Convolutional neural network battery pack classification - Gramian angular field vs. Markov transition field
H. H. Andersen, Kasper Mayntz Paasch · IET conference proceedings. · 2023
In battery pack manufacturing the main time-consuming aspects is the long test time for discharge and charge cycles. Making an AI that can assist by predicting the result of these tests while they are running is the end goal of this series of papers, where this is step one. To achieve this, the classification of the different battery pack types and the different tests must be performed. In collaboration with the company Banke Aps., who is a battery pack manufacturer, a database of tests has been build forming the foundation of this paper. Using the Gramian Angular Field (GAF) and Markov Transition Field (MTF) methods to transform the time series data into image form gives the possibility to utilize the standard convolutional neural network (CNN) structures to classify the battery pack and test type. Furthermore, building an algorithm that can distinguish between pass or fail tests with as high accuracy as possible, is important. Some question, that needs to be investigated, is which mathematical technique is best, GAF or MTF? Can these mathematical techniques identify important test features in the generated images and be used to estimate the test outcome?