Scholarlink

<p>The increasing adoption of electric vehicles has contributed significantly in addressing global environmental concerns and minimizing the emission of carbon di oxide. Among numerous energy storage systems, Lithium-ion batteries are utilizedwidely owing to its high energy density, efficiency, as well as longer lifespan. However, ensuring optimal battery performance, safety, and longevity necessitates an advanced Battery Management System that accurately monitors, regulates and optimizes both discharging and charging processes. This article presents a comprehensive review of new advancements designed to enhance battery management functionality. A systematic search methodology is employed to analyze and extract valuable insights from four major scientific literature databases, focusing on techniques for state estimation, fault diagnosis, thermal management, and charge balancing. Furthermore, the study highlights key challenges in existing technologies and discusses potential opportunities for developing next-generation intelligent algorithms and controllers. The results provide a strong foundation for designing more sophisticated solutions, ultimately improving battery efficiency, reliability, and sustainability in future technologies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Smart Algorithms and Controls for Electric Vehicle Battery Management Systems: A State-of-the-Art Review

  • 期刊论文

  • 发表日期：

    2025年6月23日

查看全文
  • Prashanta Kumar Dehury,
  • Sudhansu Kumar Samal

摘要

The increasing adoption of electric vehicles has contributed significantly in addressing global environmental concerns and minimizing the emission of carbon di oxide. Among numerous energy storage systems, Lithium-ion batteries are utilizedwidely owing to its high energy density, efficiency, as well as longer lifespan. However, ensuring optimal battery performance, safety, and longevity necessitates an advanced Battery Management System that accurately monitors, regulates and optimizes both discharging and charging processes. This article presents a comprehensive review of new advancements designed to enhance battery management functionality. A systematic search methodology is employed to analyze and extract valuable insights from four major scientific literature databases, focusing on techniques for state estimation, fault diagnosis, thermal management, and charge balancing. Furthermore, the study highlights key challenges in existing technologies and discusses potential opportunities for developing next-generation intelligent algorithms and controllers. The results provide a strong foundation for designing more sophisticated solutions, ultimately improving battery efficiency, reliability, and sustainability in future technologies.