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dc.contributor.authorBai, Guangxing
dc.contributor.authorWang, Pingfeng
dc.identifier.citationG. Bai and P. Wang, "Battery prognostics using a self-cognizant dynamic system approach," Prognostics and Health Management (PHM), 2015 IEEE Conference on, Austin, TX, 2015, pp. 1-10en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThis paper proposes a new self-cognizant dynamic system approach for Battery PHM, that incorporates an artificial neural network model into a dual extended Kalman filter (DEKF) algorithm. A feed-forward neural network (FFNN) structure is employed to approximate a complex battery system response that is mostly treated as impossible modeling work due to inaccessible system physics. After training by historical data, the FFNN model is embedded into a DEKF algorithm to track down system dynamics. The required recursive computation, which is used to update the FFNN model during collecting the online measurement, is also derived in this paper. To validate the proposed SCDS approach, a battery dynamic system is introduced as an experimental application. After modeling the battery system by an FFNN model and a state-space model, the state-of-charge (SoC) and state-of-health (SoH) are estimated with updating the FFNN model by the proposed approach. Experimental results illustrate that the proposed approach improves the efficiency and accuracy for battery PHM.en_US
dc.relation.ispartofseries2015 IEEE Conference on Prognostics and Health Management (PHM);
dc.subjectPrognostics and health managementen_US
dc.subjectSelf-cognizant dynamic systemen_US
dc.subjectFeed-forward neural networksen_US
dc.subjectDual extended Kalman filteren_US
dc.subjectLithium-ion batteryen_US
dc.titleBattery prognostics using a self-cognizant dynamic system approachen_US
dc.typeConference paperen_US
dc.rights.holder© Copyright 2016 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.en_US

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