Real-Time Implementation of BLDC Motor-Based Intelligent Tracking Control Fed from PV-Array for E-Bike Applications

Q3 Engineering

WSEAS Transactions on Power Systems Pub Date : 2023-11-20 DOI:10.37394/232016.2023.18.28

Essamudin Ali Ebrahim

{"title":"Real-Time Implementation of BLDC Motor-Based Intelligent Tracking Control Fed from PV-Array for E-Bike Applications","authors":"Essamudin Ali Ebrahim","doi":"10.37394/232016.2023.18.28","DOIUrl":null,"url":null,"abstract":"The essential goal of this research is designing and modeling a speed and position tracking system for driving an electric bike (e-bike) motordrive. This motor is a brushless DC (BLDC) motor as a high-performance drive. It is supplied from twin electric sources to drive it and charge the storage elements (i.e., batteries, super-capacitors, etc.). The first one is a renewable, neat, and clean source photovoltaic (PV) module and the second one is a pedal generator driven by the rider. The submitted design of the controllers is optimized to improve the system’s dynamic stability. The artificial bee colony (ABC) as an artificial intelligent (AI) algorithm is suggested for searching the optimal gains of the proposed proportional-integral-derivative (PID) controllers by reducing the error of its fitness function. The system behavior is studied with that controller when directly feeding from the PV array with and without batteries. The response of the proposed technique - against dynamic troubles and PV oscillations such as irradiance- is also verified. Other evolutionary computational techniques - such as ant colony optimization (ACO) and genetic algorithm (GA)- have been compared with the behavior of the proposed controller to ensure high efficiency in optimized tuning of PID gains. Then, the proposed controller that gives a high performance will be executed in real-time by using OPAL-RT 4510 RT-simulator and rapid control prototyping.","PeriodicalId":38993,"journal":{"name":"WSEAS Transactions on Power Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"WSEAS Transactions on Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37394/232016.2023.18.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}

引用次数: 0

Abstract

The essential goal of this research is designing and modeling a speed and position tracking system for driving an electric bike (e-bike) motordrive. This motor is a brushless DC (BLDC) motor as a high-performance drive. It is supplied from twin electric sources to drive it and charge the storage elements (i.e., batteries, super-capacitors, etc.). The first one is a renewable, neat, and clean source photovoltaic (PV) module and the second one is a pedal generator driven by the rider. The submitted design of the controllers is optimized to improve the system’s dynamic stability. The artificial bee colony (ABC) as an artificial intelligent (AI) algorithm is suggested for searching the optimal gains of the proposed proportional-integral-derivative (PID) controllers by reducing the error of its fitness function. The system behavior is studied with that controller when directly feeding from the PV array with and without batteries. The response of the proposed technique - against dynamic troubles and PV oscillations such as irradiance- is also verified. Other evolutionary computational techniques - such as ant colony optimization (ACO) and genetic algorithm (GA)- have been compared with the behavior of the proposed controller to ensure high efficiency in optimized tuning of PID gains. Then, the proposed controller that gives a high performance will be executed in real-time by using OPAL-RT 4510 RT-simulator and rapid control prototyping.

查看原文本刊更多论文

电动自行车应用中基于光伏阵列的无刷直流电机智能跟踪控制的实时实现

本研究的主要目标是设计和模拟一个用于驱动电动自行车（电动自行车）的速度和位置跟踪系统。该电机采用无刷直流（BLDC）电机作为高性能驱动装置。它由两个电力源驱动，并为存储元件（即电池、超级电容器等）充电。第一种是可再生、整洁、清洁的光伏（PV）模块，第二种是由骑行者驱动的脚踏发电机。提交的控制器设计经过优化，以提高系统的动态稳定性。建议采用人工蜂群（ABC）作为人工智能（AI）算法，通过减少其拟合函数的误差来搜索所提出的比例-积分-微分（PID）控制器的最佳增益。在有电池和无电池的情况下，利用该控制器对直接从光伏阵列馈电的系统行为进行了研究。此外，还验证了所提技术对动态故障和光伏振荡（如辐照度）的响应。其他进化计算技术（如蚁群优化 (ACO) 和遗传算法 (GA)）与拟议控制器的行为进行了比较，以确保 PID 增益优化调整的高效性。然后，将使用 OPAL-RT 4510 实时模拟器和快速控制原型实时执行所提出的高性能控制器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

WSEAS Transactions on Power Systems Engineering-Industrial and Manufacturing Engineering

CiteScore

1.10

自引率

0.00%

发文量

期刊介绍： WSEAS Transactions on Power Systems publishes original research papers relating to electric power and energy. We aim to bring important work to a wide international audience and therefore only publish papers of exceptional scientific value that advance our understanding of these particular areas. The research presented must transcend the limits of case studies, while both experimental and theoretical studies are accepted. It is a multi-disciplinary journal and therefore its content mirrors the diverse interests and approaches of scholars involved with generation, transmission & distribution planning, alternative energy systems, power market, switching and related areas. We also welcome scholarly contributions from officials with government agencies, international agencies, and non-governmental organizations.