State of Charge Estimation of Li-ion Batteries Based on Adaptive Extended Kalman Filter

Monowar Hossain; Md. Enamul Haque; Sajeeb Saha; Mohammad T Arif; Aman Maung Than Oo

doi:10.1109/PESGM41954.2020.9282150

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State of Charge Estimation of Li-ion Batteries Based on Adaptive Extended Kalman Filter

Conference paper

Peer reviewed

State of Charge Estimation of Li-ion Batteries Based on Adaptive Extended Kalman Filter

Monowar Hossain, Md. Enamul Haque, Sajeeb Saha, Mohammad T Arif and Aman Maung Than Oo

Proceedings of the 2020 IEEE Power & Energy Society General Meeting (PESGM), pp.1-5

IEEE Power & Energy Society General Meeting (PESGM), 2020 (Montreal, Canada, 02-Aug-2020–06-Aug-2020)

Institute of Electrical and Electronics Engineers

2020

DOI: https://doi.org/10.1109/PESGM41954.2020.9282150

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Abstract

Li-ion battery

recursive least-square

state of charge

temperature effect

adaptive extended Kalman filter

The extended Kalman filter (EKF) is widely adopted for the state-of-charge (SOC) estimation of batteries. The trial and error selection of noise covariance and variation of operating temperatures lead to convergence uncertainty and poor robustness of the EKF. This paper presents an adaptive EKF (AEKF) for online SOC estimation of lithium-ion batteries based on the Thevenin equivalent circuit model (ECM) that can mitigate the problems with EKF. The parameters of the first-order Thevenin ECM are estimated using the recursive least square (RLS) method at different operating temperatures. A pulse discharge test with lithium-iron-phosphate cell has been carried out in the LabVIEW platform, where SOC of the cell is determined by the coulomb counting method (CCM). Then the SOC is estimated using the EKF and AEKF methods and compared with the CCM method. The simulation and experimental results confirm that the AEKF shows better performance compared to the conventional EKF method.

Details

Title: State of Charge Estimation of Li-ion Batteries Based on Adaptive Extended Kalman Filter
Authors: Monowar Hossain (Author) - Deakin University
Md. Enamul Haque (Author) - Deakin University
Sajeeb Saha (Author) - Deakin University
Mohammad T Arif (Author) - Deakin University
Aman Maung Than Oo (Author) - Deakin University
Publication details: Proceedings of the 2020 IEEE Power & Energy Society General Meeting (PESGM), pp.1-5
Conference details: IEEE Power & Energy Society General Meeting (PESGM), 2020 (Montreal, Canada, 02-Aug-2020–06-Aug-2020)
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2020
DOI: 10.1109/PESGM41954.2020.9282150
ISSN: 1944-9933; 1944-9925
ISBN: 9781728155081
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
Language: English
Record Identifier: 99622138902621
Output Type: Conference paper

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