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This paper establishes a dynamic thermal model for the Air Conditioning (AC)-cabin coupled system that includes the influences of vehicle speed and external environment on the heat exchange with the cabin. An Intelligent Model Predictive Control strategy (IMPC strategy) integrating the vehicle speed previewer and the self-adaptor of passenger’s thermal comfort, is proposed and applied to the AC-cabin system. This strategy can predict both the car speed and the preferred predicted mean vote of passengers by learning the historical car speed and the passenger’s comfort temperature. With their help, the IMPC has a more dynamic response of compressor speed to the car speed change and can automatically adjust cabin temperature, making it satisfy the thermal preference of the passenger with a little control error of PMV and cabin temperature. In aspect of energy conservation, the IMPC strategy saves more energy than the other control strategies researched in this paper. Its energy consumption is 4.32% less than the traditional MPC strategy, 40.4% less than the on-off controller, and 25.6% less than the PID controller. Moreover, the IMPC algorithm can keep the surface temperature of evaporator above 0 °C by setting the restricted condition in the MPC strategy, which can avoid the frosting on the evaporator wall and make the AC system work efficiently.

This paper establishes a coupled thermal model of the air conditioning system and cabin for electric vehicles, which considers the effects of solar radiation, vehicle speed, and the external environment on the heat exchanged with the cabin. An intelligent air conditioning system control strategy that can learn passengers’ thermal comfort preferences is proposed. This strategy predicts the preferred predicted mean vote of passengers by learning their thermal comfort preferences, then converts it into a target temperature for the air conditioning system. The performance of the proposed strategy is compared with that of conventional on-off and fuzzy PID controllers. In a simulated hot environment, the proposed control algorithm directly and automatically decreased the cabin temperature to the passengers’ preferred temperature without any manual adjustment. It can also maintain stable passenger PMVs and corresponding temperatures regardless of solar radiation, vehicle speed, and external environmental changes. The proposed strategy can improve the thermal comfort of passengers, compared with the on-off and fuzzy PID controllers; moreover, it consumes less energy. In a simulated driving cycle, its energy consumption was 31.8% less than that of the on-off controller and 10% less than that of the fuzzy PID controller, and its COP was respectively 20.4% and 18.7% more than those of on-off controller and the fuzzy PID controller. Therefore, the proposed strategy can make vehicles, especially electric ones, more efficient.