# A Simple Multi-Parameter Method for Efficient Charging Scheduling of Electric Vehicles
###### Abstract  
In this article, a method for the efficient charging of electric vehicles (EVs) at the parking lot (PL) level, including V2G operation and taking into account lifetime of EV batteries, distribution network and local transformer loading, is proposed. The main targets of the method are to minimize the total charging cost of the PLs hosting the EVs and to satisfy all technical and operation constraints of EVs and PLs. The proposed method exploits particle swarm optimization (PSO) to derive the charging schedule of the EVs. The proposed method is compared with conventional charging strategies, where the EVs are charged with the maximum power of their charging power converter or the average power required to achieve their state-of-charge target, and a conventional charging scheduling method using the aggregated behavior of the plug-in EVs. Real-world data series of electricity price and parking lot activity were used. The results obtained from the study of indicative operation scenarios prove the effectiveness of the proposed method, while no sophisticated computing, measurement and communication systems are required for its application. electric vehicles; efficient charging scheduling; energy management; PSO; V2G +
Footnote †: journal: Applied system  
definition  
grid. The willingness of EV owners to participate in V2G services is studied in [14]. Several works conclude that range anxiety of the EV drivers is an important parameter for their participation in V2G operation. Range anxiety represents the anxiety of the EV owners from their uncertainty regarding the range of their next travel ensured by the energy stored in their EVs. In [15], different V2G business models were studied and five major factors for a profitable market were identified. These factors are: the battery degradation because of V2G operation, the dispatch of the economic revenue from V2G to the involved parties, V2G services such as frequency regulation, the energy used to provide V2G services and the variables which should be taken into consideration when applying V2G and creating bids to the electricity market. Smart and conventional charging of EVs have been studied taking into consideration several points of view, scenarios, methodologies and objectives. EVs' load could be characterized as shiftable, interruptible and flexible. Hence, the charging of EVs could be coordinated with the operation of the local distribution network, preventing its overload and satisfying all local charging constraints, at the same time. The studies of the previous issues could be separated into two main categories. The first one considers smart charging in terms of cost minimization, while the second one includes smart charging and discharging (V2G and ancillary services to the grid). In most studies, the charging/discharging schedule of the PEVs is optimized. However, charging/discharging at the PL level has to address more constraints, such as PL transformer power limit, grid voltage at the common point of coupling, local distribution grid loading, etc. In [16], a particle swarm optimization (PSO)-based charging schedule is developed, and its comparison with priority charging-based algorithms is made. The main objective was a fast charging process with the minimum cost, and consequently, grid overloading was not considered. The authors claimed that the proposed method outperformed the other methods regarding the optimal dispatch of the charging power to the controlled EVs. In [17], an improved PSO was introduced in order to charge/discharge EVs. Power losses of the electric network, frequent operation and smoothness of the power daily load curve were considered. At the same time, it was attempted to increase the satisfaction of EV owners' charging requirements. One type of EV was considered with constant charging power of 3.12 kW (slow charging). In [18], an innovative method is presented to obtain an efficient charging schedule of PEVs.