Nonlinear Model Predictive Control of Electric Vehicle Cabin Cooling System for Improved Thermal Comfort and Efficiency

Vehicle thermal management system is a key facilitator of driving range increase of a battery electric vehicle in extreme weather conditions. This control system should optimally coordinate control actions of heating, ventilation, and air-conditioning (HVAC) system to maintain high thermal comfort while reducing energy consumption. To this end, this paper presents a nonlinear model predictive control (NMPC) system for electric vehicle cabin cooling system. The proposed NMPC strategy optimizes trajectories of cabin inlet air temperature and air mass flow, while accounting for cabin thermal dynamics, available disturbance preview, and low-level-controlled HVAC system's dynamics and operating range constraints. The NMPC cost function includes simultaneous minimization of Predicted Mean Vote (PMV) thermal comfort index and either maximization of HVAC coefficient of performance or minimization of HVAC power consumption. The proposed NMPC system is verified through simulation for cabin cool-down scenario and compared with a hierarchical control strategy based on a superimposed cabin air temperature feedback controller and an inner control allocation algorithm. The simulation results indicate that the NMPC system outperforms the hierarchical control strategy in terms of reduced energy consumption for the same comfort or improved thermal comfort for the same energy consumption.