Improving estimation accuracy for electric vehicle energy consumption considering the effects of ambient temperature

The ability to accurately predict the energy consumption of electric vehicles (EVs) is important for alleviating the range anxiety of drivers and is a critical foundation for the spatial planning, operation and management of charging infrastructures. Based on sparse GPS observations of 68 EVs in Aichi Prefecture, Japan, an energy consumption model is proposed and verified through traditional linear regression and multilevel linear regression. In particular, the influence of the ambient temperature is considered. Based on the results, the proposed model shows good performance for energy consumption estimation. For a steeper road gradient, the parameters exhibit a greater difference between uphill energy consumption and downhill energy regeneration. The relationship between energy efficiency and ambient temperature presents an asymmetrical 'U' shape, with the best energy efficiency occurring at approximately 17.5 degrees centigrade. Considering the individual heterogeneity of driving behavior, a multilevel mixed-effects regression model exhibits a higher goodness of fit. (C) 2016 The Authors. Published by Elsevier Ltd.