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A stochastic model predictive controller (SMPC) of air conditioning (AC) system is proposed to improve the energy efficiency of electric vehicles (EVs). A Markov-chain based velocity predictor is adopted to provide a sense of the future disturbances over the SMPC control horizon. The sensitivity of electrified AC plant to solar radiation, ambient temperature, and relative air flow speed is quantificationally analyzed from an energy efficiency perspective. Three control approaches are compared in terms of the electricity consumption, cabin temperature, and comfort fluctuation, which include the proposed SMPC method, a generally used bang-bang controller, and dynamic programming as the benchmark. Real solar radiation and ambient temperature data are measured to validate the effectiveness of the SMPC. Comparison results illustrate that SMPC is able to improve the AC energy economy by 12% compared to the rule-based controller. The cabin temperature variation is reduced by more than 50.4%, resulting with a much better cabin comfort.

Stochastic Model Predictive Control of Air Conditioning System for Electric Vehicles: Sensitivity Study, Comparison, and Improvement