# Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
## 7 Control and Energy Management  
Connecting the renewable energy-based stations to the grid leads to several challenges. Besides the grid integration and fluctuation issues, the charging operation presents a critical shortage due to the inharmonious charging process concerning power quality and demand [86], specifically for fast-charging stations [87]. Hence, it is crucial to control the charging behavior to reduce these issues' impacts. For example, an analysis of electricity production conducted by [88] to calculate relevant performance indicators of the electricity supplied by the grid indicated significant variability of the CO2 emissions. It highlights the need for accurate knowledge of operational parameters to support future _smart grid_ management. Therefore, the management of the EV charging behavior would moderate the fluctuation of renewable energy, optimize the grid's peak demand, and make efficient load characteristics of the grid [89]. The literature comprises several studies on impacts of charging loads on the grid. For example, [PERSON] et al. [90] studied impacts of EVs on the distribution network, and [PERSON] et al. [91] discussed effects of large-scale charging infrastructure on the system's total loss. In [92], a probabilistic model is used to investigate incremental impacts of EV charging on the distribution network.  
Management of the charging process in a controlled mode increases the capacity of charging a large number of EVs. Using the maximum renewable energy generated and smart coordination with the grid can decrease the power load of the charging equipment on the grid and ensure cleaner energy. By contrast, integrating the energy storage with the charging station enables disengaging EV load from the grid, moving the charging time to off-peak, and controlling renewable energy's uncertainty and fluctuation [87].  
Moreover, most EVs' parking time is up to 95% per day in the charging area; thus, the V2G concept was raised [93]. EVs can be charged at low pricing hours and discharge at high pricing hours, making EVs a distributed energy facility. This mechanism offers EV drivers the chance to lower the charging cost through the price difference [94]. The excellent interaction among the grid and large-scale or high-distance range EVs leads to the smart charging and discharging strategy [95].  
The literature has many studies concerning energy management for EVs associated with renewable energy sources. For example, [PERSON] et al. [96] proposed a charging control algorithm to schedule EV charging associated with PV in smart buildings. The proposed strategy is based on predicted PV energy generation and baseload power demand. However, uncertainties of EV charging demand were not considered. Similarly, another scheduling algorithm was proposed by [97] for smart buildings that can efficiently reduce CO\({}_{2}\) emissions. However, the study did not consider the flexibility of EV charging demand.  
Several studies consider appropriate strategies for controlling EV charging. For instance, [PERSON] et al. [98] proposed a vehicle-originating-signals strategy for controlling EV charging by reducing communication overhead with minor effects on performance. [PERSON] et al. [99] proposed a scheme for EV charging control that considers the energy storage system and renewable energy power. In [100], an optimal scheduling method was introduced for charging infrastructure associated with a microgrid. [PERSON] et al. [101] evaluated various combinations of different priority control criteria on EVs' ability to charge and charging fairness. Table 3 summarizes the other recent studies concerning energy management of renewable energy-based charging infrastructure. It is noticeable that the dominant criterion is focused mainly on the energy management associated with a PV system that is connected to the grid; consequently, the wind energy and the energy storage system are not sufficiently considered.  
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