基于改进迟滞模型与可观测性自适应迭代CKF的储能电池SOC估计研究

李智超; 屈治国

doi:10.19799/j.cnki.2095-4239.2026.0632

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基于改进迟滞模型与可观测性自适应迭代CKF的储能电池SOC估计研究

储能XXXX | 更新时间：2026-09-04

- 基于改进迟滞模型与可观测性自适应迭代CKF的储能电池SOC估计研究
- State-of-charge estimation of energy storage batteries based on improved hysteresis model and observability-adaptive iterative cubature Kalman filter
- 储能科学与技术 2026年页码：1-12
- 作者机构：
  
  西安交通大学热流科学与工程教育部重点实验室，陕西西安 710049
- 作者简介：
  
  李智超（1999—），男，博士研究生在读，主要从事锂离子电池快充与状态估计研究，E-mail：leezc@stu.xjtu.edu.cn；
  屈治国，教授，主要从事电化学储能与氢能利用等研究，E-mail：zgqu@mail.xjtu.edu.cn。
- 基金信息：
  
  新型油气勘探开发国家科技重大专项“油气田生产系统再造技术与装备”(2025ZD1406700)
- DOI：10.19799/j.cnki.2095-4239.2026.0632
  中图分类号： TM 911
- 收稿：2026-07-20，
  
  修回：2026-08-31，
  
  网络首发：2026-09-03，
- 稿件说明：
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李智超, 屈治国. 基于改进迟滞模型与可观测性自适应迭代CKF的储能电池SOC估计研究[J]. 储能科学与技术, XXXX, XX(XX): 1-12.

LI Zhichao, QU Zhiguo. State-of-charge estimation of energy storage batteries based on improved hysteresis model and observability-adaptive iterative cubature Kalman filter[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-12.
李智超, 屈治国. 基于改进迟滞模型与可观测性自适应迭代CKF的储能电池SOC估计研究[J]. 储能科学与技术, XXXX, XX(XX): 1-12. DOI： 10.19799/j.cnki.2095-4239.2026.0632.

LI Zhichao, QU Zhiguo. State-of-charge estimation of energy storage batteries based on improved hysteresis model and observability-adaptive iterative cubature Kalman filter[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-12. DOI： 10.19799/j.cnki.2095-4239.2026.0632.

摘要

针对储能电池迟滞效应和电压平台导致荷电状态（SOC）估计精度下降的问题，提出了一种基于改进Prandtl-Ishlinskii（PI）迟滞模型与可观测性自适应迭代容积卡尔曼滤波（OA-ICKF）的SOC估计方法。在模型方面，采用二次修正函数改进PI模型以描述开路电压的非对称迟滞特性。在算法方面，基于开路电压-SOC灵敏度和电压曲线斜率连续调整滤波器的SOC校正策略。同时引入迭代量测更新控制迭代收敛，提高了强非线性条件下的SOC估计精度。采用RegD动态工况数据和储能电站实际运行数据对所提出的方法进行验证。RegD工况下SOC估计的绝对误差维持在2%以内。在模型初始SOC与实际值存在40%的偏差情况下，最大绝对误差仅为2.06%。储能电站0.5 C和0.25 C充电工况下SOC估计的均方根误差分别为0.718%和0.594%。在连续两次充放电的运行工况下，SOC估计的平均绝对误差和均方根误差分别为1.79%和2.10%。消融实验结果显示，去除改进PI模型、OA机制与迭代量测更新中的一个或多个组件后，SOC估计精度显著下降，表明上述改进模块对SOC估计精度的提升均不可替代。所提出的方法能够准确估计储能电池的SOC，为保证储能电站安全高效运行提供支撑。

Abstract

To address the degradation of state of charge (SOC) estimation accuracy caused by the hysteresis effect and voltage plateau of energy storage batteries

a SOC estimation method based on an improved Prandtl-Ishlinskii (PI) hysteresis model and an observability-adaptive iterative cubature Kalman filter (OA-ICKF) is proposed. On the modeling side

a quadratic correction function is adopted to improve the PI model for characterizing the asymmetric hysteresis of the open circuit voltage. On the algorithm side

the SOC correction strategy of the filter is continuously adjusted based on the open circuit voltage-SOC sensitivity index and the open-circuit voltage curve slope. Iterative measurement updates are introduced simultaneously to control the convergence to improve the SOC estimation accuracy under strongly nonlinear conditions. The proposed method was validated using RegD dynamic test data and actual operation data from an energy storage station. Under the RegD condition

the absolute error in SOC estimation remains within 2%. Even when the model's initial SOC deviates by 40% from the actual value

the maximum absolute error is only 2.06%. Under 0.5C and 0.25C charging conditions with energy storage station data

the root mean square errors of SOC estimation are 0.718% and 0.594%. Under the practical operating condition of two consecutive charge-discharge cycles

the mean absolute error and root mean square error of SOC estimation are 1.79% and 2.10%

respectively. Ablation experiments show that removing one or more components of the improved PI model

the OA mechanism

or the iterative measurement update significantly reduces the accuracy of SOC estimation

indicating that all of the aforementioned improved modules are indispensable for enhancing the SOC estimation accuracy. The proposed method accurately estimates the SOC of energy storage batteries

providing support for the safe and efficient operation of energy storage stations.

关键词

Keywords

references

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