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Approximate Dynamic Programming and Artificial Neural Network Control of Electric Vehicles: from Motor Drives to Grid Integration.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Approximate Dynamic Programming and Artificial Neural Network Control of Electric Vehicles: from Motor Drives to Grid Integration./
Author:	Sun, Yang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	142 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13882946
ISBN:	9781088301142

Approximate Dynamic Programming and Artificial Neural Network Control of Electric Vehicles: from Motor Drives to Grid Integration.
Sun, Yang.

Approximate Dynamic Programming and Artificial Neural Network Control of Electric Vehicles: from Motor Drives to Grid Integration. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 142 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--The University of Alabama, 2019.

This item is not available from ProQuest Dissertations & Theses.

The drive system of an electric vehicle (EV) includes two major parts- the powertrain and charging system. This dissertation investigates the implementation of the approximate dynamics programming (ADP) based artificial neural network (ANN) control on these two parts to increase the efficiency, stability and reliability of EVs.The major challenge of the powertrain control is to control the EV motor, which is usually an interior mounted permanent magnetic motor(IPM). By using the conventional vector controller, the IPM encounters high current distortion and speed oscillation especially when working in overmodulation area, due to the decoupling inaccuracy issue. The ADP-ANN controller resolves the decoupling issue and guarantees better speed and current tracking performance.For industrial implementation, the motor control algorithm is normally achieved by a digital signal processor (DSP), which has limited computational resources. As ADP-ANN has more complex structure than the conventional controller, whether it can be put into a DSP need to be tested. This dissertation optimized the ADP-ANN algortithm and make it successfully running in a TMS320F28335 DSP platform.To control a gird-connected solar based EV charging system, the dc-bus voltage stability of the solar inverter need to be maintained to acquire high charging efficiency and reduce the grid current distortion. This will become a challenge to conventional vector controller when the solar irradiation level changing rapidly. The implementation of the proposed controller allows the solar inverter improve the dc-bus voltage stability, energy capture efficiency, adaptivity, power conversion efficiency and power quality.Multiple EVs can be used to supply reactive power to the grid when connected with the charging system. But, a great challenge is that grid integration inverters would fight each other when operated autonomously in participating grid voltage control using the conventional control methods. The ADP-ANN control is able to properly handle the inverter constraints in achieving Voltage/Var control objectives at the grid edge and overcomes the challenges of conventional DER inverter control techniques.

ISBN: 9781088301142Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Approximate dynamic programming

Approximate Dynamic Programming and Artificial Neural Network Control of Electric Vehicles: from Motor Drives to Grid Integration.
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500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
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506 $a This item is not available from ProQuest Dissertations & Theses.
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520 $a The drive system of an electric vehicle (EV) includes two major parts- the powertrain and charging system. This dissertation investigates the implementation of the approximate dynamics programming (ADP) based artificial neural network (ANN) control on these two parts to increase the efficiency, stability and reliability of EVs.The major challenge of the powertrain control is to control the EV motor, which is usually an interior mounted permanent magnetic motor(IPM). By using the conventional vector controller, the IPM encounters high current distortion and speed oscillation especially when working in overmodulation area, due to the decoupling inaccuracy issue. The ADP-ANN controller resolves the decoupling issue and guarantees better speed and current tracking performance.For industrial implementation, the motor control algorithm is normally achieved by a digital signal processor (DSP), which has limited computational resources. As ADP-ANN has more complex structure than the conventional controller, whether it can be put into a DSP need to be tested. This dissertation optimized the ADP-ANN algortithm and make it successfully running in a TMS320F28335 DSP platform.To control a gird-connected solar based EV charging system, the dc-bus voltage stability of the solar inverter need to be maintained to acquire high charging efficiency and reduce the grid current distortion. This will become a challenge to conventional vector controller when the solar irradiation level changing rapidly. The implementation of the proposed controller allows the solar inverter improve the dc-bus voltage stability, energy capture efficiency, adaptivity, power conversion efficiency and power quality.Multiple EVs can be used to supply reactive power to the grid when connected with the charging system. But, a great challenge is that grid integration inverters would fight each other when operated autonomously in participating grid voltage control using the conventional control methods. The ADP-ANN control is able to properly handle the inverter constraints in achieving Voltage/Var control objectives at the grid edge and overcomes the challenges of conventional DER inverter control techniques.
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