Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system

Song Qiang; Zhao Ping

doi:10.1109/IAEAC.2017.8054421

Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system

Song Qiang, Zhao Ping

School of Mechanical Engineering

Beijing Institute of Technology

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

1 Citation (Scopus)

Abstract

Based on the optimization design, the mathematical model of the control strategy parameter optimization taking the minimum fuel consumption as the objective function is established. Then, taking hybrid bulldozer as an example, the genetic algorithm is used to solve the optimization problem. Through optimization, the fuel consumption reduces 4.1% further more compared with conventional bulldozer under the same working condition. Using this method, it is easier to find a set of optimal parameters to shorten the calibrated time of the controller in a real hybrid electric vehicle.

Original language	English
Title of host publication	Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2256-2260
Number of pages	5
ISBN (Electronic)	9781467389778
DOIs	https://doi.org/10.1109/IAEAC.2017.8054421
Publication status	Published - 29 Sept 2017
Event	2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017 - Chongqing, China Duration: 25 Mar 2017 → 26 Mar 2017

Publication series

Name	Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017

Conference

Conference	2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017
Country/Territory	China
City	Chongqing
Period	25/03/17 → 26/03/17

Keywords

Control strategy
Genetic algorithm
Hybrid powertrain system
Hybrid vehicle
Parameter optimization

Access to Document

10.1109/IAEAC.2017.8054421

Cite this

Qiang, S., & Ping, Z. (2017). Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system. In B. Xu (Ed.), Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017 (pp. 2256-2260). Article 8054421 (Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAEAC.2017.8054421

Qiang, Song ; Ping, Zhao. / Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system. Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2256-2260 (Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017).

@inproceedings{c1939fbf10a7405aaf04abaafd65cff3,

title = "Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system",

abstract = "Based on the optimization design, the mathematical model of the control strategy parameter optimization taking the minimum fuel consumption as the objective function is established. Then, taking hybrid bulldozer as an example, the genetic algorithm is used to solve the optimization problem. Through optimization, the fuel consumption reduces 4.1% further more compared with conventional bulldozer under the same working condition. Using this method, it is easier to find a set of optimal parameters to shorten the calibrated time of the controller in a real hybrid electric vehicle.",

keywords = "Control strategy, Genetic algorithm, Hybrid powertrain system, Hybrid vehicle, Parameter optimization",

author = "Song Qiang and Zhao Ping",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017 ; Conference date: 25-03-2017 Through 26-03-2017",

year = "2017",

month = sep,

day = "29",

doi = "10.1109/IAEAC.2017.8054421",

language = "English",

series = "Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2256--2260",

editor = "Bing Xu",

booktitle = "Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017",

address = "United States",

}

Qiang, S & Ping, Z 2017, Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system. in B Xu (ed.), Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017., 8054421, Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 2256-2260, 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017, Chongqing, China, 25/03/17. https://doi.org/10.1109/IAEAC.2017.8054421

Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system. / Qiang, Song; Ping, Zhao.
Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2256-2260 8054421 (Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017).

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

TY - GEN

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AU - Qiang, Song

AU - Ping, Zhao

PY - 2017/9/29

Y1 - 2017/9/29

N2 - Based on the optimization design, the mathematical model of the control strategy parameter optimization taking the minimum fuel consumption as the objective function is established. Then, taking hybrid bulldozer as an example, the genetic algorithm is used to solve the optimization problem. Through optimization, the fuel consumption reduces 4.1% further more compared with conventional bulldozer under the same working condition. Using this method, it is easier to find a set of optimal parameters to shorten the calibrated time of the controller in a real hybrid electric vehicle.

AB - Based on the optimization design, the mathematical model of the control strategy parameter optimization taking the minimum fuel consumption as the objective function is established. Then, taking hybrid bulldozer as an example, the genetic algorithm is used to solve the optimization problem. Through optimization, the fuel consumption reduces 4.1% further more compared with conventional bulldozer under the same working condition. Using this method, it is easier to find a set of optimal parameters to shorten the calibrated time of the controller in a real hybrid electric vehicle.

KW - Control strategy

KW - Genetic algorithm

KW - Hybrid powertrain system

KW - Hybrid vehicle

KW - Parameter optimization

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U2 - 10.1109/IAEAC.2017.8054421

DO - 10.1109/IAEAC.2017.8054421

M3 - Conference contribution

AN - SCOPUS:85034607837

T3 - Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017

SP - 2256

EP - 2260

BT - Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017

A2 - Xu, Bing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017

Y2 - 25 March 2017 through 26 March 2017

ER -

Qiang S, Ping Z. Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system. In Xu B, editor, Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2256-2260. 8054421. (Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017). doi: 10.1109/IAEAC.2017.8054421

Genetic algorithm study on control strategy parameter optimization of hybrid powertrain system

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