doi:10.16183/j.cnki.jsjtu.2021.027

Abstract

Abstract:

Aimed at the uncertainty of charging starting and ending point caused by incomplete charging and discharging in practical applications of lithium-ion battery, an estimation method of battery health based on dual charging state factors is proposed. A battery aging experiment bench is built, and eight nickel-cobalt-manganese lithium-ion batteries are subjected to aging test. Different from the traditional single state factor estimation, the average value of equal time difference current at the front end of constant voltage charging curve and the equal amplitude voltage charging time at the end of constant current charging curve are selected under different aging conditions to construct health factors. The corresponding relationship between state of charge (SOC) and open circuit voltage (OCV) of the experimental battery in different aging states is analyzed and the correctness of health factor is proved by theoretical deduction and experimental results. An improved support vector regression model with a strong generalization ability is established, and the hyperparameters of the model are optimized through the particle swarm optimization algorithm. The results show that the proposed dual-charging health factor is closely related to battery capacity aging and attenuation. The improved support vector regression model can estimate the health status in different aging states in real time, and has the ability to characterize local capacity rebound change, which can be used as an effective method for estimating the state of health of an embedded battery management system.

Key words: lithium-ion battery, state of health estimation, support vector regression, dual charging state, aging experiment

CLC Number:

TM912

LU Dihua, CHEN Ziqiang. [J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 342-352.

Figures/Tables 11

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References 20

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组合关系	Pearson 相关系数	Spearman 相关系数
H1&容量	-0.9927	-0.9913
H2&容量	0.9956	0.9926

电池编号	起始循环	优化算法	RMSE	MAPE
B5	第111次	PSO_SVR	0.0053	0.0052
B5	第111次	GA_SVR	0.0084	0.0107
B6	第111次	PSO_SVR	0.0151	0.0205
B6	第111次	GA_SVR	0.0338	0.0473
B7	第111次	PSO_SVR	0.0061	0.0062
B7	第111次	GA_SVR	0.0177	0.0206
B18	第91次	PSO_SVR	0.0106	0.0075
B18	第91次	GA_SVR	0.0142	0.0183

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