Optimization of battery recharging using clean energy and positioning logistics with machine learning and predictive models for electric personal mobility equipment in urban centers

Abstract

The objective of this experimental research is to prove the efficiency of a mediator software, based on artificial intelligence, to optimize the use of recharge points of batteries with renewable energy and parking areas for electric personal mobility equipment in urban centers, providing online information for owners and service companies sharing this equipment. The research intends to evaluate the following benefits for users and cities: § Improve the organization of individual electric mobility equipment in urban centers, measured through research among users and public control agencies; § Meet the current and future regulatory norms of cities for services of sharing of individual electric mobility equipment, measured by the number of fines; § Increase the number of individual electric mobility equipment with charge in the batteries to increase the amount of travel, compared to the beginning of the research; § Improve the level of satisfaction of owners and users of the services, as measured by research; § Reduce traffic congestion kilometers measured by transit companies in regions served by shared services; § Increase the number of users of electric personal mobility equipment sharing services, measured from the number of battery loads; § Reduce people's journey time in urban areas, as measured by satisfaction survey; § Expand the use of renewable energy to charge the batteries of individual electric mobility equipment; § Reduce the use of automotive vehicles to reposition electric modals, reducing the emission of greenhouse gases, as measured by the reduction of vehicles. The research envisages the development of software as a Service in a cloud computing environment to implement the mathematical model of mediation and suggestion, based on artificial intelligence, of recharge points and parking using a NoSQL database receiving real-time data from IOT (Internet of Things) remote devices over a mobile data communication network. The Application Program Interface (API) mediator software will provide data for other softwares, including mobile applications dedicated to the discovery of recharging and parking points, and can be integrated with companies' services for the sharing of personal mobility equipment and services such as Google Maps and Waze. The algorithms to be developed should meet the following assumptions: § Maintain individual electric mobility equipment of shared services with charged batteries close to the demand of users, by time and by company of sharing; § Maximize the use of battery recharging points and parking of individual electric mobility equipment; § Suggest a route between the starting point and the recommended recharging or parking point; § Provide information to suggest faster routes using more than one mode in the city for routing services.The algorithms will be based on Machine Learning models and predictive modeling. The models will learn from the historical data of battery recharges and will determine the likelihood of users meeting the recommendations of the mediator software, based on certain conditions measured in real time, such as rainfall forecast. With the increase of the services of sharing individual electric mobility equipment (scooters, bicycles, scooters and other modalities) the opportunity to offer a complementary service to battery recharge using photovoltaic energy of Infra Solar company. (AU)