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World Electric Vehicle Journal is published by MDPI from Volume 9 issue 1 (2018). Articles in this Issue were published by The World Electric Vehicle Association (WEVA) and its member the European Association for e-Mobility (AVERE), the Electric Drive Transportation Association (EDTA), and the Electric Vehicle Association of Asia Pacific (EVAAP). They are hosted by MDPI on mdpi.com as a courtesy and upon agreement with AVERE.
Open AccessArticle

Online Prediction of Battery Electric Vehicle Energy Consumption

Dynamics and Control Group, Department of Mechanical Engineering, Eindhoven University of Technology Den Dolech 2, 5612 AZ Eindhoven, The Netherlands
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World Electr. Veh. J. 2016, 8(1), 213-224; https://doi.org/10.3390/wevj8010213
Published: 25 March 2016
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Abstract

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.
Keywords: battery electric vehicle; energy consumption; prediction, online battery electric vehicle; energy consumption; prediction, online
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, J.; Besselink, I.; Nijmeijer, H. Online Prediction of Battery Electric Vehicle Energy Consumption. World Electr. Veh. J. 2016, 8, 213-224.

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