用强化学习优化锂电池充电，兼顾速度与寿命，还自动验证安全性能。
Reinforcement Learning for Robust Ageing-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verification
- 结合强化学习与数据驱动抽象，设计分段式充电控制器。
- 实现充电速率与电池寿命的平衡，满足预设安全约束。
- 适合电池管理、电动汽车及储能系统研发人员参考。
可充电锂离子（Li-ion）电池是现代技术的核心组件。过去几十年中，电池及其配套嵌入式充电与安全协议（即电池管理系统，BMS）的设计成为关键。一个核心挑战是快充与电池老化之间的权衡，导致容量衰减。本文基于高保真物理模型，提出一种数据驱动的充电与安全协议设计方法。采用反例引导归纳合成（CEGIS）框架，将强化学习（RL）与最新数据驱动形式化方法结合，生成混合控制策略：RL用于合成各子控制器，数据驱动抽象则指导其按电池初始输出测量值划分成切换结构。最终形成的离散控制器选择与连续电池动态构成混合系统。当设计满足预期标准时，抽象提供闭环性能的概率保证。
原文摘要 · Abstract (English)
Rechargeable lithium-ion (Li-ion) batteries are a ubiquitous element of modern technology. In the last decades, the production and design of such batteries and their adjacent embedded charging and safety protocols, denoted by Battery Management Systems (BMS), has taken central stage. A fundamental challenge to be addressed is the trade-off between the speed of charging and the ageing behavior, resulting in the loss of capacity in the battery cell. We rely on a high-fidelity physics-based battery model and propose an approach to data-driven charging and safety protocol design. Following a Counterexample-Guided Inductive Synthesis scheme, we combine Reinforcement Learning (RL) with recent developments in data-driven formal methods to obtain a hybrid control strategy: RL is used to synthesise the individual controllers, and a data-driven abstraction guides their partitioning into a switched structure, depending on the initial output measurements of the battery. The resulting discrete selection among RL-based controllers, coupled with the continuous battery dynamics, realises a hybrid system. When a design meets the desired criteria, the abstraction provides probabilistic guarantees on the closed-loop performance of the cell.
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