Next Article in Journal
The Impact and Determinants of Environmental Taxation on Economic Growth Communities in Romania
Next Article in Special Issue
A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI
Previous Article in Journal
Using Trajectory Clusters to Define the Most Relevant Features for Transient Stability Prediction Based on Machine Learning Method
Previous Article in Special Issue
Optimal Isolation Control of Three-Port Active Converters as a Combined Charger for Electric Vehicles
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2016, 9(11), 896; doi:10.3390/en9110896

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

1
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2
IVHM Centre, Cranfield University, Cranfield MK43 0AL, UK
3
Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Academic Editor: Michael Gerard Pecht
Received: 31 May 2016 / Revised: 2 October 2016 / Accepted: 25 October 2016 / Published: 1 November 2016
View Full-Text   |   Download PDF [2248 KB, uploaded 1 November 2016]   |  

Abstract

State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework. View Full-Text
Keywords: lithium-ion batteries; state of health (SOH); rest time; cycle beginning time; support vector machine; hyperplane shift lithium-ion batteries; state of health (SOH); rest time; cycle beginning time; support vector machine; hyperplane shift
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 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

Qin, T.; Zeng, S.; Guo, J.; Skaf, Z. A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena. Energies 2016, 9, 896.

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