IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v307y2024ics0360544224024940.html

Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China

Author

Listed:
  • Meng, Yihao
  • Zou, Yuan
  • Ji, Chengda
  • Zhai, Jianyang
  • Zhang, Xudong
  • Zhang, Zhaolong

Abstract

The electrification of transportation is profoundly reshaping human society and presenting new challenges in terms of travel modes, infrastructure development, and energy supply. Given the potential for large-scale scheduling of electric logistics vehicles (ELVs), it is crucial to thoroughly analyze the usage characteristics and establish reliable models. This study examines the usage patterns and charging behaviors of 29 ELVs in Shenzhen, China, encompassing 34,856 trips and 14,464 charging events. Furthermore, behavior-time probability density models were constructed based on an improved Gaussian mixture model (GMM), which avoids the fitting error caused by misclassification of time series data across time nodes. The article also provides a comprehensive analysis of other statistical findings related to the travel and charging activities of ELVs. The conclusions drawn from this research can serve as valuable references for industries involved in infrastructure construction, power grid management, battery virtual aggregation, and similar sectors.

Suggested Citation

  • Meng, Yihao & Zou, Yuan & Ji, Chengda & Zhai, Jianyang & Zhang, Xudong & Zhang, Zhaolong, 2024. "Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China," Energy, Elsevier, vol. 307(C).
  • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224024940
    DOI: 10.1016/j.energy.2024.132720
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224024940
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.132720?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Vincent Barthel & Jonas Schlund & Philipp Landes & Veronika Brandmeier & Marco Pruckner, 2021. "Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data," Energies, MDPI, vol. 14(16), pages 1-16, August.
    4. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    5. Sida Qian & Lei Li, 2023. "A Comparison of Well-to-Wheels Energy Use and Emissions of Hydrogen Fuel Cell, Electric, LNG, and Diesel-Powered Logistics Vehicles in China," Energies, MDPI, vol. 16(13), pages 1-18, July.
    6. Colmenar-Santos, Antonio & Muñoz-Gómez, Antonio-Miguel & Rosales-Asensio, Enrique & López-Rey, África, 2019. "Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario," Energy, Elsevier, vol. 183(C), pages 61-74.
    7. Yuhui Zhao & Xinyan Zhu & Wei Guo & Bing She & Han Yue & Ming Li, 2019. "Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    8. Zhao, Yang & Jiang, Ziyue & Chen, Xinyu & Liu, Peng & Peng, Tianduo & Shu, Zhan, 2023. "Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets," Energy, Elsevier, vol. 285(C).
    9. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    10. Schiffer, Maximilian & Klein, Patrick S. & Laporte, Gilbert & Walther, Grit, 2021. "Integrated planning for electric commercial vehicle fleets: A case study for retail mid-haul logistics networks," European Journal of Operational Research, Elsevier, vol. 291(3), pages 944-960.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lu, Qing-Chang & Wang, Shixin & Xu, Peng-Cheng & Li, Jing & Meng, Xu & Hussain, Adil, 2025. "Modeling the dependency relationship of coupled power and transportation networks," Energy, Elsevier, vol. 320(C).
    2. Zhu, Ming & Li, Yingkun & Chen, Xiong & Zhou, Changsheng & Mi, Junjie & Liu, Tiantian & Li, Weixuan, 2025. "Nonlinear adaptive robust control strategy of thrust regulation for pintle solid-propellant rocket motors," Energy, Elsevier, vol. 329(C).
    3. David W. Eby & Renée M. St. Louis & Jennifer S. Zakrajsek & Nicole Zanier, 2025. "Adoption and Use of Battery Electric Vehicles Among Older Drivers: A Review and Research Recommendations," Sustainability, MDPI, vol. 17(7), pages 1-13, March.
    4. Chih-Hung Hsu & Shu-Jin Chen & Ming-Qiang Huang & Qi Le, 2024. "Industry 5.0 Drivers Analysis Using Grey-DEMATEL: A Logistics Case in Emerging Economies," Mathematics, MDPI, vol. 12(22), pages 1-27, November.
    5. Meng, Yihao & Zou, Yuan & Du, Guodong & Zhang, Xudong & Zhang, Zhaolong, 2026. "Low-carbon economic dispatch for microgrid-integrated charging stations: A cost-oriented bi-layer optimization framework," Applied Energy, Elsevier, vol. 407(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cui, Dingsong & Chen, Haibo & Watling, David & Liu, Ye & Liu, Jin & Wang, Chenxi & Xu, Mengyuan & Wang, Shuo & Wang, Zhenpo & Xu, Chengcheng, 2025. "Environmental emission analysis of frugal electric vehicles from a life cycle perspective: A case study," Energy, Elsevier, vol. 335(C).
    2. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    3. Sheng, Yujie & Zeng, Hongtai & Guo, Qinglai & Yu, Yang & Li, Qiang, 2023. "Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations," Applied Energy, Elsevier, vol. 348(C).
    4. Xin, Wentao & Lu, Zhenwei & Yu, Zhe & He, Zhaoxuan & Pu, Hongjiang & Ye, Bin, 2025. "Aggregator-driven optimisation of electric vehicle charging stations in Shenzhen: Synergising smart charging, renewable energy integration and energy storage," Applied Energy, Elsevier, vol. 397(C).
    5. Peng, Chang & Xu, Chengcheng & Jiao, Lijuan, 2025. "Variational spatiotemporal factorized graph neural networks for integrated electric vehicle usage prediction and pattern recognition with missing data on regional networks," Energy, Elsevier, vol. 335(C).
    6. Thiemo Fetzer & Samuel Marden, 2017. "Take What You Can: Property Rights, Contestability and Conflict," Economic Journal, Royal Economic Society, vol. 0(601), pages 757-783, May.
    7. Zhang, Chengquan & Kitamura, Hiroshi & Goto, Mika, 2024. "Feasibility of vehicle-to-grid (V2G) implementation in Japan: A regional analysis of the electricity supply and demand adjustment market," Energy, Elsevier, vol. 311(C).
    8. Daniel Agness & Travis Baseler & Sylvain Chassang & Pascaline Dupas & Erik Snowberg, 2025. "Valuing the Time of the Self-Employed," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 92(6), pages 3471-3503.
    9. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    10. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    11. Nicoleta Serban & Huijing Jiang, 2012. "Multilevel Functional Clustering Analysis," Biometrics, The International Biometric Society, vol. 68(3), pages 805-814, September.
    12. Anahita Nodehi & Mousa Golalizadeh & Mehdi Maadooliat & Claudio Agostinelli, 2025. "Torus Probabilistic Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 42(2), pages 435-456, July.
    13. Fu, Zhi & Liu, Xiaochen & Zhang, Ji & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2025. "Orderly solar charging of electric vehicles and its impact on charging behavior: A year-round field experiment," Applied Energy, Elsevier, vol. 381(C).
    14. Orietta Nicolis & Jean Paul Maidana & Fabian Contreras & Danilo Leal, 2024. "Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    15. Wang, Zhenpo & Zhang, Dayu & Liu, Peng & Lin, Ni & Zhang, Zhaosheng & She, Chengqi, 2024. "An online inconsistency evaluation and abnormal cell identification method for real-world electric vehicles," Energy, Elsevier, vol. 307(C).
    16. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    17. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    18. Forzani, Liliana & Gieco, Antonella & Tolmasky, Carlos, 2017. "Likelihood ratio test for partial sphericity in high and ultra-high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 18-38.
    19. Yujia Li & Xiangrui Zeng & Chien‐Wei Lin & George C. Tseng, 2022. "Simultaneous estimation of cluster number and feature sparsity in high‐dimensional cluster analysis," Biometrics, The International Biometric Society, vol. 78(2), pages 574-585, June.
    20. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224024940. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.