Study of a New Quick-Charging Strategy for Electric Vehicles in Highway Charging Stations

Abstract

Abstract:To solve the problem, because of which conventional quick-charging strategies (CQCS) cannot meet the requirements of quick-charging for multiple types of electric vehicles (EV) on highways where vehicle inflow is excessive, this paper proposed a new quick-charging strategy (NQCS) for EVs: on the premise of not affecting those EVs being charged, the remaining power of the quick-charging pile with multiple power output interfaces is used to provide a synchronous charging service for EVs waiting in the queue. To verify the effectiveness of this strategy, a power distribution model of charging pile and a queuing model of charging station (CS) were constructed. In addition, based on an actual highway service area where vehicle inflow is excessive during the simulation period (0:00-24:00), charging situations of CQCS and NQCS were respectively simulated in a charging station (CS), with different number of chargers, by basic queuing algorithm and an improved queuing algorithm. The simulation results showed that when the relative EV inflow is excessive, compared to CQCS, NQCS not only can reduce user waiting time, charging time, and stay time, but also can improve the utilisation rate of charging infrastructure and service capacity of CS and reduce the queue length of CS. At the same time, NQCS can reduce the impact on the power grid. In addition, in NQCS, the on-demand power distribution method is more efficient than the average power distribution method. Therefore, NQCS is more suitable for quick-charging for multiple types of EVs on highways where vehicle inflow is excessive.

A Study on Coordinated Optimization of Electric Vehicle Charging and Charging Pile Selection

“…Calculate the available active capacity of the transformer during different periods in the k-th group according to Equation (16). Substitute these parameters and those in Step 3 into models (8) to (15) in the second stage. The IP_SOLVE package was used in MATLAB to solve the model.…”

“…With any group X = (x 1 , x 2 , · · · , x N ) as an example, with T as the study period, the decision variables of optimal charging for group X are defined as the charging power of each EV in the period of T, as shown in Equation (8). where U X denotes a matrix with the decision variables of optimal charging for group X, T = n∆t, ∆t denotes the time interval, n denotes the number of intervals within the period of T, u ti denotes the charging power of EV i within the period of t, and N denotes the sample size of group X.…”

“…According to the current mainstream configuration (with a maximum mileage of about 150 km), the specifications of the battery of Bavarian Motor Work (BMW) MINI EV were adopted, and the battery pack capacity was set to 30 kW·h for simulation. Moreover, the expected SOC of each EV was set to 0.95 with considering the charging profile defined by the manufacturer [8], and the initial charge SOC data in the MINI E test …”

“…On the one hand, EV charging method is constrained by the planning scheme of charging facilities. For instance, the type of charging piles will affect the effective charging of EVs [8]. In addition, the maximum output power of the charging piles will limit the adjustable range of the charging power.…”

“…For the power grid, such consequences include an "extra peak" of load on the grid, reduced voltage at some nodes in the grid, and increased network loss of the grid [5][6][7]. For charging facilities, such impacts are manifested in the reduced utilization and increased operating costs [8]. For end users, such consequences mean the increased charging costs and duration [9].…”