ScienceDirect Available online at

2. Related work 
The penetration of plug-in hybrid electric vehicles (PHEV) and fully electric vehicles (EV) on transportation 
markets could mitigate environmental impact, energy use, and reduction. However, further research and 
development together with real pilot testing need to be conducted in order to optimize and support the way these 
vehicles are used in different scenarios, such as traffic situation, road nature, and according to driving behavior. The 
main challenge consists in taking into consideration the environment in which these vehicles are evolving [ref]. 
Recently, several approaches have been developed and can be classified into four main categories: control-based 
charging methods, planning and scheduling-based methods, routing guidance based architectures and station 
placement methods, and energy trading profits- oriented methods. 
Control-based charging methods target the power management of an electric vehicle and its battery life cycle ([8], 
[9], [10] and [11]). Their main objective is to identify the parameters affecting the battery health degradation, such as 
aging and number of cycles, and optimizing the charging pattern of EVs. Planning and scheduling-based methods, 
either reactive or proactive, have been proposed for managing EVs at the charging station ([2], [12] and [13]). Their 
objective is to improve the satisfaction of EVs’ drivers by reducing the charging cost and waiting time as well. In the 
third category, approaches tackle routing and vehicle guidance to suitable charging stations and location assignments 
([14] and [15]). Their aim is to guide EVs’ drivers to suitable charging stations by using a charging system together 
with required information and communication technologies (ICT). Approaches, in the fourth category, tackle the 
issue of maximizing energy trading profits while minimizing battery aging costs [16]. Approaches include methods 
that take into consideration the use of the battery in an optimized way under the consideration of battery aging costs 
and variable electricity prices.
The approach proposed in this paper falls into the second category of approaches. We consider multiple requests 
for predictive charging rates and times with the objective to reduce the waiting time of EVs and consequently the 
occupation of charging stations. It allows managing large scale demands by proposing solutions to the EV drivers for 
making optimal charging processes. The aim is to charge all EVs within an accepted waiting time while reducing the 
occupation time of charging stations. To do so, we study each charging request in both cases, with and without 
 
NaitSidiMoh et al./ Procedia Computer Science 00 (2018) 000–000 
 3 
considering required energy of each battery for being fully charged. The charging planning and the interaction 
between all the system components are based on an interoperable architecture using ICT and geo-positioning 
techniques as described in [6]. This architecture describes various exchanges and communication technologies 
between all system components. EVs, charging stations and a collaborative platform are the main components of the 
system. More precisely, the interaction and communication between all system components are based on the 
following principles: real-time positioning using Geo-positioning techniques (e.g. GPS and EGNOS), bidirectional 
communication between EVs and infrastructure (V2I and I2V) via wireless technologies (e.g. GPRS, 3G/4G), and 
Web services ([17] and [18]).