## 5 Simulation Results  
The proposed charging station, shown in Figure 1, was simulated using the Matlab/Simulink platform. The specifications and parameters of the charging station are listed in Table 2. Applying step disturbances of the insolation level, the system controllers' performances were tested and compared, as shown in Figures 6-9. Figures 6 and 7 show the response using the fuzzy controller, while Figures 8 and 9 show the response using the PI controller. Figure 6a shows the variations of the insolation level, while Figure 6b shows the response of \(V_{dc}\) compared to the reverence value. It can be recognized that there is no steady-state error with a small settling time and percentage overshoot. The ESS charging current is shown in Figure 6c. It follows the reference produced by the \(V_{dc}\) controller very well, however the reference value changes according to the insolation level. When the insolation level is relatively high, \(\geq\)50%, the PV energy is sufficient to supply energy to the EV charging and store the excess energy in the ESS. The charging current is positive in this period. Nevertheless, at low insolation levels, at \(\leq\)50%, the solar energy is not sufficient to charge the EV. Hence, the ESS discharges to keep the EV charging process steady by compensating for the solar energy drop. Figure 6d shows the EV current response with the reference value produced by the voltage controller. It is seen that the EV current tracks the reference well and has nearly no disturbance corresponding to the insolation step changes. Figure 6: The system response with the fuzzy controller: (**a**) the sun insolation level, (**b**) the DC bus voltage, (**c**) the ESS battery current, and (**d**) the EV battery current. Figure 7: The system response with the fuzzy controller: (**a**) the sun insolation level, (**b**) the PV power, (**c**) the EV battery power, and (**d**) the ESS battery power. Figure 7a shows the variations of the system powers corresponding to the variations of the insolation level. The PV power, shown in Figure 7b, precisely tracks the MPPT power and follows the insolation level. However, there are steep drops in the PV power at the instant of the step change in the insolation. It can be explained as follows: The sample time of the MPPT algorithm is relatively larger than the system sample. Hence, the absorbed power from the PV will be kept at its high level until the MPPT sample occurs. Figure 7c shows the EV charging power. It is steady and has not been affected by the PV power disturbances. Figure 7d shows the ESS power response to the insolation level variations. When the insolation is \(\geq\)50%, the generated PV power is sufficient to charge the EV and store the reserve power in the ESS. However, at the insolation levels of \(\leq\)50%, the energy is not enough to charge the EV. Therefore, the ESS discharges to compensate for the drop in solar energy. It is noted that the discharge power level of the ESS is higher than the charging power. This phenomenon occurs due to the internal ESS losses. Furthermore, the charging/discharging processes follow and compensate for the insolation variations. Figure 8 shows the response of the DC bus voltage, the ESS battery current, and the EV battery current against the solar insolation level for the PI controller. All the variables  
Figure 8: The system response with the PI controller: (**a**) the sun insolation level, (**b**) the DC bus voltage, (**c**) the ESS battery current, and (**d**) the EV battery current. Figure 9: The system response with the PI controller: (**a**) the sun insolation level, (**b**) the PV power, (**c**) the EV battery power, and (**d**) the ESS battery power. track the references very well. Nevertheless, the performances are less than that of the fuzzy controller shown in Figure 6.  
Figure 9 shows the response of the PV power, the ESS battery power, and the EV battery power against the solar insolation level for the PI controller. All the variables track the references very well.