Joint State-of-charge and State-of-health Estimation of Lithium-Ion Batteries Across Varying Operational Stages on Differing Timescales with Large Language Model: a Multi-Task Prompting Method

Accurate joint estimation of state-of-charge (SOC) and state-of-health (SOH) is essential for maximizing the reliability and lifespan of Lithium-ion batteries. However, the complex coupled dynamics of SOC and SOH across diverse operational stages on differing timescales challenge the flexibility and robustness of joint estimation. Hence, this paper proposes a multi-task explicit-implicit prompt learning method that exploits a large-scale pre-trained language model (PLM) for joint estimation. A per-state task interpreter is introduced to elaborate battery data into explicit prompt texts, directing PLM to grasp measurement representations and task semantics for flexible joint state estimates across varying operational stages. A cross-state task adaptor is designed to refactor implicit prompt vectors into a base type that encodes inter-state coupling and a custom type that perceives state-specific intricacies, enhancing PLM adaptability for joint estimation. An adaptive gated integrator is constructed to discriminatively aggregate base and custom prompts, shaping calibrated multi-state knowledge. It further engages extensively with the PLM space to inject knowledge, strengthening joint-estimation robustness against coupled state changes on differing timescales. Experiments demonstrate that the proposed method delivers accurate joint estimates of the persistently changing SOC during charge-discharge processes and the fluctuating SOH over cycles, ensuring high flexibility and robust performance.