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<p>Lithium-ion batteries are central to contemporary energy storage systems, yet the precise estimation of critical states—state of charge (SOC), state of health (SOH), and remaining useful life (RUL)—remains a complex challenge under dynamic and varied conditions. Conventional methodologies often fail to meet the required adaptability and precision, leading to a growing emphasis on the application of machine learning (ML) techniques to enhance battery management systems (BMS). This review examines a decade of progress (2013–2024) in ML-based state estimation, meticulously analysing 58 pivotal publications selected from an initial corpus of 2414 studies. Unlike existing reviews, this work uniquely emphasizes the integration of novel frameworks such as Tiny Machine Learning (TinyML) and Scientific Machine Learning (SciML), which address critical limitations by offering resource-efficient and interpretable solutions. Through detailed comparative analyses, the review explores the strengths, weaknesses, and practical considerations of various ML methodologies, focusing on trade-offs in computational complexity, real-time implementation, and generalization across diverse datasets. Persistent barriers, including the absence of standardized datasets, stagnation in innovation, and scalability constraints, are identified alongside targeted recommendations. By synthesizing past advancements and proposing forward-thinking approaches, this review provides valuable insights and actionable strategies to drive the development of robust, scalable, and efficient energy storage technologies.</p>

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A decade of machine learning in lithium-ion battery state estimation: a systematic review

  • 期刊论文

  • 发表日期：

    2025年1月18日

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  • Zaina Al-Hashimi,
  • Taha Khamis,
  • Mouaz Al Kouzbary,
  • Nooranida Arifin,
  • Hamam Mokayed,
  • Noor Azuan Abu Osman

摘要

Lithium-ion batteries are central to contemporary energy storage systems, yet the precise estimation of critical states—state of charge (SOC), state of health (SOH), and remaining useful life (RUL)—remains a complex challenge under dynamic and varied conditions. Conventional methodologies often fail to meet the required adaptability and precision, leading to a growing emphasis on the application of machine learning (ML) techniques to enhance battery management systems (BMS). This review examines a decade of progress (2013–2024) in ML-based state estimation, meticulously analysing 58 pivotal publications selected from an initial corpus of 2414 studies. Unlike existing reviews, this work uniquely emphasizes the integration of novel frameworks such as Tiny Machine Learning (TinyML) and Scientific Machine Learning (SciML), which address critical limitations by offering resource-efficient and interpretable solutions. Through detailed comparative analyses, the review explores the strengths, weaknesses, and practical considerations of various ML methodologies, focusing on trade-offs in computational complexity, real-time implementation, and generalization across diverse datasets. Persistent barriers, including the absence of standardized datasets, stagnation in innovation, and scalability constraints, are identified alongside targeted recommendations. By synthesizing past advancements and proposing forward-thinking approaches, this review provides valuable insights and actionable strategies to drive the development of robust, scalable, and efficient energy storage technologies.