# EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking

arXiv: https://arxiv.org/abs/2404.01849 · HTML: https://mlsanity.com/paper/2404.01849/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure](https://mlsanity.com/paper/2401.04705/) (2024)

## Built on by (most cited first, 8 of 8)

- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [Repairing Reward Functions with Feedback to Mitigate Reward Hacking](https://mlsanity.com/paper/2510.13036/) (2025)
- [GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments](https://mlsanity.com/paper/2502.01778/) (2025)
- [Chargax: A JAX Accelerated EV Charging Simulator](https://mlsanity.com/paper/2507.01522/) (2025)
- [Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints](https://mlsanity.com/paper/2510.12335/) (2025)
- [Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset](https://mlsanity.com/paper/2604.05429/) (2026)
- [Learning to Route Electric Trucks Under Operational Uncertainty](https://mlsanity.com/paper/2604.26566/) (2026)
- [Emergent Charging Coordination in Electric Delivery Fleets](https://mlsanity.com/paper/2609.07689/) (2026)

## Abstract (arXiv)

As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL) algorithms in the form of a Gym environment, and those that do usually lack depth in modeling Vehicle-to-Grid (V2G) scenarios. To address the aforementioned issues, this paper introduces the EV2Gym, a realistic simulator platform for the development and assessment of small and large-scale smart charging algorithms within a standardized platform. The proposed simulator is populated with comprehensive EV, charging station, power transformer, and EV behavior models validated using real data. EV2Gym has a highly customizable interface empowering users to choose from pre-designed case studies or craft their own customized scenarios to suit their specific requirements. Moreover, it incorporates a diverse array of RL, mathematical programming, and heuristic algorithms to speed up the development and benchmarking of new solutions. By offering a unified and standardized platform, EV2Gym aims to provide researchers and practitioners with a robust environment for advancing and assessing smart charging algorithms.