State-of-Health, State-of-Charge, and State-of-Energy Co-Estimation of Li-Ion Batteries With Pretrained Language Model Empowered by Multistate Explicit-Implicit Prompt Learning

State-of-health (SOH), state-of-charge (SOC), and state-of-energy (SOE) co-estimation is vital for reliable operation and longevity of Li-ion batteries (LIBs). However, the intricately coupled changes occurring in multiple states across varying operating stages and differing timescales under cycle aging challenge flexible, accurate, and robust co-estimation. To tackle them, this article proposes a novel co-estimation method, leveraging a large-scale pretrained language model (PLM) empowered by multistate explicit–implicit prompt learning. Specifically, a state-wise contextual synthesizer is introduced to augment battery data into state-dependent explicit prompts, serving as situational directives for PLM to make flexible estimates over cycles and charge–discharge phases. A cross-state disentangling scheme is devised to capture coupling relationships among states and unique intricacies within states by structuring shared and individual implicit prompts. It allows PLM to perceive capacity decay for accuracy loss compensation under cycle aging. A multistate synergistic regulator is built to calibrate inter- and intrastate knowledge and interact them with PLM, strengthening co-estimation robustness to state dynamics at differing timescales. Experiments demonstrate that the proposed method yields accurate co-estimates of fluctuating SOH over the lifecycle, as well as variable SOC and SOE during charge–discharge phases, with root-mean-square error (RMSE) reductions exceeding 38%, 29%, and 29%, respectively, compared with conventional deep co-estimators.