Spatio-Temporal V2V energy sharing with Path-Speed Co-Optimization for electric fleets

• Proposes STLCSS-ACO V2V framework reducing EV fleet operational costs by 21.2 %. • STLCSS algorithm enables precise spatio-temporal matching of provider/consumer EVs. • 3.Multi-objective ACO co-optimizes paths and speeds to minimize fleet energy and cost. The electrification of delivery fleets introduces significant challenges regarding uneven energy utilization and inefficient scheduling. While Vehicle-to-Vehicle (V2V) energy sharing offers a potential solution, current strategies often lack the precision required for dynamic supply–demand matching and fail to fully integrate path-speed co-optimization. To address these limitations, this study introduces an integrated V2V energy sharing system designed to minimize total operational costs, including energy consumption and time-window penalties. We employ the spatiotemporal longest common subsequence algorithm to ensure precise trajectory and temporal alignment between vehicles. This is coupled with a multi-objective ant colony optimization model that dynamically adjusts vehicle routes and speeds. Simulation results demonstrate that, compared to conventional charging station replenishment, this approach reduces overall operating costs by 21.2 % and improves the energy utilization of provider vehicles by 5.2 %. These findings validate that integrating advanced spatio-temporal matching with path-speed co-optimization significantly enhances the sustainability and cost-effectiveness of electric fleet logistics.