Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles

This paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal solution for the integrated optimal control problem (OCP), we first build an MPC, referred to as a joint MPC, in which the goal is to minimize battery energy consumption while maintaining cabin-cooling comfort. Second, we divide the integrated OCP into two small-scale problems and devise a co-optimization MPC (co-MPC), where speed planning on hilly roads and cabin-cooling management with propulsion power information are addressed successively. Our proposed MPC methods are then validated through two case studies. The results show that both the joint MPC and co-MPC can produce significant energy benefits while maintaining driving and thermal comfort. Compared to regular constant-speed cruise control that is equipped with a proportion integral (PI)-based AC controller, the benefits to the battery energy earned by the joint MPC and co-MPC range from 2.09% to 2.72%. Furthermore, compared with the joint MPC, the co-MPC method can achieve comparable performance in energy consumption and temperature regulation but with reduced computation time.

Automated summary

This paper presents two nonlinear model predictive control methods for integrated propulsion and cabin-cooling management in electric vehicles. The proposed methods optimize system-level performance by minimizing battery energy consumption while maintaining cabin-cooling comfort. The results show that both methods offer significant energy benefits and maintain driving and thermal comfort. Additionally, the co-MPC method achieves comparable performance with reduced computation time compared to the joint MPC method.