Scholarlink

<p>The pressing environmental impact of carbon emissions from fossil fuels and diesel engines has accelerated the transition toward electric mobility and advanced energy storage solutions. Among these, lithium-ion batteries (LIBs) have emerged as the dominant technology for electric vehicles (EVs) due to their high energy density, low self-discharge, and long cycle life. Accurate estimation of the state of charge (SOC) is a critical function of the battery management system (BMS), directly influencing battery longevity, charging/discharging efficiency, and driving safety. Nevertheless, challenges such as battery aging, temperature variations, and measurement noise continue to hinder reliable SOC estimation. This review provides a comprehensive analysis of recent data-driven SOC estimation methods, encompassing classification and regression models, fuzzy logic systems, probabilistic approaches, recurrent neural networks, feedforward neural networks, and hybrid algorithms. The discussion highlights their theoretical foundations, model architectures, input features, operational mechanisms, standardized flowcharts, and performance evaluation metrics, alongside their strengths and limitations. A detailed comparative study further evaluates these approaches in terms of data sources, input–output characteristics, hyperparameter tuning strategies, and overall performance. In addition, the review identifies critical gaps in existing data-driven methodologies, particularly with respect to scalability, large-scale cell balancing, multi-state co-estimation, and real-time implementation using hardware-in-the-loop platforms. Opportunities for integrating emerging technologies such as the Internet of Things (IoT) and cloud computing into SOC estimation frameworks are also discussed. Ultimately, this study outlines key research directions that can support industry practitioners and academic researchers in developing accurate, robust, and practically deployable SOC estimation strategies, thereby advancing the reliability and sustainability of EV applications.</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

Data-Driven and Computational Intelligence Approaches for SOC Estimation in Lithium-Ion Batteries: Challenges, Opportunities, and Future Trends

  • 期刊论文

  • 发表日期：

    2026年4月21日

查看全文
  • Saibal Manna,
  • Bhupender Sharma,
  • Vivek Saxena,
  • Deepak Kumar Singh

摘要

The pressing environmental impact of carbon emissions from fossil fuels and diesel engines has accelerated the transition toward electric mobility and advanced energy storage solutions. Among these, lithium-ion batteries (LIBs) have emerged as the dominant technology for electric vehicles (EVs) due to their high energy density, low self-discharge, and long cycle life. Accurate estimation of the state of charge (SOC) is a critical function of the battery management system (BMS), directly influencing battery longevity, charging/discharging efficiency, and driving safety. Nevertheless, challenges such as battery aging, temperature variations, and measurement noise continue to hinder reliable SOC estimation. This review provides a comprehensive analysis of recent data-driven SOC estimation methods, encompassing classification and regression models, fuzzy logic systems, probabilistic approaches, recurrent neural networks, feedforward neural networks, and hybrid algorithms. The discussion highlights their theoretical foundations, model architectures, input features, operational mechanisms, standardized flowcharts, and performance evaluation metrics, alongside their strengths and limitations. A detailed comparative study further evaluates these approaches in terms of data sources, input–output characteristics, hyperparameter tuning strategies, and overall performance. In addition, the review identifies critical gaps in existing data-driven methodologies, particularly with respect to scalability, large-scale cell balancing, multi-state co-estimation, and real-time implementation using hardware-in-the-loop platforms. Opportunities for integrating emerging technologies such as the Internet of Things (IoT) and cloud computing into SOC estimation frameworks are also discussed. Ultimately, this study outlines key research directions that can support industry practitioners and academic researchers in developing accurate, robust, and practically deployable SOC estimation strategies, thereby advancing the reliability and sustainability of EV applications.