Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena

This paper presents the implementation of a particle-filtering-based prognostic framework that allows estimating the state of health (SOH) and predicting the remaining useful life (RUL) of energy storage devices, and more specifically lithium-ion batteries, while simultaneously detecting and isolati...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2013-02, Vol.62 (2), p.364-376
Main Authors: Olivares, B. E., Cerda Munoz, Matias A., Orchard, M. E., Silva, J. F.
Format: Article
Language:English
Subjects:
Batteries
Battery charge measurement
Bayesian methods
Capacity regeneration
Degradation
Electrostatic discharges
energy storage devices (ESDs)
particle filters (PFs)
Predictive models
SOH prognosis
state-of-health (SOH) monitoring
Temperature measurement
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Summary:This paper presents the implementation of a particle-filtering-based prognostic framework that allows estimating the state of health (SOH) and predicting the remaining useful life (RUL) of energy storage devices, and more specifically lithium-ion batteries, while simultaneously detecting and isolating the effect of self-recharge phenomena within the life-cycle model. The proposed scheme and the statistical characterization of capacity regeneration phenomena are validated through experimental data from an accelerated battery degradation test and a set of ad hoc performance measures to quantify the precision and accuracy of the RUL estimates. In addition, a simplified degradation model is presented to analyze and compare the performance of the proposed approach in the case where the optimal solution (in the mean-square-error sense) can be found analytically.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2012.2215142