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Artificial Intelligence and Machine Learning for Control and Operation of Electric Vehicles and Machine Drives.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial Intelligence and Machine Learning for Control and Operation of Electric Vehicles and Machine Drives./
Author:	Dong, Weizhen.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	154 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28548896
ISBN:	9798544234470

Artificial Intelligence and Machine Learning for Control and Operation of Electric Vehicles and Machine Drives.
Dong, Weizhen.

Artificial Intelligence and Machine Learning for Control and Operation of Electric Vehicles and Machine Drives. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 154 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--The University of Alabama, 2021.

This item must not be sold to any third party vendors.

Motor drive and charging system with batteries are two major parts in an electric vehicle (EV) powertrain system. This dissertation investigates the artificial intelligence-based control and operation of EV machine drives and the charging systems.There are several major challenges related the EV motor drive control such as machine parameter variations, magnetic saturations, accurate torque control, and optimal and efficient operation considering copper loss and iron loss. Regarding the charging and discharging control with DC/DC converters, the stable and robust voltage regulation under disturbances is required. The issues of how to smoothly handle the current/voltage constraints and the power limit still remain. This dissertation presents a novel machine learning strategy based on a neural network (NN) to achieve MTPA, flux-weakening, and MTPV for the most efficient IPM torque control over its full speed operating range. The NN is trained offline by using the LMBP (Levenberg-Marquardt backpropagation) algorithm, which avoids the disadvantages associated with the online NN training. A special technique is developed to generate NN training data, that is particularly suitable and favorable, to develop a high-performance NN-based IPM torque control system, and the impact of variable motor parameters is embedded into the NN system development and training. IPM machine modeling and parameter estimation are important for the controller design of high-efficient and high-performance motor drives. The accuracy of the magnetic modeling is a challenge dur to the magnetic saturation, cross saturation, iron loss, and temperature variations. The proposed ANN-based modeling method can capture the nonlinear areas of the model and generate accurate dq-axis flux linkages with saturation and iron loss considered. For the vehicle to grid (V2G) and vehicle to home (V2H) applications, the battery not only can be charged but also can provide power back to the load and systems through DC/DC converters. The ANN controller presented in this dissertation has a strong ability to track rapidly changing reference commands, maintain stable output voltage for a variable load, and manage maximum duty-ratio and current constraints properly. The presented control algorithm also has the ability of power sharing based on DG capabilities for DC microgrid applications.

ISBN: 9798544234470Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Electric vehicles

Artificial Intelligence and Machine Learning for Control and Operation of Electric Vehicles and Machine Drives.
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520 $a Motor drive and charging system with batteries are two major parts in an electric vehicle (EV) powertrain system. This dissertation investigates the artificial intelligence-based control and operation of EV machine drives and the charging systems.There are several major challenges related the EV motor drive control such as machine parameter variations, magnetic saturations, accurate torque control, and optimal and efficient operation considering copper loss and iron loss. Regarding the charging and discharging control with DC/DC converters, the stable and robust voltage regulation under disturbances is required. The issues of how to smoothly handle the current/voltage constraints and the power limit still remain. This dissertation presents a novel machine learning strategy based on a neural network (NN) to achieve MTPA, flux-weakening, and MTPV for the most efficient IPM torque control over its full speed operating range. The NN is trained offline by using the LMBP (Levenberg-Marquardt backpropagation) algorithm, which avoids the disadvantages associated with the online NN training. A special technique is developed to generate NN training data, that is particularly suitable and favorable, to develop a high-performance NN-based IPM torque control system, and the impact of variable motor parameters is embedded into the NN system development and training. IPM machine modeling and parameter estimation are important for the controller design of high-efficient and high-performance motor drives. The accuracy of the magnetic modeling is a challenge dur to the magnetic saturation, cross saturation, iron loss, and temperature variations. The proposed ANN-based modeling method can capture the nonlinear areas of the model and generate accurate dq-axis flux linkages with saturation and iron loss considered. For the vehicle to grid (V2G) and vehicle to home (V2H) applications, the battery not only can be charged but also can provide power back to the load and systems through DC/DC converters. The ANN controller presented in this dissertation has a strong ability to track rapidly changing reference commands, maintain stable output voltage for a variable load, and manage maximum duty-ratio and current constraints properly. The presented control algorithm also has the ability of power sharing based on DG capabilities for DC microgrid applications.
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