A Critical Review and Future Prospects of Control-Oriented HVAC Modeling Strategies in Electric Vehicles

Electric Vehicles (EVs) have emerged as a promising solution in the transportation industry, but their adoption is hindered by range anxiety due to uncertainty in driving range. Specifically, severe weather conditions can result in a high requirement for the use of Heating, Ventilation and Air Conditioning (HVAC) to regulate cabin’s thermal comfort, leading to significant demand for battery power. To address this, understanding real-time HVAC power usage can help precise range prediction and control. Furthermore, achieving real-time capability involves exploring simplified control-oriented models for EV HVAC systems. Therefore, this research aims to address the gap between current and previous HVAC modelling research for EVs by providing a detailed discussion of three modelling techniques: physics-based, data-driven, and hybrid models. Later, various evaluation metrics such as modelling level capability, accuracy, complexity, generalization, adaptability, cost, and required effort are defined and used to compare these models. The potential of using control-oriented models for design optimization, synthetic data generation, fault detection, diagnosis, prognosis, and Failure Mode Effect Analysis (FMEA) is also discussed, and the need for further research in this area is noted. Overall, this paper provides a comprehensive overview of control-oriented HVAC modelling for EVs and offers insights for researchers in this field.