Multi-objective cooperation optimization research on dynamic response and energy loss of high-speed solenoid valve for diesel engine injector

被引:0
|
作者
Yu, Zhiqing [1 ]
Zhao, Jianhui [1 ]
Wei, Rongqiang [1 ]
机构
[1] Harbin Engn Univ, Sch Power & Energy Engn, Harbin 150001, Peoples R China
基金
中国国家自然科学基金;
关键词
High-speed solenoid valve; dynamic response; energy loss; NSGA-II; multi-objective cooperation optimization; POWER LOSSES;
D O I
10.3233/JAE-230099
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The dynamic response and operational reliability of high-speed solenoid valve (HSV) for diesel engine injector are the main indicators to measure their performance. At high-frequency, the eddy current energy and Joule energy generated by the HSV will be converted into heat, which has a significant impact on the service life of HSV. The optimization of HSV involves the interaction between energy loss and the dynamic response of HSV. To optimize the HSV dynamic response time considering energy loss, the HSV work process simulation model was established in this paper, and the model was verified based on armature lift experimental data. Without changing the structural parameters of the HSV, the four parameters of electroconductibility, spring stiffness, damping coefficient, and coil resistance were selected as the key parameters affecting the dynamic response and energy loss. The response surface models (RSMs) of opening response time, closing response time, eddy current energy and Joule energy of the HSV were constructed by using the smoothing spline-analysis of variance method. The multi-objective cooperation optimization of HSV under the interaction of dynamic response characteristics and energy loss was completed by using non-dominated sorting genetic algorithms. After optimization, the opening and closing response times of HSV were reduced by 15.1% and 16.6% respectively, while the eddy current energy and Joule energy were reduced by 5.2% and 48.4% respectively. In this paper, the dynamic response and energy loss were jointly optimized. The presented results provide theory instruction for multi-objective cooperative optimization of HSV.
引用
收藏
页码:57 / 77
页数:21
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