## 2 Smart Grids System
### Power Supply
#### 2.2.1 Battery Management  
The battery management is worked from different perspectives, such as automatically controlling the SoG and the system that maintains battery aging and health. The rest of the research society considered the authority of the power consumption and reduced the costs of PS [71]. (GA) [72], particle swarm optimization (PSO) [73], fuzzy logic (FL) [74], metaheuristic optimization algorithms (MOA) [27], etc. have all been used to preserve battery life and control the charging process, which includes charging from 20% and stopping when it reaches 95%. These methods and algorithms use a mix of energy sources ranging from wind energy, fossil energy, solar energy, and RE [75,76]. However, the focus is to resolve the issues between battery control and energy supplies used during freightage (see Figure 3). State of Charge (SoC)  
A cell (SoC) depicts current capacity as a function of its rated capacity. The SoC's value ranges from 0 to 100 percent. The cell is fully loaded if the SoC is 100 percent, whereas an SoC of zero percent shows that the cell is entirely discharged [77]. In practical applications, the percentage or level that defines the start or end of the charging process is varied according to the charging system, whether it is manual or automatic. The beginning SoC is assigned as 0% and target charging SoC as 80% to compare improvements. In the same study, the optimal charging current series for 0-80% SoC with different setting time had charging times that ranged from 1 to 3 h, with a step of 0.5 h. Knowing the battery beginning SoC, the target SoC, and the charging time, it has been found that the current charging command can be easily calculated by the database-based method. Compared with the constant current charging strategy, the proposed method can effectively decrease the charging loss [72]. Furthermore, electrochemical techniques and post-mortem examination allowed the samples kept at 30%, 60%, and 100% SoC and 55 \({}^{\circ}\)C to be comprehensively examined. It was determined that the most severe capacity fading occurred when the batteries were kept at 55 degrees Celsius and 100% SoC [78]. In addition, higher stored SoC resulted in a more substantial rise in bulk resistance (R\({}_{\text{b}}\)) and charge-transfer resistance (R\({}_{\text{ct}}\)) of a full battery at 55 \({}^{\circ}\)C. Still, the discharge rate capability of the stored battery remained unchanged [73]. Furthermore, higher stored SoC resulted in a more substantial rise in bulk resistance (R\({}_{\text{b}}\)) and charge-transfer resistance (R\({}_{\text{ct}}\)) of a full battery at 55 \({}^{\circ}\)C. Still, the discharge rate capability of the stored battery remained unchanged [78]. However, the minimum SoC in the study never fell below 20% to avoid reducing battery life. Therefore, there was always 20% energy in the batteries in this study [73]. Factors such as charge, discharge rate, and charging/discharging hours played a significant role in correcting the load characteristic of the grid, and the islanded micro-grid was the optimal operation of energy systems [73]. The numerical simulations were used to evaluate the system's net savings for various SoC settings in the control strategy. Considering expanding data samples, the proposed approximate dynamic programming approach beat the classic dynamic programming approach [79]. The proposed approximate dynamic programming approach for microgrid power system optimization problems is a computationally efficient tool [80; 81] (Figure 4). Figure 3: Power consumption control. Determining battery aging is a crucial issue to predict the available charge in battery-operated systems [82]. According to the literature, the batteries were charged and discharged in 5 h to produce a 5 kilowatt (KW) average, while the battery life was anticipated to be around ten years [73].