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Model-Based Condition Monitoring of the Sensors and Actuators of an Electric and Automated Vehicle

Li, Shiqing 1; Frey, Michael ORCID iD icon 1; Gauterin, Frank ORCID iD icon 1
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)

Abstract:

Constant monitoring of driving conditions and observation of the surrounding area are essential for achieving reliable, high-quality autonomous driving. This requires more reliable sensors and actuators, as there is always the potential that sensors and actuators will fail under real-world conditions. The sensitive condition-monitoring methods of sensors and actuators should be used to improve the reliability of the sensors and actuators. They should be able to detect and isolate the abnormal situations of faulty sensors and actuators. In this paper, a developed model-based method for condition monitoring of the sensors and actuators in an electric vehicle is presented that can determine whether a sensor has a fault and further reconfigure the sensor signal, as well as detect the abnormal behavior of the actuators with the reconfigured sensor signals. Through the simulation data obtained by the vehicle model in complex road conditions, it is proved that the method is effective for the state detection of sensors and actuators.


Verlagsausgabe §
DOI: 10.5445/IR/1000154511
Veröffentlicht am 13.01.2023
Originalveröffentlichung
DOI: 10.3390/s23020887
Scopus
Zitationen: 9
Web of Science
Zitationen: 8
Dimensions
Zitationen: 9
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000154511
Erschienen in Sensors
Verlag MDPI
Band 23
Heft 2
Seiten Art.-Nr.: 887
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 12.01.2023
Schlagwörter electric vehicle; sensors; actuators; abnormal condition monitoring; model-based fault detection; symptom generation; fault diagnosis
Nachgewiesen in Dimensions
Web of Science
Scopus
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