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: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Trans Tech Publications, Switzerland
2014
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| Subjects: | |
| Online Access: | 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 http://irep.iium.edu.my/35311/ http://www.ttp.net/1022-6680.html |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English English |
| Summary: | 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. |
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