As the demand for faster, more efficient charging of lithium-ion batteries in electric vehicles grows, rapid charging methods that also safeguard battery health have become essential. This talk describes several system-theoretic challenges in optimal battery management, including optimal control for monotone systems, informative experiment design, and model-free fast-charging solutions.
We begin by examining the structure of optimal control for monotone systems, identifying conditions under which the optimal policy is of bang-ride type and explaining how such control laws can be implemented using selectors. This approach suggests simple policies that adhere to health-related constraints without requiring full state information or complex models. Next, we present an approach for informative charging that combines fast-charging protocols with experimental input design, allowing key model parameters to be estimated in real-time, thus maintaining parameter accuracy as the battery ages. Finally, we discuss a model-free, data-driven charging algorithm that leverages adaptive feedback to implement optimal bang-ride control without relying on pre-trained models, enabling efficient, robust, and health-conscious fast charging directly from real-time battery data.
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