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. 2024 Aug 30;24(17):5628.
doi: 10.3390/s24175628.

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

Efe Savran  1 , Esin Karpat  2 , Fatih Karpat  1
Affiliations

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

Efe Savran et al. Sensors (Basel). .

Abstract

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-Bouldin index, and Calinski-Harabasz 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-Sinai 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.

Keywords: Mahalanobis distance; anomaly detection; chaoticity; energy optimization; local outlier factor; long short-term memory; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Process flow of the study.
Figure 2
Figure 2
A scope of CAN bus record.
Figure 3
Figure 3
Speed graph of Driving-A.
Figure 4
Figure 4
Speed graph of Driving-B.
Figure 5
Figure 5
Three-dimensional plot of the real driving route.
Figure 6
Figure 6
LSTM-Autoencoder training performance curve on Driving-A.
Figure 7
Figure 7
LSTM-Autoencoder reconstruction error graph on Driving-A.
Figure 8
Figure 8
Anomalies detected by Hybrid Model-1 in Driving-A.
Figure 9
Figure 9
Anomalies detected by Hybrid Model-2 in Driving-A.
Figure 10
Figure 10
LSTM-Autoencoder training performance curve on Driving-B.
Figure 11
Figure 11
LSTM-Autoencoder reconstruction error graph on Driving-B.
Figure 12
Figure 12
Anomalies detected by Hybrid Model-1 on Driving-B.
Figure 13
Figure 13
Anomalies detected by Hybrid Model-2 on Driving-B.
Figure 14
Figure 14
Anomaly detection performances of hybrid models.
Figure 15
Figure 15
Energy-saving performances of hybrid models.
Figure 16
Figure 16
Lyapunov exponentials: Driving-A data (left) and Driving-B data (right).
Figure 17
Figure 17
Fractal dimensions: Driving-A (left) and Driving-B (right).
See this image and copyright information in PMC

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