Show simple item record

dc.contributor.authorBai, Guangxing
dc.contributor.authorWang, Pingfeng
dc.contributor.authorHu, Chao
dc.date.accessioned2015-04-02T14:20:32Z
dc.date.available2015-04-02T14:20:32Z
dc.date.issued2015-03-15
dc.identifier.citationBai, Guangxing; Wang, Pingfeng; Hu, Chao. 2015. A self-cognizant dynamic system approach for prognostics and health management. Journal of Power Sources, vol. 278, 15 March 2015:pp 163–174en_US
dc.identifier.issn0378-7753
dc.identifier.otherWOS:000350181400021
dc.identifier.urihttp://dx.doi.org/10.1016/j.jpowsour.2014.12.050
dc.identifier.urihttp://hdl.handle.net/10057/11182
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractPrognostics and health management (PHM) is an emerging engineering discipline that diagnoses and predicts how and when a system will degrade its performance and lose its partial or whole functionality. Due to the complexity and invisibility of rules and states of most dynamic systems, developing an effective approach to track evolving system states becomes a major challenge. This paper presents a new self-cognizant dynamic system (SCDS) approach that incorporates artificial intelligence into dynamic system modeling for PHM. A feed-forward neural network (FFNN) is selected to approximate a complex system response which is challenging task in general due to inaccessible system physics. The trained FFNN model is then embedded into a dual extended Kalman filter algorithm to track down system dynamics. A recursive computation technique used to update the FFNN model using online measurements is also derived. To validate the proposed SCDS approach, a battery dynamic system is considered as an experimental application. After modeling the battery system by a FFNN model and a state-space model, the state-of-charge (SoC) and state-of-health (SoH) are estimated by updating the FFNN model using the proposed approach. Experimental results suggest that the proposed approach improves the efficiency and accuracy for battery health management.en_US
dc.description.sponsorshipThis research is partially supported by National Science Foundation through Faculty Early Career Development (CAREER) award (CMMI-1351414) and the Award (CMMI-1200597), and by the Department of Transportation through University Transportation Center (UTC) Program.en_US
dc.language.isoen_USen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofseriesJournal of Power Sources;v.278
dc.subjectLithium-ion batteryen_US
dc.subjectPrognostics and health managementen_US
dc.subjectKalman filteren_US
dc.subjectState-of-health (SoH)en_US
dc.subjectState-of-charge (SoC)en_US
dc.subjectDynamic systemsen_US
dc.titleA self-cognizant dynamic system approach for prognostics and health managementen_US
dc.typeArticleen_US
dc.rights.holderCopyright © 2014 Elsevier B.V. All rights reserved.


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record