Feature Extraction, Ageing Modelling and Information Analysis of a Large-Scale Battery Ageing Experiment

Large scale testing of newly developed Li-ion cells is associated with high costs for the interested parties, and ideally, testing time should be kept to a minimum. In this work, an ageing model was developed and trained with real data from a large-scale testing experiment in order to answer how much testing time and data would have been really needed to achieve similar model generalisation performance on previously unseen data. A linear regression model was used, and the feature engineering, extraction and selection steps are shown herein, alongside accurate prediction results for the majority of the accelerated ageing experiments. Information analysis was performed to achieve the desired data reduction, obtaining similar model properties with a fifth of the number of cells and half of the testing time. The proposed ageing model uses features commonly found in the literature, and the structure is simple enough for the training to be performed online in an EV. It has good generalisation capabilities. Lastly, the data reduction approach used here is model-independent, allowing a similar methodology to be used with different modelling assumptions.

Automated summary

Aging model developed in this study, using information analysis to reduce data needed for large scale testing, achieving greater efficiency in achieving similar model performance.