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 bank w...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Trans Tech Publications
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/34390/ http://psasir.upm.edu.my/id/eprint/34390/1/Lithium%20iron%20phosphate%20intelligent%20SOC%20prediction%20for%20efficient%20electric%20vehicle.pdf |
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| author | Toha, Siti Fauziah Ismail, Nur Hazima Faezaa Mohd Azubair, Nor Aziah Md Ishak, Nizam Hanis Hassan, Mohd Khair Ksm Kader Ibrahim, Babul Salam |
| author_facet | Toha, Siti Fauziah Ismail, Nur Hazima Faezaa Mohd Azubair, Nor Aziah Md Ishak, Nizam Hanis Hassan, Mohd Khair Ksm Kader Ibrahim, Babul Salam |
| author_sort | Toha, Siti Fauziah |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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]°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. |
| first_indexed | 2025-11-15T09:23:42Z |
| format | Article |
| id | upm-34390 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:23:42Z |
| publishDate | 2014 |
| publisher | Trans Tech Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-343902016-09-15T05:28:03Z http://psasir.upm.edu.my/id/eprint/34390/ Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle Toha, Siti Fauziah Ismail, Nur Hazima Faezaa Mohd Azubair, Nor Aziah Md Ishak, Nizam Hanis Hassan, Mohd Khair Ksm Kader Ibrahim, Babul Salam 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. Trans Tech Publications 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/34390/1/Lithium%20iron%20phosphate%20intelligent%20SOC%20prediction%20for%20efficient%20electric%20vehicle.pdf 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 http://www.scientific.net/AMR.875-877.1613 10.4028/www.scientific.net/AMR.875-877.1613 |
| spellingShingle | Toha, Siti Fauziah Ismail, Nur Hazima Faezaa Mohd Azubair, Nor Aziah Md Ishak, Nizam Hanis Hassan, Mohd Khair Ksm Kader Ibrahim, Babul Salam Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle |
| title | 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_short | Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle |
| title_sort | lithium iron phosphate intelligent soc prediction for efficient electric vehicle |
| url | http://psasir.upm.edu.my/id/eprint/34390/ http://psasir.upm.edu.my/id/eprint/34390/ http://psasir.upm.edu.my/id/eprint/34390/ http://psasir.upm.edu.my/id/eprint/34390/1/Lithium%20iron%20phosphate%20intelligent%20SOC%20prediction%20for%20efficient%20electric%20vehicle.pdf |