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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Noor Iswaniza Md Siam
Universiti Teknologi Malaysia
Control & Systems Engineering Electrical & Electronics Engineering

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Lithium ferro phosphate battery state of charge estimation using particle filter Noor Iswaniza Md Siam; Tole Sutikno; Mohd Junaidi Abdul Aziz
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i2.pp975-985

Abstract

Lithium ferro phosphate (LiFePO4) has a promising battery technology with high charging/discharging behaviours make it suitable for electric vehicles (EVs) application. Battery state of charge (SOC) is a vital indicator in the battery management system (BMS) that monitors the charging and discharging operation of a battery pack. This paper proposes an electric circuit model for LiFePO4 battery by using particle filter (PF) method to determine the SOC estimation of batteries precisely. The LiFePO4 battery modelling is carried out using MATLAB software. Constant discharge test (CDT) is performed to measure the usable capacity of the battery and pulse discharge test (PDT) is used to determine the battery model parameters. Three parallel RC battery models have been chosen for this study to achieve high accuracy. The proposed PF implements recursive bayesian filter by Monte Carlo sampling which is robust for non-linear and/or non-Gaussian distributions. The accuracy of the developed electrical battery model is compared with experimental data for verification purpose. Then, the performance of the model is compared with experimental data and extended Kalman filter (EKF) method for validation purposed. A superior battery SOC estimator with higher accuracy compared to EKF method has been obtained.
Co-Authors Mohd Junaidi Abdul Aziz Tole Sutikno