Jointed SOH Estimation of Electric Bus Batteries Based on Operating Conditions and Multiple Indicators

Xiaohui Xu, Ke Deng, Jibin Yang (), Pengyi Deng, Xiaohua Wu, Linsui Cheng and Haolan Zhou
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Xiaohui Xu: Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
Ke Deng: Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
Jibin Yang: Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
Pengyi Deng: Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
Xiaohua Wu: Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
Linsui Cheng: Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China
Haolan Zhou: Key Laboratory of Automobile Measurement and Control & Safety, School of Automobile & Transportation, Xihua University, Chengdu 610039, China

Sustainability, 2025, vol. 17, issue 3, 1-24

Abstract: Accurately estimating the battery State of Health (SOH) is crucial for the safe and reliable operation of electric vehicles. Based on the actual operating data of electric buses, this article proposes a battery SOH estimation method that can be applied to multiple operating conditions and indicators. Specifically, the complex operating conditions are simplified into charging and driving conditions through data preprocessing. Under charging conditions, combined with Coulomb counting and incremental capacity analysis methods, a battery SOH estimation model of capacity indicators based on the Bayesian optimization bidirectional gated recursive unit model (BO-BiGRU) is established. Under driving conditions, the adaptive forgetting factor recursive least squares method considering the influence of current is used to identify the battery internal resistance feature. In addition, two separate battery SOH estimation models are established: one for internal resistance indicators based on BO-BiGRU and another for power indicators derived from the actual operational data feature. Finally, a joint battery SOH estimation method considering temperature and different operating conditions is proposed based on the SOH estimation results of the three battery indicators. The verification results show that the average error of the battery SOH estimation method proposed in this article is less than 2%, which has better accuracy for actual vehicles.

Keywords: lithium-ion battery; state of health estimation; real-world driving data; multiple operating conditions; multiple indicators (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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