Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering
This addresses a critical safety problem for electric vehicle and energy storage systems by providing a robust, data-efficient detection method, though it appears incremental as it builds on existing data-driven approaches.
The paper tackles thermal anomaly detection in electric vehicle and energy storage batteries to prevent thermal runaway, proposing a data-driven method using unsupervised shape clustering that shows improved accuracy over onboard BMS and early detection of unforeseen anomalies in initial experiments.
For electric vehicles (EV) and energy storage (ES) batteries, thermal runaway is a critical issue as it can lead to uncontrollable fires or even explosions. Thermal anomaly detection can identify problematic battery packs that may eventually undergo thermal runaway. However, there are common challenges like data unavailability, environment and configuration variations, and battery aging. We propose a data-driven method to detect battery thermal anomaly based on comparing shape-similarity between thermal measurements. Based on their shapes, the measurements are continuously being grouped into different clusters. Anomaly is detected by monitoring deviations within the clusters. Unlike model-based or other data-driven methods, the proposed method is robust to data loss and requires minimal reference data for different pack configurations. As the initial experimental results show, the method not only can be more accurate than the onboard BMS and but also can detect unforeseen anomalies at the early stage.