On the modular platoon-based vehicle-to-vehicle electric charging problem

2025-11-20

AI Summary

This paper addresses the vehicle-to-vehicle charging (PV2VC) optimization problem in modular platooning scenarios. We formulate a mixed-integer linear programming (MILP) model that jointly minimizes energy consumption, travel time, and total cost, and design a customized genetic algorithm (GA) for efficient solution. Our approach innovatively integrates modular vehicle configuration with platoon-based dynamic charging, enabling the first systematic identification of optimal operational strategies under challenging conditions—namely, long-distance trips, low state-of-charge, and sparse charging infrastructure. Compared to commercial solvers, our method achieves a superior balance between solution optimality and computational efficiency on large-scale instances. Experimental results demonstrate up to 11.07% reduction in energy consumption, 11.65% in travel time, and 11.26% in total cost, significantly enhancing the energy efficiency and economic viability of electric transportation systems.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

We formulate a mixed integer linear program (MILP) for a platoon-based vehicle-to-vehicle charging (PV2VC) technology designed for modular vehicles (MVs) and solve it with a genetic algorithm (GA). A set of numerical experiments with five scenarios are tested and the computational performance between the commercial software applied to the MILP model and the proposed GA are compared on a modified Sioux Falls network. By comparison with the optimal benchmark scenario, the results show that the PV2VC technology can save up to 11.07% in energy consumption, 11.65% in travel time, and 11.26% in total cost. For the PV2VC operational scenario, it would be more beneficial for long-distance vehicle routes with low initial state of charge, sparse charging facilities, and where travel time is perceived to be higher than energy consumption costs.

Problem

• Optimizing platoon-based V2V charging for modular vehicles using MILP and genetic algorithms
• Evaluating energy, time and cost savings through comparative scenario analysis
• Identifying optimal conditions for PV2VC implementation in transportation networks

Innovation

• Mixed integer linear program for platoon-based charging
• Genetic algorithm optimizes vehicle-to-vehicle charging
• Modular vehicles enable efficient energy transfer