Nonlinear Model Predictive Control of an Electric Vehicle Heat Pump System for Improved Energy Efficiency

To allow for an increased range and achieve a high level of thermal comfort in very cold ambient conditions, modern battery electric vehicles are equipped with energy-efficient heat pump systems. Featured with redundant actuators and complex thermal energy flows, optimal coordination of respected control actions is required for superior efficiency. To this end, the paper presents an advanced vehicle passenger cabin thermal management strategy based on nonlinear model predictive control (NMPC). The supervised NMPC framework optimizes the control trajectories of cabin inlet air temperature and air mass flow, while accounting for preview information on disturbance variables, and considering the thermal dynamics of vehicle cabin and HVAC system. The optimal control actions reflect the trade-off between the HVAC system power consumption and the Predicted Mean Vote (PMV) thermal comfort index. The proposed strategy is validated through simulations based on a high-fidelity, multi-physics vehicle model for different cost function and controller gain settings, and compared with a previously developed optimal allocation control strategy relying on a cabin temperature feedback controller. The performance of control strategies is evaluated both for heat-up scenario phase and quasi-steady-state driving along the WLTP certification driving cycle. The results show that the NMPC framework outperforms the optimal control allocation in terms of reducing energy consumption, while maintaining equivalent level of thermal comfort.