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锂离子电池的荷电状态精准估计是电池管理系统实现安全管控与寿命优化的核心技术瓶颈。针对现有荷电状态估计方法在动态工况适应性、模型泛化能力及计算效率方面的不足，系统综述了基于物理特征、模型驱动与数据驱动的三类方法，通过多维度交叉对比揭示技术演化规律与适用边界。每种方法具有不同的原理、优缺点和应用场景。基于物理特征的经验模型法通过实验标定建立经验映射关系，该方法具有计算效率高、实施门槛低等优势，但其高度依赖初始精度，存在抗干扰和老化适应性差等问题。基于模型的荷电状态估计方法具有高精度和快速收敛等优点，但要求精确的电池模型和参数。基于数据驱动的方法则在适应性和精度方面表现突出，但需要大量的历史数据，且可能面临过拟合和泛化能力不足的问题。通过对各类方法进行比较分析，总结了当前的研究进展，并提出了锂电池荷电状态估计面临的挑战及未来的研究方向。
Abstract：Accurate estimation of the State of Charge(SOC) of lithium-ion batteries is a critical technical bottleneck for enabling safety assessment and lifespan prediction in the Battery Management Systems(BMS). Aiming at the limitations of the existing SOC estimation approaches, particularly in terms of adaptability under dynamic operating conditions, model generalization capability, and computational efficiency, the three major methodological frameworks were systematically reviewed: physics-based approaches, model-driven approaches, and data-driven approaches. Through a comprehensive examination of the multiple dimensions, the technological advancements, strengths, limitations, and applicable boundaries of each method were revealed. Physics-based empirical models establish mapping relationships through experimental calibration, which offer advantages such as high computational efficiency and low implementation complexity. However, they are heavily reliant on the initial calibration accuracy and suffer from poor robustness against disturbances and limited adaptability to the battery aging. Model-driven approaches, while achieving high accuracy and rapid convergence, require precise battery behavior modeling and accurate parameter identification. Data-driven approaches have strong adaptability and accuracy in varying conditions but require a large number of historical data and may face challenges such as overfitting and insufficient generalization. This paper summarizes the current state of research, highlights the key challenges and the future directions for advancing SOC estimation technologies for lithium-ion batteries based on the comparative analysis.
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基本信息:
DOI：10.14106/j.cnki.1001-2028.2025.0090
中图分类号:TM912
引用信息:
[1]毕林楠,何亮,周立春,等.锂电池荷电状态估计方法研究进展[J].电子元件与材料,2025,44(07):741-750.DOI:10.14106/j.cnki.1001-2028.2025.0090.
基金信息:
国家自然科学基金(52202104); 浙江省衢州市大科创项目(2025K003,2024D028)