Online prediction of battery electric vehicle energy consumption

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5 Citations (Scopus)
11 Downloads (Pure)


The energy consumption of battery electric vehicles (BEVs) depends on a number of factors, such as vehicle characteristics, driving behavior, route information, traffic states and weather conditions. The variance of these factors and the correlation among each other make the energy consumption prediction of BEVs difficult. This paper presents an online algorithm to adjust the energy consumption prediction during driving. It includes a vehicle parameter estimation algorithm and a driving behavior correction algorithm. The vehicle parameter estimation algorithm can assess the vehicle mass and rolling resistance during driving. The driving behavior correction algorithm can adjust the energy consumption prediction based on the current driving behavior, and considers the influence of wind and road slope. The online energy consumption prediction algorithm is verified by 21 driving tests, including highway, city, rural and hilly area tests. The comparison shows that the mean absolute percentage error between the actual energy consumption value and online prediction result is within 5% for every test.

Original languageEnglish
Title of host publicationEVS 2016 29th International Electric Vehicle Symposium
PublisherElectric Vehicle Symposium and Exhibition
Number of pages12
ISBN (Electronic)9781510832701
Publication statusPublished - 1 Jan 2016
Event29th International Electric Vehicle Symposium and Exhibition (EVS 2016) - Montreal, Canada
Duration: 19 Jun 201622 Jun 2016
Conference number: 29

Publication series

NameWorld Electric Vehicle Journal
PublisherThe World Electric Vehicle Association (WEVA)
ISSN (Print)2032-6653


Conference29th International Electric Vehicle Symposium and Exhibition (EVS 2016)
Abbreviated titleEVS 2016


  • Battery electric vehicle
  • Energy consumption
  • Online
  • Prediction


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