Artículo de revista
Information-Theoretic Measures and Sequential Monte Carlo Methods for Detection of Regeneration Phenomena in the Degradation of Lithium-Ion Battery Cells
Fecha
2015Registro en:
IEEE Transactions on Reliability, Vol. 64, No. 2, June 2015
0018-9529
DOI: 10.1109/TR.2015.2394356
Autor
Orchard Concha, Marcos
Lacalle Alarcón, Matías
Olivares, Benjamín
Silva Sánchez, Jorge
Palma Behnke, Rodrigo
Estévez Valencia, Pablo
Severino Astudillo, Bernardo
Calderón Muñoz, Williams
Cortés Carmona, Marcelo
Institución
Resumen
This paper analyses and compares the performance
of a number of approaches implemented for the detection of
capacity regeneration phenomena (measured in ampere-hours)
in the degradation trend of energy storage devices, particularly
Lithium-Ion battery cells. All implemented approaches are based
on a combination of information-theoretic measures and sequential
Monte Carlo methods for state estimation in nonlinear,
non-Gaussian dynamic systems. Properties of information measures
are conveniently used to quantify the impact of process
measurements on the posterior probability density function of the
state, assuming that sub-optimal Bayesian estimation algorithms
(such as classic or risk-sensitive particle filters) are to be used to
obtain an empirical representation of the system uncertainty. The
proposed anomaly detection strategies are tested and evaluated
both in terms of (i) detection time (early detection) and (ii) false
alarm rates. Verification of detection schemes is performed using
simulated data for battery State-Of-Health accelerated degradation
tests, to ensure absolute knowledge on the time instant where
a regeneration phenomenon occurs