Innovations and prognostics in battery degradation and longevity for energy storage systems

Battery technology plays a vital role in modern energy storage across diverse applications, from consumer electronics to electric vehicles and renewable energy systems. However, challenge related to battery degradation and the unpredictable lifetime hinder further advancement and widespread adoption. Battery degradation and longevity directly affect a system's reliability, efficiency, and cost-effectiveness, ensuring stable energy supply and minimizing replacement needs. This study presents a comprehensive review of the recent developments in understanding battery degradation mechanisms and emerging prognostic approaches for lifetime prediction. Key contributions include an in-depth analysis of physical and chemical processes contributing to capacity loss, advanced diagnostic techniques, and innovative machine learning models that enhance prediction accuracy. Distinct from other reviews, this paper synthesizes various approaches - mathematical, hybrid, and data-driven by highlighting their strengths and limitations in real world applications. The study concludes by comparing findings, identifying key research gaps, and proposing future directions to enhance battery lifespan and optimize performance, providing valuable insights for researchers, engineers, and industry stakeholders. • Investigating the physical/chemical processes that lead to battery capacity loss and performance • Review the mathematical and hybrid models for battery degradation, usage, and maintenance • Highlight the advanced prognostic approaches, ML and data-driven models, for predicting battery lifespan