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|Title:||State-of-charge estimation of lithium-ion battery using square root spherical unscented kalman filter (Sqrt-UKFST) in nanosatellite||Authors:||Aung, Htet
Low, Kay Soon
Goh, Shu Ting
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Electric power||Issue Date:||2015||Source:||Aung, H., Low, K. S., & Goh, S. T. (2015). State-of-charge estimation of lithium-ion battery using square root spherical unscented kalman filter (Sqrt-UKFST) in nanosatellite. IEEE Transactions on Power Electronics, 30(9), 4774-4783.||Series/Report no.:||IEEE transactions on power electronics||Abstract:||State of charge (SOC) estimation is an important aspect for modern battery management system. Dynamic and closed loop model-based methods such as extended Kalman filter (EKF) have been extensively used in SOC estimation. However, the EKF suffers from drawbacks such as Jacobian matrix derivation and linearization accuracy. In this paper, a new SOC estimation method based on square root unscented Kalman filter (Sqrt-UKFST) using spherical transform with unit hyper sphere is proposed. The Sqrt-UKFST does not require the linearization for nonlinear model and uses fewer sigma points with spherical transform, which reduces the computational requirement of traditional unscented transform. The square root characteristics improves the numerical properties of state covariance. The proposed method has been experimentally validated. The results are compared with existing SOC estimation methods such as Coulomb counting, portable fuel gauge and extended Kalman filter. The proposed method has an absolute root mean square error (RMSE) of 1.42% and an absolute maximum error of 4.96%. These errors are lower than the other three methods. When compared with EKF, it represents 37% and 44% improvement in RMSE and maximum error respectively. Furthermore, the Sqrt-UKFST is less sensitive to parameter variation than EKF and it requires 32% less computational requirement than the regular UKF.||URI:||https://hdl.handle.net/10356/96192
|DOI:||http://dx.doi.org/10.1109/TPEL.2014.2361755||Rights:||© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TPEL.2014.2361755].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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