基于SUKF-EKF融合的锂电池状态估计方法

王期文; 盛强; 何玉财

doi:doi:10.19912/j.0254-0096.tynxb.2025-0830

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基于SUKF-EKF融合的锂电池状态估计方法

更新时间：2026-01-08

- 基于SUKF-EKF融合的锂电池状态估计方法
- Vol. 46, Issue 12, Pages: 11-21(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2025-0830
  CLC： TM912
- Published Online：07 January 2026，
  
  Published：2025
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王期文, 盛强, 何玉财. 基于SUKF-EKF融合的锂电池状态估计方法[J]. 2025, 46(12): 11-21.
DOI：

王期文, 盛强, 何玉财. 基于SUKF-EKF融合的锂电池状态估计方法[J]. 2025, 46(12): 11-21. DOI： doi:10.19912/j.0254-0096.tynxb.2025-0830.

摘要

针对锂离子电池状态估计中扩展卡尔曼滤波（EKF）精度不足及参数变化适应性差的问题

提出一种通过EKF实现电池参数的在线辨识与更新

同时融合滑模观测器（SMO）与无迹卡尔曼滤波（UKF）的锂电池状态估计方法（SUKF）。该方法构建双层时间尺度协同估计架构

利用EKF进行电池参数在线识别并估计SOH

同时结合SMO的鲁棒特性与UKF的非线性处理能力实现SOC高精度估计。通过高速公路燃效测试工况、新型欧洲行驶周期与城区动态驾驶循环3种代表性环境进行验证

结果表明

融合算法SOC估计平均误差分别为0.13%、0.25%和0.14%

最大误差控制在0.46%以内

相比传统EKF算法精度提升超过85%;SOH估计在不同复杂度工况下平均误差为0.02%~0.16%

最大误差不超过0.53%;在5%噪声强干扰环境下

算法仍保持良好的估计精度和收敛性能。随着工况复杂度增加

融合算法性能优势更为明显

显著提高了电池状态估计的准确性和鲁棒性

为动力电池管理系统提供了有效解决方案。

Abstract

To address the insufficient accuracy and poor adaptability to parameter variations of Extended Kalman Filter （EKF） in lithium-ion battery state estimation

this paper proposes a method utilizing EKF for online battery parameter identification and updating

while integrating Sliding Mode Observer （SMO） and Unscented Kalman Filter （UKF） for lithium battery state estimation （SUKF）. The method establishes a dual-layer temporal scale collaborative estimation architecture

employing EKF for online battery parameter identification and State of Health （SOH） estimation

while combining SMO’s robustness with UKF’s nonlinear processing capabilities to achieve high-precision State of Charge （SOC） estimation. Verification through three representative testing environments—highway fuel economy test conditions

New European driving cycle

and urban dynamometer driving schedule—demonstrates that the fusion algorithm achieves average SOC estimation errors of 0.13%

0.25%

and 0.14% respectively

with maximum errors constrained within 0.46%

representing over 85% improvement in accuracy compared to traditional EKF algorithms. SOH estimation under varying complexity conditions exhibits average errors ranging from 0.022% to 0.16%

with maximum errors not exceeding 0.53%. Under 5% noise disturbance conditions

the algorithm demonstrates robust estimation accuracy and convergence performance. As operational complexity increases

the performance advantages of the fusion algorithm become more pronounced

significantly enhancing the accuracy and robustness of battery state estimation

thus providing an effective solution for power battery management systems.

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