Electric vehicle energy consumption modelling and prediction based on road information

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The limited driving range is considered as a significant barrier to the spread of electric vehicles. One effective method to reduce "range anxiety" is to offer accurate information to the driver on the remaining driving range. However, the energy consumption during driving is largely determined by driving behaviour, road topography information and traffic situation, which are hard to predict. This paper will discuss an accurate electric vehicle energy consumption model validated using driving tests on different public roads, and then the model is used to predict future energy consumption based on road information. The energy consumption model includes five parts: the road load model, the powertrain loss model, the regenerative braking model, the auxiliary system model and the battery model. The parameters of these models are obtained through driving tests on public road and dynamometer tests in the TU/e Automotive Engineering Science lab. The results show that the model can calculate the energy consumption with a maximum error of 5% based on driving speed under different circumstances. To predict the future energy consumption, the road information is obtained from OpenStreetMap and Shuttle Radar Topography Mission. An offline algorithm is built to predict the energy consumption for a future trip based on the road information. The algorithm gives two energy consumption results: one is for the fastest driving speed; the other one is for the most economic driving speed. The results show that the measured energy consumption results for different types of road driving are all within the algorithm's prediction scope. Therefore, the offline algorithm can give an accurate energy consumption estimation to the driver before a trip begins.

Original languageEnglish
Pages (from-to)447-458
Number of pages12
JournalWorld Electric Vehicle Journal
Issue number3
Publication statusPublished - 1 Jan 2015


  • Electric vehicle
  • Energy consumption model
  • Energy prediction
  • Road information


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