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Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle


Citation

Toha, Siti Fauziah and Ismail, Nur Hazima Faezaa and Mohd Azubair, Nor Aziah and Md Ishak, Nizam Hanis and Hassan, Mohd Khair and Ksm Kader Ibrahim, Babul Salam (2014) Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle. Advanced Materials Research, 875-877. pp. 1613-1618. ISSN 1022-6680; ESSN: 1662-8985

Abstract

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]°C. 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.


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Official URL or Download Paper: http://www.scientific.net/AMR.875-877.1613

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.4028/www.scientific.net/AMR.875-877.1613
Publisher: Trans Tech Publications
Keywords: Elman recurrent neural network and battery supervisory system (BSS); Lithium iron phosphate; Multi-layered perceptron neural network (MLPNN); State of charge (SOC)
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 15 Sep 2016 05:28
Last Modified: 15 Sep 2016 05:28
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.4028/www.scientific.net/AMR.875-877.1613
URI: http://psasir.upm.edu.my/id/eprint/34390
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