{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:45:59Z","timestamp":1776941159801,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"TUBITAK","doi-asserted-by":"publisher","award":["119C154"],"award-info":[{"award-number":["119C154"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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. The anomaly detection performances of hybrid models created with the combination of Long Short-Term Memory (LSTM)-Autoencoder, the Local Outlier Factor (LOF), and the Mahalanobis distance were evaluated with the silhouette score, Davies\u2013Bouldin index, and Calinski\u2013Harabasz index, and the potential energy recovery rates were also determined. Two driving datasets were evaluated in terms of chaotic aspects using the Lyapunov exponent, Kolmogorov\u2013Sinai entropy, and fractal dimension metrics. The developed hybrid models are superior to the sub-methods in anomaly detection. Hybrid Model-2 had 2.92% more successful results in anomaly detection compared to Hybrid Model-1. In terms of potential energy saving, Hybrid Model-1 provided 31.26% superiority, while Hybrid Model-2 provided 31.48%. It was also observed that there is a close relationship between anomaly and chaoticity. In the literature where cyber security and visual sources dominate in anomaly detection, a strategy was developed that provides energy efficiency-based anomaly detection and chaotic analysis from data obtained without additional sensor data.<\/jats:p>","DOI":"10.3390\/s24175628","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T07:45:47Z","timestamp":1725003947000},"page":"5628","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Energy-Efficient Anomaly Detection and Chaoticity in Electric Vehicle Driving Behavior"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9518-6498","authenticated-orcid":false,"given":"Efe","family":"Savran","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Bursa Uludag University, 16059 Bursa, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2740-8183","authenticated-orcid":false,"given":"Esin","family":"Karpat","sequence":"additional","affiliation":[{"name":"Electrical-Electronics Engineering Department, Bursa Uludag University, 16059 Bursa, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8474-7328","authenticated-orcid":false,"given":"Fatih","family":"Karpat","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Bursa Uludag University, 16059 Bursa, Turkey"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"ref_1","first-page":"829","article-title":"A Comprehensive Survey of Anomaly Detection Algorithms","volume":"10","author":"Samariya","year":"2023","journal-title":"Ann. 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