The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Energy consumption analysis and prediction of electric vehicles based on real-world driving data
Abstract
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.
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Citations
Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1603.02754 · Cited by 7,104
Electric vehicles’ energy consumption measurement and estimation
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Energy consumption and cost-benefit analysis of hybrid and electric city buses
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Energy consumption estimation integrated into the Electric Vehicle Routing Problem
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Impact of driving characteristics on electric vehicle energy consumption and range
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Wang, H., Zhang, X., & Ouyang, M. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.05.057 · Cited by 207
Electric vehicles’ energy consumption estimation with real driving condition data
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Cited by
Al-Wreikat, Y., Serrano, C., & Sodré, J. R. (2022) · Energy · doi:10.1016/j.energy.2021.122028 · Cited by 220
Ramsey, D., Bouscayrol, A., Boulon, L., Desreveaux, A., & Vaudrey, A. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3144526 · Cited by 39
Simolin, T., Rauma, K., Viri, R., Mäkinen, J., Rautiainen, A., & Järventausta, P. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117651 · Cited by 36
Huang, H.-C., He, H.-D., & Peng, Z.-R. (2024) · Energy · doi:10.1016/j.energy.2024.130665 · Cited by 18
Dou, G., Ke, J., Liang, J., Wang, J., Li, J., Liu, Q., & Hao, C. (2023) · Atmosphere · doi:10.3390/atmos14101562 · Cited by 7
Metadata
- Authors
- Zhang, J., Wang, Z., Liu, P., & Zhang, Z.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 275
- Pages
- 115408
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W3037516875
- Cited by
- 322
- References
- 36
- FWCI
- 10.30
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
