Energy consumption and modelling of the climate control system in the electric vehicle. Issue 1 (January 2019)

• Record Type: Journal Article
• Authors: Doyle, Aisling; Muneer, Tariq
• Is Part Of: Energy exploration & exploitation. Volume 37:Issue 1(2019)
• Page Start: 519
• Page End: 543
• Publication Date: 2019-01
• Subjects: Electric vehicles -- thermal modelling -- solar modelling -- temperature prediction -- heating -- cooling; Power resources -- Periodicals; 333.79
• DOI: 10.1177/0144598718806458
• Languages: English
• ISSNs: 0144-5987

Abstract

With the introduction of electric vehicles in the automobile market, limited information is available on how the battery's energy consumption is distributed. This paper focuses on the energy consumption of the vehicle when the heating and cooling system is in operation. On average, 18 and 14% for the battery's energy capacity is allocated to heating and cooling requirements, respectively. The conventional internal combustion engine vehicle uses waste heat from its engine to provide for passenger thermal requirements at no cost to the vehicle's propulsion energy demands. However, the electric vehicle cannot avail of this luxury to recycle waste heat. In order to reduce the energy consumed by the climate control system, an analysis of the temperature profile of a vehicle's cabin space under various weather conditions is required. The present study presents a temperature predicting algorithm to predict temperature under various weather conditions. Previous studies have limited consideration to the fluctuation of solar radiation space heating to a vehicle's cabin space. This model predicts solar space heating with a mean bias error and root mean square error of 0.26 and 0.57°C, respectively. This temperature predicting model can potentially be developed with further research to predict the energy required by the vehicle's primary lithium-ion battery to heat and cool the vehicle's cabin space. Thus, this model may be used in a route planning application to reduce range anxiety when drivers undertake a journey under various ambient weather conditions while optimising the energy consumption of the electric vehicle.