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

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.