SOH Early Prediction of Lithium-ion Batteries Based on Voltage Interval Selection and Features Fusion

Energy, Volume 308, 2024, 132993, ISSN 0360-5442, DOI: doi.org/10.1016/j.energy.2024.132993

SOH Early Prediction of Lithium-ion Batteries Based on Voltage Interval Selection and Features Fusion

Simin Peng^b, Junchao Zhu^b, Tiezhou Wu^b, Aihua Tang^c, Jiarong Kan^a, and Michael G. Pecht^d
^aSchool of Electrical Engineering, Yancheng Institute of Technology, Yancheng, 224051, China
^bHubei Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation Control, Hubei University of Technology, Wuhan, 430068, China
^cKey Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing, 400054, China
^cCenter for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, 20742, USA

For more information about this article and related research, please contact Prof. Michael Pecht.

Abstract:

Accurate state of health (SOH) of batteries is a crucial prerequisite for ensuring the safety and stable operation of electric vehicles. However, existing conventional prediction methods do not consider the influence of early-stage capacity variations on full-cycle SOH with extensive test data. This paper develops a SOH early prediction method of lithium-ion batteries based on voltage interval selection and features fusion. To identify the battery SOH curves with high similarity under identical charge-discharge conditions, a double correlation based early-stage SOH similarity analysis method is presented. To minimize the amount of voltage training data collected during feature extraction, a voltage interval selection method considering sampling time and features correlation is introduced. Meanwhile, a feature fusion method combining entropy weight and correlation factor is used to reduce the impact of redundant health feature information. To address the problems of low accuracy and computational inefficiency using the least squares support vector machine (LSSVM) model, a grey wolf optimizer-LSSVM-adaptive boosting model is developed for battery SOH prediction. The experimental results show that the coefficients of determination remain above 0.98, indicating high SOH prediction accuracy using the developed method. Compared to other methods, the mean absolute errors based on the developed method are maintained below 1.5 %.

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