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

Zhenhua Chen, Qiong Chen, Yiying Chao and Cheng Xue ()
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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

Mathematics, 2025, vol. 13, issue 13, 1-20

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.

Keywords: PHEVs; shortest path problem; partial charging; hybrid energy mode; MILP; labeling algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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