# An Efficient GPS Algorithm for Maximizing Electric Vehicle Range
## 4 Results
### Simulation Case Studies  
#### 4.1.1 Case 1: Impact of Aerodynamics and Weight on kwh Consumption  
In this case, two scenarios will be presented to show the influence of aerodynamics, frontal area of the vehicle, drag coefficient, and weight on the energy consumption of EVs per charge. The first scenario contains a comparative analysis of the energy consumed by seven distinct electric vehicle models mentioned in Table 1, each characterized by varying aerodynamics at the same road to calculate the total energy consumed which can simultaneously affect the total time of the trip.  
In the first scenario, all vehicles adhere to a driving cycle wherein the maximum speed reached was approximately 90 km/h, and at some point, the vehicles stop due to high traffic. This can be visually depicted in Figure 6. Given the relatively moderate velocities employed in this scenario, the influence of aerodynamics remains marginal. To explore the impact of aerodynamics more comprehensively, Scenario 2 was initiated. Consequently, the outcomes and energy consumption delineated in Scenario 1 predominantly hinge on the vehicle's weight rather than its aerodynamic properties. As depicted in Table 2 and Figure 7, it is evident that vehicles with the lightest weight exhibit minimal energy consumption, while those with the heaviest weight demonstrate the highest consumption.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle Type**} & \multirow{2}{*}{**Drag Coefficient**} & \multicolumn{2}{c}{**Frontal Area**} & \multirow{2}{*}{**Weight**} & \multirow{2}{*}{**Battery Capacity**} \\  & & **(m\({}^{2}\))** & & **(kg)** & **(kWh)** \\ \hline Tesla Model S Plaid [38; 39] & 0.208 & 2.3 & 2214 & 100 \\ \hline BMW i3 [40] & 0.29 & 2.38 & 1315 & 22 \\ \hline Nissan e-NV200 [41] & 0.3 & 7.1 & 1700 & 22 \\ \hline Rivian R1T [42] & 0.42 & 3 & 2669 & 135 \\ \hline Volkswagen ID3 [43] & 0.267 & 2.36 & 1772 & 45 \\ \hline GMC Hummer [44] & 0.35 & 3.21 & 4173 & 210 \\ \hline Volkswagen E-Lavida [45] & 0.3 & 2.4 & 1500 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The data used to compare different vehicle models in Case 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Type** & **SOC\%** & **DOD\%** & **Energy Consumption** \\ \hline Tesla Model S Plaid & 95.5\% & 4.5\% & 4.5 kWh \\ \hline BMW i3 & 89\% & 11\% & 2.42 kWh \\ \hline Nissan e-NV200 & 79.5\% & 20.5\% & 4.5 kWh \\ \hline Rivian R1T & 95.5\% & 4.5\% & 6 kWh \\ \hline Volkswagen ID3 & 92.5\% & 7.5\% & 3.375 kWh \\ \hline GMC Hummer & 96.5\% & 3.5\% & 7.35 kWh \\ \hline Volkswagen E-Lavida & 91\% & 9\% & 2.7 kWh \\ \hline \hline \end{tabular}
\end{table}
Table 2: SOC% and DOD% of different vehicles used in Scenario 1.  
In the context of the second scenario, the velocity remains consistently set throughout the entirety of the 68-km journey, with the vehicle maintaining a speed of 120 km/h as shown in Figure 7b. Moreover, the duration of the journey is standardized across both scenarios within Case 2, where the temporal span remains constant at 2040 s.  
From the findings and Table 3, it becomes evident that aerodynamics significantly impacts energy consumption, with vehicle velocity emerging as the primary determinant due to the quadratic relationship expounded earlier in Equation (1). Notably, the energy expended during extended distances and at fixed speeds undergoes exponential variation, as corroborated by the results.  
Figure 7: (a) SOC% for Scenario 1 using different vehicle models. (b) Driving Cycle used in Scenario 2 for a constant 120 km/h velocity.