Abstract
With the increasing adoption of electric vehicles worldwide, some limitations have emerged in their usage. The main limitations include low autonomy and a scarcity of charging points. In this work, we describe a software architecture for planning a stop at charging stations along a trip, by prediction of battery charge to be spent along the path. We describe the main components of this architecture and evaluate regression methods for the car consumption prediction module. We also use a real dataset built from an electric vehicle usage to validate the architecture concept and its viability analyzing multiple linear regression machine learning models. To further validate the architecture, we make comparisons between simulated and a real trips.
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