Improving Fast Charging Safety With Core Temperature Estimation Via Kolmogorov-Arnold Network

Abstract

Fast charging of Lithium-ion batteries can lead to a significant temperature rise, which can cause serious risks to battery safety and lifetime. To ensure safe battery operation, thermal constraints must be enforced during the fast charging process. However, the core temperature of the battery cannot be directly measured in practice, which makes real-time safety enforcement challenging. This paper proposes a framework that incorporates core temperature estimates from Kolmogorov-Arnold Network within robust control barrier function (KAN-rCBF) constraints for battery fast-charging. The algorithm utilizes measurements from battery surface temperature, coolant temperature, coolant power, and charging current to solve a quadratic programming problem under safety constraints. We prescribe analytical safety guarantees for this optimal charging policy under KAN estimation errors and model uncertainty. Simulation results show that the proposed method maintains a safe battery temperature while achieving charging times comparable to the state-of-the-art method, where the latter fails to guarantee the same level of thermal safety.