Fine-tuning enables state of health estimation for lithium-ion batteries via a time series foundation model

Wenjie Sun, Chengke Wu, Chengde Xie, Xikang Wang, Yuanjun Guo, Yongbing Tang, Yanhui Zhang, Kang Li, Guanhao Du, Zhile Yang and Wenjiao Yao

Energy, 2025, vol. 318, issue C

Abstract: Accurate estimation of the State of Health (SOH) of lithium-ion batteries (LIBs) is of significant importance for the utilization of electrical devices powered by batteries, maintenance of battery energy storage equipment, and economic considerations for battery storage applications. Data-driven methods have gained increased attention due to their simple modeling and real-time learning capabilities. However, these methods have been constrained by cumbersome feature engineering and limited model transferability. Considering the emergence of time series foundation models, this study fine-tunes TimeGPT using cycling data from 143 LIBs with six different cathode materials to enhance the model’s accuracy and generalization capability in SOH estimation. Experimental results demonstrate that the fine-tuned model can adapt to various LIBs and operating conditions, exhibiting strong adaptability and transferability. After 100 steps of fine-tuning, the model maintains an RMSE below 1.06% and MAE below 0.59% across different Test sets, achieving an average reduction of 21.55% in RMSE and 13.55% in MAE compared to zero-shot inference. This research treats LIBs SOH estimation as a time series predication downstream task, validates the feasibility of fine-tuning, and provides a new solution for developing next-generation battery management systems.

Keywords: State of health; TimeGPT; Lithium-ion battery; Battery management system (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:318:y:2025:i:c:s0360544224039550

DOI: 10.1016/j.energy.2024.134177

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