A radial-basis-function neural network-based energy reserving strategy for energy-saving elevator

摘要

Efficiency improvement and energy consumption decrease are becoming a pivotal issue in vehicle applications. Predictive control based on a radius-basis-function neural network (RBFNN) is employed to develop a new energy-reserving strategy (ERS) that dynamically regulates the energy stored in the supercapacitor pack (SCP), which is equipped in an energy-saving elevator system. Specifically, at the beginning of every traveling period, the balance voltage (BV) of the SCP, which represents the instantaneous state of the energy stored, is predicted and managed based on the traveling distance and the load ratio of the elevator. In this way, the capacitance of the SCP can be fully used to provide or reserve as much energy as possible. The above energy optimization problem is modeled and transformed into a group of constrained optimization equations based on the energy analysis of the elevator. With the proposed control scheme, not only does the peak power, which comes out when the elevator moves with heavy load eliminate, but also part of the nominal power can be provided by the SCP. Thus, the power provided by the AC-grid pulse-width modulation (PWM) rectifier is decreased, and the energy discharged by the SCP, which has reserved the braking energy at the previous journey, is optimized. The paper includes a detailed explanation of power and energy flow of the elevator and the implementation of the proposed RBFNN-based predictive control scheme. Finally, several simulation and experimental results are presented to demonstrate the effectiveness of the presented control scheme. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.