A Framework for Multi-Variate Time Series Anomaly Detection Based on Behavioral Patterns in Electric Vehicles
Wenli Jia, Chang Rong Yang · 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · 2022
Due to the gradual increase of electric vehicle ownership, the safety of electric vehicles has received more attention. Various sudden abnormalities of electric vehicle batteries, such as sudden spontaneous combustion caused by thermal runaway, can seriously endanger people's life and property. In this paper, we propose an anomaly detection and warning method based on behavioral patterns, which can effectively improve the interpretability of multiple time series analysis. Adequate experiments were also done on the real-world dataset to verify the validity of this work.