# Dynamic Energy Consumption Modeling for HVAC Systems in Electric Vehicles
## 5 Conclusions  
This paper introduces an innovative approach for modeling the transient behavior of a vapor compression refrigerating system for automotive applications. The methodology, validated by comparisons with experiments, can give the energy consumption of the air conditioning system in different scenarios. Several factors influence the energy consumption of the HVAC system, including the characteristics of subsystems such as the evaporator and condenser, external thermal loads and the number of occupants in the vehicle cabin. This paper places particular emphasis on the behavior of HVAC subsystems, while simplified models account for heat and mass exchanges between the vehicle cabin and the external environment. The main findings and novel aspects are summarized as follows:  
1. **Model and validation:** Accurate correlations for refrigerant and air-side heat transfer coefficients are integrated into the model to represent a realistic HVAC system designed for electric vehicles. These correlations are sourced from the literature and are based on the geometry of the heat exchangers used in the experiments. The model's results were validated through comparisons with experimental measurements performed in a climatic chamber, demonstrating excellent agreement. 2. **Unsteady analysis of energy consumption:** Two different HVAC working conditions were considered for the unsteady analysis of energy consumption. The first condition involves one person in the car, driving at a constant speed for 40 min on a hot summer day. The analysis compares the energy consumed by the HVAC during the drive period in two scenarios: one in which the driver enters a hot car and starts driving immediately and another where the driver enters a car that has undergone a precooling phase. The comparison shows that the energy consumption during the driving period without a precooling phase is 16% greater than with a precooling phase. However, the total energy consumed with precooling is 25% higher than without precooling, but this does not affect the energy delivered by the batteries during the drive. The second condition simulates the behavior of the HVAC system during a regulated driving cycle (ECE 15 driving cycle) that simulates urban mobility. In this case, the HVAC system increases the total energy consumption of the electric vehicle by 40%. Figure 13: State of charge of the batteries with (red line) and without (blue line) the employment of the HVAC system. 3. **Importance and future work:** The contribution of the HVAC system is significant in the overall energy consumption of an electric vehicle. Therefore, a computational model that allows us to evaluate the electrical consumption of the HVAC system is crucial to improving the efficiency of electric mobility. The methodology framework proposed in this article can be extended to other HVAC systems and applied in different scenarios, allowing more robust optimization techniques for such systems. This approach shows potential practical implications, such as in a tool for describing the dynamic behavior of systems in the vehicle to compare different solutions. In an example, one can compare the energy consumption with different heat exchangers in the evaporator or condenser before building a prototype. Moreover, the dynamic simulation shown in this paper can be a basis for the optimization of a control system for the HVAC system. Hence, the methodologies presented in this paper are very useful for electric vehicle manufacturers or HVAC system developers. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15073514/s1](https://www.mdpi.com/article/10.3390/app15073514/s1)]([https://www.mdpi.com/article/10.3390/app15073514/s1](https://www.mdpi.com/article/10.3390/app15073514/s1)). Conceptualization, G.P., B.P. and G.S. ; methodology, G.P., B.P. and G.S. ; software, G.P. and B.P. ; validation, G.P. and B.P. ; formal analysis, G.P. and B.P. ; investigation, G.P. and B.P. ; resources, B.P. ; data curation, G.P. and B.P.