In this paper, we address the problem of explainability in the understanding of Lithium-ion battery aging for electric mobility. For this purpose, we develop a predictive model based on a convolutional neural network on battery aging data generated in our laboratory. This prediction is explained using Shaley values. A pattern mining in a temporal series of explicability, using the matrix profile approach, allows to identify the patterns responsible for the accelerated aging.