基于ECM和SGPR的高鲁棒性锂离子电池健康状态估计方法

doi:10.16183/j.cnki.jsjtu.2022.221

摘要/Abstract

摘要：

锂离子电池健康状态(SOH)的准确估计对于保障电池系统安全运行具有重要意义.针对传统SOH估计方法在可变工况下失效的问题,提出了一种基于等效电路模型和稀疏高斯过程回归的锂离子电池SOH在线估计方法.通过两个在线滤波器,在恒流充电过程中动态地辨识了锂离子电池等效电路模型的各项参数,构建了工况不敏感的健康因子,结合稀疏高斯过程回归实现SOH的间接估计.该方法在多种工况下使用统一的信号处理方法和特征映射模型,兼具鲁棒性强和冗余度低的优点.实验结果表明,该方法在多种工况下的平均绝对误差不超过0.94%,均方根误差不超过1.12%,与现有方法相比,该方法在综合性能上具有显著优势.

关键词: 锂离子电池, 健康状态, 健康因子, 粒子滤波, 稀疏高斯过程回归

Abstract:

Accurately estimating the state of health (SOH) of lithium-ion batteries is of great significance in ensuring the safe operation of the battery system. Addressing the issue where traditional SOH estimation methods fail under variable working conditions, an online SOH estimation method for lithium-ion batteries based on equivalent circuit model (ECM) and sparse Gaussian process regression (SGPR) is proposed. During the constant current charging process, the parameters of the ECM of lithium-ion battery are dynamically identified by two online filters, based on which, a condition-insensitive health indicator is constructed. In combination with the SGPR, the indirect SOH estimation is achieved. This method uses the unified signal processing method and feature mapping model under various working conditions, and features strong robustness with low redundancy. The experimental results show that the average absolute error of the method proposed under various working conditions does not exceed 0.94%, and the root mean square error stays below 1.12%. When benchmarked against existing methods, this method has significant advantages in comprehensive performance.

Key words: lithium-ion battery, state of health (SOH), health indicator, particle filter, sparse Gaussian process regression (SGPR)

中图分类号:

TM912

崔显, 陈自强. 基于ECM和SGPR的高鲁棒性锂离子电池健康状态估计方法[J]. 上海交通大学学报, 2024, 58(5): 747-759.

CUI Xian, CHEN Ziqiang. A Highly Robust State of Health Estimation Method for Lithium-Ion Batteries Based on ECM and SGPR[J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 747-759.

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电池编号	充电电流/A	放电电流/A	环境温度/℃	循环次数
NMC cell 1	5	10	25	410
NMC cell 2	5	20	25	466
NMC cell 3	10	10	25	446
NMC cell 4	5	10	5	150

SOC范围/%	工况
SOC范围/%	0.5 C,25 ℃	1 C,25 ℃	0.5 C,5 ℃
[10,20)	0.9911	0.9909	0.9910
[20,30)	0.9921	0.9930	0.9951
[40,50)	0.9938	0.9931	0.9955
[50,60)	0.9938	0.9931	0.9955
[20,40)	0.9938	0.9931	0.9955
[40,60)	0.9938	0.9931	0.9955
[20,50)	0.9937	0.9930	0.9955
[30,80)	0.9938	0.9931	0.9953

指标	训练	测试
指标	训练	常规GPR	SGPR
样本数	410×8	466×8	466×8
计算时长/s	552	639	8
MAE/%	0.1573	0.2631	0.2629
RMSE/%	0.2027	0.3183	0.3182

SOC范围/%	MAE/%			RMSE/%
SOC范围/%	0.5 C, 25 ℃	1 C, 25 ℃	0.5 C, 5 ℃	0.5C, 25 ℃	1 C, 25 ℃	0.5 C, 5 ℃
[10, 20)	0.269 8	0.399 4	0.458 9	0.329 8	0.516 2	0.538 8
[20, 30)	0.267 6	0.398 6	0.457 0	0.326 5	0.515 5	0.536 7
[40, 50]	0.265 4	0.397 8	0.455 1	0.323 4	0.514 9	0.534 6
(50, 60]	0.263 3	0.397 0	0.453 4	0.320 4	0.514 2	0.532 7
[20, 40)	0.261 5	0.396 3	0.452 3	0.317 7	0.513 6	0.531 9
[40, 60)	0.259 8	0.395 6	0.450 6	0.315 6	0.513 2	0.530 7
[20, 50)	0.258 5	0.395 3	0.449 4	0.314 0	0.512 9	0.530 4
[30, 80)	0.257 6	0.395 2	0.448 9	0.312 9	0.512 7	0.530 4

电池编号	10%~20% SOC		50%~60% SOC
电池编号	MAE/%	RMSE/%	MAE/%	RMSE/%
LCO cell 3	0.8013	0.9870	0.3432	0.4223
LCO cell 4	0.9398	1.1011	0.3692	0.4622
LCO cell 5	0.9081	1.0812	0.3643	0.4511
LCO cell 6	0.8733	1.0893	0.3904	0.4654
LCO cell 7	0.9209	1.1196	0.4047	0.5120
LCO cell 8	0.8405	1.0691	0.4392	0.5416