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Application of Fuzzy Logic in Multi-Mode Driving for a Battery Electric Vehicle Energy Management

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@article{IJASEIT1960,
   author = {T.A.T. Mohd and M.K. Hassan and Ishak Aris and Azura C.S and B.S.K.K Ibrahim},
   title = {Application of Fuzzy Logic in Multi-Mode Driving for a Battery Electric Vehicle Energy Management},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {1},
   year = {2017},
   pages = {284--290},
   keywords = {battery electric vehicle; energy management; fuzzy logic controller; multi-mode driving.},
   abstract = {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 multi-mode 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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1960},
   doi = {10.18517/ijaseit.7.1.1960}
}

EndNote

%A Mohd, T.A.T.
%A Hassan, M.K.
%A Aris, Ishak
%A C.S, Azura
%A Ibrahim, B.S.K.K
%D 2017
%T Application of Fuzzy Logic in Multi-Mode Driving for a Battery Electric Vehicle Energy Management
%B 2017
%9 battery electric vehicle; energy management; fuzzy logic controller; multi-mode driving.
%! Application of Fuzzy Logic in Multi-Mode Driving for a Battery Electric Vehicle Energy Management
%K battery electric vehicle; energy management; fuzzy logic controller; multi-mode driving.
%X 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 multi-mode 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1960
%R doi:10.18517/ijaseit.7.1.1960
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 1
%@ 2088-5334

IEEE

T.A.T. Mohd,M.K. Hassan,Ishak Aris,Azura C.S and B.S.K.K Ibrahim,"Application of Fuzzy Logic in Multi-Mode Driving for a Battery Electric Vehicle Energy Management," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 1, pp. 284-290, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.1.1960.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Mohd, T.A.T.
AU  - Hassan, M.K.
AU  - Aris, Ishak
AU  - C.S, Azura
AU  - Ibrahim, B.S.K.K
PY  - 2017
TI  - Application of Fuzzy Logic in Multi-Mode Driving for a Battery Electric Vehicle Energy Management
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 1
Y2  - 2017
SP  - 284
EP  - 290
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - battery electric vehicle; energy management; fuzzy logic controller; multi-mode driving.
N2  - 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 multi-mode 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1960
DO  - 10.18517/ijaseit.7.1.1960

RefWorks

RT Journal Article
ID 1960
A1 Mohd, T.A.T.
A1 Hassan, M.K.
A1 Aris, Ishak
A1 C.S, Azura
A1 Ibrahim, B.S.K.K
T1 Application of Fuzzy Logic in Multi-Mode Driving for a Battery Electric Vehicle Energy Management
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 1
YR 2017
SP 284
OP 290
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 battery electric vehicle; energy management; fuzzy logic controller; multi-mode driving.
AB 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 multi-mode 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1960
DO  - 10.18517/ijaseit.7.1.1960