Open AccessLithium ferro phosphate battery state of charge estimation using particle filter
Author(s) -
Noor Iswaniza Md Siam,
Tole Sutikno,
Mohd Junaidi Abdul Aziz
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijpeds.v12.i2.pp975-985
Subject(s) - battery (electricity) , extended kalman filter , state of charge , battery pack , lithium iron phosphate , particle filter , computer science , equivalent circuit , estimator , automotive engineering , simulation , kalman filter , electrical engineering , engineering , voltage , mathematics , power (physics) , artificial intelligence , physics , statistics , quantum mechanics
Lithium ferro phosphate (LiFePO 4 ) 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 LiFePO 4 battery by using particle filter (PF) method to determine the SOC estimation of batteries precisely. The LiFePO 4 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.