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A scalable energy modeling framework for electric vehicles in regional transportation networks

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  • Xu, Xiaodan
  • Aziz, H.M. Abdul
  • Liu, Haobing
  • Rodgers, Michael O.
  • Guensler, Randall

Abstract

Vehicle electrification plays a central role in reducing global energy use and greenhouse gas emissions. Predicting electric vehicle (EV) energy use for future transportation networks is critical for the planning, design, and operations of sustainable transportation systems. However, there is currently a lack of EV energy modeling approaches that are fully-scalable to large transportation network applications and consider actual on-road vehicle operating conditions. Such an approach is required for the accurate assessment of EV energy impact under various transportation scenarios. Here we present a simulation-based quasi-statistical approach to estimate EV energy consumption under various on-road vehicle operating conditions. In this approach, a Bayesian Network method is used to integrate outputs from full-system vehicle simulation tools for specific makes and models of EVs under a wide-variety of on-road operating conditions. These outputs are used to develop inference models that greatly improve computational efficiency, while maintaining most of the prediction accuracy of the complete system models. This approach is both highly scalable and transferable for analyzing the energy impact of EV fleet deployment in different regions, can facilitate the estimation of network-level EV energy consumption, and can be incorporated into a wide-variety of transportation planning models. In our case study of Atlanta, GA, the results indicate that if 6.2% of urban travel distances and 4.9% of rural travel distances were to be driven by EVs, regional fuel savings would be around 4.0% for a typical travel day in 2024.

Suggested Citation

  • Xu, Xiaodan & Aziz, H.M. Abdul & Liu, Haobing & Rodgers, Michael O. & Guensler, Randall, 2020. "A scalable energy modeling framework for electric vehicles in regional transportation networks," Applied Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:appene:v:269:y:2020:i:c:s0306261920306073
    DOI: 10.1016/j.apenergy.2020.115095
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    References listed on IDEAS

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    Cited by:

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    4. Dai, Ziyi & Liu, Haobing & Rodgers, Michael O. & Guensler, Randall, 2022. "Electric vehicle market potential and associated energy and emissions reduction benefits," Applied Energy, Elsevier, vol. 322(C).
    5. Wang, An & Xu, Junshi & Zhang, Mingqian & Zhai, Zhiqiang & Song, Guohua & Hatzopoulou, Marianne, 2022. "Emissions and fuel consumption of a hybrid electric vehicle in real-world metropolitan traffic conditions," Applied Energy, Elsevier, vol. 306(PB).
    6. Maksymilian Mądziel, 2024. "Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses," Energies, MDPI, vol. 17(5), pages 1-22, February.
    7. Sun, Bin & Zhang, Qijun & Wei, Ning & Jia, Zhenyu & Li, Chunming & Mao, Hongjun, 2022. "The energy flow of moving vehicles for different traffic states in the intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    8. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).
    9. Raymond Kene & Thomas Olwal & Barend J. van Wyk, 2021. "Sustainable Electric Vehicle Transportation," Sustainability, MDPI, vol. 13(22), pages 1-16, November.

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