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Energies 2015, 8(6), 5217-5233; doi:10.3390/en8065217

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
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
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)
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Abstract

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
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).

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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.

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