JOURNAL ARTICLE
Multi-objective cooperation optimization research on dynamic response and energy loss of high-speed solenoid valve for diesel engine injector.
Published In: International Journal of Applied Electromagnetics & Mechanics, 2024, v. 74, n. 1. P. 57 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Yu, Zhiqing; Zhao, Jianhui; Wei, Rongqiang 3 of 3
Abstract
This article focuses on the multi-objective cooperative optimization of the dynamic response and energy loss of high-speed solenoid valves (HSV) used in diesel engine injectors. A finite element simulation model of HSV was developed and validated experimentally to analyze internal energy distribution, revealing that eddy current energy and Joule energy constitute the primary sources of heat loss affecting HSV reliability. Response surface models (RSMs) for opening and closing response times, as well as eddy current and Joule energy losses, were constructed using key parameters—electroconductibility, spring stiffness, damping coefficient, and coil resistance—and optimized via the non-dominated sorting genetic algorithm II (NSGA-II). The optimization achieved reductions of approximately 15% in response times and up to nearly 50% in Joule energy loss, demonstrating a trade-off between dynamic performance and energy efficiency. These findings provide a theoretical basis for improving HSV design by balancing fast dynamic response with minimized energy loss to enhance operational reliability.
Additional Information
- Source:International Journal of Applied Electromagnetics & Mechanics. 2024/01, Vol. 74, Issue 1, p57
- Document Type:Article
- Subject Area:History
- Publication Date:2024
- ISSN:1383-5416
- DOI:10.3233/JAE-230099
- Accession Number:175033758
- Copyright Statement:Copyright of International Journal of Applied Electromagnetics & Mechanics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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