An RL-based charging controller for Li-ion batteries, refined via counterexample-guided synthesis, is verified with a data-driven abstraction to satisfy a reach-while-avoid specification with probability at least 99.956%.
Khalik, Modeling and Optimal Control for Aging-Aware Charging of Batteries, PhD Thesis (research TU/e / graduation TU/e), Eindhoven University of Technology, Eindhoven (Nov
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Reinforcement Learning for Robust Ageing-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verification
An RL-based charging controller for Li-ion batteries, refined via counterexample-guided synthesis, is verified with a data-driven abstraction to satisfy a reach-while-avoid specification with probability at least 99.956%.