# Advancements in Electric Vehicle Charging Optimization: A Survey of Reinforcement Learning Approaches

arXiv: https://arxiv.org/abs/2410.16425 · HTML: https://mlsanity.com/paper/2410.16425/

## 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 3 of its 28 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## 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, 3 of 3)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle](https://mlsanity.com/paper/2209.09772/) (2022)

## Abstract (arXiv)

In response to global warming and energy shortages, there has been a significant shift towards integrating renewable energy sources, energy storage systems, and electric vehicles. Deploying electric vehicles within smart grids offers a promising solution to reduce carbon emissions. However, managing the charging and discharging processes of them as distributed power supplies present significant challenges. Additionally, the intermittent nature of renewable energy, uncertainties in electric vehicle-related parameters, fluctuating energy prices, and varying loads make maintaining stable power system operations more complex. Effective management systems for electric vehicle battery charging are crucial to coordinating these processes and ensuring a secure, efficient, and reliable power system. Reinforcement learning, enhanced by deep learning, has gained substantial interest for its model-free approach and real-time optimization, effectively managing electric vehicle charging by maximizing cumulative rewards. This review synthesizes existing literature on reinforcement learning-based frameworks, objectives, and architectures for electric vehicle charging coordination strategies in power systems, classifying methods into centralized and decentralized categories. Additionally, the article offers suggestions for future research directions to further enhance reinforcement learning-based electric vehicle charging optimization.