Reinforcement Learning for Robust Ageing-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verification

2025-09-04

AI Summary

Fast charging of lithium-ion batteries inherently conflicts with aging mitigation. To address this, we propose a degradation-aware robust charging control framework that synergistically integrates reinforcement learning (RL) with data-driven formal verification. We construct a hybrid system based on a high-fidelity electrochemical-thermal coupled model and design a mode-switching controller. Crucially, we adopt a Counterexample-Guided Inductive Synthesis (CEGIS) paradigm to jointly synthesize RL-based control policies and learn data-driven abstractions, thereby enabling formally verifiable closed-loop control. The approach achieves high charging efficiency while significantly suppressing capacity fade. Experimental results demonstrate that the method satisfies aging constraints with high probability across diverse operating conditions. To the best of our knowledge, this is the first fast-charging strategy achieving co-optimization of charging speed and battery longevity with formal probabilistic guarantees.

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Abstract

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.

Problem

• Optimizing charging speed and battery aging trade-off
• Developing data-driven formal verification for BMS
• Synthesizing hybrid RL controllers with probabilistic guarantees

Innovation

• Reinforcement Learning for controller synthesis
• Data-driven formal methods for abstraction
• Hybrid control strategy combining RL and switching