基于双充电状态的锂离子电池健康状态估计

doi:10.16183/j.cnki.jsjtu.2021.027

摘要/Abstract

摘要：

针对锂离子电池实际应用中存在不完全充放电而导致的充电起始点及截止点不确定问题,提出一种基于双充电状态因子的电池健康状态估计方法.搭建电池老化实验台架,采用8块镍钴锰锂离子电池进行老化实验;区别于传统单状态因子估计,选取不同老化阶段下恒压充电状态前端等时间差的电流平均值,以及恒流充电状态末端等幅值电压的充电时间构造健康因子;分析不同老化阶段实验电池的荷电状态-开路电压对应关系,通过理论推导及实验结果证明健康因子的正确性;建立具备强泛化能力的改进支持向量回归模型,并通过粒子群算法优化模型超参数.实验结果表明:所提双充电状态健康因子与电池老化衰减密切相关,所建立的改进支持向量回归模型可实时估计不同老化状态下的电池健康状态,具备容量局部回弹变化的表征能力,可作为一种有效的嵌入式电池管理系统健康状态估计方法.

关键词: 锂离子电池, 健康状态估计, 支持向量回归, 双充电状态, 老化实验

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

中图分类号:

TM912

卢地华, 陈自强. 基于双充电状态的锂离子电池健康状态估计[J]. 上海交通大学学报, 2022, 56(3): 342-352.

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

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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