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<p>Exact evaluations of Li-ion battery conditions are necessary for efficient battery management. Various chemicals, capacity testing, hybrid pulse power characterization testing analysis, open-circuit voltage analysis, state of charge calculation techniques, direct shaping, adaptive including techniques, and nonlinear control techniques are all part of the research on Li-ion battery technology. Direct measurements require battery-charge designation once, while adaptive methods change estimates based on changing conditions. Nonlinear observer techniques use complex models and expect SoC to be appropriate. Learning algorithms with neural networks of deep learning for reality-based solutions for estimating specific SoCs. Hybrid methods combine particular techniques, such as adaptive neuro-fuzzy interference and artificial neural network systems, to improve the accuracy of SoC estimation. These various methods address complex conditions caused by Li-ion battery chemistry and environmental factors, and ensure reliable SoC estimation for optimal battery performance. This article describes various battery chemistries, unique quality tests, and commonly used methods for calculating energy levels, along with their advantages and disadvantages.</p>

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Characterization of Li-ion battery and state of charge Estimation methods for diverse battery chemistries: a review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月3日

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  • Shailesh Shirguppikar,
  • Pankaj Gavali,
  • Vaibhav Ganachari,
  • Prashant Jadhav,
  • M. Zubairuddin,
  • Sachin Khot,
  • Juber Mulla,
  • Amol S. Todkar,
  • S. Prabhakar

摘要

Exact evaluations of Li-ion battery conditions are necessary for efficient battery management. Various chemicals, capacity testing, hybrid pulse power characterization testing analysis, open-circuit voltage analysis, state of charge calculation techniques, direct shaping, adaptive including techniques, and nonlinear control techniques are all part of the research on Li-ion battery technology. Direct measurements require battery-charge designation once, while adaptive methods change estimates based on changing conditions. Nonlinear observer techniques use complex models and expect SoC to be appropriate. Learning algorithms with neural networks of deep learning for reality-based solutions for estimating specific SoCs. Hybrid methods combine particular techniques, such as adaptive neuro-fuzzy interference and artificial neural network systems, to improve the accuracy of SoC estimation. These various methods address complex conditions caused by Li-ion battery chemistry and environmental factors, and ensure reliable SoC estimation for optimal battery performance. This article describes various battery chemistries, unique quality tests, and commonly used methods for calculating energy levels, along with their advantages and disadvantages.