Next Article in Journal
Contribution of Small Wind Turbine Structural Vibration to Noise Emission
Next Article in Special Issue
Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model
Previous Article in Journal
Development of Clay Tile Coatings for Steep-Sloped Cool Roofs
Previous Article in Special Issue
Results from a Novel Method for Corrosion Studies of Electroplated Lithium Metal Based on Measurements with an Impedance Scanning Electrochemical Quartz Crystal Microbalance
Article Menu

Export Article

Open AccessArticle
Energies 2013, 6(8), 3654-3668; doi:10.3390/en6083654

Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction

1
Department of Automatic Test and Control, Harbin Institute of Technology, No.2, YiKuang Street, NanGang District, Harbin 150080, China
2
Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA
*
Author to whom correspondence should be addressed.
Received: 21 June 2013 / Revised: 11 July 2013 / Accepted: 12 July 2013 / Published: 25 July 2013
(This article belongs to the Special Issue Li-ion Batteries and Energy Storage Devices)
View Full-Text   |   Download PDF [661 KB, 17 March 2015; original version 17 March 2015]   |  

Abstract

Prognostics and remaining useful life (RUL) estimation for lithium-ion batteries play an important role in intelligent battery management systems (BMS). The capacity is often used as the fade indicator for estimating the remaining cycle life of a lithium-ion battery. For spacecraft requiring high reliability and long lifetime, in-orbit RUL estimation and reliability verification on ground should be carefully addressed. However, it is quite challenging to monitor and estimate the capacity of a lithium-ion battery on-line in satellite applications. In this work, a novel health indicator (HI) is extracted from the operating parameters of a lithium-ion battery to quantify battery degradation. Moreover, the Grey Correlation Analysis (GCA) is utilized to evaluate the similarities between the extracted HI and the battery’s capacity. The result illustrates the effectiveness of using this new HI for fading indication. Furthermore, we propose an optimized ensemble monotonic echo state networks (En_MONESN) algorithm, in which the monotonic constraint is introduced to improve the adaptivity of degradation trend estimation, and ensemble learning is integrated to achieve high stability and precision of RUL prediction. Experiments with actual testing data show the efficiency of our proposed method in RUL estimation and degradation modeling for the satellite lithium-ion battery application.
Keywords: satellite; lithium-ion battery; remaining useful life estimation; health indicator; echo state networks; ensemble learning satellite; lithium-ion battery; remaining useful life estimation; health indicator; echo state networks; ensemble learning
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Liu, D.; Wang, H.; Peng, Y.; Xie, W.; Liao, H. Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction. Energies 2013, 6, 3654-3668.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top