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

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Hongwen He, Hui Jia, Fengchun Sun, Chao Sun

A stochastic model predictive controller (SMPC) of air conditioning (AC)system is proposed to improve the energy efficiency of electric vehicles (EV).A Markov-chain based velocity predictor is adopted to provide a sense of thefuture disturbances over the SMPC control horizon. The sensitivity ofelectrified AC plant to solar radiation, ambient temperature and relative airflow speed is quantificationally analyzed from an energy efficiencyperspective. Three control approaches are compared in terms of the electricityconsumption, cabin temperature, and comfort fluctuation, which are (i) theproposed SMPC method, (ii) a generally used bang-bang controller and (iii)dynamic programming (DP) as the benchmark. Real solar radiation and ambienttemperature data are measured to validate the effectiveness of the SMPC.Comparison results illustrate that SMPC is able to improve the AC energyeconomy by 12% than rule-based controller. The cabin temperature variation isreduced by over 50.4%, resulting with a much better cabin comfort.

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