  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.
