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<p>The rise in demand for sustainable transportation solutions in recent years has resulted in Electric vehicles (EVs) attracting substantial interest. The rapid rise in EV adoption has led to the need for efficient prognostics and health management (PHM) solutions. To ensure reliable and safe operation, the batteries that power EVs require maintenance. This paper reviews the detailed artificial intelligence (AI)-driven PHM framework for mobility batteries. Different mobility batteries have different chemistries owing to the different battery materials used for their development. AI-driven PHM uses machine learning, deep learning, and data-driven techniques to accurately estimate key battery parameters that include state of health and remaining useful life. We review data collection strategies, data processing approaches, feature development, and battery health assessment techniques based on AI, reviewing various AI methodologies that include artificial neural networks, Gaussian process regression, convolutional neural networks, and many more. This inclusive review of the recent trends and methodologies of AI-based mobility battery PHM provides a framework to develop a future efficient PHM solution for safer, efficient, and reliable battery systems.</p>

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Recent Advances in Artificial Intelligence-Driven Prognostics and Health Management of Mobility Batteries

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Prashant Kumar,
  • Heung Soo Kim,
  • Sungjun Kim,
  • Hyun-Seok Kim,
  • Janghyuk Moon

摘要

The rise in demand for sustainable transportation solutions in recent years has resulted in Electric vehicles (EVs) attracting substantial interest. The rapid rise in EV adoption has led to the need for efficient prognostics and health management (PHM) solutions. To ensure reliable and safe operation, the batteries that power EVs require maintenance. This paper reviews the detailed artificial intelligence (AI)-driven PHM framework for mobility batteries. Different mobility batteries have different chemistries owing to the different battery materials used for their development. AI-driven PHM uses machine learning, deep learning, and data-driven techniques to accurately estimate key battery parameters that include state of health and remaining useful life. We review data collection strategies, data processing approaches, feature development, and battery health assessment techniques based on AI, reviewing various AI methodologies that include artificial neural networks, Gaussian process regression, convolutional neural networks, and many more. This inclusive review of the recent trends and methodologies of AI-based mobility battery PHM provides a framework to develop a future efficient PHM solution for safer, efficient, and reliable battery systems.