Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain

A. Hanif; Q. Ahmed; A. I. Bhatti; G. Rizzoni

doi:10.1109/CCTA.2017.8062566

Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain

A. Hanif
, Q. Ahmed
, A. I. Bhatti
, G. Rizzoni

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

3 Scopus citations

Abstract

In electrified powertrain control, meeting the torque demands and ensuring efficient Electrical Machine (EM) operations are two essential but conflicting demands. A multi-objective Linear Parameters Varying (LPV) controller is proposed to address the problem of these conflicting objectives. The synthesis of multi-objective controller is based on the selection of optimal weighting functions optimized by Genetic Algorithm (GA). The effectiveness of the proposed controller is tested and evaluated for an electrified powertrain operating in a standard urban driving cycles. The stability of the proposed Multi-Objective Controller (MOC) is established. The nonlinear simulation of the proposed controller delivers the robust performance and better efficiency of an EV Induction Machine (IM) based electric drive over the entire driving cycle.

Original language	English
Title of host publication	1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	853-858
Number of pages	6
ISBN (Electronic)	9781509021826
DOIs	https://doi.org/10.1109/CCTA.2017.8062566
State	Published - 6 Oct 2017
Externally published	Yes

Publication series

Name	1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017
Volume	2017-January

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

UN SDGs

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

SDG 7 Affordable and Clean Energy

ASJC Scopus subject areas

Theoretical Computer Science
Hardware and Architecture
Software
Control and Systems Engineering

Access to Document

10.1109/CCTA.2017.8062566

Cite this

Hanif, A., Ahmed, Q., Bhatti, A. I., & Rizzoni, G. (2017). Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain. In 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017 (pp. 853-858). (1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCTA.2017.8062566

Hanif, A. ; Ahmed, Q. ; Bhatti, A. I. et al. / Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain. 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 853-858 (1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017).

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title = "Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain",

abstract = "In electrified powertrain control, meeting the torque demands and ensuring efficient Electrical Machine (EM) operations are two essential but conflicting demands. A multi-objective Linear Parameters Varying (LPV) controller is proposed to address the problem of these conflicting objectives. The synthesis of multi-objective controller is based on the selection of optimal weighting functions optimized by Genetic Algorithm (GA). The effectiveness of the proposed controller is tested and evaluated for an electrified powertrain operating in a standard urban driving cycles. The stability of the proposed Multi-Objective Controller (MOC) is established. The nonlinear simulation of the proposed controller delivers the robust performance and better efficiency of an EV Induction Machine (IM) based electric drive over the entire driving cycle.",

author = "A. Hanif and Q. Ahmed and Bhatti, \{A. I.\} and G. Rizzoni",

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year = "2017",

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doi = "10.1109/CCTA.2017.8062566",

language = "English",

series = "1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017",

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Hanif, A, Ahmed, Q, Bhatti, AI & Rizzoni, G 2017, Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain. in 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017. 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 853-858. https://doi.org/10.1109/CCTA.2017.8062566

Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain. / Hanif, A.; Ahmed, Q.; Bhatti, A. I. et al.
1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 853-858 (1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017; Vol. 2017-January).

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

TY - GEN

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AU - Hanif, A.

AU - Ahmed, Q.

AU - Bhatti, A. I.

AU - Rizzoni, G.

PY - 2017/10/6

Y1 - 2017/10/6

N2 - In electrified powertrain control, meeting the torque demands and ensuring efficient Electrical Machine (EM) operations are two essential but conflicting demands. A multi-objective Linear Parameters Varying (LPV) controller is proposed to address the problem of these conflicting objectives. The synthesis of multi-objective controller is based on the selection of optimal weighting functions optimized by Genetic Algorithm (GA). The effectiveness of the proposed controller is tested and evaluated for an electrified powertrain operating in a standard urban driving cycles. The stability of the proposed Multi-Objective Controller (MOC) is established. The nonlinear simulation of the proposed controller delivers the robust performance and better efficiency of an EV Induction Machine (IM) based electric drive over the entire driving cycle.

AB - In electrified powertrain control, meeting the torque demands and ensuring efficient Electrical Machine (EM) operations are two essential but conflicting demands. A multi-objective Linear Parameters Varying (LPV) controller is proposed to address the problem of these conflicting objectives. The synthesis of multi-objective controller is based on the selection of optimal weighting functions optimized by Genetic Algorithm (GA). The effectiveness of the proposed controller is tested and evaluated for an electrified powertrain operating in a standard urban driving cycles. The stability of the proposed Multi-Objective Controller (MOC) is established. The nonlinear simulation of the proposed controller delivers the robust performance and better efficiency of an EV Induction Machine (IM) based electric drive over the entire driving cycle.

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M3 - Conference contribution

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BT - 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017

PB - Institute of Electrical and Electronics Engineers Inc.

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Hanif A, Ahmed Q, Bhatti AI, Rizzoni G. Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain. In 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 853-858. (1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017). doi: 10.1109/CCTA.2017.8062566

Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain

Abstract

Publication series

Bibliographical note

UN SDGs

ASJC Scopus subject areas

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Cite this