摘要
为解决扩展卡尔曼滤波算法估算锂电池荷电状态(State of charge,SOC)时存在的系统噪声统计不确定性和电池模型不准确性问题,该文提出了一种基于改进型Sage-Husa自适应强跟踪卡尔曼滤波的SOC估算算法。以参数辨识得到的锂电池等效电路模型为基础,在扩展卡尔曼滤波算法中引入一个强跟踪滤波器的渐消因子来加强跟踪能力,结合可对时变噪声进行特征统计的Sage-Husa自适应滤波器来调整系统噪声参数,实现了锂电池SOC的估算。最后通过锂电池模拟工况实验,验证了该算法相比于扩展卡尔曼滤波具有更高的精度和实用性。
In order to solve the problem of systematic noise statistical uncertainty and battery model inaccuracy in estimating the state of charge(SOC)of lithium battery by the extended Kalman filter algorithm.A state-of-charge estimation algorithm based on the improved Sage-Husa adaptive strong tracking Kalman filter is proposed.Based on the equivalent circuit model of lithium battery obtained by parameter identification,a fading factor of strong tracking filter is introduced into the extended Kalman filter algorithm to enhance the tracking ability of the system.Combined with the sage Husa adaptive filter which can be used to analyze the characteristics of time-varying noise,the system noise parameters are adjusted,and the SOC estimation of lithium battery is realized.Through the lithium battery simulation working condition experiment,it is verified that the algorithm is more accurate and practical than the extended Kalman filter.
作者
盛国良
翁朝阳
陆宝春
Sheng Guoliang;Weng Chaoyang;Lu Baochun(Industrial Center,Nanjing Institute of Technology,Nanjing 211167,China;School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2020年第6期689-695,共7页
Journal of Nanjing University of Science and Technology
基金
国家重点研发计划资助项目(2018YFB1308300)。
