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Temperature Effects on IPM Machines:...

Li, Silong.

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Temperature Effects on IPM Machines: Influence, Modeling, and Compensation Control Method for Automotive Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Temperature Effects on IPM Machines: Influence, Modeling, and Compensation Control Method for Automotive Applications./
Author:	Li, Silong.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	444 p.
Notes:	Source: Dissertations Abstracts International, Volume: 78-11, Section: B.
Contained By:	Dissertations Abstracts International78-11B.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10281864
ISBN:	9781369766745

Temperature Effects on IPM Machines: Influence, Modeling, and Compensation Control Method for Automotive Applications.
Li, Silong.

Temperature Effects on IPM Machines: Influence, Modeling, and Compensation Control Method for Automotive Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 444 p.

Source: Dissertations Abstracts International, Volume: 78-11, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.

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

In automotive applications, high-performance interior permanent magnet (IPM) machine control which is robust to temperature variation is critical since the operation temperature could vary significantly. As a prerequisite for developing high-performance IPM machine control robust to temperature variation, an in-depth understanding of the temperature effects on the characteristics and performance of IPM machine is essential. In this research, the influence of temperature variation on the characteristics and performance of a baseline traction IPM machine are analyzed over a wide operating range. Trends of the performance variation due to temperature change are examined analytically and validated by both FEA and experimental results. The influence of temperature variation on different PM machine topologies and PM machine using different magnet materials have also been investigated and compared. An accurate high-fidelity nonlinear IPM machine model which includes the temperature effects is developed in this research. With this model, the control algorithms can be accurately simulated and evaluated at different temperatures. Additionally, it is much less time-consuming by using the proposed model compared to circuit-field-coupled time-stepping FEA simulation. Both open-loop and closed-loop control methods are proposed to compensate the performance variations in IPM machines due to temperature change. Simulation and experimental results show that the proposed compensation control methods can successfully maintain torque production accuracy and MTPA operation if the operating point is within voltage and current constraints, and produce maximum available torque if the operating point violates voltage and current constraints. Driving cycle analysis shows considerable loss reduction and fuel economy improvement by using the proposed compensation control. Precise knowledge of machine parameters is essential for accurate closed-loop compensation control. In this research, an online multi-parameter estimation method based on the adaptive linear neuron (ADALINE) neural network (NN) algorithm is proposed for IPM machines. The ADALINE NN is combined with current pulse injection to estimate more than two machine parameters without ill-convergence issues. The proposed current pulse injection pattern is specially designed for automotive applications, which has very small impact on the normal operation of the vehicle.

ISBN: 9781369766745Subjects--Topical Terms:

649834
Electrical engineering.

Temperature Effects on IPM Machines: Influence, Modeling, and Compensation Control Method for Automotive Applications.
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300 $a 444 p.
500 $a Source: Dissertations Abstracts International, Volume: 78-11, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Sarlioglu, Bulent.
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506 $a This item must not be sold to any third party vendors.
520 $a In automotive applications, high-performance interior permanent magnet (IPM) machine control which is robust to temperature variation is critical since the operation temperature could vary significantly. As a prerequisite for developing high-performance IPM machine control robust to temperature variation, an in-depth understanding of the temperature effects on the characteristics and performance of IPM machine is essential. In this research, the influence of temperature variation on the characteristics and performance of a baseline traction IPM machine are analyzed over a wide operating range. Trends of the performance variation due to temperature change are examined analytically and validated by both FEA and experimental results. The influence of temperature variation on different PM machine topologies and PM machine using different magnet materials have also been investigated and compared. An accurate high-fidelity nonlinear IPM machine model which includes the temperature effects is developed in this research. With this model, the control algorithms can be accurately simulated and evaluated at different temperatures. Additionally, it is much less time-consuming by using the proposed model compared to circuit-field-coupled time-stepping FEA simulation. Both open-loop and closed-loop control methods are proposed to compensate the performance variations in IPM machines due to temperature change. Simulation and experimental results show that the proposed compensation control methods can successfully maintain torque production accuracy and MTPA operation if the operating point is within voltage and current constraints, and produce maximum available torque if the operating point violates voltage and current constraints. Driving cycle analysis shows considerable loss reduction and fuel economy improvement by using the proposed compensation control. Precise knowledge of machine parameters is essential for accurate closed-loop compensation control. In this research, an online multi-parameter estimation method based on the adaptive linear neuron (ADALINE) neural network (NN) algorithm is proposed for IPM machines. The ADALINE NN is combined with current pulse injection to estimate more than two machine parameters without ill-convergence issues. The proposed current pulse injection pattern is specially designed for automotive applications, which has very small impact on the normal operation of the vehicle.
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