Hierarchical Model Predictive Control for Battery Thermal Management and Lifetime Protection in Connected Electric Vehicles

The battery performance is vulnerable to temperature, so an efficient battery thermal management (BTM) system needs to be designed to maintain the battery temperature. However, due to the slow thermodynamics of the battery thermal system, the optimization for BTM requires a long-term reliable prediction of the future velocity to improve performance and reduce energy consumption. This paper presents an optimization strategy based on multi-horizon hierarchical model predictive control (MH-H-MPC) for BTM systems considering battery lifetime protection, which uses multi-horizon to optimize BTM system in connected electric vehicles (CEV). The approach takes advantage of both long-term and short-term decision-making to address multi-timescale problems of thermal systems and ensure high computational efficiency. By incorporating a multi-horizon optimization approach, the upper layer efficiently plans the real-time the battery temperature trajectory over a long horizon. And multi-objective optimization of energy consumption, battery life, and reference temperature trajectory deviation is achieved to reduce energy consumption further in the lower layer. The effectiveness of the proposed approach is validated by simulation using traffic data. The results show that the proposed MH-HMPC method reduces the BTM energy consumption by 5.74%, 14.34%, and 29.20% compared to hierarchical MPC, single MPC, and PID on real-world cycles with minimal battery degradation rate and total economic cost.