Deep Transfer Learning for Detecting Electric Vehicles Highly Correlated Energy Consumption Parameters

Implementation of advanced intelligent deep learning techniques for electric vehicles (EVs) energy consumption analysis is obstructed by two main subjects. First, the problem of finding a very similar collection of datasets to the actual EVs energy usage in terms of feature space and data distribution. Second, training a retrained model from scratch requires a massive amount of computational power; however, this does not guarantee to catch rare events included in datasets. To mitigate the aforementioned concerns, this article aims to present a model based on deep transfer learning (DTL) between domain-variant datasets, to reduce the need for the existence of a vast amount of EVs data, including driving characteristics and patterns. Also, this model applies a distributed cooperative learning approach to identify highly correlated energy consumption parameters by building the model on previously acquired knowledge from preceding learning phases in order to enhance the artificial intelligence (AI) accuracy level of the proposed energy management system.