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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Trans Tech Publications, Switzerland
2014
|
| 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 |
| _version_ | 1825647860872380416 |
|---|---|
| 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 |
| collection | IIUM |
| 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. |
| first_indexed | 2024-03-05T23:23:49Z |
| format | Article |
| id | oai:generic.eprints.org:35311 |
| institution | International Islamic University Malaysia |
| language | English English |
| last_indexed | 2024-03-05T23:23:49Z |
| publishDate | 2014 |
| publisher | Trans Tech Publications, Switzerland |
| record_format | dspace |
| spelling | oai:generic.eprints.org: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 |
| 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 |
| 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 |
| topic | T59.5 Automation |
| url | 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 |
| work_keys_str_mv | AT tohasitifauziah lithiumironphosphateintelligentsocpredictionforefficientelectricvehicle AT faezanorhazima lithiumironphosphateintelligentsocpredictionforefficientelectricvehicle AT mohdazubairnoraziah lithiumironphosphateintelligentsocpredictionforefficientelectricvehicle AT nizamhanis lithiumironphosphateintelligentsocpredictionforefficientelectricvehicle AT hassanmohdkhair lithiumironphosphateintelligentsocpredictionforefficientelectricvehicle AT ibrahimbabulsalamksm lithiumironphosphateintelligentsocpredictionforefficientelectricvehicle |