Control Trajectory Optimization of Electric Vehicle Heat Pump-based Cabin Heating System

Optimal control of battery electric vehicle thermal management systems is essential for maximizing the driving range in extreme weather conditions. Vehicles equipped with advanced heating, ventilation and air-conditioning (HVAC) systems based on heat pumps with secondary coolant loops are more challenging to control due to actuator redundancy and increased thermal inertia. This paper presents the dynamic programming (DP)-based offline control trajectory optimization of heat pump-based HVAC aimed at maximizing thermal comfort and energy efficiency. Besides deriving benchmark results, the goal of trajectory optimization is to gain insights for practical hierarchical control strategy modifications to further improve real-time controllers’ performance. DP optimizes cabin inlet air temperature and flow rate to set the trade-off between thermal comfort and energy efficiency while considering the nonlinear dynamics and operating limits of HVAC system in addition to typically considered cabin thermal dynamics. The energy efficiency term is investigated by two optimization approaches: maximizing the HVAC system's coefficient of performance or minimizing its power consumption. Detailed Dymola-based HVAC system and cabin models are used to parameterize low-order models for control trajectory optimization. The parameters of these models are identified through numerical procedures, leveraging responses from the detailed simulation model across a range of operating points. They are subsequently fitted with analytical functions to further facilitate the computational efficiency of DP algorithm implemented in C++. Additionally, nonlinear regression models for HVAC power consumption and the Predicted Mean Vote thermal comfort index are developed to construct the optimization cost function. Repeating the optimization for multiple cost function settings yields the Pareto optimal frontiers expressed in terms of aggregated thermal comfort index and energy consumption. Dymola simulation results confirm that incorporating DP-derived insights into a hierarchical control strategy leads to significant improvements in both thermal comfort and energy efficiency compared to baseline hierarchical control strategy.