HELECAR-D: A dataset for urban electro mobility in Moroccan context

Abstract

The integration of electric vehicles (EV) into the global car fleet has seen a major shift boosted by the new environmental regulations. The adoption of this low-carbon vehicle is hampered by several constraints in emerging countries and particularly in Morocco, e.g. constraints related to the infrastructure (land purchasing for charging stations, integration with existing power infrastructures, funding and optimized deployment) [1], and constraints related to the lack of standards and regulatory frameworks [2]. For this purpose, our objective is to share with the community a dataset about EV exploitation in the Moroccan context. This dataset [3] could be used to improve the energy management system characterized by a limited driving range and restrictive charging infrastructures. Subsequently, several driving cycles have been done in three main trajectories using data collection in the region of Rabat-Salé-Kénitra (RSK). The collected data contains mainly the date, time, Battery State of Charge (SoC), speed, vehicle's position, weather information, traffic conditions and road speed limits. The dataset collection is done using an onboard developed electronic card that collects the vehicle's internal and external data. Collected data are pre-processes and then stored in a Comma Separated Values (CSV) file. The collected dataset could be used in applications that are related to EV management and planning, such as speed prediction, speed control strategies, rerouting and EV charging scheduling, vehicle-to-grid and grid-to-vehicle, and energy demand forecasting.

Keywords

• Electric vehicles
• EV Speed profile
• Battery state of charge
• Machine Learning