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Decision Making for Energy Acquisition of Electric Vehicle Taxi with Profit Maximization

Author

Listed:
  • Li Cui

    (School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
    Center of Engineering Training, Nanjing Institute of Technology, Nanjing 211167, China)

  • Yanping Wang

    (School of Artificial Intelligence and Computer Science, North China University of Technology, Beijing 100144, China)

  • Hongquan Qu

    (School of Artificial Intelligence and Computer Science, North China University of Technology, Beijing 100144, China)

  • Yiqiang Li

    (Center of Engineering Training, Nanjing Institute of Technology, Nanjing 211167, China)

  • Mingshen Wang

    (Center of Engineering Training, Nanjing Institute of Technology, Nanjing 211167, China)

  • Qingyuan Wang

    (Center of Engineering Training, Nanjing Institute of Technology, Nanjing 211167, China)

Abstract

With the emergence of joint business operations involving electric vehicle taxis (EVTs) and charging/swapping stations (CSSTs), a unified decision-making method has become essential for an EVT to select both the driving path and the energy acquisition mode (EAM). The decision making is influenced by energy acquisition cost and potential operation profit. The energy acquisition cost is closely related to the driving time required to reach a CSST, and existing prediction methods for driving time ignore the spatial–temporal interactions of traffic flows on different roads and fail to account for traffic congestion differences across various sections of a road. Existing estimation methods for potential operation income ignore the distributions of taxi orders in different areas. To address these issues, a traffic flow prediction model is first proposed based on the long short-term memory–generative adversarial network (LSTM-GAN) deep learning algorithm. A refined driving time model is developed by segmenting a road into different sections. Then, an expected operation income model is developed considering the distributions of origins and destinations of taxi orders in different areas. Then, a decision-making method for path planning and the charging/swapping mode is proposed, aiming to maximize the total profit of EVTs. Finally, the effectiveness of the proposed decision-making method for EVTs is validated with a city’s traffic network.

Suggested Citation

  • Li Cui & Yanping Wang & Hongquan Qu & Yiqiang Li & Mingshen Wang & Qingyuan Wang, 2025. "Decision Making for Energy Acquisition of Electric Vehicle Taxi with Profit Maximization," Sustainability, MDPI, vol. 17(11), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:5116-:d:1670590
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    References listed on IDEAS

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