@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",
}