# Dynamic Energy Consumption Modeling for HVAC Systems in Electric Vehicles
## 1 Introduction
The results indicate that the primary modeling methods used in this field are Computational Fluid Dynamics (CFD) and lumped parameter methods, accounting for the majority of the surveyed literature. Additionally, data-driven approaches utilizing machine learning techniques have gained increasing attention due to the rapid development of machine learning. Among the studies surveyed, solar radiation, cabin occupants and vehicle driving conditions are the primary factors considered when modeling vehicle thermodynamics. However, the influence of humidity is largely overlooked despite its potential correlation with the thermal environment and passenger comfort. They identify two major research gaps: the need for a well-balanced trade-off between model complexity and accuracy, which can be used for optimal control of the cabin thermal environment in real time, and the absence of standardized boundary conditions to compare and validate various models. They identify three promising directions for future research: the development of control-oriented simulation techniques, the establishment of a standardized model repository that captures typical internal and external thermal characteristics of automotive cabins and the utilization of machine learning algorithms to develop surrogate models of the cabin thermal environment. The review reveals a notable gap in the dynamic analysis of HVAC system elements. [PERSON] et al. [9] analyze the distribution of heat sources in the vehicle, the requirements of the heat load, the modeling approaches for the main thermal management system, the control methods and the developments of refrigerants from the perspective of the entire vehicle. Finally, they conclude that there is still much work to do and that the electric vehicle thermal management system will develop toward integration and efficiency in the future. [PERSON] et al. [10] introduce three advanced model-based control strategies for the cabin active heating thermal system of an EV, namely, linear and nonlinear. For this purpose, a physics-based model of the aforementioned thermal system is derived and validated using data generated from a high-fidelity thermal plant model created in GT-suite software. The parameters of the physics-based model are identified using some optimization algorithms. They show that the nonlinear dynamics of the thermal system has been accurately captured. A comprehensive test study is performed, and the performance of the proposed approaches is evaluated using various indicators such as reference tracking, energy consumption, robustness, computation time and implementation complexity. The results of the test study demonstrate the high performance and efficiency of the proposed control strategies, which offer significant advancements in the control of the thermal system of electrified vehicles. [PERSON] et al. [11] propose a unified thermal management framework, developed for the entire EV, which addresses both the heating and cooling requirements for the battery pack and the HVAC system of an electric vehicle. They present a detailed 1D simulation model in which different parts of the vehicle, including the powertrain components, battery pack, cabin, compressor, battery chiller, expansion valves, pumps, radiator fan, etc., are accurately modeled and integrated into the EV general model in the GT-Suite environment. Considering different heating/cooling scenarios in various environmental conditions (including air temperature, solar flux, metabolic rate, vehicle speed, etc. ), effective control systems are designed for each subsystem, particularly the compressor, heaters and pumps, using the gain-scheduling PI control method in the MATLAB/Simulink R2023B environment coupled with the GT-Suite model. They show that the system can maintain the battery temperature within a range while reducing the cabin temperature. They show preliminary tests conducted on an actual vehicle to evaluate its performance under real-world driving conditions.