SOH Online Estimation of Lithium-Ion Batteries Based on Fusion Health Factor and Integrated Extreme Learning Machine

doi:10.16183/j.cnki.jsjtu.2022.306

Abstract

Abstract:

Online estimation of the state of health (SOH) of lithium-ion batteries (LIB) is crucial for the security and stability operation of battery management systems. In order to overcome the problem such as long training time, large amount of computation, and complex debugging process of the LIB SOH estimation methods based on traditional data-driven, an LIB SOH estimation method based on fusion health factor (HF) and integrated extreme learning machine is proposed. The interval data with a high correlation with the SOH was found by analyzing the dQ/dV and dT/dV curves of the battery. Multi-dimensional HFs are extracted from the interval data, and the indirect HF are obtained by principal component analysis. The stochastic learning algorithm of extreme learning machine is used to establish the nonlinear mapping relationship between indirect HF and SOH. Considering the unstable output of a single model, an integrated extreme learning machine model is proposed. The unreliable output is eliminated by setting credibility evaluation rules for the estimation results, and the estimation accuracy of the model is improved. Finally, the method proposed in this paper is validated using the NASA LIB aging dataset and the LIB aging dataset of Oxford University. The results show that the average absolute percentage error of SOH estimation method proposed is less than 1%, and it has a high accuracy and reliability.

Key words: lithium-ion battery (LIB), health factor (HF), integrated extreme learning machine (IELM) model, online estimation of state of health (SOH)

CLC Number:

TM912

QU Keqing, DONG Hao, MAO Ling, ZHAO Jinbin, YANG Jianlin, LI Fen. SOH Online Estimation of Lithium-Ion Batteries Based on Fusion Health Factor and Integrated Extreme Learning Machine[J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 263-272.

Figures/Tables 12

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电池编号	Pearson系数	Spearman系数
B0005	0.9950	0.9952
B0006	0.9931	0.9965
B0007	0.9941	0.9964
B0018	0.9878	0.9805
Cell 1	0.9941	0.9977
Cell 2	0.9839	0.9937
Cell 3	0.9961	0.9992
Cell 4	0.9976	0.9993
Cell 5	0.9990	0.9989
Cell 6	0.9941	0.9989
Cell 7	0.9975	0.9990
Cell 8	0.9953	0.9990

电池编号	MAE	MAPE	RMSE
B0005	0.0059	0.0073	0.0075
B0006	0.0094	0.0125	0.0117
B0007	0.0058	0.0063	0.0073
B0018	0.0110	0.0130	0.0127
Cell 1	0.0024	0.0032	0.0033
Cell 2	0.0073	0.091	0.0089
Cell 3	0.0025	0.0029	0.0031
Cell 4	0.0032	0.0037	0.0035
Cell 5	0.0021	0.0023	0.0028
Cell 6	0.0038	0.0046	0.0043
Cell 7	0.0016	0.0019	0.0021
Cell 8	0.0021	0.0024	0.0027

数据集	HF	平均RMSE/%
NASA	M1	0.98
	M2	1.66
	M3	1.35
	M4	1.58
Oxford	M1	0.38
	M2	0.76
	M3	0.53
	M4	0.71

数据集	模型	平均RMSE/%	t/s
NASA	IELM	0.98	2.21
	ELM	1.51	0.83
	LSTM	2.23	16.47
	GPR	1.66	3.03
Oxford	IELM	0.38	2.07
	ELM	0.68	0.98
	LSTM	1.11	15.89
	GPR	0.63	2.93