用安全扰动层让电池充电更稳更快，不牺牲安全性。
Balancing SoC in Battery Cells using Safe Action Perturbations
- 在深度强化学习上加安全层，动态修正动作避免危险状态。
- 实验显示安全违规减少，且适用于多种电池配置。
- 适合关注电池安全与通用控制策略的研究者。
锂离子电池充电时，保持各电芯荷电状态均衡极具挑战。荷电不均会损害电池健康，还可能引发热失控和火灾。传统方法在安全与充电速度间做权衡；另一些方法针对特定电池设计，难以推广。本文提出一种新方法：在深度强化学习（Deep RL）代理之上添加安全层，通过动作扰动防止电池进入危险状态。该框架专注于学习可泛化的控制策略，适用于不同电池配置。实验表明，基于安全层的动作扰动显著减少安全违规，同时在多种电池配置下均能学习到稳健的充电策略。
原文摘要 · Abstract (English)
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.
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