Abstract:

Lithium-ion batteries (LIBs) play an essential role in much of today's portable electronics, industrial products, energy storage systems and electric vehicles. Nevertheless, there have been unacceptable numbers of fires and explosions stemming from LIBs, and numerous recalls of products that were broken due to swollen batteries or failed or had premature performance degradation due to the LIBs. This study reviews the state-of-the-art methods and techniques in the reliability and safety analysis of LIBs with a focus on emerging computational methods to manage and predict battery health and safety in real-time environments. Discussions follow to identify the key failure mechanisms and how proper design, manufacture, testing, and health monitoring are needed to ensure the reliability and safety of products which incorporate LIBs. Moreover, the review provides a detailed discussion on data-driven prognostics, machine learning algorithms, and their applicability in real-time monitoring and predictive maintenance of batteries. Notably, this review contrasts conventional approaches with new trends integrating LIBs with the internet of things for enhanced monitoring and safety, showcasing novel pathways for achieving UN Sustainable Development Goals related to energy and sustainability. Presenting strategies that could greatly aid in achieving global emission reduction targets, this review offers a distinctive viewpoint on the changing technology landscape in LIB reliability management. The integration of predictive and preventative techniques is crucial for improving battery safety and enabling the broad adoption of LIB-based technologies in a net-zero future, according to significant findings.

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