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EVTradeMatch: A Mobility-Aware Multi-Objective Matching Framework for EV--EV Energy Trading

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Updated 3 h ago · first seen 11 Sept 2026

paper_01M294FPKSCB94KTGKHW6YEYSY

Published: 11 Sept 2026; T1 · 3 h ago
arXiv: 2609.10551; T1 · 3 h ago
Category: math.OC; T1 · 3 h ago

Abstract

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.

Authors 4

Md. Mahfujur Rahman, Alistair Barros, Raja Jurdak, Darshika Koggalahewa

Specification

Official page: arxiv.org/abs/2609.10551
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
arXiv id: 2609.10551
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Categories: math.OC, cs.LG, cs.SY, eess.SY
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
PDF: arxiv.org/pdf/2609.10551
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Primary category: math.OC
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

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T19

Freshest observation

3 h ago

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None

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Claim history · Abstract

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Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
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.	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	1 h ago	1

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