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GAO Wenkai,ZHENG Yuejiu,XU Shuangshuang,ZHOU Long.Entire Range State-of-charge Estimation Based on Incremental Error Using Kalman Filtering Algorithm[J].JOURNAL OF POWER SUPPLY,2019,17(5):162-169
Entire Range State-of-charge Estimation Based on Incremental Error Using Kalman Filtering Algorithm
Received:December 11, 2017  Revised:April 20, 2018
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DOI:10.13234/j.issn.2095-2805.2019.5.162
Keywords:battery management system  equivalent circuit model  Kalman filtering algorithm  lithium battery  low state-of-charge(SOC) range
Fund Project:Project Supported by the National Natural Science Foundation of China (NSFC) under the Grant number of 51507102; “Chenguang Pro-gram” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under the grant number of 16CG52; The State Key Laboratory of Automotive Safety and Energy under Project No. KF16022.
           
AuthorInstitutionEmail
GAO Wenkai College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai , China
ZHENG Yuejiu College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai , China;State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing , China yuejiu_zheng@163.com
XU Shuangshuang College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai , China
ZHOU Long College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai , China
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Abstract:
      In the field of battery management system, the accurate estimation of state-of-charge(SOC) is one of ba-sic techniques among many state estimations. However, the equivalent circuit model(ECM), which is convenient for pra-ctical applications, cannot well simulate the battery characteristics in some specific SOC ranges. Therefore, the accuracy of SOC estimation based on the traditional Kalman filtering algorithm will decrease. The accurate estimation in the entire SOC range is realized using a novel Kalman filtering algorithm based on incremental error. Based on the off-line analysis of the ECM performance in different SOC ranges, the control table for noise covariance in each SOC range is obtained. The specific noise covariance is applied in the corresponding SOC range so that the entire SOC range is accurately estimated using the proposed Kalman filtering algorithm. Experimental results showed that the estimation accuracy in the range with SOC lower than 20% can be significantly improved using the Kalman filtering algorithm based on incremental error.
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