A Highly Robust State of Health Estimation Method for Lithium-Ion Batteries Based on ECM and SGPR

doi:10.16183/j.cnki.jsjtu.2022.221

Abstract

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)

CLC Number:

TM912

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