Staff View: Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)

Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)

State of charge (SOC) is a vital indicator in the battery management system (BMS) that monitoring the charging and discharging operation of the battery pack. It is crucial for optimizing the performance and extend the lifespan of battery for EVs/HEVs applications. Lithium Ferro Phosphate (LiFePO4) i...

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Main Author:	Md. Siam, Noor Iswaniza
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93013/1/NoorIswanizaMSKE2020.pdf http://eprints.utm.my/id/eprint/93013/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135884
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id	my.utm.93013
record_format	eprints
spelling	my.utm.930132021-11-07T06:00:26Z http://eprints.utm.my/id/eprint/93013/ Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF) Md. Siam, Noor Iswaniza TK Electrical engineering. Electronics Nuclear engineering State of charge (SOC) is a vital indicator in the battery management system (BMS) that monitoring the charging and discharging operation of the battery pack. It is crucial for optimizing the performance and extend the lifespan of battery for EVs/HEVs applications. Lithium Ferro Phosphate (LiFePO4) is one of the battery technology that has good performance, excellent life cycles, fast charging with lesser time as compared to other batteries. Many uncertainties and noises such as fluctuating current, sensor measurement, ambient temperature effect, and calibration error pose a challenge to determine the accuracy of SOC estimation. The objective of this research is to develop a battery model for LiFePO4 battery by using Particle Filter (PF) method to determine the SOC estimation of the lithium-ion battery precisely. The LiFePO4 battery modeling carried out using MATLAB software. Constant discharge test (CDT) is performed to measure and study the usable capacity of the battery. Then, pulse discharge test (PDT) is used to extract the dynamic characteristics of battery and calculate the battery model parameters. Three parallel RC battery model has been chosen for this study due to high accuracy is needed. The proposed PF implements Recursive Bayesian Filter by Monte Carlo sampling which is robust for non-linear and/or non-Gaussian distributions. The simulation result is compared with experimental data of dynamic behaviors of LiFePO4battery for verification purpose. Then, the performance of the algorithm which is in PF is compared to experimental data outcome and Extended Kalman Filter (EKF) method. An accurate SOC estimator with minimum error compared to EKF has been obtained. 2020 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/93013/1/NoorIswanizaMSKE2020.pdf Md. Siam, Noor Iswaniza (2020) Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF). Masters thesis, Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135884
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Md. Siam, Noor Iswaniza Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)
description	State of charge (SOC) is a vital indicator in the battery management system (BMS) that monitoring the charging and discharging operation of the battery pack. It is crucial for optimizing the performance and extend the lifespan of battery for EVs/HEVs applications. Lithium Ferro Phosphate (LiFePO4) is one of the battery technology that has good performance, excellent life cycles, fast charging with lesser time as compared to other batteries. Many uncertainties and noises such as fluctuating current, sensor measurement, ambient temperature effect, and calibration error pose a challenge to determine the accuracy of SOC estimation. The objective of this research is to develop a battery model for LiFePO4 battery by using Particle Filter (PF) method to determine the SOC estimation of the lithium-ion battery precisely. The LiFePO4 battery modeling carried out using MATLAB software. Constant discharge test (CDT) is performed to measure and study the usable capacity of the battery. Then, pulse discharge test (PDT) is used to extract the dynamic characteristics of battery and calculate the battery model parameters. Three parallel RC battery model has been chosen for this study due to high accuracy is needed. The proposed PF implements Recursive Bayesian Filter by Monte Carlo sampling which is robust for non-linear and/or non-Gaussian distributions. The simulation result is compared with experimental data of dynamic behaviors of LiFePO4battery for verification purpose. Then, the performance of the algorithm which is in PF is compared to experimental data outcome and Extended Kalman Filter (EKF) method. An accurate SOC estimator with minimum error compared to EKF has been obtained.
format	Thesis
author	Md. Siam, Noor Iswaniza
author_facet	Md. Siam, Noor Iswaniza
author_sort	Md. Siam, Noor Iswaniza
title	Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)
title_short	Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)
title_full	Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)
title_fullStr	Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)
title_full_unstemmed	Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)
title_sort	lithium ferro phosphate (lifepo4) battery soc estimation using particle filter (pf)
publishDate	2020
url	http://eprints.utm.my/id/eprint/93013/1/NoorIswanizaMSKE2020.pdf http://eprints.utm.my/id/eprint/93013/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135884
_version_	1717093407176785920
score	13.211869

Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)

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