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

TL;DR

This paper introduces a stochastic model predictive control approach for EV air conditioning systems, utilizing a Markov-chain velocity predictor to enhance energy efficiency and cabin comfort under varying environmental conditions.

Contribution

It presents a novel SMPC method with a Markov-chain predictor for EV AC systems, outperforming traditional controllers in energy savings and comfort.

Findings

SMPC improves energy efficiency by 12% over rule-based controllers.

Cabin temperature variation is reduced by over 50%.

The approach is validated with real solar and temperature data.

Abstract

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.