小波降噪卡尔曼滤波锂电池荷电状态估计

发布时间：2018-04-22 10:30

本文选题：离散小波变换 + 降噪　；参考：《西安交通大学学报》2017年10期

【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。
[Abstract]:Aiming at the problem that the noise signal in the charge / discharge voltage signal of lithium ion battery (DCV) leads to the decrease of the state of charge (SOC) estimation accuracy and the large fluctuation, a DWT-based denoising extended Kalman filter (EKF) algorithm is proposed. The DCV signal with noise is decomposed by Multiresolution Analysis (MRA), and the noise reduction effect of DCV signal with noise is compared by comparing four threshold hard threshold de-noising rules. The Stein unbiased risk threshold hard threshold denoising rule is selected to adjust the wavelet coefficients. After the parameters of the battery model are identified by the recursive least square method with adaptive forgetting factor, the extended Kalman filter algorithm is used to estimate the SOC. The simulation results show that using the Stein unbiased risk threshold hard threshold de-noising rule can effectively reduce the noise signal in the DCV signal, and the proposed algorithm is robust and can effectively improve the accuracy of SOC estimation. The error range of SOC estimation is controlled within 3%.
【作者单位】：西安交通大学陕西省智能机器人重点实验室;西安交通大学机械制造系统工程国家重点实验室;西安交通大学机械工程学院;
【基金】：国家自然科学基金资助项目(51405374) 中国博士后基金资助项目(2014M560763) 中国博士后科学基金特别资助项目(2016T90904) 陕西省博士后基金资助项目(2014M560763)
【分类号】：TM912

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