks up and down to obtain a BRNN network, wherein the BRNN network comprises an input layer, a forward hidden layer, a backward hidden layer and an output layer; for each time t, signals input into the BRNN network are simultaneously input into two RNN networks with opposite directions, and output signals of the BRNN network are jointly determined by the two RNN networks;
4.2, setting six weights of an input layer, a forward hidden layer, a backward hidden layer and an output layer of the BRNN network respectively, wherein the six weights are respectively as follows: w1 and w3 from the input layer to the forward hidden layer and the backward hidden layer, w2 and w5 from the hidden layer to the hidden layer, and w4 and w6 from the forward hidden layer and the backward hidden layer to the output layer;
4.3, taking the average absolute error as a target function, adopting an RAdam adaptive optimizer to minimize the target function, and then setting the step length and the iteration times of the BRNN network;
constructing and training an estimation model;
5.1, the SE-CNN network constructed in the third step is used for estimating the SOH of the lithium ion battery, an objective function and an optimization algorithm are set for training, and network parameters are learned;
5.2, the BRNN network constructed in the fourth step is used for estimating the SOC of the lithium ion battery, an objective function and an optimization algorithm are set for training, and network parameters are learned;
step six, carrying out combined estimation on the SOC and the SOH of the lithium ion battery;
6.1, carrying out full process quantity of voltage, current and temperature and electric quantity based on current integration, which are obtained in the process of fully charging the lithium ion battery with constant current and constant voltage
Inputting the SE-CNN network trained in the step 5.1, and outputting to obtain the SOH value of the lithium ion battery in the current state;
6.2, obtaining the SOH estimated value of the lithium ion battery at the time t and the SOH estimated value from the time t to the time t from the output of the 6.1 0 Voltage, current, temperature measurements and electrical quantities based on current integration over a period of time
SOH t Inputting the BRNN network trained in the step 5.2, and outputting to obtain the SOC estimated value of the lithium ion battery at the time t.
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| CN113391225B (en) * | 2021-05-19 | 2022-06-03 | 北京航空航天大学 | Lithium battery state-of-charge estimation method considering capacity degradation |
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| CN113884905B (en) * | 2021-11-02 | 2022-06-14 | 山东大学 | Power battery state of charge estimation method and system based on deep learning |
| CN114236410B (en) * | 2021-11-24 | 2022-06-24 | 广东工业大学 | Lithium battery SOH estimation method based on novel generation type antagonistic neural network |
| CN114114052A (en) * | 2021-11-25 | 2022-03-01 | 福州大学 | Method for rapidly estimating SOH and SOC of battery based on shock response characteristic |
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