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New framework optimizes EV-to-EV energy trading using multi-objective optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EVTradeMatch, a new framework designed to optimize peer-to-peer energy trading among electric vehicles (EVs). This system uses a prediction-guided, multi-objective optimization approach to match EV providers and consumers, considering factors like mobility, energy transfer, and charging node suitability. The framework employs a tailored non-dominated sorting genetic algorithm II (NSGA-II) to find Pareto-efficient solutions, aiming to balance objectives such as maximizing coverage, transferred energy, and suitability while minimizing mobility costs. Experimental results indicate significant improvements in transferred energy and charging node suitability compared to existing methods. AI

IMPACT This framework could enhance the efficiency and flexibility of energy management in electric vehicle networks.

RANK_REASON The item is an academic paper detailing a new framework and algorithm for a specific optimization problem. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework optimizes EV-to-EV energy trading using multi-objective optimization

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The item is an academic paper detailing a new framework and algorithm for a specific optimization problem. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Md. Mahfujur Rahman, Alistair Barros, Raja Jurdak, Darshika Koggalahewa ·

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

    arXiv:2609.10551v1 Announce Type: cross 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…

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