  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.
