Design of fuzzy controller of induction motor for electric vehicle application

Variable speed drives are growing, varying and consuming energy at the same time. From this response, the vehicle’s explicit capabilities as well as its contribution to the system performance of the driver/vehicle combination are obtained. Two different controllers, a conventional controllers and ar...

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Bibliographic Details
Main Author: Noh, Muhamed Fauzie
Format: Thesis
Published: 2014
Subjects:
Online Access:http://eprints.uthm.edu.my/5515/
http://eprints.uthm.edu.my/5515/1/MUHAMED_FAUZIE_BIN_NOH.pdf
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Summary:Variable speed drives are growing, varying and consuming energy at the same time. From this response, the vehicle’s explicit capabilities as well as its contribution to the system performance of the driver/vehicle combination are obtained. Two different controllers, a conventional controllers and artificial intelligence for an electric vehicle (EV) are developed in this project to control the vehicle, namely a PI controller and a Fuzzy Logic Controller (FLC). Artificial intelligent has found high application in most nonlinear systems same as motors drive. On top of that artificial intelligent techniques can use as controller for any system without requirement to system mathematical model. Thus the performances of the aforesaid two controllers have been studied extensively in this project. For achieving an improved response, parameters of both the PID and FLC have been tuned and their performances have been compared. Further, the effect of major components power consumption response is also presented. To validate the above two control performances, a nonlinear simulation model of an EV is developed and is used in the simulation studies. Both the controllers track the desired directional signal efficiently. Both PI and Fuzzy controllers provide competitive performances. Although with the assumption of all parameters of the vehicle available PID controller exhibits slightly better dynamic performance but in the real-world scenario the fuzzy controller is preferred due to its robustness i.e. it does not depend on the parameters of the vehicle.