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research-article

A new method for on-line estimation of the piston maximum temperature in diesel-nature gas dual fuel engine

[+] Author and Article Information
Youyao Fu

School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang 330013, China
fuyouyao828@126.com

Bing Xiao

College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
aubxiao@scut.edu.cn

Chengwei Zhang

Electrical Engineering College, Guizhou Institute of Technology, Guiyang 550003, China
weiwei433410@sina.com

Jun Liu

School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang 330013, China
liujun@ecit.cn

Jiangxiong Fang

School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang 330013, China
fangchj2002@163.com

1Corresponding author.

ASME doi:10.1115/1.4038836 History: Received November 05, 2015; Revised November 17, 2017

Abstract

Diesel-natural gas dual fuel engine has gained increasing interesting in recent years because of its excellent power and economy. However, the reliability of the dual fuel engine does not meet the requirements of practical application. The piston maximun temperature(PMT) of the dual fuel engine easily exceeds the security border. In view of this, this paper proposes a method based on the lasso regression to estimate the PMT of the dual fuelengine, so as to real-timely monitor the health state of the dual fuel engine. Specifically, PMTs under some working conditions were off-line acquired by the finite element analysis with ANSYS. A model is presented to describe the relationship between the PMT and some indirect engine variables, including NOx emission, excess air coefficient, engine speed and inlet pressure, and the model parameters are optimized using the lasso regression algorithm, which can be easily implemented by the electronic control unit(ECU). Finally, the model is employed to real-timely estimate the PMT of the dual fuel engine. Experiments reveal that the proposed model produces satisfying predictions with deviations less than11

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