Balancing SoC in Battery Cells using Safe Action Perturbations
This work addresses safety and generalization challenges in battery management systems, but it is incremental as it builds on existing Deep RL methods with a safety add-on.
The paper tackles the problem of managing equal charge levels in active cell balancing for Li-ion batteries to prevent safety hazards like thermal runaway, by proposing a safety-layer that perturbs actions from a Deep Reinforcement Learning agent to avoid unsafe states, resulting in fewer safety violations and a robust policy across various battery configurations.
Managing equal charge levels in active cell balancing while charging a Li-ion battery is challenging. An imbalance in charge levels affects the state of health of the battery, along with the concerns of thermal runaway and fire hazards. Traditional methods focus on safety assurance as a trade-off between safety and charging time. Others deal with battery-specific conditions to ensure safety, therefore losing on the generalization of the control strategies over various configurations of batteries. In this work, we propose a method to learn safe battery charging actions by using a safety-layer as an add-on over a Deep Reinforcement Learning (RL) agent. The safety layer perturbs the agent's action to prevent the battery from encountering unsafe or dangerous states. Further, our Deep RL framework focuses on learning a generalized policy that can be effectively employed with varying configurations of batteries. Our experimental results demonstrate that the safety-layer based action perturbation incurs fewer safety violations by avoiding unsafe states along with learning a robust policy for several battery configurations.