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Mathematical analysis of battery data for development of data-driven degradation model

Degradation models of battery are required for reliable, optimized and safe deployment of battery systems in different large scale applications, such as grid-integrated energy storage systems. Considering the non-linear degradation pattern, and its dependency on multiple stress factors, data-driven degradation models are preferred. However, there are some issues associated with the extraction and utilization of data for development of the data-driven models, such as redundancy of information due to uncorrelated data, and computational burden due to multi-dimensions of data. This study applies two data-analysis methods to improve the quality of data to be used for degradation model. The Grey Relational Analysis (GRA) is used to remove the redundant data, and the Principal component analysis (PCA) is used to reduce the dimensions of the data.