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Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

Author

Listed:
  • Qiong Bao

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Minghao Gao

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Jianming Chen

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Xu Tan

    (School of Transportation, Southeast University, Nanjing 211189, China)

Abstract

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

Suggested Citation

  • Qiong Bao & Minghao Gao & Jianming Chen & Xu Tan, 2024. "Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization," Mathematics, MDPI, vol. 12(19), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3143-:d:1493763
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    References listed on IDEAS

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    1. Zhang, Xinfang & Zhang, Zhe & Liu, Yang & Xu, Zhigang & Qu, Xiaobo, 2024. "A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation," Renewable Energy, Elsevier, vol. 234(C).
    2. Kuang, Haoxuan & Qu, Haohao & Deng, Kunxiang & Li, Jun, 2024. "A physics-informed graph learning approach for citywide electric vehicle charging demand prediction and pricing," Applied Energy, Elsevier, vol. 363(C).
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