Health State Prediction of Lithium-Ion Battery Based on Improved Sparrow Search Algorithm and Support Vector Regression

Deyang Yin, Xiao Zhu, Wanjie Zhang and Jianfeng Zheng ()
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Deyang Yin: The School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Xiao Zhu: The School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Wanjie Zhang: The School of Intelligent Manufacturing, Changzhou Technician College Jiangsu Province, Changzhou 213164, China
Jianfeng Zheng: The School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China

Energies, 2024, vol. 17, issue 22, 1-14

Abstract: The state of health (SOH) prediction of lithium-ion batteries is a pivotal function within the battery management system (BMS), and achieving accurate SOH predictions is crucial for ensuring system safety and prolonging battery lifespan. To enhance prediction performance, this paper introduces an SOH prediction model based on an improved sparrow algorithm and support vector regression (ISSA-SVR). The model uses nonlinear weight reduction (NWDM) to enhance the search capability of the Sparrow algorithm and combines a mixed mutation strategy to reduce the risk of local optimal traps. Then, by analyzing the characteristics of different voltage ranges, the charging time from 3.8 V to 4.1 V, the discharge time of the battery, and the number of cycles are selected as the feature set of the model. The model’s prediction capabilities are validated across various working environments and training sample sizes, and its performance is benchmarked against other algorithms. Experimental findings indicate that the proposed model not only confines SOH prediction errors to within 1.5% but also demonstrates robust adaptability and widespread applicability.

Keywords: lithium-ion battery; improved sparrow search algorithm; state of health; support vector regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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