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dc.contributor.authorTampier, Carlos
dc.contributor.authorPerez, Aramis
dc.contributor.authorJaramillo, Francisco
dc.contributor.authorOrchard, Marcos
dc.contributor.authorSilva, Jorge
dc.contributor.authorL. Quintero, Vanessa
dc.date.accessioned2019-07-02T18:01:43Z
dc.date.available2019-07-02T18:01:43Z
dc.date.issued2015-08-24
dc.date.issued2015-08-24
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/6156
dc.descriptionBattery energy systems are currently one of the most common power sources found in mobile electromechanical devices. In all these equipment, assuring the autonomy of the system requires to determine the battery state-of-charge (SOC) and predicting the end-of-discharge time with a high degree of accuracy. In this regard, this paper presents a comparative analysis of two well-known Bayesian estimation algorithms (Particle filter and Unscented Kalman filter) when used in combination with Outer Feedback Correction Loops (OFCLs) to estimate the SOC and prognosticate the discharge time of lithium-ion batteries. Results show that, on the one hand, a PF-based estimation and prognosis scheme is the method of choice if the model for the dynamic system is inexact to some extent; providing reasonable results regardless if used with or without OFCLs. On the other hand, if a reliable model for the dynamic system is available, a combination of an Unscented Kalman Filter (UKF) with OFCLs outperforms a scheme that combines PF and OFCLs.en_US
dc.description.abstractBattery energy systems are currently one of the most common power sources found in mobile electromechanical devices. In all these equipment, assuring the autonomy of the system requires to determine the battery state-of-charge (SOC) and predicting the end-of-discharge time with a high degree of accuracy. In this regard, this paper presents a comparative analysis of two well-known Bayesian estimation algorithms (Particle filter and Unscented Kalman filter) when used in combination with Outer Feedback Correction Loops (OFCLs) to estimate the SOC and prognosticate the discharge time of lithium-ion batteries. Results show that, on the one hand, a PF-based estimation and prognosis scheme is the method of choice if the model for the dynamic system is inexact to some extent; providing reasonable results regardless if used with or without OFCLs. On the other hand, if a reliable model for the dynamic system is available, a combination of an Unscented Kalman Filter (UKF) with OFCLs outperforms a scheme that combines PF and OFCLs.en_US
dc.languageeng
dc.language.isoengen_US
dc.publisherA Comparative Analysis. In the Annual Conference of the Prognostics and Health Management Society 2015en_US
dc.publisherA Comparative Analysis. In the Annual Conference of the Prognostics and Health Management Society 2015
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectparticle filteren_US
dc.subjectunscented Kalman filteren_US
dc.subjectBattery discharge prognosticsen_US
dc.subjectparticle filter
dc.subjectunscented Kalman filter
dc.subjectBattery discharge prognostics
dc.titleLithium-Ion Battery End-of-Discharge Time Estimation and Prognosis based on Bayesian Algorithms and Outer Feedback Correction Loops: A Comparative Analysisen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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