IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v299y2024ics0360544224006169.html

An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing

Author

Listed:
  • Zhang, Lei
  • Huang, Zhijia
  • Wang, Zhenpo
  • Li, Xiaohui
  • Sun, Fengchun

Abstract

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling.

Suggested Citation

  • Zhang, Lei & Huang, Zhijia & Wang, Zhenpo & Li, Xiaohui & Sun, Fengchun, 2024. "An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing," Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:energy:v:299:y:2024:i:c:s0360544224006169
    DOI: 10.1016/j.energy.2024.130844
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224006169
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130844?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. Maki, Seiya & Fujii, Minoru & Fujita, Tsuyoshi & Shiraishi, Yasushi & Ashina, Shuichi & Gomi, Kei & Sun, Lu & Budi Nugroho, Sudarmanto & Nakano, Ryoko & Osawa, Takahiro & Immanuel, Gito & Boer, Rizald, 2022. "A deep reinforced learning spatiotemporal energy demand estimation system using deep learning and electricity demand monitoring data," Applied Energy, Elsevier, vol. 324(C).
    2. Liu, Yuechen Sophia & Tayarani, Mohammad & Gao, H. Oliver, 2022. "An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand," Energy, Elsevier, vol. 258(C).
    3. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    4. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    5. Sunoh Kim & Jin Hur, 2020. "A Probabilistic Modeling Based on Monte Carlo Simulation of Wind Powered EV Charging Stations for Steady-States Security Analysis," Energies, MDPI, vol. 13(20), pages 1-13, October.
    6. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    7. Alexis Gerossier & Robin Girard & George Kariniotakis, 2019. "Modeling and Forecasting Electric Vehicle Consumption Profiles," Energies, MDPI, vol. 12(7), pages 1-14, April.
    8. Yin, Wanjun & Ji, Jianbo & Wen, Tao & Zhang, Chao, 2023. "Study on orderly charging strategy of EV with load forecasting," Energy, Elsevier, vol. 278(C).
    9. Akhtar Hussain & Petr Musilek, 2022. "Reliability-as-a-Service Usage of Electric Vehicles: Suitability Analysis for Different Types of Buildings," Energies, MDPI, vol. 15(2), pages 1-18, January.
    10. 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).
    11. Pareschi, Giacomo & Küng, Lukas & Georges, Gil & Boulouchos, Konstantinos, 2020. "Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data," Applied Energy, Elsevier, vol. 275(C).
    12. Koeneman, Scott H. & Cavanaugh, Joseph E., 2022. "An improved asymptotic test for the Jaccard similarity index for binary data," Statistics & Probability Letters, Elsevier, vol. 184(C).
    13. Dillman, Kevin Joseph & Fazeli, Reza & Shafiei, Ehsan & Jónsson, Jón Örvar G. & Haraldsson, Hákon Valur & Davíðsdóttir, Brynhildur, 2021. "Spatiotemporal analysis of the impact of electric vehicle integration on Reykjavik's electrical system at the city and distribution system level," Utilities Policy, Elsevier, vol. 68(C).
    14. Li, Guoyuan & Chen, Anthony, 2022. "Frequency-based path flow estimator for transit origin-destination trip matrices incorporating automatic passenger count and automatic fare collection data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    15. Ren, Fei & Tian, Chenlu & Zhang, Guiqing & Li, Chengdong & Zhai, Yuan, 2022. "A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features," Energy, Elsevier, vol. 250(C).
    16. Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    17. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    18. Heng, Pei & Sun, Yi, 2023. "Algorithms for Convex Hull Finding in Undirected Graphical Models," Applied Mathematics and Computation, Elsevier, vol. 445(C).
    19. Majidpour, Mostafa & Qiu, Charlie & Chu, Peter & Pota, Hemanshu R. & Gadh, Rajit, 2016. "Forecasting the EV charging load based on customer profile or station measurement?," Applied Energy, Elsevier, vol. 163(C), pages 134-141.
    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. Sijiang Ma & Jin Ning & Ning Mao & Jie Liu & Ruifeng Shi, 2024. "Research on Machine Learning-Based Method for Predicting Industrial Park Electric Vehicle Charging Load," Sustainability, MDPI, vol. 16(17), pages 1-18, August.
    2. Kılkış, Şiir, 2025. "Urban scenario approaches with quartiles of benchmarked performance and co-benefits," Energy, Elsevier, vol. 335(C).
    3. Neto, Daywes Pinheiro & Coimbra, António Paulo & Moura, Pedro & de Almeida, Tony Richard & de Almeida, Aníbal Traça, 2026. "Multi-objective techno-economic optimization of hybrid photovoltaic and battery energy storage systems for electric vehicle charging stations," Renewable Energy, Elsevier, vol. 259(C).
    4. Lee, Wonjong & Koo, Yoonmo & Kim, Yong-gun, 2024. "Environmental time-of-use scheme: Strategic leveraging of financial and environmental incentives for greener electric vehicle charging," Energy, Elsevier, vol. 309(C).
    5. Zhuang, Yingrui & Cheng, Lin & Qi, Ning & Wang, Xinyi & Chen, Yue, 2025. "Real-time hosting capacity assessment for electric vehicles: A sequential forecast-then-optimize method," Applied Energy, Elsevier, vol. 380(C).
    6. Bruno Knevitz Hammerschmitt & Clodomiro Unsihuay-Vila & Jordan Passinato Sausen & Marcelo Bruno Capeletti & Alexandre Rasi Aoki & Mateus Duarte Teixeira & Carlos Henrique Barriquello & Alzenira da Ros, 2024. "Adaptive Charging Simulation Model for Different Electric Vehicles and Mobility Patterns," Energies, MDPI, vol. 17(16), pages 1-21, August.
    7. Shengcong Wu & Hang Li & Hang Wang, 2025. "Seasonal Load Statistics of EV Charging and Battery Swapping Stations Based on Gaussian Mixture Model for Charging Strategy Optimization in Electric Power Distribution Systems," Energies, MDPI, vol. 18(20), pages 1-24, October.
    8. Zhang, Qi & Yang, Kexin & Fang, Siying, 2025. "Stochastic optimization of electric vehicle charging strategy based on day-ahead high precision forecast for renewable power and charging demand," Energy, Elsevier, vol. 338(C).
    9. Wu, Hongbin & Lan, Xinjie & He, Ye & Wu, Andrew Y. & Ding, Ming, 2025. "Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation," Applied Energy, Elsevier, vol. 378(PA).
    10. Guo, Hongxia & Chen, Lingxuan & Wang, Zhaocai & Li, Lin, 2025. "Day-ahead prediction of electric vehicle charging demand based on quadratic decomposition and dual attention mechanisms," Applied Energy, Elsevier, vol. 381(C).
    11. Liu, Bingchun & Gao, Xu & Wang, Ying & Zhao, Shunfan, 2025. "Co-construction strategy of battery swapping stations and charging piles in China," Transport Policy, Elsevier, vol. 169(C), pages 56-73.
    12. Chen, Fangjian & Wang, Yubin & Deng, Hang & Sun, Qianhao & Chen, Qifang & Xia, Mingchao, 2025. "Probabilistic prediction of electric vehicle load in service area incorporating highway spatiotemporal traffic information," Energy, Elsevier, vol. 329(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. 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).
    2. Yamaguchi, Yohei & Ochi, Yudai & Ohara, Ryotaro & Uchida, Hideaki & Yoshizawa, Shinya & Sakai, Katsuya & Ota, Yutaka & Shimoda, Yoshiyuki, 2025. "Large-scale spatiotemporal modeling of travel behavior, electric vehicle charging demand, and flexibility based on human mobility big data," Energy, Elsevier, vol. 335(C).
    3. Zhuang, Yingrui & Cheng, Lin & Qi, Ning & Wang, Xinyi & Chen, Yue, 2025. "Real-time hosting capacity assessment for electric vehicles: A sequential forecast-then-optimize method," Applied Energy, Elsevier, vol. 380(C).
    4. Jiang, An & Qiu, Jiehong & Li, Aiyuan & Zhang, Guangnan, 2025. "An empirical analysis framework to evaluate the impact of residential electric vehicles on power grid," Transport Policy, Elsevier, vol. 173(C).
    5. Liu, Yuechen Sophia & Tayarani, Mohammad & Gao, H. Oliver, 2022. "An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand," Energy, Elsevier, vol. 258(C).
    6. Du, Bojun & Jia, Hongyang & Zhang, Bochao & Li, Yaowang & Wang, Han & Yan, Jie & Du, Ershun & Zhang, Ning, 2025. "Evaluating the carbon-emission reduction potential of employing low-carbon demand response to guide electric-vehicle charging: A Chinese case study," Applied Energy, Elsevier, vol. 397(C).
    7. Mao, Yaqi & Yu, Xiaobing & Wang, Feng & Zhu, Junhua, 2026. "Electric vehicle charging demand forecasting: A data-driven integrated learning approach," Renewable Energy, Elsevier, vol. 256(PD).
    8. Zhang, Fan & Lv, Huitao & Xing, Qiang & Ji, Yanjie, 2024. "Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles," Energy, Elsevier, vol. 290(C).
    9. Shang, Yitong & Li, Duo & Li, Yang & Li, Sen, 2025. "Explainable spatiotemporal multi-task learning for electric vehicle charging demand prediction," Applied Energy, Elsevier, vol. 384(C).
    10. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    11. Xu, Maosheng & Gao, Shan & Zheng, Junyi & Huang, Xueliang & Wu, Chuanshen, 2024. "Day-ahead electric vehicle charging behavior forecasting and schedulable capacity calculation for electric vehicle parking lot," Energy, Elsevier, vol. 309(C).
    12. Byungsung Lee & Haesung Lee & Hyun Ahn, 2020. "Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation," Energies, MDPI, vol. 13(18), pages 1-15, September.
    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. Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
    15. Zhang, Tianren & Huang, Yuping & Liao, Hui & Liang, Yu, 2023. "A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network," Applied Energy, Elsevier, vol. 351(C).
    16. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    17. Quintero Fuentes, Abel & Hickman, Mark & Whitehead, Jake, 2025. "Zone substations' readiness to embrace electric vehicle adoption: Brisbane case study," Energy, Elsevier, vol. 322(C).
    18. Yu, Gang & Ye, Xianming & Gong, Dunwei & Xia, Xiaohua, 2025. "Stochastic planning for transition from shopping mall parking lots to electric vehicle charging stations," Applied Energy, Elsevier, vol. 379(C).
    19. Cao, Tingwei & Xu, Yinliang & Liu, Guowei & Tao, Shengyu & Tang, Wenjun & Sun, Hongbin, 2024. "Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station," Applied Energy, Elsevier, vol. 371(C).
    20. Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.

    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:299:y:2024:i:c:s0360544224006169. 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.