Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management
Energy management system is an area of emerging interest in a full electric vehicle research. With the increasing moves to a more sustainable vehicle, there is a need to extend the battery range that simultaneously satisfying the conflicting demand between battery capacity and vehicle weight or volu...
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| Format: | Article |
| Language: | English |
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Insight - Indonesian Society for Knowledge and Human Development
2017
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| Online Access: | http://eprints.uthm.edu.my/3889/ http://eprints.uthm.edu.my/3889/1/AJ%202017%20%28522%29.pdf |
| _version_ | 1848888139993579520 |
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| author | Tengku Mohd, Tengku Azman Hassan, Mohd Khair Aris, Ishak C.S, Azura K. K. Ibrahim, Babul Salam |
| author_facet | Tengku Mohd, Tengku Azman Hassan, Mohd Khair Aris, Ishak C.S, Azura K. K. Ibrahim, Babul Salam |
| author_sort | Tengku Mohd, Tengku Azman |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Energy management system is an area of emerging interest in a full electric vehicle research. With the increasing moves to a more sustainable vehicle, there is a need to extend the battery range that simultaneously satisfying the conflicting demand between battery capacity and vehicle weight or volume. This paper presents a research conducted in the Universiti Putra Malaysia, focusing on the energy management strategy of a battery-powered electric vehicle. Three vehicle driving modes; sport, comfort, and eco have been individually modelled. Each mode is capable to dominate different driving environments; highway, suburban, and urban. In European driving cycle simulation test, comfort and eco modes have shown large extension in driving range with the maximum of 7.33% and 19.70% respectively. However the speeds have been confined by certain specific limits. The proposed of integrated multimode driving using fuzzy logic has enabled an adaptive driving by automatically select the driving parameters based on the speed conditions. The results have proven its ability in reducing the energy consumption as much as 32.25%, and increasing the driving range of 4.21% without downgrading the speed performance. |
| first_indexed | 2025-11-15T20:05:32Z |
| format | Article |
| id | uthm-3889 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:05:32Z |
| publishDate | 2017 |
| publisher | Insight - Indonesian Society for Knowledge and Human Development |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-38892021-11-22T06:28:35Z http://eprints.uthm.edu.my/3889/ Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management Tengku Mohd, Tengku Azman Hassan, Mohd Khair Aris, Ishak C.S, Azura K. K. Ibrahim, Babul Salam TK1001-1841 Production of electric energy or power. Powerplants. Central stations TK7800-8360 Electronics Energy management system is an area of emerging interest in a full electric vehicle research. With the increasing moves to a more sustainable vehicle, there is a need to extend the battery range that simultaneously satisfying the conflicting demand between battery capacity and vehicle weight or volume. This paper presents a research conducted in the Universiti Putra Malaysia, focusing on the energy management strategy of a battery-powered electric vehicle. Three vehicle driving modes; sport, comfort, and eco have been individually modelled. Each mode is capable to dominate different driving environments; highway, suburban, and urban. In European driving cycle simulation test, comfort and eco modes have shown large extension in driving range with the maximum of 7.33% and 19.70% respectively. However the speeds have been confined by certain specific limits. The proposed of integrated multimode driving using fuzzy logic has enabled an adaptive driving by automatically select the driving parameters based on the speed conditions. The results have proven its ability in reducing the energy consumption as much as 32.25%, and increasing the driving range of 4.21% without downgrading the speed performance. Insight - Indonesian Society for Knowledge and Human Development 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/3889/1/AJ%202017%20%28522%29.pdf Tengku Mohd, Tengku Azman and Hassan, Mohd Khair and Aris, Ishak and C.S, Azura and K. K. Ibrahim, Babul Salam (2017) Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management. International Journal on Advanced Science Engineering Information Technology, 7 (1). pp. 284-290. ISSN 2088-5334 https://dx.doi.org/10.18517/ijaseit.7.1.1960 |
| spellingShingle | TK1001-1841 Production of electric energy or power. Powerplants. Central stations TK7800-8360 Electronics Tengku Mohd, Tengku Azman Hassan, Mohd Khair Aris, Ishak C.S, Azura K. K. Ibrahim, Babul Salam Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management |
| title | Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management |
| title_full | Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management |
| title_fullStr | Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management |
| title_full_unstemmed | Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management |
| title_short | Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management |
| title_sort | application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management |
| topic | TK1001-1841 Production of electric energy or power. Powerplants. Central stations TK7800-8360 Electronics |
| url | http://eprints.uthm.edu.my/3889/ http://eprints.uthm.edu.my/3889/ http://eprints.uthm.edu.my/3889/1/AJ%202017%20%28522%29.pdf |