Elsevier

Energy Procedia

Volume 105, May 2017, Pages 2904-2909
Energy Procedia

Improving Estimation Accuracy for Electric Vehicle Energy Consumption Considering the Effects of Ambient Temperature

https://doi.org/10.1016/j.egypro.2017.03.655Get rights and content
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Abstract

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.

Keywords

Electric vehicle
energy consumption model
real-world observations
individual heterogeneity
multilevel regression

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Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy.