AI 中文总结
本文针对锂离子电池快充核心温度无法直接测量的问题，提出结合KAN核心温度估计的KAN-rCBF框架，经仿真验证可在保证热安全的同时实现与现有最优方法相当的充电时间。
AI 中文摘要
锂离子电池快充会导致温度显著上升，对电池安全性和寿命构成严重风险，因此快充过程中必须实施热约束以确保电池安全运行。但实际中无法直接测量电池核心温度，这使得实时安全约束的实施颇具挑战。本文提出一种框架，将柯尔莫哥洛夫-阿诺德网络（Kolmogorov-Arnold Network，KAN）估计的核心温度纳入鲁棒控制障碍函数（KAN-rCBF）约束，用于电池快充。该算法利用电池表面温度、冷却液温度、冷却液功率及充电电流的测量值，在安全约束下求解二次规划问题。针对KAN估计误差与模型不确定性，本文为该最优充电策略提供了解析安全保障。仿真结果表明，所提方法在维持电池温度安全的同时，充电时间与现有最优方法相当，而现有最优方法无法保证同等水平的热安全性。
英文摘要
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.
Comments14 pages, 3 figures
