Next Article in Journal
Environmentally Sustainable Biogas? The Key Role of Manure Co-Digestion with Energy Crops
Next Article in Special Issue
Research on a Novel Power Inductor-Based Bidirectional Lossless Equalization Circuit for Series-Connected Battery Packs
Previous Article in Journal
Security of Supply in European Electricity Markets—Determinants of Investment Decisions and the European Energy Union
Previous Article in Special Issue
A Rule-Based Energy Management Strategy for a Plug-in Hybrid School Bus Based on a Controller Area Network Bus
Article Menu

Export Article

Open AccessArticle
Energies 2015, 8(6), 5217-5233;

Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation

Hawaii Natural Energy Institute, University of Hawaii at Manoa, 1680 East-West Road, Post 105, Honolulu, HI 96822, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: K. T. Chau
Received: 1 May 2015 / Revised: 23 May 2015 / Accepted: 26 May 2015 / Published: 3 June 2015
(This article belongs to the Collection Electric and Hybrid Vehicles Collection)
PDF [1141 KB, uploaded 4 June 2015]


As the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accurate online estimation of the state of charge (SOC) in a battery pack. This estimation is difficult, especially after substantial battery aging. In order to address this problem, this paper utilizes SOC estimation of Li-ion battery packs using a fuzzy-improved extended Kalman filter (fuzzy-IEKF) for Li-ion cells, regardless of their age. The proposed approach introduces a fuzzy method with a new class and associated membership function that determines an approximate initial value applied to SOC estimation. Subsequently, the EKF method is used by considering the single unit model for the battery pack to estimate the SOC for following periods of battery use. This approach uses an adaptive model algorithm to update the model for each single cell in the battery pack. To verify the accuracy of the estimation method, tests are done on a LiFePO4 aged battery pack consisting of 120 cells connected in series with a nominal voltage of 432 V. View Full-Text
Keywords: Li-ion battery; aged cell; state of charge; extended Kalman filter; fuzzy Li-ion battery; aged cell; state of charge; extended Kalman filter; fuzzy

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).

Share & Cite This Article

MDPI and ACS Style

Sepasi, S.; Roose, L.R.; Matsuura, M.M. Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation. Energies 2015, 8, 5217-5233.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



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