Tuning of Kalman filter parameters via genetic algorithm for state-of-charge estimation in battery management system.
In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of...
| Publicado en: | Scientific World Journal pp. 176052 - 176053 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103842848&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103842848 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103842848 103842848 NLM25162041 2012704770 10.1155/2014/176052 NLM25162041 PMC4138764 103842848 ppf: 176052 ppct: 1 formats: tig: atl: Tuning of Kalman filter parameters via genetic algorithm for state-of-charge estimation in battery management system. aug: au: Ting, T O Man, Ka Lok Lim, Eng Gee Leach, Mark affil: Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, No. 111, Ren'ai Road, HET, SIP, Suzhou, Jiangsu 215123, China. sug: subj: Algorithms Energy-Generating Resources Models, Theoretical ab: In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets of Q and R values (KF's parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune Q and R parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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