Affiliations: [a] Department of Artificial Intelligence and Applied Electronics, Instituto Tecnológico de Castilla y León, Polígono Industrial de Villalonquejar, Burgos, Spain | [b] Computer Science Department, University of Oviedo, Campus de Viesques s/n, Gijón, Spain
Correspondence:
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Corresponding author: José R. Villar, Computer Science Department, University of Oviedo, Campus de Viesques s/n 33204 Gijón, Spain. E-mail: villarjose@uniovi.es.
Abstract: Electric Vehicles (EV) represent a completely open new field full of promising opportunities that are nowadays arising; one of the most challenging is the driver support for charging stations (CS). The usage of EVs in the city requires specific infrastructures and computational support to ensure an efficient and correct deployment. Consequently, new services are required to fulfil with the EV needs that will arise: e.g. booking a service in a CS, selecting the most appropriate CS in relation to current EV location, booking a CS, or finding the best route to the CS. All the support services should consider not only the user preferences but also the best route restrictions, which are related with minimum energy requirements and avoiding the EV running out of energy before reaching the CS. In this study, a Case Based Reasoning decision support system considering the EV's restrictions in routing to CS is proposed and developed to validate the need of service enhancement. Furthermore, a multi-agent approach to deal with these services is described: agents should interact based on the traffic and energy information – e.g., their states, the current and destination locations, traffic data and events. All these studies are embedded in a more ambitious project to provide EV infrastructures in Spain. Computational experiments performed for real-data study cases using the proposed CBR system compared to traditional routing methods confirm the need to develop dedicated models and consider EV's features and restrictions in designing supporting algorithms.
Keywords: Electrical vehicles, multi-agent systems, dynamic path planning, case based reasoning
