The application of fuzzy-neural network on control strategy of hybrid vehicles

Rongguang Chen; Chunsheng Li; Xia Meng; Yongguang Yu

doi:10.1109/CHICC.2008.4604992

The application of fuzzy-neural network on control strategy of hybrid vehicles

Rongguang Chen
, Chunsheng Li
, Xia Meng
, Yongguang Yu

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

Abstract

In order to increase the fuel economy and decrease the emissions of hybrid vehicles, firstly a fuzzy logic control system is presented in this paper. In parallel hybrid vehicles, the whole required torque comes from internal combustion engine and motor engine respectively. Based on the desired torque for driving and state of charge, the fuzzy logic control system determines how the power splits between the dual sources, which is the key point for hybrid vehicles. Then, Adaptive Neural-Fuzzy Inference System (ANFIS) method is applied to optimize fuzzy logic control system based on the data of driving cycle. The main contribution of this paper is well application of fuzzy-neural network to improve original control system, which minimized the fuel consumption and emissions. The simulation results show very good performance of the proposed method.

Original language	English
Title of host publication	Proceedings of the 27th Chinese Control Conference, CCC
Pages	281-284
DOIs	https://doi.org/10.1109/CHICC.2008.4604992
Publication status	Published - 2008
Event	27th Chinese Control Conference, CCC - Kunming, Yunnan, China Duration: 16 Jul 2008 → 18 Jul 2008

Publication series

Name	Proceedings of the 27th Chinese Control Conference, CCC

Conference

Conference	27th Chinese Control Conference, CCC
Place	China
City	Kunming, Yunnan
Period	16/07/08 → 18/07/08

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

Control strategy
Fuzzy logic controller
Hybrid electric vehicles

Access Output

10.1109/CHICC.2008.4604992

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{4a3e0ce08c594f5baa70fa5923fab6cc,

title = "The application of fuzzy-neural network on control strategy of hybrid vehicles",

abstract = "In order to increase the fuel economy and decrease the emissions of hybrid vehicles, firstly a fuzzy logic control system is presented in this paper. In parallel hybrid vehicles, the whole required torque comes from internal combustion engine and motor engine respectively. Based on the desired torque for driving and state of charge, the fuzzy logic control system determines how the power splits between the dual sources, which is the key point for hybrid vehicles. Then, Adaptive Neural-Fuzzy Inference System (ANFIS) method is applied to optimize fuzzy logic control system based on the data of driving cycle. The main contribution of this paper is well application of fuzzy-neural network to improve original control system, which minimized the fuel consumption and emissions. The simulation results show very good performance of the proposed method.",

keywords = "Control strategy, Fuzzy logic controller, Hybrid electric vehicles",

author = "Rongguang Chen and Chunsheng Li and Xia Meng and Yongguang Yu",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 27th Chinese Control Conference, CCC ; Conference date: 16-07-2008 Through 18-07-2008",

year = "2008",

doi = "10.1109/CHICC.2008.4604992",

language = "English",

isbn = "9787900719706",

series = "Proceedings of the 27th Chinese Control Conference, CCC",

pages = "281--284",

booktitle = "Proceedings of the 27th Chinese Control Conference, CCC",

}

Chen, R, Li, C, Meng, X & Yu, Y 2008, The application of fuzzy-neural network on control strategy of hybrid vehicles. in Proceedings of the 27th Chinese Control Conference, CCC., 4604992, Proceedings of the 27th Chinese Control Conference, CCC, pp. 281-284, 27th Chinese Control Conference, CCC, Kunming, Yunnan, China, 16/07/08. https://doi.org/10.1109/CHICC.2008.4604992

The application of fuzzy-neural network on control strategy of hybrid vehicles. / Chen, Rongguang; Li, Chunsheng; Meng, Xia et al.
Proceedings of the 27th Chinese Control Conference, CCC. 2008. p. 281-284 4604992 (Proceedings of the 27th Chinese Control Conference, CCC).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - The application of fuzzy-neural network on control strategy of hybrid vehicles

AU - Chen, Rongguang

AU - Li, Chunsheng

AU - Meng, Xia

AU - Yu, Yongguang

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2008

Y1 - 2008

N2 - In order to increase the fuel economy and decrease the emissions of hybrid vehicles, firstly a fuzzy logic control system is presented in this paper. In parallel hybrid vehicles, the whole required torque comes from internal combustion engine and motor engine respectively. Based on the desired torque for driving and state of charge, the fuzzy logic control system determines how the power splits between the dual sources, which is the key point for hybrid vehicles. Then, Adaptive Neural-Fuzzy Inference System (ANFIS) method is applied to optimize fuzzy logic control system based on the data of driving cycle. The main contribution of this paper is well application of fuzzy-neural network to improve original control system, which minimized the fuel consumption and emissions. The simulation results show very good performance of the proposed method.

AB - In order to increase the fuel economy and decrease the emissions of hybrid vehicles, firstly a fuzzy logic control system is presented in this paper. In parallel hybrid vehicles, the whole required torque comes from internal combustion engine and motor engine respectively. Based on the desired torque for driving and state of charge, the fuzzy logic control system determines how the power splits between the dual sources, which is the key point for hybrid vehicles. Then, Adaptive Neural-Fuzzy Inference System (ANFIS) method is applied to optimize fuzzy logic control system based on the data of driving cycle. The main contribution of this paper is well application of fuzzy-neural network to improve original control system, which minimized the fuel consumption and emissions. The simulation results show very good performance of the proposed method.

KW - Control strategy

KW - Fuzzy logic controller

KW - Hybrid electric vehicles

UR - https://www.scopus.com/pages/publications/52449124753

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-52449124753&origin=recordpage

U2 - 10.1109/CHICC.2008.4604992

DO - 10.1109/CHICC.2008.4604992

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9787900719706

T3 - Proceedings of the 27th Chinese Control Conference, CCC

SP - 281

EP - 284

BT - Proceedings of the 27th Chinese Control Conference, CCC

T2 - 27th Chinese Control Conference, CCC

Y2 - 16 July 2008 through 18 July 2008

ER -

The application of fuzzy-neural network on control strategy of hybrid vehicles

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this