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 the 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 modeling research for EVs by providing a detailed discussion of three modeling techniques: physics-based, data-driven, and hybrid models. Later, various evaluation metrics, such as modeling 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 article provides a comprehensive overview of control-oriented HVAC modeling for EVs and offers insights for researchers in this field.