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

arXiv: https://arxiv.org/abs/2103.08796 · HTML: https://mlsanity.com/paper/2103.08796/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [A data-driven approach for discovering heat load patterns in district heating](https://mlsanity.com/paper/1901.04863/) (2019)

## Built on by (most cited first, 2 of 2)

- [Battery Cloud with Advanced Algorithms](https://mlsanity.com/paper/2203.03737/) (2022)
- [Deep Learning Methods for Detecting Thermal Runaway Events in Battery Production Lines](https://mlsanity.com/paper/2504.08632/) (2025)

## Abstract (arXiv)

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.