Joint State-of-Charge and State-of-Health Estimation via Actor–Critic Reinforcement Learning

Accurate estimation of state-of-charge (SoC) and state-of-health (SoH) is important for lithium-ion battery operation. This study investigates an actor–critic formulation of joint SoC/SoH estimation using TD3 and SAC with a composite reward and compares the observed results with four supervised baselines. On the supplied XJTU run, the actor–critic models achieved lower observed SoC RMSE than the supervised baselines, while the best supervised model remained competitive or superior for SoH. The current evaluation uses a limited cell cohort and three random seeds, and the per-cell normalization and the OCV calibration used the records of held-out cells (the protocol is not training-only); therefore, the results are exploratory and do not establish confirmatory statistical superiority or generalization to a larger battery population. The planned Tsinghua label-retention experiment could not be completed because the supplied capacity field had no usable variance. The resulting Tsinghua analysis is limited to label-free output diagnostics and does not establish target-domain estimation accuracy or validate reward-model transfer under label scarcity. The reward terms, including a coulomb-counting term applied to SoH that is provisional and not a validated physical constraint, are exploratory, and any mechanistic explanation of the SoC–SoH difference is a hypothesis rather than an established result. The reward-component ablation, the hyperparameter-sensitivity analysis, the matched non-RL comparison, and the Tsinghua label-retention sweep remain future validation requirements.