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

• Published in: IEEE transactions on transportation electrification 2024-09, Vol.10 (3), p.6715-6725
• Main Authors: Ma, Baolin, Chu, Fanyuan, Guo, Lulu, Hu, Yunfeng, Xu, Fang, Chen, Hong
• Format: Article
• Language: English
• Subjects: Air flow; Air quality; Air temperature; Atmospheric modeling; Cabin thermal and air quality management; electric vehicles (EVs); Energy consumption; Impact analysis; model predictive control (MPC); Passenger comfort; Predictive control; Real time; Real-time systems; Safety management; Strategy; Thermal analysis; Thermal comfort; Thermal management; Thermal simulation; Traffic control; Vehicle dynamics; Vehicle safety; Ventilation
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2023.3339146

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.