Remaining Useful Life Prediction of Lithium-ion Batteries Based on Expansion Stress

doi:10.11985/2024.01.005

Abstract

Abstract:

Accurate and fast prediction of the remaining useful life(RUL) of lithium-ion batteries is crucial for safe and stable system operation. However, the complex internal degradation mechanism and the changeable external operating conditions of the battery bring great challenges to RUL prediction. Therefore, a RUL prediction method based on battery expansion stress is proposed in this paper. The battery expansion stress information is extracted, the relationship between reversible expansion as well as irreversible expansion and capacity is analyzed respectively, and the correlation is calculated. The reversible expansion and irreversible expansion are used as feature parameters, and long short-term memory(LSTM) neural network is constructed and trained to achieve accurate and fast RUL prediction. Through the verification on UMBL public dataset, the use of expansion stress features enables better learning of the battery aging state and captures the battery capacity degradation trend. The results show that the RMSE and MAE are within 0.82% and 0.70%, respectively, under different cycle starting points and various aging conditions. The proposed method can predict RUL with strong robustness accurately and quickly.

Key words: Lithium-ion battery, remaining useful life, battery expansion, long short-term memory network

CLC Number:

TM912

YU Miao, ZHU Yuhao, GU Xin, SHANG Yunlong. Remaining Useful Life Prediction of Lithium-ion Batteries Based on Expansion Stress[J]. Journal of Electrical Engineering, 2024, 19(1): 49-56.

Figures/Tables 13

References 23

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电池编号	测试温度/℃	充电倍率	放电倍率
01	25	C/5	C/5
05	-5	1.5C	1.5C
09	45	2C	2C
10	25	C/5	1.5C

特征参数	01	05	09	10
可逆膨胀	0.983	0.996	0.982	0.988
不可逆膨胀	-0.999	-0.993	-0.993	-0.996
内阻	-0.881	-0.894	-0.974	-0.947
充电时间	0.908	0.824	0.984	0.908

电池编号	训练集占比(%)	预测方法	RUL 预测值	RMSE (%)	MAE (%)	AE
01	45	A	154	0.554	0.412	7
	45	B	161	0.896	0.634	14
	55	A	110	0.358	0.298	2
	55	B	124	0.778	0.516	10
05	45	A	122	0.474	0.410	10
	45	B	112	0.892	0.750	20
	55	A	100	0.420	0.384	3
	55	B	112	0.660	0.532	15
09	45	A	62	0.818	0.699	4
	45	B	74	2.182	1.860	16
	55	A	47	0.490	0.458	2
	55	B	52	1.176	1.015	7
10	45	A	195	0.480	0.368	4
	45	B	242	1.486	1.026	51
	55	A	150	0.296	0.268	3
	55	B	157	0.501	0.394	10

预测方法	RUL预测值	RMSE(%)	MAE(%)	AE
文献[21]	156	0.664	0.525	9
文献[22]	135	0.734	0.516	12
文献[23]	160	0.931	0.652	13
本文	155	0.554	0.412	8