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

arXiv: https://arxiv.org/abs/2609.10551 · HTML: https://mlsanity.com/paper/2609.10551/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems under Demand and Supply Uncertainties](https://mlsanity.com/paper/2211.13797/) (2022)
- [Reducing Waiting Times at Charging Stations with Adaptive Electric Vehicle Route Planning](https://mlsanity.com/paper/2102.06503/) (2021)
- [Electric Vehicle Charge Scheduling Mechanism to Maximize Cost Efficiency and User Convenience](https://mlsanity.com/paper/1803.04750/) (2018)
- [Multi-objective Scheduling of Electric Vehicle Charging/Discharging with Time of Use Tariff](https://mlsanity.com/paper/2108.05062/) (2021)
- [Flexible Energy Management Protocol for Cooperative EV-to-EV Charging](https://mlsanity.com/paper/1802.06059/) (2018)
- [EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading](https://mlsanity.com/paper/2603.26688/) (2026)

## Abstract (arXiv)

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±0.76% matching coverage and transfer 1701.94±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.