基于耦合半经验电-热与老化模型的电池老化感知自适应模型预测控制

Abstract This paper presents an aging-rate aware nonlinear model predictive control (MPC) strategy for battery energy storage systems, integrating a semi-empirical, experimentally validated electro-thermal and degradation model to account for both calendar and cycle aging factors, often neglected in conventional energy management approaches. A key contribution is the introduction of a adaptive weighting method that dynamically adjusts the weights of the MPC cost function according to the battery’s aging state, primarily driven by time-dependent degradation factors. This adaptive mechanism improves control decisions across varying prediction horizons, leading to reductions in both battery degradation and total operating costs by up to 262.7 % and 44.51 %, respectively, when compared to a standard MPC.