東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Diagnosis of the powertrain systems ...

Shen, Tunan.

FindBook

Google Book

Amazon

博客來

Diagnosis of the powertrain systems for autonomous electric vehicles

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Diagnosis of the powertrain systems for autonomous electric vehicles/ by Tunan Shen.
作者:	Shen, Tunan.
出版者:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2022.,
面頁冊數:	xxxii, 120 p. :ill., digital ;24 cm.
內容註:	Background and State of the Art -- Diagnosis of Electrical Faults in Electric Machines -- Diagnosis of Mechanical Faults in Electric Machines.
Contained By:	Springer Nature eBook
標題:	Electric vehicles - Power trains. -
電子資源:	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. - Wiesbaden :Springer Fachmedien Wiesbaden :2022. - xxxii, 120 p. :ill., digital ;24 cm. - Wissenschaftliche Reihe Fahrzeugtechnik Universitat Stuttgart,2567-0352. - Wissenschaftliche Reihe Fahrzeugtechnik Universitat 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:

3590093
Electric vehicles
--Power trains.

LC Class. No.: TL220 / .S54 2022

Dewey Class. No.: 629.2293

Diagnosis of the powertrain systems for autonomous electric vehicles
LDR:02461nmm a2200337 a 4500 001 2298773
003 DE-He213
005 20220302103151.0
006 m d
007 cr nn 008maaau
008 230324s2022 gw s 0 eng d
020 $a 9783658369927 $q (electronic bk.)
020 $a 9783658369910 $q (paper)
024 7 $a 10.1007/978-3-658-36992-7 $2 doi
035 $a 978-3-658-36992-7
040 $a GP $c GP
041 0 $a eng
050 4 $a TL220 $b .S54 2022
072 7 $a TRC $2 bicssc
072 7 $a TEC009090 $2 bisacsh
072 7 $a TRC $2 thema
082 0 4 $a 629.2293 $2 23
090 $a TL220 $b .S546 2022
100 1 $a Shen, Tunan. $3 3595660
245 1 0 $a Diagnosis of the powertrain systems for autonomous electric vehicles $h [electronic resource] / $c by Tunan Shen.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Vieweg, $c 2022.
300 $a xxxii, 120 p. : $b ill., digital ; $c 24 cm.
490 1 $a Wissenschaftliche Reihe Fahrzeugtechnik Universitat 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 0 $a Electric vehicles $x Power trains. $3 3590093
650 0 $a Automated vehicles $x Power trains. $3 3595661
650 1 4 $a Automotive Engineering. $3 928032
650 2 4 $a Engine Technology. $3 1774726
650 2 4 $a Electrical Power Engineering. $3 3592498
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Wissenschaftliche Reihe Fahrzeugtechnik Universitat Stuttgart. $3 2195151
856 4 0 $u https://doi.org/10.1007/978-3-658-36992-7
950 $a Engineering (SpringerNature-11647)