  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 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, but also can detect unforeseen anomalies at the early
stage.
