Transferable Generative Pretrained Transformer Using Low-Rank Adaptation (TransLoRA-GPT): A Framework for Predicting Battery Aging Trajectories from Charging Fragments

Abstract

Accurate battery lifetime prediction is critical for operational safety and battery management. However, limited data and poor model transferability make this task challenging. This paper proposes the transferable generative pretrained transformer using low-rank adaptation (TransLoRA-GPT) to address the challenges. Specifically, depending on whether the source and target domains are the same, a base model and a transferred model are fine-tuned from the large language model (LLM), respectively. In the evaluation phase, by applying feature engineering from the charging data of the first 20 cycles, the model can predict the state of health (SOH) aging trajectory for the subsequent 100 cycles. The results indicate that by fine-tuning only 3.33% of the total parameters, the model outperforms traditional models with an average root-mean-square error (RMSE) of 0.0128. Moreover, it maintains robustness against data scarcity and delivers reliable predictions across the entire battery life cycle. Furthermore, it demonstrates strong transferability across different charging/discharging strategies (average RMSE of 0.0171) and battery types (average RMSE of 0.0594). Overall, this work highlights the potential of LLM-based transferable predictive models.