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Dynamic Pricing for Wireless Charging Lane Management Based on Deep Reinforcement Learning

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  • Fan Liu

    (Shandong Hi-Speed Group Co., Ltd., Jinan 250098, China
    Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo 315100, China
    State Key Laboratory of Intelligent Transportation System, Beijing 100191, China)

  • Zhen Tan

    (Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo 315100, China
    Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo 315100, China)

  • Hing Kai Chan

    (College of Business & Public Management, Wenzhou-Kean University, Wenzhou 325060, China)

Abstract

We consider a dynamic pricing problem in a double-lane system consisting of one general purpose lane and one wireless charging lane (WCL). The electricity price is dynamically adjusted to affect the lane-choice behaviors of incoming electric vehicles (EVs), thereby regulating the traffic assignment between the two lanes with both traffic operation efficiency and charging service efficiency considered in the control objective. We first establish an agent-based dynamic double-lane traffic system model, whereby each EV acts as an agent with distinct behavioral and operational characteristics. Then, a deep Q-learning algorithm is proposed to derive the optimal pricing decisions. A regression tree (CART) algorithm is also designed for benchmarking. The simulation results reveal that the deep Q-learning algorithm demonstrates superior capability in optimizing dynamic pricing strategies compared to CART by more effectively leveraging system dynamics and future traffic demand information, and both outperform the static pricing strategy. This study serves as a pioneering work to explore dynamic pricing issues for WCLs.

Suggested Citation

  • Fan Liu & Zhen Tan & Hing Kai Chan, 2025. "Dynamic Pricing for Wireless Charging Lane Management Based on Deep Reinforcement Learning," Sustainability, MDPI, vol. 17(21), pages 1-30, November.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:21:p:9831-:d:1787388
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