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Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles

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  • Zhang, Fan
  • Lv, Huitao
  • Xing, Qiang
  • Ji, Yanjie

Abstract

As the delivery sector increasingly relies on electric micromobility vehicles (EMVs), the urgency for efficient battery-swapping infrastructure becomes critical. This study develops a comprehensive framework for predicting battery-swapping demand for delivery EMVs (DEMVs) based on an activity-based travel chain simulation model and devises a multi-objective optimization model for the strategic placement of battery-swapping stations. The simulation model integrates submodules such as the EMV generation and attraction model, Agent-based EMV travel chain, and EMV and battery-swapping behavior model to capture the nuanced travel patterns and battery-swapping demand. Leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the study optimizes the network design of battery-swapping stations considering both construction and travel costs. A case study in Nanjing City, representative of the diverse delivery sector's operations, substantiates the simulation's accuracy, maps out the spatiotemporal distribution of swapping demand, and analyzes the Pareto optimal set derived from the optimization model. Sensitivity analysis focuses on the facility planning model, assessing how uncertainties in swapping demand and battery charging rates within stations impact operational efficacy. This research melds demand forecasting with infrastructure optimization, providing actionable insights for the planning and management of battery-swapping stations.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544224000239
    DOI: 10.1016/j.energy.2024.130252
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    References listed on IDEAS

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    1. Bo Zhang & Meng Zhao & Xiangpei Hu, 2023. "Location planning of electric vehicle charging station with users’ preferences and waiting time: multi-objective bi-level programming model and HNSGA-II algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 61(5), pages 1394-1423, March.
    2. R.P. Manatkar & Kondapaneni Karthik & Sri Krishna Kumar & Manoj Kumar Tiwari, 2016. "An integrated inventory optimization model for facility location-allocation problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(12), pages 3640-3658, June.
    3. 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).
    4. Metais, M.O. & Jouini, O. & Perez, Y. & Berrada, J. & Suomalainen, E., 2022. "Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    5. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    6. Fan, Zhang & Yanjie, Ji & Huitao, Lv & Yuqian, Zhang & Blythe, Phil & Jialiang, Fan, 2022. "Travel satisfaction of delivery electric two-wheeler riders: Evidence from Nanjing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 253-266.
    7. Qiang Xing & Zhong Chen & Ziqi Zhang & Xiao Xu & Tian Zhang & Xueliang Huang & Haiwei Wang, 2020. "Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior," Energies, MDPI, vol. 13(6), pages 1-32, March.
    8. 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).
    9. Michael Kuby & Seow Lim, 2007. "Location of Alternative-Fuel Stations Using the Flow-Refueling Location Model and Dispersion of Candidate Sites on Arcs," Networks and Spatial Economics, Springer, vol. 7(2), pages 129-152, June.
    10. 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).
    11. Noor-E-Alam, Md. & Mah, Andrew & Doucette, John, 2012. "Integer linear programming models for grid-based light post location problem," European Journal of Operational Research, Elsevier, vol. 222(1), pages 17-30.
    12. Guo, Fang & Yang, Jun & Lu, Jianyi, 2018. "The battery charging station location problem: Impact of users’ range anxiety and distance convenience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 1-18.
    13. Guo, Fang & Zhang, Jingjing & Huang, Zhihong & Huang, Weilai, 2022. "Simultaneous charging station location-routing problem for electric vehicles: Effect of nonlinear partial charging and battery degradation," Energy, Elsevier, vol. 250(C).
    14. 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.
    15. Liu, Shan & Jiang, Hai & Chen, Shuiping & Ye, Jing & He, Renqing & Sun, Zhizhao, 2020. "Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
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