Demonstration of Model Predictive Control to optimise cabin thermal comfort in a battery electric vehicle

The focus on thermal system efficiency has increased with the introduction of electric vehicles (EV) where the heating and cooling of the cabin represents a major energy requirement that has a direct impact on vehicle range in hot and cold ambient conditions. This is further exacerbated during heating where EVs do not have an engine to provide a source of heat and instead use stored electrical energy from the battery to heat the vehicle. This paper considers two approaches to reduce the energy required by the climate control and hence increase the range of the vehicle. The first approach considers minimizing the energy to keep the passengers comfortable, whilst the second approach optimizes the heating and ventilation system to minimize the energy required to achieve the target setpoints. Finally, these two approaches are combined to minimize both the passenger’s demand and the energy required to meet the demand. This paper covers the development process from simulation to demonstration on a state of the art production vehicle in a climate test chamber leading to an improvement in total energy consumption of 8-10% under the varied driving conditions of the UDDS at -7°C. Key innovations include using thermal comfort and vehicle system models to identify the opportunities for energy savings and the demonstration of a Model Predictive Controller to implement an optimal control strategy.