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Network-level energy consumption estimation for electric vehicles considering vehicle and user heterogeneity

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  • Wang, Hua
  • Zhao, De
  • Meng, Qiang
  • Ong, Ghim Ping
  • Lee, Der-Horng

Abstract

As a green transport means, electric vehicle (EV) has received widespread attention in recent years and an increasing number of cities have been establishing their EV transport systems including EV fleet management and charging infrastructure. Transport policy-makers are concerning about the system performance of an EV charging system measured by energy consumption and user experience. We in this paper aim to develop an effective method for the expected total energy consumption estimation (ETEC) of EV charging systems deployed in a dense city. To achieve this objective, we firstly introduce the nonlinear charging profile, its impacts on energy consumption as well as user experience, and present an approach for estimating the nonlinear charging time. We then elaborate our method to estimate energy consumption for one-time EV charging by addressing four kinds of energy losses in charging process. Next, charging frequency by considering multi-type EVs, and their heterogeneities and different charging needs for normal and fast charging systems is analyzed in depth. These two together determine the network-wide energy consumption and a pro-rated approach is used to figure out the spatial distribution of energy consumption. A case study of Singapore is conducted in the end to validate the proposed methodology. Numerical results reveal a trade-off between energy saving and user experience, and also demonstrate the importance of considering heterogeneities of driving range, charging preference and daily travel mileage.

Suggested Citation

  • Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2020. "Network-level energy consumption estimation for electric vehicles considering vehicle and user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 30-46.
    • Handle: RePEc:eee:transa:v:132:y:2020:i:c:p:30-46
    DOI: 10.1016/j.tra.2019.10.010
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    References listed on IDEAS

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    4. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    5. Li, Baicheng & Szeto, W.Y. & Zou, Liang, 2022. "Optimal fare and fleet size regulation in a taxi/ride-sourcing market with congestion effects, emission externalities, and gasoline/electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 215-243.
    6. Liu, Yang & Zhang, Qi & Lyu, Cheng & Liu, Zhiyuan, 2021. "Modelling the energy consumption of electric vehicles under uncertain and small data conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 313-328.
    7. Xu, Min & Meng, Qiang, 2020. "Optimal deployment of charging stations considering path deviation and nonlinear elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 120-142.

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