Chargax: A JAX Accelerated EV Charging Simulator

TL;DR

Chargax is a JAX-based EV charging simulation environment that significantly accelerates reinforcement learning training and supports diverse real-world station configurations, aiding sustainable energy solutions.

Contribution

Introduces Chargax, a novel JAX-based EV charging simulator that offers over 100x performance improvements and modular design for realistic, scalable RL training.

Findings

Achieves 100x-1000x faster simulation performance.

Validates effectiveness with real data scenarios.

Supports diverse charging station configurations.

Abstract

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.