Particle-Filtering-Based Discharge Time Prognosis for Lithium-Ion Batteries With a Statistical Characterization of Use Profiles
Author
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Pola Contreras, Daniel
Author
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Navarrete Echeverría, Hugo
Author
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Orchard Concha, Marcos
Author
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Rabié Durán, Ricardo
Author
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Cerda Muñoz, Matías
Author
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Olivares Rubio, Benjamín
Author
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Silva Sánchez, Jorge
Author
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Espinoza, Pablo
Author
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Pérez Mora, Aramis
Admission date
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2015-08-22T20:06:58Z
Available date
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2015-08-22T20:06:58Z
Publication date
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2015
Cita de ítem
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IEEE Transactions on Reliability, vol. 64, no. 2, June 2015
en_US
Identifier
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0018-9529
Identifier
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DOI: 10.1109/TR.2014.2385069
Identifier
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https://repositorio.uchile.cl/handle/2250/133035
General note
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Artículo de publicación ISI
en_US
Abstract
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We present the implementation of a particle-filteringbased
prognostic framework that utilizes statistical characterization
of use profiles to (i) estimate the state-of-charge (SOC), and (ii)
predict the discharge time of energy storage devices (lithium-ion
batteries). The proposed approach uses a novel empirical statespace
model, inspired by battery phenomenology, and particle-filtering
algorithms to estimate SOC and other unknown model parameters
in real-time. The adaptation mechanism used during the
filtering stage improves the convergence of the state estimate, and
provides adequate initial conditions for the prognosis stage. SOC
prognosis is implemented using a particle-filtering-based framework
that considers a statistical characterization of uncertainty for
future discharge profiles based on maximum likelihood estimates
of transition probabilities for a two-state Markov chain. All algorithms
have been trained and validated using experimental data
acquired from one Li-Ion 26650 and two Li-Ion 18650 cells, and
considering different operating conditions.