Enhancing Remaining Useful Life in Solid-State Batteries: Smart Battery Management System Strategies

This paper focuses on the critical role of Smart Battery Management Systems (SBMS) in enhancing the Remaining Useful Life (RUL) of solid-state batteries (SSBs), a key component in the next generation of energy storage technology. SSBs, known for their high energy density and safety, require advanced management to maximize their lifespan and efficiency. We examine the latest developments and strategies in SBMS, designed explicitly for SSBs, to extend their RUL. The paper comprehensively overviews current techniques, including state-of-the-art predictive algorithms and real-time monitoring systems. Special attention is given to innovative approaches such as Artificial Neural Networks and Adaptive Fuzzy Logic, which have shown significant potential in accurately predicting battery degradation and failure. We also discuss the unique challenges associated with SSBs, such as their sensitivity to various operational and environmental conditions, and how SBMS can effectively address these issues. By comparing different methodologies and highlighting their strengths and limitations, this review aims to identify the most promising strategies for improving the RUL of SSBs in the future. The collected information in the paper can be a valuable resource for researchers and industry professionals looking to enhance battery management practices, contributing to the advancement of sustainable and efficient energy storage solutions.