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

• URL: http://arxiv.org/abs/2103.08796v1
• Date: Tue, 16 Mar 2021 01:29:41 GMT
• Title: Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering
• Authors: Xiaojun Li, Jianwei Li, Ali Abdollahi, Trevor Jones and Asif Habeebullah
• Abstract summary: We propose a data-driven method to detect battery thermal anomaly based on comparing shape-similarity between thermal measurements. 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.
• Score: 4.805591270997103
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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