# Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols

arXiv: https://arxiv.org/abs/2406.12309 · HTML: https://mlsanity.com/paper/2406.12309/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 18 Jun 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, 10 of 10)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [A Lyapunov-based Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/1805.07708/) (2018)
- [Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?](https://mlsanity.com/paper/2004.00915/) (2020)
- [Gaussian process regression for forecasting battery state of health](https://mlsanity.com/paper/1703.05687/) (2017)
- [Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints](https://mlsanity.com/paper/2304.08743/) (2023)
- [Reinforcement Learning-based Fast Charging Control Strategy for Li-ion Batteries](https://mlsanity.com/paper/2002.02060/) (2020)
- [Optimal Charging Method for Effective Li-ion Battery Life Extension Based on Reinforcement Learning](https://mlsanity.com/paper/2005.08770/) (2020)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning for Robust Ageing-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verification](https://mlsanity.com/paper/2509.04288/) (2025)

## Abstract (arXiv)

Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL-based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this work proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions.