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Design and Control of an Electrical Vehicle Traction Inverter to Address the Opportunity and Challenge of SiC Wide Bandgap Device.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Design and Control of an Electrical Vehicle Traction Inverter to Address the Opportunity and Challenge of SiC Wide Bandgap Device./
作者:	Yang, Li.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	163 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Contained By:	Dissertations Abstracts International82-08B.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28306161
ISBN:	9798698599036

Design and Control of an Electrical Vehicle Traction Inverter to Address the Opportunity and Challenge of SiC Wide Bandgap Device.
Yang, Li.

Design and Control of an Electrical Vehicle Traction Inverter to Address the Opportunity and Challenge of SiC Wide Bandgap Device. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 163 p.

Source: Dissertations Abstracts International, Volume: 82-08, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2020.

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

Aiming at expediting the adoption of Silicon Carbide (SiC) device in Electrical Vehicle (EV) industry, this research evaluates the opportunity and challenge posed by SiC device to traction inverter development. Opportunities of high power density design and novel PMSM motor drive algorithm taking advantage of the high switching frequency capability are elaborated in the following chapters. The challenge of increased common mode noise due to the high switching speed is also investigated. The common mode noise reduction by active gate driving is discussed taking a current source gate driver as an example. A recurrent neural network based approach is developed to predict the optimal active gate driving sequence.The work starts from designing a 100kW planarized high power density SiC traction inverter. Unlike the traditional IGBT based traction inverter of the same power level that usually operates with low voltage and high current, the designed SiC inverter adopts 1.7kV SiC MOSFET module and operates on a high DC-link of 1000V. The power-PCB based busbar is devised, which not only achieves higher power density, it helps reduce commutation loop inductance as well. The current detection is achieved by shunt based current sensor. The circuit size and cost are reduced. With exceptional attention paid on the Common Mode Transient Immunity (CMTI) and Common Mode Rejection Ratio (CMRR) of the circuit, the shunt current sensor generates current feedback with high quality and robustness. The increased common mode noise caused by high voltage fast switching is an issue for the SiC traction inverter. To tackle this problem, a low profile planar transformer is designed with high CMTI . Planarized controller, gate driver, current sensor and busbar are the key components of the designed high power density traction inverter.Interior Permanent Magnet Synchronous Machine (IPMSM) is one of the most commonly used driving motors in EVs and its maximum torque generation over the whole speed range can be formulated as a nonlinear optimization problem. The problem takes inverter voltage and current limitations as the constraints. A geometrical linearization method is proposed to solve the optimization problem without numerical iterations and unifies the Maximum Torque per Ampere (MTPA) and flux weakening control on IPMSM into the same framework. The high switching frequency capability of SiC device provides new possibility on implementing this type of algorithm involving model linearization. Compared with conventional IGBT based traction inverter, the control signal can be updated at a higher frequency by SiC inverter, which is the key to obtain an accurate solution on the nonlinear problem through linearization approach. Principles of the algorithm and simulations will be presented in the dissertation.To investigate Active Gate Driving (AGD) on the SiC MOSFET, the device is first modeled by a new approach based on the I-V characteristic curve and the nonlinear capacitance maps. The half bridge circuit driven by current source gate driver is modeled by the differential equations with the dynamics of gate driver incorporated. The modeled circuit achieves high fidelity simulation results compared with LTspice, the AGD effects on individual interval for both turn-on and turn-off switching transients are analyzed, active driving sequences are obtained and verified. With the high accuracy half bridge circuit model, large amount of data for excitation sequence vs. switching results is obtained and used as training data for a recurrent neural network. The recurrent neural network takes the encoder-decoder structure, and is capable of predicting an AGD excitation sequence given a target switching results. The proposed deep learning model is verified by circuit SPICE simulation.

ISBN: 9798698599036Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Electrical vehicle

Design and Control of an Electrical Vehicle Traction Inverter to Address the Opportunity and Challenge of SiC Wide Bandgap Device.
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500 $a Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
500 $a Advisor: Wu, Fen;Lukic, Srdjan;Yu, Wensong;Husain, Iqbal.
502 $a Thesis (Ph.D.)--North Carolina State University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Aiming at expediting the adoption of Silicon Carbide (SiC) device in Electrical Vehicle (EV) industry, this research evaluates the opportunity and challenge posed by SiC device to traction inverter development. Opportunities of high power density design and novel PMSM motor drive algorithm taking advantage of the high switching frequency capability are elaborated in the following chapters. The challenge of increased common mode noise due to the high switching speed is also investigated. The common mode noise reduction by active gate driving is discussed taking a current source gate driver as an example. A recurrent neural network based approach is developed to predict the optimal active gate driving sequence.The work starts from designing a 100kW planarized high power density SiC traction inverter. Unlike the traditional IGBT based traction inverter of the same power level that usually operates with low voltage and high current, the designed SiC inverter adopts 1.7kV SiC MOSFET module and operates on a high DC-link of 1000V. The power-PCB based busbar is devised, which not only achieves higher power density, it helps reduce commutation loop inductance as well. The current detection is achieved by shunt based current sensor. The circuit size and cost are reduced. With exceptional attention paid on the Common Mode Transient Immunity (CMTI) and Common Mode Rejection Ratio (CMRR) of the circuit, the shunt current sensor generates current feedback with high quality and robustness. The increased common mode noise caused by high voltage fast switching is an issue for the SiC traction inverter. To tackle this problem, a low profile planar transformer is designed with high CMTI . Planarized controller, gate driver, current sensor and busbar are the key components of the designed high power density traction inverter.Interior Permanent Magnet Synchronous Machine (IPMSM) is one of the most commonly used driving motors in EVs and its maximum torque generation over the whole speed range can be formulated as a nonlinear optimization problem. The problem takes inverter voltage and current limitations as the constraints. A geometrical linearization method is proposed to solve the optimization problem without numerical iterations and unifies the Maximum Torque per Ampere (MTPA) and flux weakening control on IPMSM into the same framework. The high switching frequency capability of SiC device provides new possibility on implementing this type of algorithm involving model linearization. Compared with conventional IGBT based traction inverter, the control signal can be updated at a higher frequency by SiC inverter, which is the key to obtain an accurate solution on the nonlinear problem through linearization approach. Principles of the algorithm and simulations will be presented in the dissertation.To investigate Active Gate Driving (AGD) on the SiC MOSFET, the device is first modeled by a new approach based on the I-V characteristic curve and the nonlinear capacitance maps. The half bridge circuit driven by current source gate driver is modeled by the differential equations with the dynamics of gate driver incorporated. The modeled circuit achieves high fidelity simulation results compared with LTspice, the AGD effects on individual interval for both turn-on and turn-off switching transients are analyzed, active driving sequences are obtained and verified. With the high accuracy half bridge circuit model, large amount of data for excitation sequence vs. switching results is obtained and used as training data for a recurrent neural network. The recurrent neural network takes the encoder-decoder structure, and is capable of predicting an AGD excitation sequence given a target switching results. The proposed deep learning model is verified by circuit SPICE simulation.
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856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28306161