## 5 Results
### EV Degradation Analysis and Forecast  
This section serves as an example of the valuable insights that can be derived from the DT-EV outputs, which offer essential information regarding the state of degradation of the EV. To enable this analysis, the DT-EV requires measurements of battery status parameters, such as current, voltage, temperature or SOC. These variables have been recorded over the course of one week, while the EVs listed in Table 2 were in operation under the baseline scenario.  
Assuming that the vehicles follow the same usage pattern and operating conditions over the course of a year, the DT-EV is able to estimate how the vehicles will degrade. Figure 12 presents the SOH evolution of the EV considered over the course of one year, assuming an initial SOH of 100%. The vehicle V7 exhibits the lowest level of degradation, maintaining a SOH of 98%, followed by V9 and V5, both of which remain close to 97%. These three vehicles typically perform shorter trips with lower energy demands, which results in reduced battery stress.  
In contrast, vehicle V2 shows the highest rate of degradation, losing over 4% of its initial capacity after one year. This is due to its intensive usage, as it travels the longest daily distance and completes two trips per day. The remaining vehicles degrade at a similar rate, with final SOH values between 96% and 97%.  
The same analysis was conducted under the EV charging optimization scenario. The resulting degradation pattern is very similar to that observed in the baseline case, indicating that the usage while the EVs are on route has a greater impact on battery degradation than charging or resting periods.  
Nevertheless, a slight reduction in degradation is observed across all vehicles if the EVO schedules the charging. Figure 13 shows the difference in SOH between the DT-EV estimations for the baseline and the EV charging optimization scenarios, after 365 days. Vehicles V7 and V9 benefit the most from the optimization, with reductions in degradation of 0.091% and 0.085%, respectively. These vehicles follow shorter routes and therefore spend more time parked. This reduction may be attributed to the fact that, under the EV charging optimization strategy, vehicles spend less time at high SOC levels while resting  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline Number of EVs & 10 & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \hline Exec. time (s) & 1.5 & 2.4 & 3.8 & 5.8 & 9.0 & 13.8 & 21.4 & 33 & 50 & 80 \\ \hline \end{tabular}
\end{table}
Table 4: EVO scalability assessment: analysis of the number of EVs considered and the impact on the final execution time.  
Figure 12: SOH evolution over one year of EV fleet operation, based on the DT-EV forecasts under the baseline scenario usage profiles.  
in the parking lot, which helps mitigating degradation mechanisms such as calendar aging [44; 45].  
Vehicles V1, V2, and V3 are the least affected by the optimized charging schedule, as they spend less time parked and their degradation is primarily driven by route usage.  
These outputs enable end users to gain a comprehensive understanding of the effects associated with specific vehicle operation patterns. In addition, the DT-EV can predict future battery degradation based on current operational patterns and assess the SOH under alternative usage scenarios.