Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller

Basically, a speed sensor is used to sense an electric vehicle’s motor speed at the rated value in order to achieve a high tracking accuracy of the speed, but the use of a sensor is costly and it is sensitive to vibrations. Therefore, this project proposed a new mechanism in order to eliminate the s...

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Main Author:	Sepeeh, Muhamad Syazmie
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.uthm.edu.my/8444/1/24p%20MUHAMAD%20SYAZMIE%20SEPEEH.pdf http://eprints.uthm.edu.my/8444/2/MUHAMAD%20SYAZMIE%20SEPEEH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8444/3/MUHAMAD%20SYAZMIE%20SEPEEH%20WATERMARK.pdf
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spelling	my-uthm-ep.84442023-02-27T00:55:53Z Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller 2022-06 Sepeeh, Muhamad Syazmie TA Engineering (General). Civil engineering (General) Basically, a speed sensor is used to sense an electric vehicle’s motor speed at the rated value in order to achieve a high tracking accuracy of the speed, but the use of a sensor is costly and it is sensitive to vibrations. Therefore, this project proposed a new mechanism in order to eliminate the speed sensor by adopting an enhanced hybrid flux estimation. The voltage signal was modified using the rotor-flux-oriented current model’s output for the internal stator flux controller to minimise the back-EMF error to represent a sensorless control. Artificial neural network (ANN)-field-oriented control (FOC) was used in the hybrid flux system. The function of the ANN was to improve speed-tracking performance, and the learning rate of the ANN inside the indirect FOC’s structure trained using the Levenberg-Marquardt (LM) algorithm was varied in order to increase speed-tracking accuracy when combined with the improved ANN speed controller. The hyperparameters of ANNs, such as weights and biases, were randomly initialised and updated using the backpropagation (BP) algorithm in order to increase the convolution of the ANNs. The sensorless ANN-IFOC was modelled, simulated, and tested using MATLAB/Simulink for a 20Hp EV motor based on a small Renault Twizy EV model and triggered by the space-vector pulse-width modulation (SVPWM). The results of the ANN-IFOC hybrid estimator were obtained in four cases, which were 1) constant high and low speeds, 2) constant speed against parameter variation, 3) variable speed, and 4) variable load torque disturbances. All results showed that the proposed method gave excellent agreement, as compared with ANN- and PI-based conventional voltage model estimators, with increased tracking accuracy (1500 rpm: 99.23% and 99.60% to 99.85%; 1000 rpm: 98.90% and 99.45% to 99.85%; and 500 rpm: 97.92% and 99.10% to 99.85%). The proposed model with the sensorless speed controller showed consistent tracking accuracy with faster speed responses and gave the shortest settling time and fewer overshoots compared with the existing PI controller. Furthermore, the drive system was able to control and improve the transient response of the EV motor. 2022-06 Thesis http://eprints.uthm.edu.my/8444/ http://eprints.uthm.edu.my/8444/1/24p%20MUHAMAD%20SYAZMIE%20SEPEEH.pdf text en public http://eprints.uthm.edu.my/8444/2/MUHAMAD%20SYAZMIE%20SEPEEH%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/8444/3/MUHAMAD%20SYAZMIE%20SEPEEH%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Sepeeh, Muhamad Syazmie Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller
description	Basically, a speed sensor is used to sense an electric vehicle’s motor speed at the rated value in order to achieve a high tracking accuracy of the speed, but the use of a sensor is costly and it is sensitive to vibrations. Therefore, this project proposed a new mechanism in order to eliminate the speed sensor by adopting an enhanced hybrid flux estimation. The voltage signal was modified using the rotor-flux-oriented current model’s output for the internal stator flux controller to minimise the back-EMF error to represent a sensorless control. Artificial neural network (ANN)-field-oriented control (FOC) was used in the hybrid flux system. The function of the ANN was to improve speed-tracking performance, and the learning rate of the ANN inside the indirect FOC’s structure trained using the Levenberg-Marquardt (LM) algorithm was varied in order to increase speed-tracking accuracy when combined with the improved ANN speed controller. The hyperparameters of ANNs, such as weights and biases, were randomly initialised and updated using the backpropagation (BP) algorithm in order to increase the convolution of the ANNs. The sensorless ANN-IFOC was modelled, simulated, and tested using MATLAB/Simulink for a 20Hp EV motor based on a small Renault Twizy EV model and triggered by the space-vector pulse-width modulation (SVPWM). The results of the ANN-IFOC hybrid estimator were obtained in four cases, which were 1) constant high and low speeds, 2) constant speed against parameter variation, 3) variable speed, and 4) variable load torque disturbances. All results showed that the proposed method gave excellent agreement, as compared with ANN- and PI-based conventional voltage model estimators, with increased tracking accuracy (1500 rpm: 99.23% and 99.60% to 99.85%; 1000 rpm: 98.90% and 99.45% to 99.85%; and 500 rpm: 97.92% and 99.10% to 99.85%). The proposed model with the sensorless speed controller showed consistent tracking accuracy with faster speed responses and gave the shortest settling time and fewer overshoots compared with the existing PI controller. Furthermore, the drive system was able to control and improve the transient response of the EV motor.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Sepeeh, Muhamad Syazmie
author_facet	Sepeeh, Muhamad Syazmie
author_sort	Sepeeh, Muhamad Syazmie
title	Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller
title_short	Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller
title_full	Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller
title_fullStr	Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller
title_full_unstemmed	Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller
title_sort	sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2022
url	http://eprints.uthm.edu.my/8444/1/24p%20MUHAMAD%20SYAZMIE%20SEPEEH.pdf http://eprints.uthm.edu.my/8444/2/MUHAMAD%20SYAZMIE%20SEPEEH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8444/3/MUHAMAD%20SYAZMIE%20SEPEEH%20WATERMARK.pdf
_version_	1776103347261014016

Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller

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