With the increasing development and deployment of new systems for efficient and clean mobility, Electric Vehicles (EVs) are becoming more and more common among people. Those produce large amounts of data streams that need to be collected and analyzed to understand user needs and improve their performance. For this purpose, Artificial Intelligence (AI) techniques are playing a very important role. Within this context, Kafka-ML is a Machine Learning (ML) framework that enables the consumption and processing of data streams and allows the flexible management and deployment of neural networks throughout their entire life cycle. Kafka-ML can work with Distributed Neural Networks (DNN) which reduce latency and response times, perform incremental training over time allowing models to adapt to data on the fly, and carry out Federated Learning (FL) processes for this type of algorithms so a more robust global model can be created while maintaining data privacy and security, but all this separately. This work has considered the joint implementation of FL, for anonymous data sharing, incremental learning for continuous training of the models, and DNN for distribution of the models across different points on the map. All this applied within a Vehicle-to-everything (V2X) domain where EV usage and charge data can be shared to improve the user experience, as well as to better understand the behavior of this type of vehicles and their charging points to achieve savings, and how it affects people daily lives. An evaluation of the system related to this EV use case is presented to demonstrate the viability of the tool.
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