Coordinating V2V Energy Sharing for Electric Fleets via Multi-Granularity Modeling and Dynamic Spatiotemporal Matching

Zhaonian Ye, Qike Han, Kai Han (), Yongzhen Wang (), Changlu Zhao, Haoran Yang and Jun Du
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Zhaonian Ye: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Qike Han: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Kai Han: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Yongzhen Wang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Changlu Zhao: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Haoran Yang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Jun Du: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Sustainability, 2025, vol. 17, issue 19, 1-43

Abstract: The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem.

Keywords: V2V energy sharing; optimization of energy systems; energy management optimization; electric fleets (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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