Next Article in Journal
Fast Thermal Runaway Detection for Lithium-Ion Cells in Large Scale Traction Batteries
Previous Article in Journal
On-Demand Micro-Power Generation from an Origami-Inspired Paper Biobattery Stack
Previous Article in Special Issue
Statistical Characterization of the State-of-Health of Lithium-Ion Batteries with Weibull Distribution Function—A Consideration of Random Effect Model in Charge Capacity Decay Estimation
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessArticle
Batteries 2018, 4(2), 15; https://doi.org/10.3390/batteries4020015

Prognosis and Remaining Useful Life Estimation of Lithium-Ion Battery with Optimal Multi-Level Particle Filter and Genetic Algorithm

Independent Researcher, Lockhart Street, Como 6152, Australia
Received: 14 February 2018 / Revised: 4 March 2018 / Accepted: 16 March 2018 / Published: 23 March 2018
(This article belongs to the Special Issue Battery Management Systems)
Full-Text   |   PDF [2481 KB, uploaded 3 May 2018]   |  

Abstract

Prognosis and remaining useful life (RUL) estimation of components and systems (C&S) are vital for intelligent asset-integrity management. The implementation of the traditional multi-level particle filter (TRMPF) has improved prognosis when compared with the one-step traditional particle filter that depended on the first-order state equation. However, despite this improvement, the need to enhance the accuracy of fault prognosis, diagnosis and detection cannot be overemphasized. To this end, an optimal multi-level particle filter (OPMPF) algorithm that combines genetic algorithm (GA) optimization and multi-level particle filter (MPF) techniques is used to predict the RUL of the C&S in order to enhance the accuracy of the estimation at different forms of deterioration in operation. A 9-fold cross-validation ensemble MPF that utilized lithium-ion (Li+) batteries’ charge capacity decay to test the developed OPMPF algorithm showed an improvement of over 200% in the estimated RUL when compared with the TRMPF estimation. View Full-Text
Keywords: end-of-life; genetic algorithm; lithium-ion battery; multi-level particle filter; prognosis; remaining useful life end-of-life; genetic algorithm; lithium-ion battery; multi-level particle filter; prognosis; remaining useful life
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ossai, C.I. Prognosis and Remaining Useful Life Estimation of Lithium-Ion Battery with Optimal Multi-Level Particle Filter and Genetic Algorithm. Batteries 2018, 4, 15.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Batteries EISSN 2313-0105 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top