Corrigendum

Abstract

International Journal of Circuit Theory and ApplicationsEarly View CORRIGENDUMFree Access Corrigendum This article corrects the following: Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium-ion battery state-of-charge estimation Xiao Yang, Shunli Wang, Wenhua Xu, Jialu Qiao, Chunmei Yu, Carlos Fernandez, Volume 50Issue 2International Journal of Circuit Theory and Applications pages: 614-632 First Published online: October 26, 2021 First published: 10 May 2022 https://doi.org/10.1002/cta.3318AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat In Section 2.3, the second and third paragraphs should read as follows: In Equation (5), yk represents the measured value of the terminal voltage and yk* represents the estimated value of the terminal voltage. In the fuzzy adaptive system, under ideal conditions, the system innovation is zero; when the innovation and its rate of change are large, the output value will increase, and thereby, the Kalman gain is increased.1 When the innovation and its rate of change are small, the output value decreases, making the Kalman gain smaller and increasing the proportion of the measured value, to achieve the purpose of making the algorithm stable and converging quickly. The theoretical residual covariance matrix expression after introducing the adjustment factor is shown in Equation 6. P zz , k ∣ k − 1 = 1 2 n ∑ i = 1 2 n z i , k ∣ k − 1 z i , k ∣ k − 1 T − z ̂ i , k ∣ k − 1 z ̂ i , k ∣ k − 1 T + R α $$ {P}_{zz,k\mid k-1}=\frac{1}{2n}\sum \limits_{i=1}^{2n}\left({z}_{i,k\mid k-1}{z}_{i,k\mid k-1}^T-{\hat{z}}_{i,k\mid k-1}{\hat{z}}_{i,k\mid k-1}^T\right)+\frac{R}{\alpha } $$ (6) It is assumed that the innovation interval is [0, 0.04], and divide it into six segments: very small, small, medium, big, and very big. The domain of innovation change rate is set as [0, 0.035], which is divided as small, medium, big, and very big. The membership functions of ek and Δek are shown in Figure 4. The Kalman gain, innovation interval, and innovation change rate have been corrected accordingly. REFERENCE 1Yang X, Wang S, Xu W, Qiao J, Yu C, Fernandez C. Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium-ion battery state-of-charge estimation. Int J Circ Theor Appl. 2022; 50(2): 614- 632. doi:10.1002/cta.3166Wiley Online LibraryWeb of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation