Next Article in Journal
Thermal Decomposition of Anhydrous Alkali Metal Dodecaborates M2B12H12 (M = Li, Na, K)
Previous Article in Journal
A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search
Article Menu

Export Article

Open AccessArticle
Energies 2015, 8(11), 12409-12428; doi:10.3390/en81112327

Fuzzy Sliding Mode Observer with Grey Prediction for the Estimation of the State-of-Charge of a Lithium-Ion Battery

1
Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 790-784, Korea
2
Department of Creative IT Excellence Engineering and Future IT Innovation Laboratory, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 790-784, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Sheng S. Zhang
Received: 17 August 2015 / Revised: 5 October 2015 / Accepted: 27 October 2015 / Published: 3 November 2015
View Full-Text   |   Download PDF [1655 KB, uploaded 3 November 2015]   |  

Abstract

We propose a state-of-charge (SOC) estimation method for Li-ion batteries that combines a fuzzy sliding mode observer (FSMO) with grey prediction. Unlike the existing methods based on a conventional first-order sliding mode observer (SMO) and an adaptive gain SMO, the proposed method eliminates chattering in SOC estimation. In this method, which uses a fuzzy inference system, the gains of the SMO are adjusted according to the predicted future error and present estimation error of the terminal voltage. To forecast the future error value, a one-step-ahead terminal voltage prediction is obtained using a grey predictor. The proposed estimation method is validated through two types of discharge tests (a pulse discharge test and a random discharge test). The SOC estimation results are compared to the results of the conventional first-order SMO-based and the adaptive gain SMO-based methods. The experimental results show that the proposed method not only reduces chattering, but also improves estimation accuracy. View Full-Text
Keywords: lithium-ion battery; state-of-charge (SOC); fuzzy sliding mode observer (FSMO); grey prediction lithium-ion battery; state-of-charge (SOC); fuzzy sliding mode observer (FSMO); grey prediction
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

Kim, D.; Goh, T.; Park, M.; Kim, S.W. Fuzzy Sliding Mode Observer with Grey Prediction for the Estimation of the State-of-Charge of a Lithium-Ion Battery. Energies 2015, 8, 12409-12428.

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