CN116243172A - Method for rapidly detecting reliability state of lithium battery - Google Patents

Method for rapidly detecting reliability state of lithium battery Download PDF

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CN116243172A
CN116243172A CN202211651017.4A CN202211651017A CN116243172A CN 116243172 A CN116243172 A CN 116243172A CN 202211651017 A CN202211651017 A CN 202211651017A CN 116243172 A CN116243172 A CN 116243172A
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battery
lithium battery
voltage
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孙权
冯静
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Hunan Gingko Reliability Technology Research Institute Co ltd
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    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
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    • 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
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Abstract

The invention discloses a method for rapidly detecting the reliability state of a lithium battery, which comprises the following steps: s1, acquiring a lithium battery charge-discharge curve through various charge-discharge modes, and defining performance characteristic parameters based on the charge-discharge curve; s2, analyzing the correlation between the performance characteristic parameters and the SOH of the battery to obtain correlation coefficients, sequencing the correlation coefficients, and obtaining key performance characteristics based on sequencing; s3, modeling the SOH of the battery health state based on the key performance characteristics, and reducing the dimension of the performance characteristic parameters based on a principal component analysis method to obtain comprehensive performance characteristics; and S4, defining an index for judging the health state of the lithium battery, and judging the health state of the lithium battery.

Description

Method for rapidly detecting reliability state of lithium battery
Technical Field
The invention relates to the technical field of lithium battery detection, in particular to a method for rapidly detecting the reliability state of a lithium battery.
Background
The reliability state of the lithium battery is evaluated, and most of the reliability state is judged through the healthy capacity of the battery, and the main judging index of the healthy state of the battery capacity is SOH, and the reliability state is generally defined through the number of charge and discharge cycles or the internal resistance. SOH is usually evaluated under standard laboratory conditions and cannot be accurately evaluated under normal use environments or online conditions. There are several classes of methods to assess the state of aging of a battery, enabling indirect assessment of SOH. The first type of method directly measures quantitative indexes capable of indicating the aging degree of the battery, such as the battery capacity and the battery internal resistance, and the test methods are simple, but the test needs to be off-line and cannot be evaluated on line in real time. The second type of method takes a quantization index representing the aging degree of the battery as a model parameter, and acquires the aging state of the closed-loop parameter by means of parameter estimation. Common approaches mainly include designing dual observers based on battery equivalent circuit models or electrochemical models. Specific typical methods include voltage curve fitting, dual extended Kalman filtering, dual unscented Kalman filtering, dual sliding mode filtering, and the like. However, these methods are less versatile and some methods are difficult to engineer. The third type of method is based on an open-loop measurement method of "empirical/semi-empirical formula aging model", and calculates the capacity fade of the battery based on the open-loop accumulation of recorded time and temperature. However, the method has poor universality, and the established model for estimating SOH is only suitable for the lithium ion power battery of the model. The fourth type of method is based on a method of a cycle number mapping model, firstly, a functional relation between cycle numbers and ageing quantization indexes is constructed, and then, a parameter estimation method is utilized to estimate the health degree of the battery and predict the probability density distribution of the residual service life. Such methods require a large amount of test data, including data for a new battery, to obtain a model. The fifth type of method utilizes a data driving model to extract the mapping relation between the aging characteristics and the quantization indexes, and further infers the aging degree and the residual life of the battery according to the prediction of the data driving model, and the method is model-independent and parameter-free. The disadvantage is that a large amount of reference data is required for training, and estimation errors are greatly affected by the training data and the training method.
In the disclosed patent, CN106423919B calculates a composite score by measuring parameters of open circuit voltage, charging time, terminal voltage, etc. of the battery during charging and standing, and classifies the SOH determination of the battery according to the score range. However, the range and standard of the comprehensive score calculation are not described in the examples, and the practicability is not high. CN110752410a uses the balanced lithium ion battery to perform series constant current charging, removes internal resistance interference from the voltage at the end of constant current charging, combines with the capacity as the input of a support vector regression model (SVR) based on a Particle Swarm Optimization (PSO) to perform model training, and establishes a rapid SOH discrimination and sorting model. On the basis of obtaining the battery capacity and internal resistance parameters, a K-means method (K-means) with a weight delta is adopted to conduct clustering recombination on the lithium ion battery, and the voltage consistency of the battery pack after the K-means clustering recombination with different weights delta is evaluated, so that sorting recombination under different application scenes is achieved. The method avoids testing parameters of single batteries one by one, improves sorting speed, and is suitable for quick sorting of large-batch retired lithium batteries. However, this method requires equalization and standard capacity testing of a plurality of batteries, which is time-consuming and not applicable to batteries of different specifications.
These methods and examples are described above in order to establish a mapping between measurable quantitative indicators and non-measurable aging characteristics to estimate and predict the state of health of the battery. However, the first and third methods need to test under specific working conditions, are relatively suitable for testing and calibrating in a laboratory, and are difficult to put into engineering application for measurement in an online closed-loop state; the accuracy and stability of the observer of the second class of methods are affected by the initial value; the fourth and fifth methods are well able to predict battery life, but do not explicitly account for dynamic changes in charge-discharge cycles. Moreover, various existing evaluation methods focus on considering cycle life of the battery and evaluate the state of health of the battery with SOH; data from a single discharge or single charge-discharge cycle is often used to estimate the charge of the cycle, underutilization of other performance parameters or performance characteristics of the battery.
Therefore, how to define dynamic index parameters to quickly determine the reliability state of the battery, avoiding measurement errors caused by the interference of precision of the equipment used in methods such as internal resistance measurement and the like, and shortening the test time are problems to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a method for rapidly detecting the reliability state of a lithium battery, which fully utilizes battery usage data, extracts characteristic parameters highly related to the state of health (SOH) of the battery, and adopts a dimension reduction processing method, so that the obtained index can rapidly reflect the comprehensive performance of the SOH of the battery, and the reliability evaluation is realized through the comprehensive performance. The method has the advantages of short test time, simple algorithm and strong engineering practicability.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a method for rapidly detecting the reliability state of a lithium battery comprises the following steps:
s1, acquiring a lithium battery charge-discharge curve through various charge-discharge modes, and defining performance characteristic parameters based on the charge-discharge curve;
s2, analyzing the correlation between the performance characteristic parameters and the SOH of the battery to obtain correlation coefficients, sequencing the correlation coefficients, and obtaining key performance characteristics based on sequencing;
s3, modeling the SOH of the battery health state based on the key performance characteristics, and reducing the dimension of the performance characteristic parameters based on a principal component analysis method to obtain comprehensive performance characteristics;
and S4, defining an index for judging the health state of the lithium battery, and judging the health state of the lithium battery.
Preferably, in the step S1, the charge-discharge mode includes constant current and constant voltage, HPPC, and constant voltage of a transformer, the performance characteristic parameter includes physical characteristic parameter and mathematical characteristic parameter, the physical characteristic parameter includes capacity, ohmic internal resistance, polarized voltage, and polarized capacitance, and the mathematical characteristic parameter includes voltage rising rate, voltage rising time, charging time, voltage falling rate, voltage drop time, and discharging time.
Preferably, the step S3 specifically includes:
the key performance characteristics are standardized to be used,
Figure BDA0004010622480000041
wherein X represents normalized parameter data, d is parameter data, mu is average value, sigma is standard deviation, and the corresponding parameter matrix after normalization processing is X= { X 1 ,x 2 ,…,x 7 }，x 1 ,x 2 ,…,x 7 The SOH, the discharge voltage drop rate, the power average value, the discharge end voltage difference, the discharge voltage kurtosis, the discharge voltage skewness and the power effective value are respectively corresponding.
Preferably, the step S4 specifically includes:
judging the health state of the lithium battery:
Figure BDA0004010622480000042
wherein Y is 1 Is the first principal component of the current state obtained by seven parameters, Y 1New Is the first main component obtained by seven parameters when the battery leaves the factory, Y 1EOL Is the first principal component obtained by seven parameters at the time of battery end life.
Compared with the prior art, the invention discloses a method for rapidly detecting the reliability state of the lithium battery, which fully utilizes battery use data, extracts characteristic parameters highly related to the state of health (SOH) of the battery, and adopts a dimension reduction processing method, so that the obtained index can rapidly reflect the comprehensive performance of the SOH of the battery, and the reliability evaluation is realized through the comprehensive performance. The method has the advantages of short test time, simple algorithm and strong engineering practicability.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a detection flow structure provided by the present invention.
Fig. 2 is a schematic diagram of a charge-discharge voltage-current curve of a battery according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a method for rapidly detecting the reliability state of a lithium battery, which comprises the following steps:
s1, acquiring a lithium battery charge-discharge curve through various charge-discharge modes, and defining performance characteristic parameters based on the charge-discharge curve;
s2, analyzing the correlation between the performance characteristic parameter and the SOH of the battery, wherein a method for analyzing the correlation comprises, but is not limited to, solving a linear correlation coefficient. The linear correlation coefficients of the series X and the series Y are according to the formula
Figure BDA0004010622480000061
Solving (x) i And y i The i-th element in X and Y respectively, and (2)>
Figure BDA0004010622480000062
And->
Figure BDA0004010622480000063
Respectively, the average value of X and Y, and n is the number of elements in the array), obtaining a correlation coefficient, sorting the correlation coefficient, and obtaining key performance characteristics based on sorting;
s3, modeling the SOH of the battery health state based on the key performance characteristics, and reducing the dimension of the performance characteristic parameters based on a principal component analysis method to obtain comprehensive performance characteristics;
s4, defining an index for judging the health state of the lithium battery, and basically performing the following steps of:
(1) M pieces of n-dimensional data are set;
(2) Integrating the data into an n x m matrix;
(3) Subtracting the average value of each line from each element of each line of the matrix, namely performing zero-mean treatment to obtain a matrix X;
(4) Using the matrix X after zero-mean processing to obtain covariance matrix
Figure BDA0004010622480000064
(5) Obtaining the eigenvalue and corresponding eigenvector of covariance matrix C;
(6) According to the size of the characteristic value, arranging corresponding characteristic vectors in sequence according to rows to form a matrix, and taking the first k rows to form a matrix P;
(7) The matrix P is used for dimension reduction, and the matrix y=px after dimension reduction. .
In order to further optimize the above technical solution, in step S1, the charge-discharge mode includes constant current and constant voltage, HPPC, and constant voltage of the current transformer, the performance characteristic parameters include physical characteristic parameters and mathematical characteristic parameters, and the physical characteristic parameters include capacity, ohmic internal resistance, polarized voltage, polarized capacitance, and the physical characteristic parameters need to be measured by a testing method such as an external internal resistance tester, an unbalanced bridge circuit, and the like. The mathematical characteristic parameters include a charging process voltage rise rate, a voltage rise time, a charging time, a discharging process voltage fall rate, a voltage drop time, a discharging time, and the like. The charge time and the discharge time are obtained by timing. The charging process voltage rising rate and the discharging process voltage falling rate are obtained by calculating the first derivative of the charging and discharging voltage to the charging and discharging time. The voltage rise time and the voltage drop time refer to a charge time or a discharge time that elapses from a specified start voltage to a specified end voltage. The maximum value, the minimum value, the average value, the effective value, the rectification average value, the peak-to-peak value, the kurtosis, the skewness, the peak factor, the pulse factor, the waveform factor, the margin factor and the like of the voltage belong to common statistical indexes, and the range of each statistics is the voltage value data in the time range of one charge (discharge).
In order to further optimize the above technical solution, step S3 specifically includes:
the key performance characteristics are standardized to be used,
Figure BDA0004010622480000071
wherein X represents normalized parameter data, d is parameter data, mu is average value, sigma is standard deviation, and the corresponding parameter matrix after normalization processing is X= { X 1 ,x 2 ,…,x 7 }，x 1 ,x 2 ,…,x 7 The SOH, the discharge voltage drop rate, the power average value, the discharge end voltage difference, the discharge voltage kurtosis, the discharge voltage skewness and the power effective value are respectively corresponding.
In order to further optimize the above technical solution, step S4 specifically includes:
judging the health state of the lithium battery:
Figure BDA0004010622480000081
wherein Y is 1 Is the first principal component of the current state obtained by seven parameters, Y 1New Is the first main component obtained by seven parameters when the battery leaves the factory, Y 1EOL Is the first principal component obtained by seven parameters at the time of battery end life.
The example battery performs a charge-discharge test on 10 groups of batteries according to the charge-discharge single-step setting of 7.1.1 and 7.1.2 step cycle in GB/T34015-2017, and based on the voltage-current curve record, parameter data such as SOH, voltage drop in the discharge process, change rate of discharge power and the like are counted as shown in figure 1. Besides, there are time domain parameters of battery voltage and power including maximum, minimum, average, effective, rectified average, peak-to-peak, kurtosis, skewness, peak factor, pulse factor, waveform factor, margin factor, etc.
Each parameter was analyzed for linear correlation with SOH. Taking into account certain parameter definitionsThe same term is included and so the correlation coefficient is very high, and the selection of these parameter combinations has information duplication and overlap. Therefore, a parameter combination is selected which does not include the same term and has a high correlation coefficient. Finally, the parameters with higher linear correlation coefficient are obtained as follows: discharge voltage drop rate x 1 Rate of change of power x 2 Mean value x of discharge power 3 Voltage difference x of discharge end 4 Kurtosis of discharge voltage x 5 Degree of discharge voltage deviation x 6 Effective power value x 7 . These seven parameters are taken as key performance parameters.
And reducing the dimension of the key performance parameters to obtain the comprehensive performance parameters. The dimension reduction is performed by Principal Component Analysis (PCA), and the ratio of the first two principal components obtained exceeds 85%, so that the two principal components are used as comprehensive performance parameters for rapidly reflecting SOH.
First main component Y 1 The relation with each parameter is as follows:
Y 1 ＝0.3628x 1 -0.3478x 2 +0.3979x 3 -0.3131x 4 +0.4264x 5 -0.4140x 6 +0.3713x 7
second main component Y 2 The relation with each parameter is as follows:
Y 2 ＝-0.2346x 1 +0.4788x 2 +0.3574x 3 +0.5572x 4 +0.0462x 5 -0.2049x 6 +0.4830x 7
each main component contains information of original seven parameters of the corresponding battery, so that the main component can be regarded as a comprehensive parameter of original multiple performance parameters. The principal component analysis is used for comprehensive evaluation, and the mathematical statistics community generally advocates that the first principal component is adopted, because the first principal component is the "most comprehensive ability", is the "size factor", and the other principal components are only the "form factors". Therefore, the first main component is used to define an index for rapidly judging the health state of the battery, and the index is shown as the following formula:
Figure BDA0004010622480000091
wherein Y is 1 Is the first principal component of the current state obtained by seven parameters, Y 1New Is the first main component obtained by seven parameters when the battery leaves the factory, Y 1EOL Is the first principal component obtained by seven parameters at the time of battery end life. When the new battery and the scrapped battery are measured to obtain corresponding values, the indexes calculated in the formula 5 can be used for judging the health state; when the data of the new battery and the scrapped battery are lacking, the value of the first main component can be used for judging the health state. The higher the principal component value, the better the reliability state of the battery.
The calculated first principal components of the example 10-pack battery are shown in table 1. Since the main component contains a plurality of parameters, which have a correlation with SOH, the change of the main component can reflect the change trend of SOH. The main component value of the same battery becomes smaller, indicating that both the state of health and the state of reliability are deteriorating. However, the numerical comparison of the main components of different batteries cannot directly explain the SOH of the two batteries due to individual differences, especially in the case of similar numerical values. According to engineering experience and measurement data, the main component value is generally above 1, SOH is generally above 92%, and the method belongs to a health usable state; below-1, SOH is typically less than 83%, which is a condition requiring or imminent recovery (the standard for power cell recovery is typically SOH below 80%); in between, belonging to a state that is available but has begun to gradually degenerate. Therefore, the reliability state of the battery can be rapidly judged by the value of the main component.
TABLE 1 Main Components of Battery
Battery numbering First main component
1 3.06
2 1.55
3 2.81
4 -3.81
5 -2.11
6 0.70
7 -2.70
8 0.87
9 -0.03
10 -0.33
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (4)

1. The method for rapidly detecting the reliability state of the lithium battery is characterized by comprising the following steps of:
s1, acquiring a lithium battery charge-discharge curve through various charge-discharge modes, and defining performance characteristic parameters based on the charge-discharge curve;
s2, analyzing the correlation between the performance characteristic parameters and the SOH of the battery to obtain correlation coefficients, sequencing the correlation coefficients, and obtaining key performance characteristics based on sequencing;
and S3, modeling the SOH of the battery state of health based on the key performance characteristics, and reducing the dimension of the key performance characteristic parameters based on a principal component analysis method to obtain the comprehensive performance characteristics.
And S4, defining an index for judging the health state of the lithium battery, and judging the health state of the lithium battery.
2. The method for rapidly detecting the reliability state of the lithium battery according to claim 1, wherein in the step S1, the charge-discharge mode includes constant current and constant voltage, HPPC, and constant voltage of the transformation current, the performance characteristic parameter includes physical characteristic parameter and mathematical characteristic parameter, the physical characteristic parameter includes capacity, ohmic internal resistance, polarized voltage, and polarized capacitance, and the mathematical characteristic parameter includes a charging process voltage rising rate, a voltage rising time, a charging time, a discharging process voltage falling rate, a voltage drop time, and a discharging time.
3. The method for rapidly detecting the reliability state of a lithium battery according to claim 1, wherein the step S3 specifically includes:
the key performance characteristics are standardized to be used,
Figure FDA0004010622470000011
wherein X represents normalized parameter data, d is parameter data, mu is average value, sigma is standard deviation, and the corresponding parameter matrix after normalization processing is X= { X 1 ,x 2 ,…,x 7 }，x 1 ,x 2 ,…,x 7 The SOH, the discharge voltage drop rate, the power average value, the discharge end voltage difference, the discharge voltage kurtosis, the discharge voltage skewness and the power effective value are respectively corresponding.
4. The method for rapidly detecting the reliability state of a lithium battery according to claim 1, wherein the step S4 specifically includes:
judging the health state of the lithium battery:
Figure FDA0004010622470000021
wherein Y is 1 Is the first principal component of the current state obtained by seven parameters, Y 1New Is the first main component obtained by seven parameters when the battery leaves the factory, Y 1EOL Is the first principal component obtained by seven parameters at the time of battery end life.
CN202211651017.4A 2022-12-21 2022-12-21 Method for rapidly detecting reliability state of lithium battery Pending CN116243172A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116430244A (en) * 2023-06-14 2023-07-14 聊城大学 Power battery health state estimation method based on voltage and current characteristics

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116430244A (en) * 2023-06-14 2023-07-14 聊城大学 Power battery health state estimation method based on voltage and current characteristics
CN116430244B (en) * 2023-06-14 2023-08-15 聊城大学 Power battery health state estimation method based on voltage and current characteristics

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