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A novel battery state of charge estimation based on the joint unscented Kalman filter and support vector machine algorithms.

Xie, Fei; Wang, Shunli; Xie, Yanxin; Fernandezb, Carlos; Li, Xiaoxia; Zou, Chuanyun

Authors

Fei Xie

Shunli Wang

Yanxin Xie

Xiaoxia Li

Chuanyun Zou



Abstract

With the development of new energy sources becoming the mainstream of energy development strategies, the role of electric vehicle-powered lithium-ion batteries in the field of automobile transportation is becoming more and more obvious. An efficient the Battery Management System is necessary for the real-time usage monitor of each battery cell, which analyzes the battery status to ensure its safe operation. A complex equivalent circuit model is proposed and established. The Improved Equivalent Circuit Model is used to realize the precise mathematical expression of the power lithiumion battery packs under special conditions. The State of Charge estimation method which is based on Unscented Kalman Filter has a good filtering effect on the nonlinear systems. Based on the State of Charge estimation of Support Vector Machine, the samples in the nonlinear space of lithium-ion battery are mapped to the linear space. It can be seen from the experimental analysis that a joint Unscented Kalman Filter and Support Vector Machine algorithms for State of Charge estimation has higher accuracy. The experimental results show that the tracking error is less than 1.00%.

Citation

XIE, F., WANG, S., XIE, Y., FERNANDEZB, C., LI, X. and ZOU, C. 2020. A novel battery state of charge estimation based on the joint unscented Kalman filter and support vector machine algorithms. International journal of electrochemical science [online], 15(8), pages 7935-7953. Available from: https://doi.org/10.20964/2020.08.83

Journal Article Type Article
Acceptance Date Feb 12, 2020
Online Publication Date Jul 10, 2020
Publication Date Aug 31, 2020
Deposit Date Sep 8, 2020
Publicly Available Date Sep 8, 2020
Journal International journal of electrochemical science
Electronic ISSN 1452-3981
Publisher Electrochemical Science Group
Peer Reviewed Peer Reviewed
Volume 15
Issue 8
Pages 7935-7953
DOI https://doi.org/10.20964/2020.08.83
Keywords Battery management system; State of charge; Improved equivalent circuit model; Unscented kalman filter; Support vector machine
Public URL https://rgu-repository.worktribe.com/output/966441