# Chargax: A JAX Accelerated EV Charging Simulator

arXiv: https://arxiv.org/abs/2507.01522 · HTML: https://mlsanity.com/paper/2507.01522/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 15 of 15)

- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Improving Sample Efficiency in Model-Free Reinforcement Learning from Images](https://mlsanity.com/paper/1910.01741/) (2019)
- [Equinox: neural networks in JAX via callable PyTrees and filtered transformations](https://mlsanity.com/paper/2111.00254/) (2021)
- [Discovered Policy Optimisation](https://mlsanity.com/paper/2210.05639/) (2022)
- [XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX](https://mlsanity.com/paper/2312.12044/) (2023)
- [Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning](https://mlsanity.com/paper/2303.17503/) (2023)
- [Podracer architectures for scalable Reinforcement Learning](https://mlsanity.com/paper/2104.06272/) (2021)
- [NAVIX: Scaling MiniGrid Environments with JAX](https://mlsanity.com/paper/2407.19396/) (2024)
- [Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach](https://mlsanity.com/paper/2107.07772/) (2021)
- [Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging](https://mlsanity.com/paper/2012.02636/) (2020)
- [EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking](https://mlsanity.com/paper/2404.01849/) (2024)
- [Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration](https://mlsanity.com/paper/2111.01294/) (2021)
- [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)
- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)

## Abstract (arXiv)

Deep Reinforcement Learning can play a key role in addressing sustainable energy challenges. For instance, many grid systems are heavily congested, highlighting the urgent need to enhance operational efficiency. However, reinforcement learning approaches have traditionally been slow due to the high sample complexity and expensive simulation requirements. While recent works have effectively used GPUs to accelerate data generation by converting environments to JAX, these works have largely focussed on classical toy problems. This paper introduces Chargax, a JAX-based environment for realistic simulation of electric vehicle charging stations designed for accelerated training of RL agents. We validate our environment in a variety of scenarios based on real data, comparing reinforcement learning agents against baselines. Chargax delivers substantial computational performance improvements of over 100x-1000x over existing environments. Additionally, Chargax' modular architecture enables the representation of diverse real-world charging station configurations.