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Optimizing PHEV Routing with Hybrid Mode and Partial Charging via Labeling-Based Methods

Author

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  • Zhenhua Chen

    (College of Transport & Communications, Shanghai Maritime University, Shanghai 201306, China)

  • Qiong Chen

    (Navigation College, Jimei University, Xiamen 361021, China)

  • Yiying Chao

    (Zhoushan Campus, Zhejiang University, Zhoushan 316021, China)

  • Cheng Xue

    (Zhoushan Campus, Zhejiang University, Zhoushan 316021, China)

Abstract

This study investigates a variant of the shortest path problem (SPP) tailored for plug-in hybrid electric vehicles (PHEVs), incorporating two practical features: hybrid energy mode switching and partial charging. A novel modeling framework is proposed that enables PHEVs to dynamically switch between electricity and fuel along each edge and to recharge partially at charging stations. Unlike most prior studies that rely on more complex modeling approaches, this paper introduces a compact mixed-integer linear programming (MILP) model that remains directly solvable using commercial solvers such as Gurobi. To address large-scale networks, a customized labeling algorithm is developed for an efficient solution. Numerical results on benchmark networks show that the hybrid mode and partial charging can reduce total cost by up to 29.76% and significantly affect route choices. The proposed algorithm demonstrates strong scalability, solving instances with up to 33,000 nodes while maintaining near-optimal performance, with less than 5% deviation in smaller cases.

Suggested Citation

  • Zhenhua Chen & Qiong Chen & Yiying Chao & Cheng Xue, 2025. "Optimizing PHEV Routing with Hybrid Mode and Partial Charging via Labeling-Based Methods," Mathematics, MDPI, vol. 13(13), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2092-:d:1687532
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    References listed on IDEAS

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