State of health estimation and remaining useful life prediction of lithium-ion battery based on characteristic voltage model

doi:10.3969/j.issn.1674-8484.2022.01.020

Abstract

Abstract:

Capacity estimation and remaining useful life (RUL) prediction of lithium-ion batteries are of great significance to improve their safety. An on-line capacity estimation and off-line prediction of RUL method was proposed based on improved particle filter (PF) algorithm and characteristic voltage model. The characteristic voltage from the discharge curve was extracted, and two correlation models of characteristic voltage-cycle times and characteristic voltage-capacity were established. The characteristic voltage was estimated in real time using an improved PF algorithm, in which the initial value of the probability density was optimized by fitting the sample battery aging data to improve the accuracy of the model parameter identification to improve the accuracy of the established correlation models. The results show that the capacity estimation error can be kept within 3%, and the RUL prediction error can be kept within 5%.

Key words: lithium-ion battery, capacity estimation, remaining useful life prediction, improved particle filter (PF) algorithm

CLC Number:

U469.72

LAI Xin, MENG Zheng, HAN Xuebing, ZHENG Yuejiu. State of health estimation and remaining useful life prediction of lithium-ion battery based on characteristic voltage model[J]. Journal of Automotive Safety and Energy, 2022, 13(1): 194-201.

Figures/Tables 8

References 17

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电池	PF算法			改进PF算法
电池	a ×10^-5	b ×10^-4	c	a ×10^-5	b ×10^-4	c
Cell#5	-1.366	-2.315	3.510	-8.601	-3.801	3.522
Cell#6	-3.164	-0.273	3.528	-2.320	-4.657	3.524

电池	PF算法			改进PF算法
电池	a ×10^-5	b ×10^-4	c	a ×10^-5	b ×10^-4	c
Cell#5	-1.366	-2.315	3.510	-8.601	-3.801	3.522
Cell#6	-3.164	-0.273	3.528	-2.320	-4.657	3.524

	PF算法				改进PF算法
	m	n	p	q	m	n	p	q
Cell#5	1.05×10^-2	1.452 6	7.941 8×10^-5	1.367 7	0.849 5	0.119 4	7.725×10^-13	7.757 9
Cell#6	1.257 4	3.649×10^-2	2.038 5×10^-14	8.772 9	0.242 7	0.530 8	1.025×10^-15	9.541 1

	PF算法				改进PF算法
	m	n	p	q	m	n	p	q
Cell#5	1.05×10^-2	1.452 6	7.941 8×10^-5	1.367 7	0.849 5	0.119 4	7.725×10^-13	7.757 9
Cell#6	1.257 4	3.649×10^-2	2.038 5×10^-14	8.772 9	0.242 7	0.530 8	1.025×10^-15	9.541 1

电池	阈值 / %	标准PF算法				改进PF算法				参考值
电池	阈值 / %	MAE / %	RMSE / %	PCC	RUL	MAE / %	RMSE / %	PCC	RUL	TCC	RUL
Cell #5	80~70	1.88	2.25	151	51	1.22	1.37	168	68	162	62 99
Cell #5	90~70	3.39	3.97	134	71	3.03	3.24	170	107	162	62 99
Cell #6	80~70	2.88	3.42	114	54	2.60	4.17	93	33	102	42 57
Cell #6	85~70	4.52	5.06	76	31	3.50	4.23	106	61	102	42 57