Abstract
As the demand for high-speed elevators grows, the requirements of elevator performance have also increased. Most of these are single variables that do not consider the comprehensive impact of multiple variables on performance, especially comfort. To overcome this problem, a stepped segmentation method for a theoretical high-speed elevator car air pressure curve (THEC-APC) adjustment is proposed that could actively help to select a suitable theoretical elevator car air pressure adjustment curve. By utilizing the proposed Particle Swarm Optimization (PSO) algorithm, the theoretical elevator car air pressure curve is optimized for multiple performances (including passenger comfort, energy consumption, and aerodynamic characteristics). In addition, the THEC-APC is smoothed by the Bezier curve for the variable destination floor. To verify the proposed method, the KLK2 (Canny Elevator Co., Ltd., 2015, Suzhou) high-speed elevator design process is applied. The numerical experiment results show that the proposed method can improve the accuracy and search efficiency of the optimal solution. Meanwhile, the proposed method helps to promote further air pressure compensation design for high-speed elevators.