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Usage pattern analysis of Beijing private electric vehicles based on real-world data

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  • Zhang, Xudong
  • Zou, Yuan
  • Fan, Jie
  • Guo, Hongwei

Abstract

Developing electric vehicles, as one of the most effective measures in constructing clean transportation, has been vigorously prompted by China's government recently through series of beneficial policies. Thus, both the sales and manufacturing of electric vehicles have witnessed prosperous growth in the last decade, especially in Beijing, whose pure electric vehicles ownership has exceeded 170,000 by the end of 2017 and ranked first in China. However, large-scale deployment of electric vehicles may also bring about troublesome problems concerning the construction of charging infrastructure and stability of electric grid. A comprehensive analysis of usage pattern of electric vehicles, especially the majority for private usage, is useful in predicting the charging load and understanding the driving characteristics, thus guiding location of charging facilities and assisting energy management of electric grid. By collecting operational data of forty-one private electric vehicles with 33,041 trips and 4738 charging events, sixteen characteristic parameters including charge consumption, state of charge before/after charging, single-trip distance, daily distance travelled, specific energy consumption, etc. are analyzed in detail. The analysis results are useful in facilitating charging infrastructures construction, management of state gird, evaluation of emerging vehicular technology and so forth in Beijing and even other metropolises with similar situation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:1074-1085
    DOI: 10.1016/j.energy.2018.11.005
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    References listed on IDEAS

    as
    1. Zhang, Xiang & Bai, Xue, 2017. "Incentive policies from 2006 to 2016 and new energy vehicle adoption in 2010–2020 in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 24-43.
    2. Hewu Wang & Xiaobin Zhang & Lvwei Wu & Cong Hou & Huiming Gong & Qian Zhang & Minggao Ouyang, 2015. "Beijing passenger car travel survey: implications for alternative fuel vehicle deployment," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 20(5), pages 817-835, June.
    3. Gong, Huiming & Zou, Yuan & Yang, Qingkai & Fan, Jie & Sun, Fengchun & Goehlich, Dietmar, 2018. "Generation of a driving cycle for battery electric vehicles：A case study of Beijing," Energy, Elsevier, vol. 150(C), pages 901-912.
    4. Li, Wenbo & Long, Ruyin & Chen, Hong, 2016. "Consumers’ evaluation of national new energy vehicle policy in China: An analysis based on a four paradigm model," Energy Policy, Elsevier, vol. 99(C), pages 33-41.
    5. 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.
    6. Ross Morrow, W. & Gallagher, Kelly Sims & Collantes, Gustavo & Lee, Henry, 2010. "Analysis of policies to reduce oil consumption and greenhouse-gas emissions from the US transportation sector," Energy Policy, Elsevier, vol. 38(3), pages 1305-1320, March.
    7. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    8. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    9. 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.
    10. Schroeder, Andreas & Traber, Thure, 2012. "The economics of fast charging infrastructure for electric vehicles," Energy Policy, Elsevier, vol. 43(C), pages 136-144.
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    14. Yang, Xiong & Zhuge, Chengxiang & Shao, Chunfu & Huang, Yuantan & Hayse Chiwing G. Tang, Justin & Sun, Mingdong & Wang, Pinxi & Wang, Shiqi, 2022. "Characterizing mobility patterns of private electric vehicle users with trajectory data," Applied Energy, Elsevier, vol. 321(C).
    15. Zhou, Yuekuan & Liu, Xiaohua & Zhao, Qianchuan, 2024. "A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid," Renewable Energy, Elsevier, vol. 221(C).
    16. Zhao, Li & Li, Yuqi & Li, Shuai & Ke, Hanchen, 2023. "A frequency item mining based embedded feature selection algorithm and its application in energy consumption prediction of electric bus," Energy, Elsevier, vol. 271(C).
    17. Ruisheng Wang & Qiang Xing & Zhong Chen & Ziqi Zhang & Bo Liu, 2022. "Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    18. Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo, 2022. "Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle," Energy, Elsevier, vol. 238(PC).
    19. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Li, Shaojie & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2023. "Building-centric investigation into electric vehicle behavior: A survey-based simulation method for charging system design," Energy, Elsevier, vol. 271(C).
    20. Powell, Siobhan & Vianna Cezar, Gustavo & Apostolaki-Iosifidou, Elpiniki & Rajagopal, Ram, 2022. "Large-scale scenarios of electric vehicle charging with a data-driven model of control," Energy, Elsevier, vol. 248(C).
    21. 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).
    22. Ortega-Cabezas, Pedro-Miguel & Colmenar-Santos, Antonio & Borge-Diez, David & Blanes-Peiró, Jorge-Juan, 2021. "Can eco-routing, eco-driving and eco-charging contribute to the European Green Deal? Case Study: The City of Alcalá de Henares (Madrid, Spain)," Energy, Elsevier, vol. 228(C).

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