2018/02/20 by Hongwen He, He, Hongwen, Hui Jia +5
Engineering · #Advanced Battery Technologies Research #Electric Vehicles and Infrastructure #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Refrigeration and Air Conditioning Technologies #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1802.07634
openalex publication_date 2018/02/20 · openalex created_date 2018/03/06 · openalex updated_date 2026/07/28
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.