Monte Carlo Tree Search-Driven Optimized Preventive Maintenance in Battery Storage Systems

Battery Energy Storage Systems (BESS) play a crucial role in modern power grid. However, battery aging significantly impacts the long-term performance and reliability of BESS. Consequently, the search for effective battery pack maintenance strategies has become essential for ensuring the economic viability of BESS. Traditional maintenance methods struggle to balance reliability and cost effectively. This study introduces the Monte Carlo Tree Search (MCTS) algorithm and combine it with Universal Generating Function (UGF) calculations for battery pack reliability to develop an efficient battery replacement decision-making framework. UGF is used to assess battery pack reliability and employ MCTS to identify the minimum number of new battery units needed to meet or exceed a predefined reliability threshold, thereby minimizing costs. Experiments conducted on battery packs of diverse scales and initial states demonstrate that our method not only significantly enhances battery pack reliability but also effectively manages the consumption of healthy individual units, exhibiting excellent performance in searching for suitable replacement actions. This study offers critical theoretical and practical support for battery pack maintenance and holds significant implications for the future development of BESS.