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。
An extended Kalman filter（EKF） algorithm based on discrete wavelet transform（DWT） denoising is proposed for the problem of the lower accuracy and larger fluctuation of SOC（state of charge）estimation caused by the noise of the discharging/charging voltage（DCV）signals of lithium-ion batteries.The DCV signal with noise is decomposed through the multiresolution analysis（MRA）.Effects of four hard-thresholding-based denoising rules on reducing the noise of DCV signal are compared,and the hard-thresholding-based denoising rule based on Stein＇s unbiased risk estimation is selected to adjust wavelet coefficients.The parameters of the battery model are identified by the recursive least square method with an adaptive forgetting factor and then the SOC is estimated using EKF.Simulation results show that the selected denoising rule effectively reduces the noise of the DCV signal.The proposed algorithm effectively improves the accuracy of SOC estimation with a strong robustness and the estimation error is less than 3%.
作者
王霄
徐俊
曹秉刚
赵云飞
鲁伟
梅雪松
WANG Xiao;XUJun;CAO Binggang;ZHAO Yunfei;LU Wei;MEI Xuesong(Shaanxi Provincial Key Laboratory of Intelligent Robots, Xi？an Jiaotong University, Xi？an 710049, China;State Key Laboratory for Manufacturing Systems Engineering, Xi？an Jiaotong University, Xi’ an 710049, China;School of Mechanical Engineering, Xi？an Jiaotong University, Xi？an 710049, China)
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2017年第10期71-76,共6页
Journal of Xi'an Jiaotong University
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)
关键词
离散小波变换
降噪
荷电状态
扩展卡尔曼滤波算法
discrete wavelet transform
denoising
state of charge
extended Kalman algorithm
