## 4 Results and Discussion
### Case 2: Validation of the Proposal, Prioritizing Li-Ion Battery Charging  
A charging condition was implemented for the Li-Ion BESS to harness all peaks of RES power generated for its charging. In this case, an SoC of 20% was set, and over time, it was observed how the Li-Ion BESS prioritized its charging. The goal was to ensure that the Li-Ion BESS was charged for use in the EV through the V2G system. In Figure 13a, the power smoothing through the R-R filter with FLC can be appreciated, achieving a 10% reduction in variance. In Figure 13b, it is observed how the SoC of the Li-ion battery started charging until it reaches 48%, prioritizing its charging over power smoothing and thus taking advantage of renewable energy peaks.  
Figure 11: Li-Ion BESS’s SoC response results via V2G to PV and WP power variations, employing: MA filter, R-R filter, and R-R filter using FLC (V2 GSmooth), respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**(50\%) \(So_{SC}\)** & **Variance without** & **Variance with** & **Variance** & **Renewable Energy** & **Renewable Energy** & **Energy** & **Utilizedized with** \\
**Initial** & **Compensation** & **Compensation** & **Reduction (\%)** & **Compensation** & **Compensation** & **Compensation** & **(KWh)** & **Cycles** \\ \hline MA & 8.54 & 7.61 & 10.87 & 9.49 & 9.3 & 0.19 & 2.803 \\ R-R & 8.54 & 7.57 & 11.31 & 9.4 & 9.24 & 0.16 & 1.459 \\ V2 GSmooth & 8.54 & 7.43 & 12.99 & 9.43 & 9.3 & 0.13 & 1.369 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power generation results from applying the power smoothing methodologies studied.  
Figure 12: Real-time adjustment of the smoothing factor (%R-R) achieved by the proposed method.  
Table 3 presents the results of case 2, designed for power smoothing, intending to mitigate variability in RES generation by applying a condition based on the initial percentage of SoC in the Li-Ion BESS. This condition prioritized EV charging, starting at 20% SoC and reaching 48% SoC. By implementing this methodology, the generated energy was smoothed, providing more or less energy depending on the SoC percentage within the specified range.  
The results indicate that the enhanced R-R filter method through FLC achieved a variance reduction of 14%. Figure 14 illustrates the time evolution of the resultant smoothing factor, expressed as a variable %R-R, at the output of the FLC.  
Figure 13: Outcome of SoC prioritized charging for the Li-Ion BESS using V2G, simultaneously mitigating peaks in power generated by RES. (**a**) Power smoothing through the R-R filter with FLC. (**b**) SoC of the Li-ion battery.  
The impact of a specific condition on the V2 GSmooth method was investigated, emphasizing the prioritization of battery charging. This strategic refinement yielded a significant improvement, lowering the Utilized Life Cycles to 1.170 cycles. This result implied that proactive energy management, focusing on battery charging, can profoundly influence battery durability. Implementing this condition emerged as a promising strategy to optimize the efficiency and longevity of batteries in dynamic environments, where precise responses to electrical demand variations are crucial.