Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering

TL;DR

This paper introduces a robust, data-driven shape clustering method for early detection of thermal anomalies in batteries, addressing challenges like data loss and variability, with promising initial experimental results.

Contribution

The paper presents a novel shape similarity clustering approach for battery thermal anomaly detection that is robust to data loss and requires minimal reference data.

Findings

More accurate than onboard BMS in initial tests

Can detect unforeseen anomalies early

Robust to data unavailability and environmental variations

Abstract

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.