A Comprehensive Review of Battery Fault Diagnosis, State-of- Health Estimation, and Remaining Useful Life Prediction

Battery systems are critical components in various applications, including electric vehicles (EVs), uninterruptible power supplies (UPS), and smart grids, where safety, reliability, and performance are paramount. Accurate fault diagnosis, precise State-of-Health (SOH) estimation, and reliable prediction of Remaining Useful Life (RUL) are essential for preventing unexpected failures, optimizing maintenance schedules, and extending battery lifespan. This paper provides a comprehensive review of existing methods and emerging trends in battery monitoring, covering physical modeling, data-driven approaches, and hybrid techniques. The study also addresses degradation mechanisms, fault indicators, and aging models, along with industrial applications and case studies. Finally, key challenges, limitations, and future research directions are discussed, offering valuable insights for the development of advanced Battery Management Systems (BMS) and next-generation energy storage technologies.

کلیدواژه ها:

Battery monitoring ، fault diagnosis ، state-of-health (SOH) ، remaining useful life (RUL) ، machine learning ، degradation modeling ، battery management system (BMS)