001444961 000__ 03479cam\a2200553Ii\4500
001444961 001__ 1444961
001444961 003__ OCoLC
001444961 005__ 20230310003805.0
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001444961 019__ $$a1302110295$$a1302184909$$a1302953635$$a1302988533
001444961 020__ $$a9783658369927$$q(electronic bk.)
001444961 020__ $$a3658369922$$q(electronic bk.)
001444961 020__ $$z9783658369910
001444961 020__ $$z3658369914
001444961 0247_ $$a10.1007/978-3-658-36992-7$$2doi
001444961 035__ $$aSP(OCoLC)1302135338
001444961 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dUKAHL$$dOCLCQ
001444961 049__ $$aISEA
001444961 050_4 $$aTL220$$b.S54 2022
001444961 08204 $$a629.22/93$$223
001444961 1001_ $$aShen, Tunan,$$eauthor.
001444961 24510 $$aDiagnosis of the powertrain systems for autonomous electric vehicles /$$cTunan Shen.
001444961 264_1 $$aWiesbaden :$$bSpringer,$$c[2022]
001444961 264_4 $$c©2022
001444961 300__ $$a1 online resource :$$billustrations (some color).
001444961 336__ $$atext$$btxt$$2rdacontent
001444961 337__ $$acomputer$$bc$$2rdamedia
001444961 338__ $$aonline resource$$bcr$$2rdacarrier
001444961 4901_ $$aWissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart,$$x2567-0352
001444961 504__ $$aIncludes bibliographical references.
001444961 5050_ $$aBackground and State of the Art -- Diagnosis of Electrical Faults in Electric Machines -- Diagnosis of Mechanical Faults in Electric Machines.
001444961 506__ $$aAccess limited to authorized users.
001444961 520__ $$aTunan 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.
001444961 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 10, 2022).
001444961 650_0 $$aElectric vehicles$$xPower trains.
001444961 650_0 $$aAutomated vehicles$$xPower trains.
001444961 650_6 $$aVéhicules électriques$$xGroupes motopropulseurs.
001444961 650_6 $$aVéhicules autonomes$$xGroupes motopropulseurs.
001444961 655_0 $$aElectronic books.
001444961 77608 $$iPrint version: $$z3658369914$$z9783658369910$$w(OCoLC)1295380828
001444961 830_0 $$aWissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart.$$x2567-0352
001444961 852__ $$bebk
001444961 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-658-36992-7$$zOnline Access$$91397441.1
001444961 909CO $$ooai:library.usi.edu:1444961$$pGLOBAL_SET
001444961 980__ $$aBIB
001444961 980__ $$aEBOOK
001444961 982__ $$aEbook
001444961 983__ $$aOnline
001444961 994__ $$a92$$bISE