Adaptive engine optimisation using NSGA-II and MODA based on a sub-structured artificial neural network

Songshan Guo; Mark Dooner; Jihong Wang; Hongming Xu; Guoxiang Lu

doi:10.23919/IConAC.2017.8082008

Adaptive engine optimisation using NSGA-II and MODA based on a sub-structured artificial neural network

Songshan Guo, Mark Dooner, Jihong Wang, Hongming Xu, Guoxiang Lu

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The trend in the automotive industry is towards electric vehicles (EV), however, the industry will depend on gasoline engines for many years to come. There is also increased demand for the reduction of greenhouse gases. This work develops an adaptive model-based optimal control algorithm based on Sub-Structured Neural Network (SSANN), Multi-Objective Genetic Algorithms (GA), Multi-Objective Dragonfly Algorithm (MODA) and a fuzzy based inference system. The SSANN based on an individual engine speed are combined into a SSANN, whose output is connected to a fuzzy based inference system which extrapolates between trained engine speeds. The SSANN outputs are then used to evaluate the objective function of the optimisation process, which is performed using GA and MODA. The SSANN is retrained if the error is out of defined limit, which allows the system to adapt with engine ageing. The purpose of this work is to contribute to online engine calibration and control to improve engine performance and reduce greenhouse gas emissions.

Original language	English
Title of host publication	ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing
Subtitle of host publication	Addressing Global Challenges through Automation and Computing
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9780701702618
DOIs	https://doi.org/10.23919/IConAC.2017.8082008
Publication status	Published - 23 Oct 2017
Event	23rd IEEE International Conference on Automation and Computing, ICAC 2017 - Huddersfield, United Kingdom Duration: 7 Sept 2017 → 8 Sept 2017

Conference

Conference	23rd IEEE International Conference on Automation and Computing, ICAC 2017
Country/Territory	United Kingdom
City	Huddersfield
Period	7/09/17 → 8/09/17

Keywords

Adaptive engine calibration
MODA
NSGA
SSANN

ASJC Scopus subject areas

Control and Optimization
Safety, Risk, Reliability and Quality
Health Informatics
Computer Networks and Communications
Computer Science Applications

Access to Document

10.23919/IConAC.2017.8082008Licence: None: All rights reserved

https://ieeexplore.ieee.org/document/8082008/Licence: None: All rights reserved

Cite this

Guo, S., Dooner, M., Wang, J., Xu, H., & Lu, G. (2017). Adaptive engine optimisation using NSGA-II and MODA based on a sub-structured artificial neural network. In ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing Article 8082008 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.23919/IConAC.2017.8082008

@inproceedings{d5308fc2df95409e80f17725434122cf,

title = "Adaptive engine optimisation using NSGA-II and MODA based on a sub-structured artificial neural network",

abstract = "The trend in the automotive industry is towards electric vehicles (EV), however, the industry will depend on gasoline engines for many years to come. There is also increased demand for the reduction of greenhouse gases. This work develops an adaptive model-based optimal control algorithm based on Sub-Structured Neural Network (SSANN), Multi-Objective Genetic Algorithms (GA), Multi-Objective Dragonfly Algorithm (MODA) and a fuzzy based inference system. The SSANN based on an individual engine speed are combined into a SSANN, whose output is connected to a fuzzy based inference system which extrapolates between trained engine speeds. The SSANN outputs are then used to evaluate the objective function of the optimisation process, which is performed using GA and MODA. The SSANN is retrained if the error is out of defined limit, which allows the system to adapt with engine ageing. The purpose of this work is to contribute to online engine calibration and control to improve engine performance and reduce greenhouse gas emissions.",

keywords = "Adaptive engine calibration, MODA, NSGA, SSANN",

author = "Songshan Guo and Mark Dooner and Jihong Wang and Hongming Xu and Guoxiang Lu",

year = "2017",

month = oct,

day = "23",

doi = "10.23919/IConAC.2017.8082008",

language = "English",

booktitle = "ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

note = "23rd IEEE International Conference on Automation and Computing, ICAC 2017 ; Conference date: 07-09-2017 Through 08-09-2017",

}

Guo, S, Dooner, M, Wang, J, Xu, H & Lu, G 2017, Adaptive engine optimisation using NSGA-II and MODA based on a sub-structured artificial neural network. in ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing., 8082008, Institute of Electrical and Electronics Engineers (IEEE), 23rd IEEE International Conference on Automation and Computing, ICAC 2017, Huddersfield, United Kingdom, 7/09/17. https://doi.org/10.23919/IConAC.2017.8082008

Adaptive engine optimisation using NSGA-II and MODA based on a sub-structured artificial neural network. / Guo, Songshan; Dooner, Mark; Wang, Jihong et al.
ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing. Institute of Electrical and Electronics Engineers (IEEE), 2017. 8082008.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Adaptive engine optimisation using NSGA-II and MODA based on a sub-structured artificial neural network

AU - Guo, Songshan

AU - Dooner, Mark

AU - Wang, Jihong

AU - Xu, Hongming

AU - Lu, Guoxiang

PY - 2017/10/23

Y1 - 2017/10/23

N2 - The trend in the automotive industry is towards electric vehicles (EV), however, the industry will depend on gasoline engines for many years to come. There is also increased demand for the reduction of greenhouse gases. This work develops an adaptive model-based optimal control algorithm based on Sub-Structured Neural Network (SSANN), Multi-Objective Genetic Algorithms (GA), Multi-Objective Dragonfly Algorithm (MODA) and a fuzzy based inference system. The SSANN based on an individual engine speed are combined into a SSANN, whose output is connected to a fuzzy based inference system which extrapolates between trained engine speeds. The SSANN outputs are then used to evaluate the objective function of the optimisation process, which is performed using GA and MODA. The SSANN is retrained if the error is out of defined limit, which allows the system to adapt with engine ageing. The purpose of this work is to contribute to online engine calibration and control to improve engine performance and reduce greenhouse gas emissions.

AB - The trend in the automotive industry is towards electric vehicles (EV), however, the industry will depend on gasoline engines for many years to come. There is also increased demand for the reduction of greenhouse gases. This work develops an adaptive model-based optimal control algorithm based on Sub-Structured Neural Network (SSANN), Multi-Objective Genetic Algorithms (GA), Multi-Objective Dragonfly Algorithm (MODA) and a fuzzy based inference system. The SSANN based on an individual engine speed are combined into a SSANN, whose output is connected to a fuzzy based inference system which extrapolates between trained engine speeds. The SSANN outputs are then used to evaluate the objective function of the optimisation process, which is performed using GA and MODA. The SSANN is retrained if the error is out of defined limit, which allows the system to adapt with engine ageing. The purpose of this work is to contribute to online engine calibration and control to improve engine performance and reduce greenhouse gas emissions.

KW - Adaptive engine calibration

KW - MODA

KW - NSGA

KW - SSANN

UR - http://www.scopus.com/inward/record.url?scp=85040010001&partnerID=8YFLogxK

U2 - 10.23919/IConAC.2017.8082008

DO - 10.23919/IConAC.2017.8082008

M3 - Conference contribution

AN - SCOPUS:85040010001

BT - ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 23rd IEEE International Conference on Automation and Computing, ICAC 2017

Y2 - 7 September 2017 through 8 September 2017

ER -

Guo S, Dooner M, Wang J, Xu H, Lu G. Adaptive engine optimisation using NSGA-II and MODA based on a sub-structured artificial neural network. In ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing. Institute of Electrical and Electronics Engineers (IEEE). 2017. 8082008 doi: 10.23919/IConAC.2017.8082008

Adaptive engine optimisation using NSGA-II and MODA based on a sub-structured artificial neural network

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this