**5.3 Simulink and results for the cascaded controller with PSO**

The cascaded PID, PI, and lead–lag compensator were used to simulate the SynRM model based on the results of the PSO algorithm discovered by using the

**Figure 12.** *Simulink model for cascade-PI and lead- lag controller.*


**Table 3.**

*The parameters and values of the cascade controller.*

**Figure 13.** *SynRM speed control at 1500 rpm with no load condition.*

**Figure 14.**

*The SynRM torque control at1500 rpm with no load condition.*

**Figure 15.** *The SynRM speed control at steps of speed.*

**Figure 16.** *The SynRM torque control at steps of speed.*

**Figure 17.** *The SynRM speed control with 1500* rpm *and 50* N*:*m *of load applied at 3.5 second.*


#### **Table 4.**

*Manually tuned of the cascade controller.*

**Figure 19.** *Simulink model for PI, PID and lead–lag controllers.*

**Figure 20.** *The SynRM speed control at 1500 rpm with no load condition.*

Simulink method with the PSO algorithm. The Simulink findings show characteristics and feature the PSO algorithm, which offers optimal PID, PI, and lead–lagcompensator parameter values to improve system functionality as shown in **Table 5**. Besides, **Table 6** shows the parameters of the PSO strategy tuned in cascade controller. **Figure 26** displays the technique parameters PID and PSO. Furthermore, the **Figures 27**–**32** show the SynRM speed control and torque control from PSO algorithm parameters of the cascaded control with approximately 50 integrations.

**Figure 21.**

*The SynRM torque control at 1500 rpm with no load condition.*

**Figure 22.** *The SynRM speed control with steps of speed.*

**Figure 23.** *The SynRM torque control with steps of speed.*

**Figure 24.** *The SynRM speed control with 1500 rpm and 50 N:m step of load applied at 3.5 second.*

#### **Figure 25.**

*The SynRM torque control with 1500* rpm *and 50* N*:*m *step of load applied at 3.5 second.*


**Table 5.** *The characteristics of PSO algorithm.*


### **Table 6.**

*PSO strategy tuned.in-cascade-controller-parameters.*

**Figure 26.** *Simulink for PSO fitness-function model.*

**Figure 27.** *The SynRM speed control with 1500 rpm and no-load condition.*

#### **5.4 Conclusion of SynRM controllers**

The cascaded-PI and PID controls were used in conjunction with a lead–lag compensator to control the motor speed. Furthermore, the Simulink controls have been utilized to regulate the motor speed in a wide range and can provide adequate

**Figure 28.**

*The SynRM torque control with 1500 rpm with no load condition.*

**Figure 29.** *The SynRM speed control with steps of speed.*

**Figure 30.** *The SynRM torque control with steps of speed.*

**Figure 31.** *The SynRM speed control with 1500 rpm and 50 N:m steps of load at 3.5 second.*

speed and torque reaction or demonstrate their validity in regulating the motor speed rate in various operating conditions. Furthermore, the PSO algorithm simulated manipulating the parameters of their cascaded controls to achieve a more important output than traditional controllers. The simulation depicts the testing and study of each of the controller's structures under various circumstances. As a result, it is possible to infer that the proposed PSO strategy offers the best control parameters for improving system efficiency, especially in the loading state. The simulation depicts the testing and study of each of the controller's structures under various circumstances. As a result, it is possible to infer that the proposed PSO strategy offers the best control parameters for improving system efficiency, especially in the loading state.

### **6. The propulsion construction of EV**

#### **6.1 The simulation and design of the EV system**

The simulation and design of the system have done via Matlab/Simulink depended on the above equations and the input parameters of these equations that have utilized in the design as showing in **Table 7** below:


#### **Table 7.**

*The design information of the EV.*

**Figure 33.** *Modeling and simulation of the resistive force.*

**Figure 34.** *The simulation diagram of the EDC system model.*

#### **6.2 Simulink model of the resistive torque in EV**

The important task for the EDC is the distribution of the torque, where the resistive forces are dispersed evenly on both electric motors in the case of a straight road. Besides, the complete resistive of the torque is divided into two parts

"two-halves", which are each half distributed on a single motor. The torques supply is the EDC assignment. The modulation and simulation of the resistive force are represented in **Figure 33**.

**Figure 35.** *The EV moving with 80 km=h speed rate at the straight road.*

**Figure 37.** *The EV turn left with 80 km=h speed-rate at the curve-road with +11-degree steering angle.*
