# A Novel Optimal Control Strategy of Four Drive Motors for an Electric Vehicle
## 3 Discussion of Simulation Results
### Performance Analysis of the Optimal Energy Management for the Electric Vehicle
This rule-based control considers the total demanded torque (\(T_{d}\)) and actual vehicle velocity (\(V_{act}\)) to determine the relationship between the output torques of the front axle motor (\(T_{f}\)) and the rear axle motor (\(T_{r}\)), as shown in Figure 10:0  
Simulink simulated the four-wheel average torque mode, the optimal energy distribution of the front and rear axle motor torques, and the optimal energy distribution of the four-wheel-side motors separately. The feasibility of optimization was assessed using two power combinations. The results show that, under the NEDC driving cycle for a 45 kW motor, compared to the four-wheel average torque mode, rule-based control and the optimal energy distribution of the front and rear axle motor torques and the four-wheel-side motors increased by 3.944%, 6.811%, and 10.704%, respectively, as shown in Figure 10:1 The results show that, under the NEDC driving cycle for a 95 kW motor, compared to the four-wheel average torque mode, rule-based control and the optimal energy distribution of the front and rear axle motor torques and the four-wheel-side motors increased by 0.800%, 4.770%, and 5.584%, respectively, as shown in Figure 10:2 \begin{table}
\begin{tabular}{c c c} \hline
**Mode** & **Condition** & **Power Distribution** \\ \hline Mode1 & \(T_{d}<5\) Nm and & \(T_{f}=0\); \(T_{r}=T_{d}\) \\ Mode2 & \(T_{d}\geq 5\) Nm & \(T_{f}=0.3T_{d}\); \(T_{r}=0.7T_{d}\) \\ Mode3 & \(T_{d}\geq 50\) Nm and & \(T_{f}=0.4T_{d}\); \(T_{r}=0.6T_{d}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Control conditions for rule-based control. Figure 15: Parameter result of the power distribution ratio \(\alpha\) for front and rear axle motors. In this study, the HIL system of the EV consisted of two real-time controllers. One served as the embedded system of the vehicle, called the energy management system, and the other acted as the vehicle simulation platform comprising the driving cycle, driver, motor, lithium-ion battery, and motorcycle dynamics models. The two real-time controllers and the software structure for the energy management system controller were managed by the insufficient analog outputs of the microcontroller. The results show that under the NEDC driving cycle for a 45 kW motor, compared to the four-wheel average torque mode, rule-based control and the optimal energy distribution of the front and rear axle motors and the optimal energy distribution of the four-wheel-side motors increased by 3.595%, 5.775%, and 6.080%, respectively, as shown in Table 5. The results show that, under the NEDC driving cycle for a 95 kW motor, compared to the four-wheel average torque mode, the rule-based control and the optimal energy distribution of the front and rear axle motor torques and the four-wheel-side motors increased by 0.597%, 4.835%, and 6.025%, respectively, as shown in Table 6.  
This research confirmed a new software model developed for electric vehicles powered by four-wheel-side motors. The model includes the following models: the driving cycle model, the driver model, the lithium battery model, the vehicle dynamics model, and the energy management system model. The following achievements were made: \begin{table}
\begin{tabular}{c c c c c} \hline  & \begin{tabular}{c} **Average Torque** \\ **(Four-Wheel)** \\ \end{tabular} & \begin{tabular}{c} **Rule-Based Control** \\ **(Front-Rear Axle)** \\ \end{tabular} & \
\begin{tabular}{c} **Optimal** \\ **(Front-Rear Axle)** \\ \end{tabular} &
\begin{tabular}{c} **Optimal** \\ **(Four-Wheel)** \\ \end{tabular} \\ \hline Energy Efficiency (km/kWh) & 4.7966 & 4.8252 & 5.0285 & 5.0856 \\ Improvement Rate (\%) & – & 0.6 & 4.8 & 6.0 \\ \hline \end{tabular}
\end{table} 
Table 6: The Simulation results of different control strategy for a 95 kW motor under the NEDC driving cycle.