# Influencing Factors of Solar-Powered Electric Vehicle Charging Stations in Hail City, Saudi Arabia
## 3 Methodology
### Station Demand  
A geographic information system (GIS) with OpenStreetMap data and Open Route Service (ORS) was employed, with questionnaire data from [31]. The design and study of the factors affecting the number and distribution of stations were based on real data obtained from a questionnaire and the technical specifications of electric vehicles available in the global market, which may make these results closer to reality than modeling methods. Table 1 describes the detailed multistep method used in Hail City to determine the required number of EVCSs. Using Google Earth, the procedure started with geographic measurements of the city zones. After this, calculations were performed that incorporated demographic data, vehicle ownership data, and EV conversion rates. Formulas that determine the total population, average family size, number of cars overall, and anticipated number of EVs in the city were essential to this process. Furthermore, the study used average trip length per day, L, and consumption rate, R, which account for the daily battery use of EVs, to determine the total required number of stations, NSs, and the necessary capacity of charging stations. Adjustments were made for station upkeep and idle times, ensuring a robust system supporting the anticipated quantity of EVs. \begin{table}
\begin{tabular}{l l l c} \hline \hline
**Step** & **Calculation Method** & **Equation** \\ \hline
1. & Measure area (A) via Google Earth & Utilizing Google Earth to understand the & N/A \\  & & geographic extent. & \\ \hline
2. & Calculate total population (P) & Multiplying the area (A) by the average & \(P=A\times D\) \\  & & population density (D) as per [42]. & \\ \hline
3. & Determine average family size (M) & Survey data were used to calculate a & \\  & & weighted average of family sizes as & \\  & & per [42]. & \\ \hline
4. & Compute the total number of & Dividing the entire population (P) by the & \\  & families (F) & typical family size (M). & \\ \hline
5. & Calculate average car ownership (O) & Utilizing survey data on car ownership to & \\  & & calculate a weighted average per the & \\  & & questionnaire. & \\ \hline
6. & Estimate total number of cars (C) & Multiplying the number of families overall & \\  & & (F) by the typical car ownership (O). & \\ \hline
7. & Forecasted number of EVs (E) & Multiplying the total number of cars (C) by & \\  & & the conversion percentage to EVs (T). & \\ \hline
8. & Determine average trip length per & Survey data will be used to calculate a & \\  & day (L) & weighted average of trip lengths as per & \\  & & the questionnaire. & \\ \hline
9. & Calculate the car battery & Multiplying the EV consumption rate (R) & \\  & consumption (CBC) & by (L). & \\ \hline
10. & Determine average recharging & Dividing the average car battery capacity & \\  & days (RD) & (BC) by the daily CBC. & \\ \hline
11. & Calculate hours needed for full & \\  & charge (HF) & Dividing BC by the charger power (CP). & \\ \hline
12. & Compute the fully charged number of & Dividing the operational hours by the & \\  & charging cycles (Nf) & hours needed for a full charge (HF). & \\ \hline
13. & Estimate station capacity per & Multiplying the number of car slots (CSs) & \\  & day (SD) & by the number of complete charging & \\  & & cycles (Nf). & \\ \hline
14. & Calculate station capacity (SC) & Multiplying the station capacity per day & \\  & & (SD) by the average recharging days (RDs). & \\ \hline
15. & Determine the number of & Dividing the expected number of EVs (E) & \\  & stations (NS) & by the station capacity (SC). & \\ \hline
16. & Adjust the number of stations for & Increasing the number of stations (NS) by & \\  & maintenance (NS’) & 10\% to account for maintenance. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculations of station numbers. This study examined how essential factors change and affect the number of EVCSs that are needed. Al-Wosyataa's central location (zone 7) made it a perfect sample area for this study.