电动汽车用锂离子电池SOC估算方法

刘娜; 王聪杰; 张锟; 梁晓嫱; 孙文浩; 田君; 曹宝森; 田崔钧

doi:10.19799/j.cnki.2095-4239.2026.0378

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电动汽车用锂离子电池SOC估算方法

储能测试与评价 | 更新时间：2026-10-22

- 电动汽车用锂离子电池SOC估算方法
- Research on SOC estimation methods for lithium-ion batteries in electric vehicles
- 储能科学与技术 2026年15卷第10期页码：4129-4147
- 作者机构：
  
  中国北方车辆研究所，北京 100072
- 作者简介：
  
  刘娜（1991—），女，博士，高级工程师，研究方向为动力及储能电池测试与评价，E-mail：liunaw1021@163.com；
  田君，研究员，研究方向为动力及储能电池测试与评价，E-mail：juntian@noveri.com.cn。
- 基金信息：
  
  中国科协青年人才托举工程(2024QNRC001)
- DOI：10.19799/j.cnki.2095-4239.2026.0378
  中图分类号： TH 3
- 收稿：2026-05-06，
  
  修回：2026-09-10，
  
  纸质出版：2026-10-28
- 稿件说明：
移动端阅览
刘娜, 王聪杰, 张锟, 等. 电动汽车用锂离子电池SOC估算方法[J]. 储能科学与技术, 2026, 15(10): 4129-4147.

LIU Na, WANG Congjie, ZHANG Kun, et al. Research on SOC estimation methods for lithium-ion batteries in electric vehicles[J]. Energy Storage Science and Technology, 2026, 15(10): 4129-4147.
刘娜, 王聪杰, 张锟, 等. 电动汽车用锂离子电池SOC估算方法[J]. 储能科学与技术, 2026, 15(10): 4129-4147. DOI： 10.19799/j.cnki.2095-4239.2026.0378.

LIU Na, WANG Congjie, ZHANG Kun, et al. Research on SOC estimation methods for lithium-ion batteries in electric vehicles[J]. Energy Storage Science and Technology, 2026, 15(10): 4129-4147. DOI： 10.19799/j.cnki.2095-4239.2026.0378.

摘要

锂离子电池荷电状态（SOC）估算是电动汽车电池管理系统（BMS）的核心技术。SOC用于表征电池剩余可用容量，受电池内部电化学非线性特性、全生命周期老化衰减、车载复杂工况扰动以及电池模组内部单体一致性差异等多重因素耦合作用，无法依靠传感器直接测得，只能通过电压、电流、温度等外部参数实现间接估算。估算精度与鲁棒性直接影响整车运行安全、续航预估可靠性，也是制约BMS性能提升与电动汽车技术迭代的瓶颈。本文系统综述电动汽车锂离子电池SOC估算技术，首先明确SOC在BMS中的核心定位与功能价值；将现有估算方法划分为可测参量经验估算法、模型驱动法、数据驱动法及模型-数据融合法四大类，深入剖析各类方法的原理、优缺点及适用边界；重点聚焦模型-数据融合这一主流发展方向，对比分析串行补偿、特征提取、约束嵌入与并行融合四类核心策略的逻辑架构、技术优势及工程应用难点；最后结合智能BMS升级及数字孪生、边缘计算等跨学科融合趋势，提出SOC估算向终身学习、云-边-端垂直整合、物理-数据深度融合及多状态联合估计演进等方向。本文旨在系统梳理SOC估算技术的发展脉络，剖析当前研究瓶颈与车载工程痛点，整合各类方法的适配场景与优化路径，为下一代BMS的研发、优化及工程化应用提供理论参考。

Abstract

State-of-charge (SOC) estimation for lithium-ion batteries is the core technology of the battery management system (BMS) in electric vehicles. SOC indicates the remaining capacity of a battery and is affected by multiple coupled factors

including the internal electrochemical nonlinearity of the battery

aging degradation over the full life cycle

disturbances under complex on-vehicle operating conditions

and cell inconsistency within battery modules. SOC cannot be directly measured by sensors and can only be estimated indirectly through external parameters such as voltage

current

and temperature. Its estimation accuracy and robustness directly affect vehicle operational safety and the reliability of driving range prediction

and constitute a bottleneck restricting BMS performance improvement and the technological advancement of electric vehicles. This paper systematically reviews SOC estimation technologies for lithium-ion batteries in electric vehicles. First

the core role and functional significance of SOC in BMS are clarified. Second

existing estimation methods are classified into four categories: measurable-parameter-based empirical estimation methods

model-driven methods

data-driven methods

and model-data fusion methods; the principles

advantages

disadvantages

and application boundaries of each category are analyzed in depth. Focusing on model-data fusion as the mainstream development direction

this paper compares and analyzes the logical architecture

technical advantages

and engineering challenges of four core strategies: serial compensation

feature extraction

constraint embedding

and parallel fusion. Finally

in the context of the upgrading of intelligent BMS and interdisciplinary integration trends such as digital twins and edge computing

this paper proposes several future directions for SOC estimation

including lifelong learning

cloud-edge-end vertical integration

deep physics-data fusion

and multi-state joint estimation. This paper systematically reviews the evolution of SOC estimation technologies

analyzes current research bottlenecks and vehicle-oriented engineering challenges

summarizes the applicable scenarios and optimization paths for various methods

and provides theoretical references for the research

development

optimization

and engineering application of next-generation BMS.

关键词

Keywords

references

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