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Multi-Objective Design Optimization of Sic-Based Inverter and Simulation Modelling of an Electrical Vehicle Drive-Train.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-Objective Design Optimization of Sic-Based Inverter and Simulation Modelling of an Electrical Vehicle Drive-Train./
Author:	Ming, Kang Hean.
Description:	1 online resource (86 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-09, Section: A.
Contained By:	Dissertations Abstracts International84-09A.
Subject:	Design optimization. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30340142click for full text (PQDT)
ISBN:	9798374484199

Multi-Objective Design Optimization of Sic-Based Inverter and Simulation Modelling of an Electrical Vehicle Drive-Train.
Ming, Kang Hean.

Multi-Objective Design Optimization of Sic-Based Inverter and Simulation Modelling of an Electrical Vehicle Drive-Train. - 1 online resource (86 pages)

Source: Dissertations Abstracts International, Volume: 84-09, Section: A.

Thesis (M.E.)--National University of Singapore (Singapore), 2022.

Includes bibliographical references

The traditional internal combustion engines (ICEs) for passenger cars are gradually being phased out globally, due to the concerns of global warming and limited supply of natural fossil fuels. Electric vehicles (EVs) are quickly replacing the ICEvehicles (ICEVs). As a result, there is a shift in paradigm towards the research and development needs of EVs, focusing on the optimal EV drive-train design. The state-of-the-art EV drive-trains consist of the high-voltage (HV) battery, DC-link capacitor, 3-phase DC-AC voltage-source-inverter (VSI), AC electric motor(s), mechanical torque converters (connecting the motor(s) to the tires), and other peripherals. The optimal EV drive-train design aims to minimize losses, weight and volume of each component without compromising the required design and safety specifications. The VSI is the power electronics component which dominates the overall drive-train performance. Hence, optimization of the VSI is necessary for a good EV drive-train design. The best VSI designs utilizes silicon-carbide (SiC) MOSFETs for power switching, as the wide-bandgap material have a superior performance over standard silicon. Amongst wide-bandgap materials, SiC devices have a higher current carrying capacity and better thermal performance, which make them the best choice for an EV application.There is a research gap in terms of a multi-objective design optimization using quantitative evaluation for power converter, specifically for the EVs' VSI application. The main performance indices of the VSI design are efficiency (η) and power density (ρ). A simple analytical method for optimization is required, as the numerical method requires an extensive computational effort. In addition, simulation models of the EV drive-trains are not readily available or easily accessible to comprehensively simulate the performance of the entire drive-train at a system level inclusive of the mechanical components.This research aims to find (i) the optimal EV inverter design with superior η-ρ performance, and corresponding (ii) blocking voltage (Vb) of the SiC-MOSFET dictating the DC-link voltage, Vdc and (iii) switching frequency (fsw), for both a high-power 300 kW EV and low-power 80 kW EV. The result of this research is then implemented on a designed simplified simulation model, suitable for simulating the complete EV drive-train, inclusive of the mechanically coupled loads.The optimization process involves converter modelling, in terms of power loss and volume, used to calculate η and ρ respectively. The η-ρ calculations is iterated over (i) a range of fsw to obtain optimal η-ρ dataset of one SiC device category, and (ii) across different categories to obtain the optimal η-ρ datasets for all categories, which is used to evaluate the corresponding global optimal design for both 300 kW and 80 kW application. The most optimal design for a 300 kW drive-train inverter design exhibits η = 96.45%, ρ = 4.37 kW/L, at fsw = 40 kHz, and the most optimal design for an 80 kW drive-train inverter design exhibits η = 96.87%, ρ = 4.08 kW/L, at fsw = 35 kHz.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798374484199Subjects--Topical Terms:

3681984
Design optimization.
Index Terms--Genre/Form:

542853
Electronic books.

Multi-Objective Design Optimization of Sic-Based Inverter and Simulation Modelling of an Electrical Vehicle Drive-Train.
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100 1 $a Ming, Kang Hean. $3 3701369
245 1 0 $a Multi-Objective Design Optimization of Sic-Based Inverter and Simulation Modelling of an Electrical Vehicle Drive-Train.
264 0 $c 2022
300 $a 1 online resource (86 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-09, Section: A.
500 $a Advisor: Poh, Loh Ai; Panda, Sanjib Kumar.
502 $a Thesis (M.E.)--National University of Singapore (Singapore), 2022.
504 $a Includes bibliographical references
520 $a The traditional internal combustion engines (ICEs) for passenger cars are gradually being phased out globally, due to the concerns of global warming and limited supply of natural fossil fuels. Electric vehicles (EVs) are quickly replacing the ICEvehicles (ICEVs). As a result, there is a shift in paradigm towards the research and development needs of EVs, focusing on the optimal EV drive-train design. The state-of-the-art EV drive-trains consist of the high-voltage (HV) battery, DC-link capacitor, 3-phase DC-AC voltage-source-inverter (VSI), AC electric motor(s), mechanical torque converters (connecting the motor(s) to the tires), and other peripherals. The optimal EV drive-train design aims to minimize losses, weight and volume of each component without compromising the required design and safety specifications. The VSI is the power electronics component which dominates the overall drive-train performance. Hence, optimization of the VSI is necessary for a good EV drive-train design. The best VSI designs utilizes silicon-carbide (SiC) MOSFETs for power switching, as the wide-bandgap material have a superior performance over standard silicon. Amongst wide-bandgap materials, SiC devices have a higher current carrying capacity and better thermal performance, which make them the best choice for an EV application.There is a research gap in terms of a multi-objective design optimization using quantitative evaluation for power converter, specifically for the EVs' VSI application. The main performance indices of the VSI design are efficiency (η) and power density (ρ). A simple analytical method for optimization is required, as the numerical method requires an extensive computational effort. In addition, simulation models of the EV drive-trains are not readily available or easily accessible to comprehensively simulate the performance of the entire drive-train at a system level inclusive of the mechanical components.This research aims to find (i) the optimal EV inverter design with superior η-ρ performance, and corresponding (ii) blocking voltage (Vb) of the SiC-MOSFET dictating the DC-link voltage, Vdc and (iii) switching frequency (fsw), for both a high-power 300 kW EV and low-power 80 kW EV. The result of this research is then implemented on a designed simplified simulation model, suitable for simulating the complete EV drive-train, inclusive of the mechanically coupled loads.The optimization process involves converter modelling, in terms of power loss and volume, used to calculate η and ρ respectively. The η-ρ calculations is iterated over (i) a range of fsw to obtain optimal η-ρ dataset of one SiC device category, and (ii) across different categories to obtain the optimal η-ρ datasets for all categories, which is used to evaluate the corresponding global optimal design for both 300 kW and 80 kW application. The most optimal design for a 300 kW drive-train inverter design exhibits η = 96.45%, ρ = 4.37 kW/L, at fsw = 40 kHz, and the most optimal design for an 80 kW drive-train inverter design exhibits η = 96.87%, ρ = 4.08 kW/L, at fsw = 35 kHz.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Design optimization. $3 3681984
650 4 $a Silicon carbide. $3 827236
650 4 $a Emissions. $3 3559499
650 4 $a Electric vehicles. $3 1613392
650 4 $a Carbon dioxide. $3 587886
650 4 $a Heat. $3 573595
650 4 $a Alternative energy sources. $3 3561089
650 4 $a Energy resources. $3 3561090
650 4 $a Transistors. $3 713271
650 4 $a Bridges. $3 868720
650 4 $a Climate change. $2 bicssc $3 2079509
650 4 $a Alternative energy. $3 3436775
650 4 $a Design. $3 518875
650 4 $a Energy. $3 876794
650 4 $a Transportation. $3 555912
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0404
690 $a 0363
690 $a 0389
690 $a 0791
690 $a 0709
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a National University of Singapore (Singapore). $3 3352228
773 0 $t Dissertations Abstracts International $g 84-09A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30340142 $z click for full text (PQDT)

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