# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
## 5 Experimental Results
### SOC Prediction Using FMI-Based EV Simulation  
After predicting the speed of the EV, we subsequently utilized the predicted results and our simulator to predict the SOC. Figure 8: Speed prediction of route C to D: (**a**) cycle 8; (b) cycle 9; (c) cycle 10; (**d**) cycle 11. Figure 10: Speed prediction of route E to F: (**a**) cycle 8; (**b**) cycle 9; (**c**) cycle 10; (**d**) cycle 11. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **AtoC** & **CtoD** & **DtoE** & **EtoF** \\ \hline TA-RNN & 0.4412 & 0.3614 & 0.4515 & 0.4870 \\ TA-GRU & 0.4295 & 0.3586 & 0.4527 & 0.4832 \\ \hline TA-LSTM & 0.4171 & 0.3456 & 0.4461 & 0.4821 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMSE results of temporal attention with different models. Figure 9: Speed prediction of route D to E: (**a**) cycle 8; (**b**) cycle 9; (**c**) cycle 10; (**d**) cycle 11. Figure 7: Speed prediction of route A to C: (**a**) cycle 8; (**b**) cycle 9; (**c**) cycle 10; (**d**) cycle 11. We used FMI-based EV simulation with the previously predicted velocity and acceleration as inputs to predict the EV's SOC. Our EV Simulation model is shown in Figure 11. As can be seen in the figure, given the inputs, the FMU2 to FMU6 are responsible for computing the SOC using the Formulas (14)-(38), whose variables are detailed in Table 6. Additionally, the lookup2d function in Formulas (29)-(31) performs the same operation as lookup2d in MATLAB/Simulink. The necessary efficiency values of the electric machine were obtained through the efficiency curves given in [9]. \[F_{t}=F_{rr}+F_{ad}+F_{lbc}+F_{la}+F_{nu} \tag{14}\]  
\[F_{rr}=\mu_{rr}mg \tag{15}\]  
\[F_{ad}=\frac{1}{2}\rho AC_{d}v^{2} \tag{16}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Values** \\ \hline \(m\) & Mass of the vehicle (kg) & 1000 \\ \(r_{w}\) & Wheel radius (m) & 0.2736 \\ \(g\) & Gravity acceleration (m/s\({}^{2}\)) & 9.81 \\ \(\rho\) & Air density & 1.2 \\ \(A\) & Front area of vehicle (m\({}^{2}\)) & 2.36 \\ \(\alpha\) & Angle of driving surface (rad) & 0 \\ \(\mu_{rr}\) & Rolling resistance coefficient & 0.015 \\ \(C_{d}\) & Aerodynamic drag coefficient & 0.3 \\ \(\eta_{g}\) & Gearbox efficiency & 0.98 \\ \(G\) & Gearbox ratio & 8.59 \\ \(C_{0}\) & Initial capacity (C) & 720,000 \\ \(R_{bi}\) & Internal resistance (\(\Omega\)) & 0.008 \\ \(E_{b0}\) & Open-circuit voltage (V) & 53.6 \\ \(K_{cl}\) & Linear t dependency of the capacity C & 6.084436 \(\times\) 10\({}^{-10}\) \\ \(t_{bu}\) & battery usage time (s) & 0 \\ \(T_{ref}\) & Reference temperature (\({}^{\circ}\)C) & 20 \({}^{\circ}\)C \\ \(T_{curr}\) & Ambient temperature (\({}^{\circ}\)C) & 20 \({}^{\circ}\)C \\ \(alphaC\) & Linear temperature coefficient & 0 \\  & of capacity (K\({}^{-1}\)) & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters for FMU-based EV simulation (Default setting). Figure 11: FMI-based model for EV power consumption. \[F_{hc}=mg\sin(\alpha) \tag{17}\]  
\[F_{la}=ma \tag{18}\]  
\[F_{wa}=0.05\times F_{la} \tag{19}\]  
\[T_{t}=F_{t}\times r_{w} \tag{20}\]  
\[P_{t}=F_{t}\times v \tag{21}\]  
\[\omega_{w}=\frac{v}{r_{w}} \tag{22}\]  
\[S_{w}=\frac{30}{\pi}\times\omega_{w} \tag{23}\]  
\[T_{s}=\frac{T_{t}}{\eta_{g}\times G}(P_{t}>0) \tag{24}\]  
Figure 11:0  
Figure 11:1  
Figure 11:2  
Figure 11:3  
Figure 11:4  
Figure 11:5  
Figure 11:6  
Figure 11:7  
Figure 11:8  
\[P_{bc}(t)=\left\{ \begin{array}{ll}P_{bm}(t)+P_{aux},&F_{t}(t)>0\\ P_{br}(t)+P_{aux},&F_{t}(t)<0\end{array} \right.