Filter-Augmented Reinforcement Learning for Battery Fast Charging with State of Health and Plating-Aware Reward Design

Abstract: Fast charging of lithium-ion batteries is crucial for electric vehicle adoption, yet it increases safety risks and accelerates battery ageing. To address this, we propose a filter-augmented reinforcement learning (RL) framework for lifelong health-aware fast charging, based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. The RL agent is guided by a reward function that balances charging speed and battery longevity through the incorporation of state of health (SoH) information and an SoH-dependent voltage penalty term derived from a constant--current constant--overpotential charging strategy. Since onboard SoH estimates are inevitably corrupted by noise, two filtering strategies are integrated: an exponential moving average (EMA) filter when noise statistics are unknown, and a Kalman filter when they are available, with the filtered estimate embedded in both the agent's state and reward computation. The RL policy is trained using data from high-fidelity life-cycle simulations. The evaluation uses a published electrochemical and degradation parameterisation for a commercial LG M50 cell. Results show that the proposed method achieves up to 21.4\% more equivalent full cycles (EFCs) than conventional constant current--constant voltage (CC--CV) charging under 2\% SoH estimation noise, and maintains this lifetime advantage even under 5\% noise, while sustaining comparable charging speed.