國立虎尾科技大學 |

Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles/ by Tunan Shen.
Author:	Shen, Tunan.
Description:	XXXII, 120 p. 61 illus., 4 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Electrical Power Engineering. -
Online resource:	https://doi.org/10.1007/978-3-658-36992-7
ISBN:	9783658369927

Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles
Shen, Tunan.

Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles[electronic resource] /by Tunan Shen. - 1st ed. 2022. - XXXII, 120 p. 61 illus., 4 illus. in color.online resource. - Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart,2567-0352. - Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart,.

Background and State of the Art -- Diagnosis of Electrical Faults in Electric Machines -- Diagnosis of Mechanical Faults in Electric Machines.

Tunan Shen aims to increase the availability of powertrain systems for autonomous electric vehicles by improving the diagnostic capability for critical faults. Following the fault analysis of powertrain systems in battery electric vehicles, the focus is on the electrical and mechanical faults of the electric machine. A multi-level diagnostic approach is proposed, which consists of multiple diagnostic models, such as a physical model, a data-based anomaly detection model, and a neural network model. To improve the overall diagnostic capability, a decision making function is designed to derive a comprehensive decision from the predictions of various operating points and different models. Contents Background and State of the Art Diagnosis of Electrical Faults in Electric Machines Diagnosis of Mechanical Faults in Electric Machines Target Groups Researchers and students of mechanical engineering, especially automotive powertrains in electric vehicles Research and development engineers in this field About the Author Tunan Shen did his PhD project at the Institute of Automotive Engineering (IFS), University of Stuttgart, Germany. Currently he is Software Developer for Cross Domain Computing Solutions at a German automotive supplier.

ISBN: 9783658369927

Standard No.: 10.1007/978-3-658-36992-7doiSubjects--Topical Terms:

1365891
Electrical Power Engineering.

LC Class. No.: TL1-483

Dewey Class. No.: 629.2

Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles
LDR:02779nam a22003975i 4500 001 1089379
003 DE-He213
005 20220302103151.0
007 cr nn 008mamaa
008 221228s2022 gw | s |||| 0|eng d
020 $a 9783658369927 $9 978-3-658-36992-7
024 7 $a 10.1007/978-3-658-36992-7 $2 doi
035 $a 978-3-658-36992-7
050 4 $a TL1-483
072 7 $a TRC $2 bicssc
072 7 $a TEC009090 $2 bisacsh
072 7 $a TRC $2 thema
082 0 4 $a 629.2 $2 23
100 1 $a Shen, Tunan. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1396632
245 1 0 $a Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles $h [electronic resource] / $c by Tunan Shen.
250 $a 1st ed. 2022.
264 1 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Vieweg, $c 2022.
300 $a XXXII, 120 p. 61 illus., 4 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart, $x 2567-0352
505 0 $a Background and State of the Art -- Diagnosis of Electrical Faults in Electric Machines -- Diagnosis of Mechanical Faults in Electric Machines.
520 $a Tunan Shen aims to increase the availability of powertrain systems for autonomous electric vehicles by improving the diagnostic capability for critical faults. Following the fault analysis of powertrain systems in battery electric vehicles, the focus is on the electrical and mechanical faults of the electric machine. A multi-level diagnostic approach is proposed, which consists of multiple diagnostic models, such as a physical model, a data-based anomaly detection model, and a neural network model. To improve the overall diagnostic capability, a decision making function is designed to derive a comprehensive decision from the predictions of various operating points and different models. Contents Background and State of the Art Diagnosis of Electrical Faults in Electric Machines Diagnosis of Mechanical Faults in Electric Machines Target Groups Researchers and students of mechanical engineering, especially automotive powertrains in electric vehicles Research and development engineers in this field About the Author Tunan Shen did his PhD project at the Institute of Automotive Engineering (IFS), University of Stuttgart, Germany. Currently he is Software Developer for Cross Domain Computing Solutions at a German automotive supplier.
650 2 4 $a Electrical Power Engineering. $3 1365891
650 2 4 $a Engine Technology. $3 881220
650 1 4 $a Automotive Engineering. $3 683772
650 0 $a Electric power production. $3 555956
650 0 $a Engines. $3 1253521
650 0 $a Automotive engineering. $3 1104081
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783658369910
776 0 8 $i Printed edition: $z 9783658369934
830 0 $a Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart, $x 2567-0042 $3 1253937
856 4 0 $u https://doi.org/10.1007/978-3-658-36992-7
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)