An intelligent route management system for electric vehicle charging

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.

Sustainable Building Design

“…It is estimated that over 70% of the city's greenhouse gases are emitted from buildings (ARUP 2013), and buildings are responsible for 70% of the energy use in the U.S. (DOE 2007). Just like hybrid and electric cars are changing the face of the auto industry (Lim et al 2012;Sedano et al 2013) the concept of green or sustainable building design is also transforming the construction industry albeit much more slowly.…”

Data-Driven Intelligent Management of Energy Constrained Autonomous Vehicles in Smart Cities

“…Since the idle EAVs can also be arranged to charge, the location and status of the charging station are also significant for the management of EVs [9]. To avoid energy exhaustion before reaching the destination, Sedano et al proposed a reservation plan of charging services for the electric-powered taxis [12]. To minimize the infrastructure investment, Yang et al presented a datadriven optimization algorithm to allocate chargers for the battery electric vehicle (BEV) taxis throughout a city [14].…”

Solving Detour‐Based Fuel Stations Location Problems

“…Moreover, the ever increasing automobile use worldwide has put enormous pressure on the supply of crude oil, which is expected to peak in the near future (Chen, ; Hill, ). To meet transportation needs in the long run, promoting the use of alternative fuel vehicles seems inevitable (see Pinto et al., ; Sedano et al., ; Pinto et al., ; Nan and Chi, for studies on electric vehicles routing and marketing problems). Yet, the supporting infrastructure that would facilitate the transition to alternative fuel vehicles, such as a network of fuel stations, is yet to be developed in most countries.…”