loading page

Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols
  • Myisha A. Chowdhury,
  • Saif S.S. Al-Wahaibi,
  • Jay Lu
Myisha A. Chowdhury
Texas Tech University Department of Chemical Engineering
Author Profile
Saif S.S. Al-Wahaibi
Texas Tech University Department of Chemical Engineering
Author Profile
Jay Lu
Texas Tech University Department of Chemical Engineering

Corresponding Author:[email protected]

Author Profile

Abstract

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.
27 May 2024Submitted to AIChE Journal
01 Jul 2024Review(s) Completed, Editorial Evaluation Pending
07 Aug 20241st Revision Received
10 Aug 2024Review(s) Completed, Editorial Evaluation Pending
10 Aug 2024Submission Checks Completed
10 Aug 2024Assigned to Editor
12 Aug 2024Reviewer(s) Assigned
27 Aug 2024Editorial Decision: Accept