Improving efficiency is one of the most important objectives of the Smart Cities standards, and Electric Vehicles (EVs). TICS can help to soften one of their main limitations –autonomy– planning efficient driving strategies. This paper evaluates the physical variables that have an impact on the EV consumption and presents an electronic architecture to monitor them in an Experimental Ultralight Electric Vehicle. This system includes a set of very low cost sensors integrated with a data logger and a GPRS transmission system that can connect, in real time, with a control center where a route-finding reinforcement-learning algorithm that helps to find the most-effective route and reduce the time spent on urban environment tips.
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