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dc.contributor.authorPola, Daniel
dc.contributor.authorGuajardo, Felipe
dc.contributor.authorJofré, Esteban
dc.contributor.authorL. Quintero, Vanessa
dc.contributor.authorPerez, Aramis
dc.contributor.authorAcuña, David
dc.contributor.authorOrchard, Marcos
dc.date.accessioned2019-07-02T17:57:20Z
dc.date.available2019-07-02T17:57:20Z
dc.date.issued2016-08-15
dc.date.issued2016-08-15
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/6155
dc.descriptionWe present the implementation of a particle-filtering-based framework that estimates the State-of-Health (SOH) and predicts the End-of-Life (EOL) of Lithium-Ion batteries, efficiently incorporating variations of ambient temperature in the analysis. The proposed approach uses an empirical state-space model, in which inputs are explicitly defined as the average temperature of operation and the output of an external module that detects self-recharge phenomena, on the other hand the output is a function that relates the current SOH and temperature with the Usable Capacity in that cycle. In addition, this approach allows to deal with data losses and outliers. In order to correct erroneous initial conditions in state estimates, an Outer Feedback Correction Loop is implemented. Finally, this framework is validated using degradation data from four sources: experimental degradation data from two Li-Ion 18650 cells, accelerated degradation data openly provided by NASA Ames Research Center, and artificially generated degradation data at different ambient temperatures.en_US
dc.description.abstractWe present the implementation of a particle-filtering-based framework that estimates the State-of-Health (SOH) and predicts the End-of-Life (EOL) of Lithium-Ion batteries, efficiently incorporating variations of ambient temperature in the analysis. The proposed approach uses an empirical state-space model, in which inputs are explicitly defined as the average temperature of operation and the output of an external module that detects self-recharge phenomena, on the other hand the output is a function that relates the current SOH and temperature with the Usable Capacity in that cycle. In addition, this approach allows to deal with data losses and outliers. In order to correct erroneous initial conditions in state estimates, an Outer Feedback Correction Loop is implemented. Finally, this framework is validated using degradation data from four sources: experimental degradation data from two Li-Ion 18650 cells, accelerated degradation data openly provided by NASA Ames Research Center, and artificially generated degradation data at different ambient temperatures.en_US
dc.languageeng
dc.language.isoengen_US
dc.publisherAnnual Conference of the PHM Society 2016en_US
dc.publisherAnnual Conference of the PHM Society 2016
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectState of Health Estimationen_US
dc.subjectBattery Remaining Useful Lifeen_US
dc.subjecttemperatureen_US
dc.subjectLithium-ion batteryen_US
dc.subjectparticle filteringen_US
dc.subjectState of Health Estimation
dc.subjectBattery Remaining Useful Life
dc.subjecttemperature
dc.subjectLithium-ion battery
dc.subjectparticle filtering
dc.titleParticle-Filtering-Based State-of-Health Estimation and End-of-Life Prognosis for Lithium-Ion Batteries at Operation Temperatureen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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