Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model

Binghan Cui, Han Wang, Renlong Li, Lizhi Xiang, Huaian Zhao, Rang Xiao, Sai Li, Zheng Liu, Geping Yin, Xinqun Cheng, Yulin Ma, Hua Huo, Pengjian Zuo, Taolin Lu, Jingying Xie and Chunyu Du

Applied Energy, 2024, vol. 353, issue PA, No S0306261923014447

Abstract: Forecasting the battery performance accurately in the ultra-early stage can avoid safety incidents, analyze degradation patterns, and prolong battery cycle life, which is crucially essential for battery management. In this work, a mechanism and data-driven fusion model is developed to predict charging capacity and energy curves over the full life cycle of batteries in the case of only knowing the planned cycling protocol without any usage history. The proposed method can achieve accurate and robust prediction of three types of batteries under different working conditions and ambient temperatures with the root-mean-square error (RMSE) of 73.7, 100.9, and 45 mAh. The maximum charging capacity and energy trajectory can be extracted further. Moreover, the proposed method can also detect battery faults without setting a safety threshold in advance due to the inconsistency of the voltage and capacity evolutions of normal and faulty batteries.

Keywords: Lithium-ion battery; Battery performance prediction; Ultra-early stage; Mechanism model; Deep learning (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014447

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DOI: 10.1016/j.apenergy.2023.122080

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