Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle

This paper presents modelling techniques for Lithium Iron Phosphate (LiFePO4) battery in an electric vehicle. Artificial intelligence techniques namely multi-layered perceptron neural network (MLPNN) and Elman recurrent neural network are devised to estimate the energy remained in the battery ban...

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Main Authors:	Toha, Siti Fauziah, Faeza, Nor Hazima, Mohd Azubair, Nor Aziah, Nizam, Hanis, Hassan, Mohd. Khair, Ibrahim, Babul Salam KSM
Format:	Article
Language:	English English
Published:	Trans Tech Publications, Switzerland 2014
Subjects:	T59.5 Automation
Online Access:	http://irep.iium.edu.my/35311/ http://irep.iium.edu.my/35311/ http://irep.iium.edu.my/35311/ http://irep.iium.edu.my/35311/1/AMR_FinalPaper.pdf http://irep.iium.edu.my/35311/4/35311_Lithium%20iron%20phosphate%20intelligent%20SOC%20prediction%20for%20efficient%20electric%20vehicle_SCOPUS.pdf

id	iium-35311
recordtype	eprints
spelling	iium-353112017-09-18T02:23:43Z http://irep.iium.edu.my/35311/ Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle Toha, Siti Fauziah Faeza, Nor Hazima Mohd Azubair, Nor Aziah Nizam, Hanis Hassan, Mohd. Khair Ibrahim, Babul Salam KSM T59.5 Automation This paper presents modelling techniques for Lithium Iron Phosphate (LiFePO4) battery in an electric vehicle. Artificial intelligence techniques namely multi-layered perceptron neural network (MLPNN) and Elman recurrent neural network are devised to estimate the energy remained in the battery bank which referred to state of charge (SOC). The New European Driving Cycle (NEDC) test data is used to excite the cells in driving cycle-based conditions under varied temperature range [0-55]0C. Accurate SOC prediction is a key function for satisfactory implementation of Battery Supervisory System (BSS). It is demonstrated that artificial intelligence methods can be effectively used with highly accurate results. The accuracy of the modeling results is demonstrated through validation and correlation tests. Trans Tech Publications, Switzerland 2014 Article PeerReviewed application/pdf en http://irep.iium.edu.my/35311/1/AMR_FinalPaper.pdf application/pdf en http://irep.iium.edu.my/35311/4/35311_Lithium%20iron%20phosphate%20intelligent%20SOC%20prediction%20for%20efficient%20electric%20vehicle_SCOPUS.pdf Toha, Siti Fauziah and Faeza, Nor Hazima and Mohd Azubair, Nor Aziah and Nizam, Hanis and Hassan, Mohd. Khair and Ibrahim, Babul Salam KSM (2014) Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle. Advanced Materials Research, 875. pp. 1613-1618. ISSN 1022-6680 http://www.ttp.net/1022-6680.html 10.4028/www.scientific.net/AMR.875-877.1613
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T59.5 Automation
spellingShingle	T59.5 Automation Toha, Siti Fauziah Faeza, Nor Hazima Mohd Azubair, Nor Aziah Nizam, Hanis Hassan, Mohd. Khair Ibrahim, Babul Salam KSM Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle
description	This paper presents modelling techniques for Lithium Iron Phosphate (LiFePO4) battery in an electric vehicle. Artificial intelligence techniques namely multi-layered perceptron neural network (MLPNN) and Elman recurrent neural network are devised to estimate the energy remained in the battery bank which referred to state of charge (SOC). The New European Driving Cycle (NEDC) test data is used to excite the cells in driving cycle-based conditions under varied temperature range [0-55]0C. Accurate SOC prediction is a key function for satisfactory implementation of Battery Supervisory System (BSS). It is demonstrated that artificial intelligence methods can be effectively used with highly accurate results. The accuracy of the modeling results is demonstrated through validation and correlation tests.
format	Article
author	Toha, Siti Fauziah Faeza, Nor Hazima Mohd Azubair, Nor Aziah Nizam, Hanis Hassan, Mohd. Khair Ibrahim, Babul Salam KSM
author_facet	Toha, Siti Fauziah Faeza, Nor Hazima Mohd Azubair, Nor Aziah Nizam, Hanis Hassan, Mohd. Khair Ibrahim, Babul Salam KSM
author_sort	Toha, Siti Fauziah
title	Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle
title_short	Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle
title_full	Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle
title_fullStr	Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle
title_full_unstemmed	Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle
title_sort	lithium iron phosphate intelligent soc prediction for efficient electric vehicle
publisher	Trans Tech Publications, Switzerland
publishDate	2014
url	http://irep.iium.edu.my/35311/ http://irep.iium.edu.my/35311/ http://irep.iium.edu.my/35311/ http://irep.iium.edu.my/35311/1/AMR_FinalPaper.pdf http://irep.iium.edu.my/35311/4/35311_Lithium%20iron%20phosphate%20intelligent%20SOC%20prediction%20for%20efficient%20electric%20vehicle_SCOPUS.pdf
first_indexed	2023-09-18T20:50:38Z
last_indexed	2023-09-18T20:50:38Z
_version_	1777409970859409408

Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle

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