Root-cause analysis with data-driven methods and machine learning in lithium-ion battery tests : Master's thesis about detecting deviations with PCA

The increased demand of energy storage systems and electric vehicles on the market result in high demand of lithium-ion batteries. As a lithium-ion battery manufacturer, Northvolt runs quality tests on the products to assess their performance, life and safety. Batteries that are tested are most often behaving as expected, but sometimes deviations occur. Anomaly detection is today most often performed by plotting and comparing produced data to other test-data to find which parameters that are deviating. The purpose of this thesis is to automatize anomaly detection and a proposed solution is to use state-of-the-art machine learning methods. These include using supervised and unsupervised machine learning. Before applying machine learning, the feature engineering is presented. It describes what parameters are extracted from the experiment data sets. Then the supervised machine learning framework is described. For the unsupervised machine learning, a principal component analysis is presented to locate deviations. This thesis also presents a differential capacity analysis, as this could be incorporated with the features in the future. The results shows that the subset of labeled data for supervised learning is too small to produce a model that predicts future deviations. The extracted features are also used in the principal component analysis, where the results show deviations (outliers) and aid targeting the anomalies. These can then be used to determine the root-cause of particular anomalies and mitigate future deviations.