mlsanityData to 14 Oct 2026

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

arXiv 2406.12309 ↗First posted 18 Jun 2024eess.SY cs.SYcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

Key facts. Cited by 1 paper 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.

Builds on

10 of 34 references.

References · 10

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  5. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
  6. 2020Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?cited by 34
  7. 2017Gaussian process regression for forecasting battery state of healthcited by 28
  8. 2023Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraintscited by 11
  9. 2020Reinforcement Learning-based Fast Charging Control Strategy for Li-ion Batteriescited by 1
  10. 2020Optimal Charging Method for Effective Li-ion Battery Life Extension Based on Reinforcement Learningcited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Reinforcement Learning for Robust Ageing-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Physics-Informed Reinforcement Learning Approach for Degradation-Aware Long-Term Charging Optimization in Batteries
  2. 2024Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network
  3. 2025Balancing SoC in Battery Cells using Safe Action Perturbations
  4. 2025A Hybrid Physics-Based and Reinforcement Learning Framework for Electric Vehicle Charging Time Prediction
  5. 2025Lifelong reinforcement learning for health-aware fast charging of lithium-ion batteries ↗
  6. 2024Advancements in Electric Vehicle Charging Optimization: A Survey of Reinforcement Learning Approaches
  7. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
  8. 2025Deep Reinforcement Learning-Based Optimization of Second-Life Battery Utilization in Electric Vehicles Charging Stations