Energy consumption analysis and prediction of electric vehicles based on real-world driving data

Jin Zhang, Zhenpo Wang, Peng Liu and Zhaosheng Zhang

Applied Energy, 2020, vol. 275, issue C, No S030626192030920X

Abstract: With increasing mass-adoption of electric vehicles, the energy consumption has become a key performance index to electric vehicle drivers, automakers and policy-makers. Accurate and real-time energy consumption prediction under real-world driving conditions is essential for alleviating the ‘range anxiety’ and can provide support for optimal battery sizing, energy-efficient route planning and charging infrastructures operation. In this paper, real-world driving data collected from fifty-five electric taxis in Beijing city are obtained and divided into three-level driving fragments. The influencing factors of energy consumption, including vehicle-, environment-, and driver-related factors, are extracted and studied. With the extracted key influencing factors, a novel machine learning-based energy consumption prediction framework integrated with driving condition prediction is proposed and used in actual energy consumption prediction. The real-world trip test results show that a root mean squared error of 0.159kWh (RMSE) and a mean absolute percentage error 12.68% (MAPE) are reached, the RMSE and the MAPE are respectively reduced by 32.05% and by 30.14% compared to the conventional method.

Keywords: Electric vehicles; Energy consumption prediction; Influencing factors; Driving condition (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (44)

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DOI: 10.1016/j.apenergy.2020.115408

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