Research on Multi-Objective Optimization of High-Speed Solenoid Valve Drive Strategies under the Synergistic Effect of Dynamic Response and Energy Loss

• Published in: Energies (Basel) Vol. 17; no. 2; p. 300
• Main Authors: Yu, Zhiqing, Yang, Li, Zhao, Jianhui, Grekhov, Leonid
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2024
• Subjects: Algorithms; Analysis; BPNN; Design; dynamic response; Energy; energy loss; Force and energy; Genetic algorithms; high speed solenoid valve; Magnetic fields; multi-objective optimization; Neural networks; NSGA-II; Optimization; Response time; Valves; Vibration
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17020300

Under high-frequency operating conditions, the high-speed solenoid valve (HSV) experiences energy loss and heat generation, which significantly impacts its operational lifetime. Reducing the energy loss of an HSV without compromising its opening response characteristics poses a significant challenge. To address this issue, a finite element simulation model of an HSV coupled with a current feedback model is constructed to investigate the synergistic effects of dynamic response and energy loss. Prediction models for the opening response time, HSV driving energy, and Joule energy using a back propagation neural network (BPNN) are established. Furthermore, a multi-objective optimization study on the current driving strategy using a non-dominated sorting genetic algorithm II (NSGA-II) is conducted. After optimization, although there was a 6.24% increase in the opening response time, both HSV drive energy and Joule energy were significantly reduced by 15.67% and 22.49%, respectively. The proposed multi-objective optimization method for an HSV driving strategy holds great significance for improving its working durability.