Energy consumption analysis and prediction of electric vehicles based on real-world driving data
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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03062619
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Supplemental Notes:
- © 2020 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
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Authors:
- Zhang, Jin
- Wang, Zhenpo
- Liu, Peng
- Zhang, Zhaosheng
- Publication Date: 2020-10-1
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 115408
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Serial:
- Applied Energy
- Volume: 275
- Publisher: Elsevier
- ISSN: 0306-2619
- Serial URL: http://www.sciencedirect.com/science/journal/03062619
Subject/Index Terms
- TRT Terms: Data analysis; Electric vehicles; Energy consumption; Machine learning; Predictive models
- Geographic Terms: Beijing (China)
- Subject Areas: Data and Information Technology; Energy; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01835135
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 31 2022 4:50PM