Real-Time Predictive Control for EVs Cabin Thermal Management Considering Air Quality

Cabin thermal comfort and air quality are critical factors influencing the driver and passengers' comfort and even driving safety. Existing studies predominantly focus on long-term planning of cabin air temperature, considering the substantial temperature inertia, while disregarding the impact of heat load generated from ventilation or at a constant fresh air flow rate. This article proposes a real-time predictive control strategy that integrates the planning of cabin air quality with thermal management. The precise control-oriented model tracks the cabin's optimal thermal load in the lower layer. The simulation results demonstrate an approximate 2% reduction in energy consumption, on average, compared to the conventional rule-based control strategy. The energy-saving improvement is particularly pronounced under heavy traffic conditions. Besides the effectiveness and computational efficiency performances, the robustness and real-time capability of the proposed strategy are analyzed. In addition, the simulation results provide a quantitative assessment of the energy consumption impact associated with different desired cabin air temperatures and qualities.