Aiming to address the poor heating performance and high ventilation load of electric vehicles in low-temperature environments, a control strategy combining fresh air ratio control with motor waste heat recovery is proposed, which can realize the real-time control of fresh air ratio with fresh air volume as well as the fresh air preheating. Firstly, an integrated thermal management system for electric vehicles was set up via AMESim software, and the heating capacity of the heat pump air conditioning model was verified with public data. Next, by introducing the carbon dioxide concentration feedback control logic, the fresh air ratio could be dynamically adjusted according to the cabin fresh air volume and air quality, forming the dynamic fresh air mode, and the operating characteristics in the dynamic fresh air mode and the fixed fresh air mode under typical working conditions were analyzed. Then, the motor waste heat utilization was introduced to the dynamic fresh air mode, using the coolant to preheat incoming air to the internal heat exchanger, forming the fresh air preheating mode, and the waste heat utilization characteristics were analyzed. Finally, the required heating capacity, the energy consumption of the compressor and the average coefficient of performance(COP) were used as the comprehensive evaluation indicators to measure the performance differences of the three modes. The simulation results show that as the ambient temperature rises from -5 ℃ to 10 ℃, the required heating capacity of the fresh air preheating mode decreases from 21.49% to 47.82%, the energy consumption of the compressor decreases from 25.73% to 37.20%, and the average COP increases from 9.53% to 22.96%, compared with the fixed fresh air mode. In terms of energy-saving effect, the maximum relative driving range of the fresh air preheating mode is 32.39 km, an increase of 9.61%. It indicates that the proposed control strategy is feasible in terms of improving the heating performance of the system and reducing energy consumption.
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基金资助
国家自然科学基金资助项目(52375093)
National Natural ScienceFoundation of China(52375093)
广东省自然科学基金资助项目(2023A15 15011585)
Natural Science Foundation of Guangdong Province(2023A1515011585)