Variable damping energy regenerative damper for self-powered sensors and self-sensing devices in smart electric buses

阻尼器 整流器(神经网络) 能量收集 功率(物理) 电气工程 电压 悬挂(拓扑) 能量(信号处理) 工程类 同轴 电力 控制理论(社会学) 汽车工程 计算机科学 结构工程 物理 人工神经网络 随机神经网络 数学 控制(管理) 量子力学 机器学习 同伦 人工智能 循环神经网络 纯数学
作者
Mansour Abdelrahman,Chengliang Fan,Mingliang Yi,Zutao Zhang,Asif Ali,Xiaofeng Xia,Abeer S. Ahmed,Shoukat Ali Mugheri,Ammar Ahmed
出处
期刊:Smart Materials and Structures [IOP Publishing]
标识
DOI:10.1088/1361-665x/ad72bf
摘要

Abstract In recent years, the increasing adoption of electric buses (EBs) worldwide has contributed significantly to reducing environmental pollution. Nevertheless, the most challenging obstacle hindering the efficiency of EBs is their power supply. In this study, a multi-purpose variable damping energy regenerative damper (VD-ERD) using a double coaxial slotted link motion conversion mechanism was proposed for health monitoring of the EBs suspension system, tunning the damping during the operation on different road conditions while providing electric energy for self-powered sensors in EBs. The VD-ERD consists of two identical generators; one is connected to optimal constant resistance for maximum energy harvesting, and the other is linked to adjustable resistance for fine-tuning the damping. Consequently, both generators connect to a rectifier and storage circuits. Furthermore, VD-ERD was developed in MATLAB/Simulink to evaluate its performance in damping and energy harvesting in different road excitations. The VD-ERD achieved an 11.59W peak and 1.84W RMS power at 50km/h on an ISO class A road and a 36.38W peak and 6.34W RMS power on an ISO class B road. In addition, the experimental finding indicated that controlling the external resistance is capable of tuning the damping. Simultaneously, the prototype achieved a peak power output of 10.29W at 12 mm and 3 Hz. Furthermore, the voltage signals received from the generators were analyzed using a deep learning model to monitor the condition of the suspension system in four different modes, namely slow, medium, fast, and failure; the result shows 99.37% training accuracy. Feasibility analysis and performance testing showed that VD-ERD provides sufficient power to 10 sensors, indicating that it can power the self-powered and self-sensing devices of EBs.
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