EVTradeMatch：一种面向电动汽车间能量交易的运动感知多目标匹配框架
EVTradeMatch: A Mobility-Aware Multi-Objective Matching Framework for EV--EV Energy Trading
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- Queensland University of Technology（昆士兰科技大学）
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中文总结 AI 辅助
EVTradeMatch提出一种预测引导的多目标优化框架，通过NSGA-II算法在移动性约束下优化电动汽车间能量交易的匹配覆盖率、转移能量和充电节点适宜性，显著提升交易效率。
中文摘要 AI 辅助
电动汽车（EV）之间的点对点能量交易可以在充电基础设施有限的情况下提高充电灵活性，但有效的电动汽车间交易需要在行程特定条件下进行协调的提供者-消费者匹配。本文提出了EVTradeMatch，一种用于运动感知的电动汽车间能量交易的预测引导多目标优化框架。基于EVNextTrade研究（一种先前的用于充电节点推荐的排序学习模型），我们将充电节点适宜性定义为一种预测衍生的分数，该分数基于移动性、能量和上下文交易特征，反映将提供者-消费者对分配到候选充电节点的适当性。该分数被用作引导信号和显式优化目标，而非硬性选择规则。电动汽车间匹配问题被建模为一个多目标混合整数线性规划，该规划在空间、时间、一对一匹配和充电节点容量约束下，最大化匹配覆盖率、转移能量和充电节点适宜性，同时最小化移动成本。为了在广域动态环境中逼近帕累托有效解，我们开发了一种定制的非支配排序遗传算法II（NSGA-II）。实验结果表明，与基于邻近度和拍卖的最先进方法相比，EVTradeMatch将转移能量提高了74.2%至82.8%，充电节点适宜性提高了8.3%至84.4%，同时将匹配覆盖率提高了3.74至25.07个百分点。平衡的NSGA-II解决方案实现了53.07±0.76%的匹配覆盖率，并转移了1701.94±17.03千瓦时的能量，但以更高的移动成本作为与最小化行程方法的显式权衡。帕累托前沿分析表明，该框架支持根据运营优先级在高覆盖率、高能量、低移动成本和高适宜性解决方案之间进行灵活选择。
英文摘要
Peer-to-peer energy trading among electric vehicles (EVs) can improve charging flexibility under limited charging infrastructure, but effective EV--EV trading requires coordinated provider--consumer matching under journey-specific conditions. This paper proposes EVTradeMatch, a prediction-guided multi-objective optimization framework for mobility-aware EV--EV energy trading. Building on the EVNextTrade study, a prior learning-to-rank model for charging-node recommendation, we define charging-node suitability as a prediction-derived score reflecting the appropriateness of assigning a provider--consumer pair to a candidate charging node based on mobility, energy, and contextual trading features. This score is used as a guidance signal and as an explicit optimization objective rather than as a hard selection rule. The EV--EV matching problem is formulated as a multi-objective mixed-integer linear program that maximizes matching coverage, transferred energy, and charging-node suitability while minimizing mobility cost under spatial, temporal, one-to-one matching, and charging-node capacity constraints. To approximate Pareto-efficient solutions in wide-area dynamic settings, we develop a tailored non-dominated sorting genetic algorithm II (NSGA-II). Experimental results show that EVTradeMatch improves transferred energy by 74.2--82.8% and charging-node suitability by 8.3--84.4% compared with proximity- and auction-based state-of-the-art methods, while improving matching coverage by 3.74--25.07 percentage points. Balanced NSGA-II solutions achieve 53.07$\pm$0.76% matching coverage and transfer 1701.94$\pm$17.03 kWh, with higher mobility cost as an explicit trade-off against travel-minimizing methods. Pareto-front analysis shows that the framework supports flexible selection among high-coverage, high-energy, low-mobility-cost, and high-suitability solutions according to operational priorities.
