Stochastic Model Predictive Control of Air Conditioning System for Electric Vehicles: Sensitivity Study, Comparison and Improvement
For electric vehicle manufacturers, this work offers a practical method to improve AC energy efficiency and comfort, though it is an incremental application of existing SMPC techniques.
This paper proposes a stochastic model predictive controller (SMPC) for air conditioning in electric vehicles, achieving 12% energy savings and over 50.4% reduction in cabin temperature variation compared to a rule-based controller.
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 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 are (i) the proposed SMPC method, (ii) a generally used bang-bang controller and (iii) dynamic programming (DP) 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% than rule-based controller. The cabin temperature variation is reduced by over 50.4%, resulting with a much better cabin comfort.