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<p>Battery safety, longevity, and reliability are enhanced through accurate assessment of the state of health (SOH) and remaining useful life (RUL). Traditional research often relies on a single model framework to estimate SOH/RUL in lithium-ion batteries (LIBs). However, due to the complex internal mechanisms of LIBs and varying external conditions, a single model may not provide reliable predictions. Recently, increasing attention has been given to hybrid techniques that combine data-driven and model-based approaches with filtering methods such as Kalman filters (KF) and particle filters (PFs), given their accuracy and robustness across different environments. Still, relatively few studies focus on filtering-based assessments of SOH/RUL. This work provides a review of hybrid approaches integrated with KF and PF for SOH/RUL estimation. It also examines co-estimation methods and discusses applications in electric vehicles, while outlining key challenges and future research directions.</p>

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Filtering techniques for lithium-ion battery state of health and remaining useful life prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • Md Ibrahim,
  • Mohd Zeeshan Hasan,
  • Mohd Salman,
  • M. S. Hossain Lipu,
  • Iftiab Ahammed Sarker,
  • Shaheer Ansari,
  • Chiara Bordin

摘要

Battery safety, longevity, and reliability are enhanced through accurate assessment of the state of health (SOH) and remaining useful life (RUL). Traditional research often relies on a single model framework to estimate SOH/RUL in lithium-ion batteries (LIBs). However, due to the complex internal mechanisms of LIBs and varying external conditions, a single model may not provide reliable predictions. Recently, increasing attention has been given to hybrid techniques that combine data-driven and model-based approaches with filtering methods such as Kalman filters (KF) and particle filters (PFs), given their accuracy and robustness across different environments. Still, relatively few studies focus on filtering-based assessments of SOH/RUL. This work provides a review of hybrid approaches integrated with KF and PF for SOH/RUL estimation. It also examines co-estimation methods and discusses applications in electric vehicles, while outlining key challenges and future research directions.