# Energy Management Scheme for an EV Smart
## 1 Introduction
Calculation of \(P\) and \(Q\) references for voltage regulation has been studied in [19]. With the advancement of EV infrastructure, EVs with different battery capacities can be connected to a single high-power fast charger simultaneously to act as an aggregated storage unit. A method for computing \(P\) reference for V2G is discussed in [8], which considers two EVs connected to the photovoltaic inverter DC-bus in a single-phase system. The EVs discharge to compensate for the low solar power; however, the strategy through which the power is shared among the EVs has not been demonstrated. Various discharging rates for V2G are allocated to multiple EVs connected to the same charger in [15]. The rates are designated by the customers, which is impracticable for a dynamic grid scenario, where the allocated power should be based on the grid requirements. A charging scenario of multiple EVs has been considered in compliance with voltage regulation from a smart charger in [16]. Charging of EVs is executed without considering any coordination with the grid. In [20], the control schemes of a three-phase four-quadrant converter and bi-directional converter of EVs are described, and the study lacks the power allocation control from the EV perspective. Overall, the proper designation of charging or discharging rates to the integrated EVs at the charger level while keeping in view the ongoing grid conditions is absent in the previous studies. The concept of EV power allocation and aggregation has been previously explored in parking lot-level studies. In [21], the charging power allotment to EVs within the aggregator is determined by a heuristic algorithm. The charging costs are minimized, while the peak loading is also avoided. Linear programming is used in [22] to allocate the charging power of each EV in a parking lot. Five discrete charging rates are considered, which are decided by the customer. To reduce the power fluctuation level in a residential area, an aggregated charging model has been proposed in [23]. The model considers the total number of EVs, as well as the SoC of each EV under an updatable optimization method. These studies are executed considering EV fleets, with day-ahead energy forecasts and long time resolutions (in the range of 5-15 min). Thus, these methods are unfeasible for instantaneous power allocation during renewables' power transients, which are short term (within the seconds range) and cannot be forecasted. Furthermore, all of these schemes only consider the same type of EVs or batteries, and management between different EV battery types is yet to be explored at the power allocation level. Instantaneous power allocation to multiple BESSs with several battery sizes has been studied in [24]. The batteries charge or discharge in descending order of their rated capacity. It is natural to assume that a larger battery should be allotted a higher share of power. However, EV power allocation from this perspective should be analyzed with different battery capacity EVs. In this paper, we have proposed a comprehensive energy management scheme (EMS) to govern the four-quadrant operation of a smart EV charger and the aggregation of various types of EVs connected to the charger. The EMS communicates with the EVs, smart charger, grid and DSO. The EMS can coordinate with a variable-power DG in the close vicinity of the charger, as well as a reactive load onset simultaneously to generate the \(PQ\) references instantaneously. Furthermore, a method is proposed within the EMS to allocate active power appropriately among multiple EVs connected to the smart charger DC-bus. The proposed method (PM) allots power references to an individual EV based on the battery state-of-charge (SoC) and its capacity. The power reference calculation for the EVs is derived from the active power reference (\(P\)) of the smart charger. The power can be aggregated from several types of EVs for V2G/G2V operation.