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<p>This review delves into the latest advancements in smart battery management for lithium-ion batteries (LiBs), which are essential for powering modern technologies and sustainable energy solutions. Smart battery management systems (BMS) are designed to monitor the health of batteries and predict their remaining lifespan. However, accurately gauging battery health and predicting its remaining life remains a challenge due to factors like charging and discharging cycles, chemical reactions, and battery aging. To develop robust and intelligent BMS that can make informed decisions about battery health, addressing the challenges of accurately estimating battery health and predicting its lifespan is critical. This review provides a thorough analysis of current methods used to estimate battery health and predict its remaining lifespan. It also explores the potential of digital twin (DT) technology to enhance smart battery health management. Furthermore, the review proposes a novel digital twin framework for LiBs that combines virtual-real modeling and continuous updating techniques to overcome limitations in existing battery health management methods. This framework offers promising solutions for real-time wireless monitoring and improved battery health management for LiBs.</p>

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Intelligent Health Management of Lithium-Ion Batteries: A Review and Future Perspectives with Digital Twin Technology

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Mengmeng Wang,
  • Shizhe Feng,
  • Darius Andriukaitis,
  • Zhixiong Li,
  • Sumika Chauhan,
  • Govind Vashishtha

摘要

This review delves into the latest advancements in smart battery management for lithium-ion batteries (LiBs), which are essential for powering modern technologies and sustainable energy solutions. Smart battery management systems (BMS) are designed to monitor the health of batteries and predict their remaining lifespan. However, accurately gauging battery health and predicting its remaining life remains a challenge due to factors like charging and discharging cycles, chemical reactions, and battery aging. To develop robust and intelligent BMS that can make informed decisions about battery health, addressing the challenges of accurately estimating battery health and predicting its lifespan is critical. This review provides a thorough analysis of current methods used to estimate battery health and predict its remaining lifespan. It also explores the potential of digital twin (DT) technology to enhance smart battery health management. Furthermore, the review proposes a novel digital twin framework for LiBs that combines virtual-real modeling and continuous updating techniques to overcome limitations in existing battery health management methods. This framework offers promising solutions for real-time wireless monitoring and improved battery health management for LiBs.