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Battery electric vehicle usage pattern analysis driven by massive real-world data

Author

Listed:
  • Cui, Dingsong
  • Wang, Zhenpo
  • Liu, Peng
  • Wang, Shuo
  • Zhang, Zhaosheng
  • Dorrell, David G.
  • Li, Xiaohui

Abstract

Electric vehicles (EVs) are playing a key role in supporting transportation electrification and reducing air pollution and greenhouse gas emissions. The increased number of EVs may also bring about some issues concerning energy system structure optimization and efficiency enhancement. User behavior analysis and simulation is an important method to solve these issues. A stochastic model for describing the usage of vehicle is essential to handle simulation models and behavior models. Therefore, a more comprehensive understanding of EV usage patterns is necessary for the model establishment. The paper focuses on the 2,047,222 charging events and 8,382,032 travel events collected from 26,606 battery electric vehicles operating in Beijing, China, in 2018, based on the open lab of National Big Data Alliance of New Energy Vehicles. With the large-scale data resource rather than limited samples, we provide some robust statistical results and some multi-dimensional comparative analysis in the paper, which can be applied in large-scale deployment environments and large population cities. The results can also provide information for charging infrastructures construction, gird management, vehicle charging scheduling, and so forth in Beijing and even other metropolises with similar situations.

Suggested Citation

  • Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhang, Zhaosheng & Dorrell, David G. & Li, Xiaohui, 2022. "Battery electric vehicle usage pattern analysis driven by massive real-world data," Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s036054422200740x
    DOI: 10.1016/j.energy.2022.123837
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    References listed on IDEAS

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    1. Shen, Zuo-Jun Max & Feng, Bo & Mao, Chao & Ran, Lun, 2019. "Optimization models for electric vehicle service operations: A literature review," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 462-477.
    2. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    3. Zhang, Xudong & Zou, Yuan & Fan, Jie & Guo, Hongwei, 2019. "Usage pattern analysis of Beijing private electric vehicles based on real-world data," Energy, Elsevier, vol. 167(C), pages 1074-1085.
    4. Lin, Haiyang & Liu, Yiling & Sun, Qie & Xiong, Rui & Li, Hailong & Wennersten, Ronald, 2018. "The impact of electric vehicle penetration and charging patterns on the management of energy hub – A multi-agent system simulation," Applied Energy, Elsevier, vol. 230(C), pages 189-206.
    5. Zou, Yuan & Wei, Shouyang & Sun, Fengchun & Hu, Xiaosong & Shiao, Yaojung, 2016. "Large-scale deployment of electric taxis in Beijing: A real-world analysis," Energy, Elsevier, vol. 100(C), pages 25-39.
    6. Iversen, Emil B. & Morales, Juan M. & Madsen, Henrik, 2014. "Optimal charging of an electric vehicle using a Markov decision process," Applied Energy, Elsevier, vol. 123(C), pages 1-12.
    7. Wang, Hewu & Zhang, Xiaobin & Ouyang, Minggao, 2015. "Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing," Applied Energy, Elsevier, vol. 157(C), pages 710-719.
    8. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    9. Yanyan Xu & Serdar Çolak & Emre C. Kara & Scott J. Moura & Marta C. González, 2018. "Planning for electric vehicle needs by coupling charging profiles with urban mobility," Nature Energy, Nature, vol. 3(6), pages 484-493, June.
    Full references (including those not matched with items on IDEAS)

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