Research papers · 2021.06.20
Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering
Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering
Overview
The method clusters battery thermal measurements by shape similarity and monitors within-cluster deviations. Initial experiments examine early thermal-anomaly detection for EV and storage batteries with limited reference data and missing measurements.
Author-supplied source information
Author-supplied information on arXiv.
2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), 2021, pp. 1-6
6 pages
Version: arXiv v2 — manuscript revision