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

Abstract

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.

An Optimum Design for a Fast-Response Solenoid Valve: Application to a Limaçon Gas Expander

“…Wu et al used a different algorithm called the particle swarm optimization method to optimize the design parameters of a hollow plunger-type solenoid HSV [38]. In a recent study by Yu et al, the authors used a back propagation neural network (BPNN) to optimize an HSV [39]. They reported a 15.67% and 22.49% reduction in drive and joule energy, respectively; however, the opening time response was increased by 6.24%.…”