Lithium-Ion Battery End-of-Discharge Time Estimation and Prognosis based on Bayesian Algorithms and Outer Feedback Correction Loops: A Comparative Analysis

Carlos Tampier, Aramis Perez, Francisco Jaramillo, Vanessa Quintero, Marcos E. Orchard, and Jorge F. Silva
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Conicyt-FONDECYT; Advanced Center for Electrical and Electronic Engineering, Basal Project FB0008
phmc_15_052.pdf942.1 KBAugust 24, 2015 - 1:48pm

Battery 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.

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Submission Keywords: 
particle filter
unscented Kalman filter
Battery discharge prognostics
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Model-based methods for fault detection, diagnostics, and prognosis
Uncertainty Quantification and Management in PHM

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