DRL For Battery Cycle Optimization: Theory and Practice

Xinrui Hu

doi:10.54097/6zm94b42

Authors

Xinrui Hu

DOI:

https://doi.org/10.54097/6zm94b42

Keywords:

Deep Reinforcement Learning (DRL), Battery Optimization, Charge/Discharge Cycles, Markov Decision Process (MDP). Energy Efficiency, Lifespan Extension.

Abstract

In this paper, we investigate the use of Deep Reinforcement Learning (DRL) to optimize the charge/discharge cycles of batteries, aiming to improve their operational efficiency and lifespan. By casting the battery management problem within a Markov Decision Process framework, we apply a DRL algorithm to learn an effective policy for cycle optimization. Our empirical results demonstrate the superiority of the DRL approach over traditional methods in terms of energy efficiency and cycle life. We also provide a theoretical analysis of the algorithm's stability and convergence properties. The paper concludes with a discussion on the implications of our findings and potential directions for future research.

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