# MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange

arXiv: https://arxiv.org/abs/2308.14111 · HTML: https://mlsanity.com/paper/2308.14111/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Parameter Space Noise for Exploration](https://mlsanity.com/paper/1706.01905/) (2017)
- [Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning](https://mlsanity.com/paper/2106.00854/) (2021)
- [Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration](https://mlsanity.com/paper/2111.01294/) (2021)
- [Reliability of Open Public Electric Vehicle Direct Current Fast Chargers](https://mlsanity.com/paper/2203.16372/) (2022)

## Built on by (most cited first, 4 of 4)

- [Safe Decentralized Operation of EV Virtual Power Plant with Limited Network Visibility via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.03278/) (2026)
- [Deep Reinforcement Learning for Community Battery Scheduling under Uncertainties of Load, PV Generation, and Energy Prices](https://mlsanity.com/paper/2312.03008/) (2023)
- [Agent-Based Decentralized Energy Management of EV Charging Station with Solar Photovoltaics via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2505.18750/) (2025)
- [Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.22363/) (2026)

## Abstract (arXiv)

Effective energy management of electric vehicle (EV) charging stations is critical to supporting the transport sector's sustainable energy transition. This paper addresses the EV charging coordination by considering vehicle-to-vehicle (V2V) energy exchange as the flexibility to harness in EV charging stations. Moreover, this paper takes into account EV user experiences, such as charging satisfaction and fairness. We propose a Multi-Agent Reinforcement Learning (MARL) approach to coordinate EV charging with V2V energy exchange while considering uncertainties in the EV arrival time, energy price, and solar energy generation. The exploration capability of MARL is enhanced by introducing parameter noise into MARL's neural network models. Experimental results demonstrate the superior performance and scalability of our proposed method compared to traditional optimization baselines. The decentralized execution of the algorithm enables it to effectively deal with partial system faults in the charging station.