Energy-Efficient Anomaly Detection and Chaoticity in Electric Vehicle Driving Behavior

Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. Th...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 24; číslo 17; s. 5628
Hlavní autoři:	Savran, Efe, Karpat, Esin, Karpat, Fatih
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 30.08.2024
Témata:	anomaly detection Datasets Electric vehicles Energy consumption Energy efficiency energy optimization local outlier factor long short-term memory machine learning Mahalanobis distance local outlier factor chaoticity energy optimization anomaly detection machine learning Mahalanobis distance long short-term memory
ISSN:	1424-8220, 1424-8220
On-line přístup:	Získat plný text
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