小波降噪卡尔曼滤波锂电池荷电状态估计

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小波降噪卡尔曼滤波锂电池荷电状态估计

更新时间：2025-07-09

- 小波降噪卡尔曼滤波锂电池荷电状态估计
- Issue 10, Pages: 71-76(2017)
- 作者机构：
  
  1. 西安交通大学陕西省智能机器人重点实验室
  2. 西安交通大学机械制造系统工程国家重点实验室
  3. 西安交通大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM912
- Published：2017
- 稿件说明：
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[J]. 2017, (10): 71-76.
DOI：

[J]. 2017, (10): 71-76. DOI：

摘要

针对锂离子电池充放电电压信号(DCV)中存在的噪声信号导致荷电状态(SOC)估计精度降低、波动较大的问题

提出了一种基于离散小波变换(DWT)的降噪扩展卡尔曼滤波(EKF)算法。该算法利用多分辨率分析(MRA)分解携带噪声的DCV信号

通过对比4种阈值硬阈值降噪规则对携带噪声的DCV信号的降噪处理效果

选择Stein无偏风险阈值硬阈值降噪规则调整小波系数

通过含自适应遗忘因子的递推最小二乘法辨识电池模型参数后

利用扩展卡尔曼滤波算法估计SOC。仿真结果表明:使用Stein无偏风险阈值硬阈值降噪规则有效地降低了DCV信号中的噪声信号;所提算法具有较好的鲁棒性

能够有效地提高SOC估计精度

使SOC估计误差范围控制在3%之内。

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references

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Related Institution

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