Optimization design and analysis of the proportional solenoid valve for the power shift transmission of cotton picker using NSGA-II algorithm - Meng, Xiangchao; Li, Yiqing; Ni, Xiangdong; Chen, Huajun; Zhang, Guangqing; Zhao, Wei; Li, Sihang (International Journal of Agricultural and Biological Engineering, 2026)
In order to further improve the dynamic response performance of the proportional solenoid valve of power shift transmission of cotton picker, the structure and working principle of the proportional solenoid valve were analyzed, and the simulation model of electro-hydraulic shift system of the proportional solenoid valve was built based on Amesim software. In addition, the dynamic response curve of oil pressure output of shift valve was obtained and verified by bench. In Isight software, the optimal Latin hypercube test design method was used to analyze the key parameters affecting the oil pressure response characteristics of the proportional solenoid valve, and the key parameters were identified. Taking the oil pressure step response time and oil pressure response overshoot of the proportional solenoid valve as the optimization objectives, the NSGA-II algorithm was used to optimize the key parameters of the proportional solenoid valve. The results show that after optimization, the oil step response time of the proportional solenoid valve is shortened by 4.55%, the oil response overshoot is reduced by 68.28%, and the flow response time is shortened by 4.76%, which improves the dynamic response characteristics of the proportional solenoid valve. The optimization results have certain significance for improving the shift quality of the cotton picker power shift system. Keywords: power shift transmission, proportional solenoid valve, multi-objective optimization, cotton picker, NSGA-II DOI: 10.25165/j.ijabe.20261902.10167 Citation: Meng X C, Li Y Q, Ni X D, Chen H J, Zhang G Q, Zhao W, et al. Optimization design and analysis of proportional solenoid valve for power shift transmission of cotton picker using NSGA-II algorithm. Int J Agric & Biol Eng, 2026; 19(2): 158–169.