CN112858918A - Power lithium ion battery health state online estimation strategy based on optimization multi-factor - Google Patents

Power lithium ion battery health state online estimation strategy based on optimization multi-factor Download PDF

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CN112858918A
CN112858918A CN202110059118.1A CN202110059118A CN112858918A CN 112858918 A CN112858918 A CN 112858918A CN 202110059118 A CN202110059118 A CN 202110059118A CN 112858918 A CN112858918 A CN 112858918A
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battery
factor
charging
health state
estimation
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夏向阳
邓子豪
张嘉诚
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Changsha University of Science and Technology
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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]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • 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]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • 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 relates to an optimized multi-factor-based power lithium ion battery health state online estimation strategy, which solves the problem that the health state online estimation precision of the conventional electric vehicle power battery is low in the charging process. The method mainly comprises the following steps: 1: setting constraint conditions under different factors, dynamically searching and optimizing a charging voltage segment U by using a genetic algorithm with minimum estimation error as an objective functionA ’‑UB ’(ii) a 2: in optimizing the charging section UA ’‑UB ’Based on the above, the influence of the three factors of the charging electric quantity, the charging time and the battery internal resistance on the estimation of the battery health state is normalized, and the health state H corresponding to each factor is respectively obtainedi1、Hi2、Hi3. 3: optimizing weight coefficient based on least square method and calculating comprehensive health factor Hi。

Description

Power lithium ion battery health state online estimation strategy based on optimization multi-factor
Technical Field
The invention relates to an optimization multi-factor based power lithium ion battery health state online estimation strategy, and belongs to the technical field of electric power.
Background
Accurate assessment of the state of health of a power lithium ion battery is critical to ensuring the safety and reliability of the battery. However, in the actual use of the electric automobile, the actual working conditions are complex, the discharging process is greatly influenced by user behaviors, the uncertainty is prominent, and the accurate evaluation of the health state of the battery is not facilitated. But the charging phase is controllable and the charging voltage and current data is preserved, so the estimation of the state of health of the battery during the charging process is more reliable.
The existing estimation method is generally divided into two types, namely a data driving type and an equivalent circuit type, wherein the data driving type is based on an empirical model, an Arrhenius capacity aging model and the like, and the estimation precision is not high; the estimation method based on the battery equivalent circuit model mostly uses the ohmic internal resistance as a key technical index for estimating the battery health state according to the approximate linear relation between the ohmic internal resistance and the battery health state, but the situations of parameter mismatching and large estimation error are easy to occur in the long-term health estimation of the power battery. Furthermore, while parameters such as polarization capacitance and polarization impedance may help improve the accuracy of the estimation of battery health, accurate on-board measurement of such parameters requires additional experimentation under specific conditions, which is difficult to achieve in the practical deployment of electric vehicles, and which may cause unnecessary additional damage to the battery.
Disclosure of Invention
In order to improve the estimation precision of the charging process of the power battery of the electric automobile, the invention provides an optimized multi-factor-based online estimation strategy for the health state of the power lithium ion battery.
The method comprises the following basic steps:
step S1: in the actual use process of the electric automobile, the situation of full charge and full discharge rarely occurs during charging, the actual battery charging range of a user is considered, and a proper voltage segment, 85% UNThe corresponding SOC is about 30%, 95% UNThe corresponding SOC is about 90%, considering the practicality of the proposed strategy, therefore the boundary of the preset optimized voltage is set to 85% UN～95％UN。
The estimation accuracy of the internal resistance of the battery under different open-circuit voltage segments is considered to be different, the impedance characteristic change of the battery in the voltage segment corresponding to the SOC of 60% -80% is relatively smooth, the error of the internal resistance estimation is relatively small, the estimation accuracy of the health state of the battery can be improved, the internal chemical reaction of the battery under other voltage segments is more severe, the impedance characteristic change is more unstable, and the estimation of the health state of the battery is also inaccurate.
In addition, if the charging voltage segment is too narrow, the corresponding charged electric quantity is too small, and the robustness of the actual capacity of the battery estimated by the factor is not high; if the charging voltage segment range is too wide, the factor is not well suited for actual vehicle conditions. In summary, the boundary conditions for setting the charging voltage are as follows:
Figure BDA0002900109320000021
after the voltage boundary conditions are determined, the optimal charging segment is found by a genetic algorithm. Firstly, sequencing battery serial numbers i of 1,2,3, n in a training set, wherein the actual capacity of each battery in the k-th charge-discharge cycle is C1k,C2k,...,CnkThe charging voltage segment corresponds to a charging capacity of
Figure BDA0002900109320000022
Because the two have strong linear correlation, the least square method can be used for carrying out linear fitting on the two, and the fitting formula is as follows.
Figure BDA0002900109320000023
In the formula ki,biRespectively, the slope and intercept of the linear fit.
Taking the average slope and average intercept of n cells in the training set,
Figure BDA0002900109320000024
calculating an estimated battery capacity of each battery in the training set as
Figure BDA0002900109320000025
Wherein i is 1,2,3.
The root mean square error of the estimated battery capacity of i batteries at the training concentration point is calculated as
Figure BDA0002900109320000026
The objective function of the genetic algorithm is
Figure BDA0002900109320000027
Figure BDA0002900109320000028
Wherein r ispDenotes the Pearson index, rsThe Spearman index is shown and is obtained by the following formula.
Figure BDA0002900109320000031
x represents the input charging time t, y represents the battery state of health Hi2。Rp’,Rs' denotes the correlation index under the optimized voltage segment, and the closer the two indexes are to 1, the stronger the correlation between the two variables is, and the smaller the correlation is vice versa.
Step S2: charging the electric quantity in the optimized voltage segment
Figure BDA0002900109320000032
And battery capacity Ci' has strong homogeneous linear relation, and can further estimate the state of health H of the battery after calculating the battery capacity through the charge capacityi1。
Figure BDA0002900109320000033
Figure BDA0002900109320000034
Selecting a mean kernel function and a covariance kernel function based on a Gaussian regression (GPR) model trained by a data driving method, and optimizing a charging segment U after initialization settingA’-UB' corresponding charging time tiAs input, its correlation index rpAnd rsCloser to 1, the regression value, i.e. the state of health H of the battery, is obtained by regression through the Gaussian processi2The accuracy of the method is higher, a GPR model is described in detail in many documents at present, a specific regression algorithm is not described in detail, and the optimized battery state of health H is obtained by using the model directly based on the optimization parametersi2。
Because the battery impedance characteristic change in the optimized voltage segment is relatively smooth and the error of the internal resistance estimation is smaller, the corresponding internal resistance r under the optimized voltage segment can be obtained by reading the data of the vehicle-mounted BMS system0iThereby improving the estimation accuracy of the battery state of health. Calculating the State of health H of the Batteryi3As shown in the following formula.
Figure BDA0002900109320000035
In the formula: r isnewThe internal resistance of a brand new battery; r is0iIs the current internal resistance of the battery; r isoldThe internal resistance of the battery in retirement.
Therefore, the influence of the three factors of the charged electric quantity, the charging time and the internal resistance of the battery under the optimized voltage segment on the estimated battery health state is normalized, and the health state H corresponding to each factor is obtained respectivelyi1、 Hi2、Hi3。
Step S3: and calculating the comprehensive health state of the battery by optimizing the setting of the weight coefficient based on a least square method. Since the influence of each factor on the state of health of the battery has been normalized in step S2, the integrated state of health H is assumediIs an independent variable Hi1、Hi2、Hi3Is a linear function of (a).
Hi＝α1Hi1+α2Hi2+α3Hi3
Wherein Hi1For optimizing the quantity of electricity C charged in the voltage segmenti' estimated State of health of Battery, Hi2Charging time t required for optimizing voltage segmentiEstimated state of health of the battery, Hi3For optimizing the corresponding internal resistance r under the voltage segment0iAn estimated state of health of the battery. Alpha is alpha1、α2、α3Are weight coefficients. The multi-factor model that calculates the i battery health states may be expressed as
Figure BDA0002900109320000041
Is converted into a matrix type,
Figure BDA0002900109320000042
then, the vector is simplified into a vector expression,
Hii·α＝H
with the minimum estimation error as the target, the objective function is set as follows
Figure BDA0002900109320000043
Based on the principle of least square method, adding Lagrange multiplier constraint condition,
Figure BDA0002900109320000044
wherein, mujλ is lagrange multiplier, and c is penalty factor. Simplifying to obtain a multiplier iterative formula of
Figure BDA0002900109320000045
Setting the calculation precision, and then the iteration end condition is
Figure BDA0002900109320000046
Wherein epsilon is calculation precision, and when the estimation error reaches minimum, a weight coefficient alpha is obtained1、α2、α3The optimal solution of (1).
Advantageous effects
Compared with the existing method for estimating the health state of the power battery in the charging process, the method takes the minimum estimation error as a target function, sets the multi-factor constraint condition, optimizes the charging voltage segment through a genetic algorithm, improves the influence of three factors, namely the charging electric quantity, the charging time and the internal resistance of the battery, on the accurate estimation of the health state of the battery on the basis, performs normalization processing, and finally comprehensively estimates the health state of the battery through a least square method, so that the overall estimation precision can be improved. And whole estimation process just accomplishes when electric automobile charges, and the practicality is strong, and the easy popularization realizes in each electric automobile charging station.
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FIG. 1 is a flow chart of policies provided by the present invention
FIG. 2 is a schematic diagram of optimizing voltage slice
Detailed Description
For the convenience of understanding the contents of the embodiments of the present invention, the present invention will be described with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Step S1: setting voltage boundary and different factor constraint conditions, taking minimum estimation error as objective function, and dynamically optimizing charging voltage segment U through genetic algorithmA’-UB' the specific optimization procedure is shown in FIG. 2.
Setting boundary conditions
Figure BDA0002900109320000051
Determining an objective function
Figure BDA0002900109320000052
Thereby obtaining an optimized charging voltage segment UA’-UB’。
Step S2: in optimizing the charging section UA’-UBOn the basis of' the method, the influence of three factors of the charging electric quantity, the charging time and the battery internal resistance on the estimation of the battery health state is normalized, and the health state H corresponding to each factor is respectively solvedi1、Hi2、Hi3. The brief procedure is as follows.
Figure BDA0002900109320000053
Gaussian regression (GPR) model trained based on data-driven method to optimize charging segment UA’-UB' corresponding charging time tiFor input, the regression value, namely the battery state of health H, is obtained through the regression of a Gaussian processi2。
Figure BDA0002900109320000061
Step S3: suppose a comprehensive health state HiIs an independent variable Hi1、Hi2、Hi3Is a linear function of (a).
Hi＝α1Hi1+α2Hi2+α3Hi3
Conversion to matrix form
Hii·α＝H
Then based on the least squares method, the objective function is as follows
Figure BDA0002900109320000062
Adding a Lagrange multiplier constraint condition,
Figure BDA0002900109320000063
wherein, mujλ is lagrange multiplier, and c is penalty factor. Simplifying to obtain a multiplier iterative formula of
Figure BDA0002900109320000064
The iteration end condition is
Figure BDA0002900109320000065
Wherein epsilon is calculation precision, and when the estimation error reaches minimum, a weight coefficient alpha is obtained1、α2、α3The optimal solution of (1).
Finally, the weight coefficient alpha is calculated1、α2、α3Is substituted back to the following formula
Hi＝α1Hi1+α2Hi2+α3Hi3
Finally obtaining the comprehensive health state H of the power battery under the optimized charging segmenti. The overall flow chart of the optimization-based multi-factor online estimation strategy for battery state of health is shown in fig. 1.
The above description is only an example of the present invention and should not be construed as limiting the present invention, and any modifications, equivalents, improvements and the like made within the spirit of the present invention should be included in the scope of the claims of the present invention.

Claims (1)

1. An optimization multi-factor based online estimation strategy for the state of health of a power lithium ion battery is characterized by comprising the following steps:
step S1: setting voltage boundaries and different factor constraints with the aim of minimum estimation errorCalibration function, dynamically searching for optimized charging voltage segment U by genetic algorithmA’-UB’；
Step S2: in optimizing the charging section UA’-UBOn the basis of' the method, the influence of three factors of the charging electric quantity, the charging time and the battery internal resistance on the estimation of the battery health state is normalized, and the health state H corresponding to each factor is respectively solvedi1、Hi2、Hi3。
Step S3: optimizing weight coefficient based on least square method and calculating comprehensive health factor Hi。
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113820615A (en) * 2021-09-30 2021-12-21 国网福建省电力有限公司龙岩供电公司 Battery health degree detection method and device
CN114236413A (en) * 2021-12-15 2022-03-25 湖北德普电气股份有限公司 Method for evaluating health state of power battery of electric vehicle
WO2023169134A1 (en) * 2022-03-07 2023-09-14 宁德时代新能源科技股份有限公司 Battery soh value calculation model generation method, battery soh value calculation method, apparatus, and system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101459925B1 (en) * 2013-07-05 2014-11-07 현대자동차주식회사 Control method of Low DC/DC Converter for electric vehicle, and Low DC/DC Converter control system using the same
CN107024664A (en) * 2017-04-01 2017-08-08 湖南银杏数据科技有限公司 Lithium battery residual life fast prediction method based on constant-current charge fragment
CN107121643A (en) * 2017-07-11 2017-09-01 山东大学 Health state of lithium ion battery combined estimation method
CN107329094A (en) * 2017-08-23 2017-11-07 北京新能源汽车股份有限公司 Electrokinetic cell health status evaluation method and device
CN107831444A (en) * 2017-10-26 2018-03-23 哈尔滨工业大学 A kind of health state of lithium ion battery method of estimation
KR20200122628A (en) * 2019-04-18 2020-10-28 현대모비스 주식회사 Method for managing battery for vehicle and apparatus for the same

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101459925B1 (en) * 2013-07-05 2014-11-07 현대자동차주식회사 Control method of Low DC/DC Converter for electric vehicle, and Low DC/DC Converter control system using the same
CN107024664A (en) * 2017-04-01 2017-08-08 湖南银杏数据科技有限公司 Lithium battery residual life fast prediction method based on constant-current charge fragment
CN107121643A (en) * 2017-07-11 2017-09-01 山东大学 Health state of lithium ion battery combined estimation method
CN107329094A (en) * 2017-08-23 2017-11-07 北京新能源汽车股份有限公司 Electrokinetic cell health status evaluation method and device
CN107831444A (en) * 2017-10-26 2018-03-23 哈尔滨工业大学 A kind of health state of lithium ion battery method of estimation
KR20200122628A (en) * 2019-04-18 2020-10-28 현대모비스 주식회사 Method for managing battery for vehicle and apparatus for the same

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113820615A (en) * 2021-09-30 2021-12-21 国网福建省电力有限公司龙岩供电公司 Battery health degree detection method and device
CN114236413A (en) * 2021-12-15 2022-03-25 湖北德普电气股份有限公司 Method for evaluating health state of power battery of electric vehicle
WO2023169134A1 (en) * 2022-03-07 2023-09-14 宁德时代新能源科技股份有限公司 Battery soh value calculation model generation method, battery soh value calculation method, apparatus, and system

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