## 4 Discussion
The proposed methodology proves that the machine operation in its loaded form can be considered for practical applications, demonstrating good efficiency when compared with a hybrid vehicle. The loaded and unloaded models are compared in Figure 5:4 The minimum error percentage for the loaded model is 21.7%, while the efficiency of other IM models [15; 20; 26; 27] is compared under the same operating conditions. The analysis suggests that the FM-PI-based proposed loaded model performs better for a given operational environment. The \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sr. No.**} & \multirow{2}{*}{**Power**} & \multirow{2}{*}{**Speed**} & \multirow{2}{*}{**Design**} & **INS.** & **Measured** & **Estimated** & **Error** \\  & & & & **CLASS** & **Efficiency** & **Efficiency** & **[100\%]** \\ \hline
1 & 1 & 1745 & B & F & 0.844 & 0.853 & 0.69 \\ \hline
2 & 3 & 1745 & B & F & 0.865 & 0.865 & 1.223 \\ \hline
3 & 7.5 & 1750 & B & F & 0.877 & 0.881 & 0.744 \\ \hline
4 & 10 & 1745 & B & F & 0.9 & 0.903 & 0.511 \\ \hline
5 & 20 & 1770 & B & F & 0.923 & 0.924 & 0.596 \\ \hline
6 & 30 & 1775 & B & B & 0.938 & 0.942 & 0.486 \\ \hline
7 & 40 & 1180 & B & F & 0.932 & 0.928 & 1.509 \\ \hline
8 & 50 & 1770 & B & F & 0.943 & 0.938 & 1.292 \\ \hline
9 & 75 & 1777 & B & F & 0.92 & 0.926 & 0.042 \\ \hline
10 & 100 & 1780 & B & F & 0.933 & 0.94 & 0.049 \\ \hline
11 & 150 & 1780 & B & F & 0.942 & 0.935 & 1.177 \\ \hline
12 & 200 & 1785 & B & F & 0.946 & 0.946 & 0.26 \\ \hline
13 & 300 & 1790 & B & F & 0.952 & 0.952 & 0.361 \\ \hline
14 & 400 & 1788 & B & F & 0.949 & 0.953 & 0.083 \\ \hline
15 & 500 & 1185 & B & F & 0.947 & 0.947 & 0.398 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Efficiency results were calculated and measured for 15 different induction motors under test conditions, ranging from 1–500 hp. analysis shows that IM is more efficient under a no-load scenario, whereas its efficiency drops if a load is introduced. A multiple regression algorithm analysis was performed to calculate the required speed (\(\omega_{\rm r}\)) (40, 60, and 80 km/h) from the input parameters of voltage (V\({}_{\rm ab}\)), phase current (I), and torque (T). After taking the dataset values into consideration, an accuracy of 98% was achieved. The dataset value for the output speed at 40, 60, and 80 km/h matched with the values of the input parameters of voltage (V\({}_{\rm ab}\)), phase current (I), and torque (T), respectively. Therefore, the current drawn from the Li-ion battery is reduced, compared to the traditional EV model when operating at a variable speed. Hence, the larger discharging time of the Li-ion battery represents the smart handling of power consumption. The waveforms of all driving scenarios suggest that the proposed model can achieve the desired speed levels with adequate precision. The fuzzy POPID controller performance was validated by the New European Driving Cycle (NEDC) test [17]. The (NEDC) test is used to validate the speed tracking performance of the EV model under test. Kent mapping and adaptive weights--the whale optimization algorithm-based proportional integral derivative (KW-WOA-PID) algorithm was developed to enhance the convergence speed and accuracy of the speed controller [28]. The support vector regression-based proportional integral (SVR-PI) controller exhibits robust speed control against external perturbations [29]. Table 4 compares the speed response parameters of POPID, KW-WOA-PID, and SVR-PI speed controllers with the proposed FM-PI speed controller for a speed of 40 km/h. The speed response parameters of the FM-PI speed controller are taken from Figure 5. The comparison shows that the proposed FM-PI controller has a better rising time and settling time than the FOPID, KW-WOA-PID, and SVR-PI controllers. As the settling time is lower, the overshoot impact is neglected as the controller reaches the desired speed more rapidly than the above-mentioned controllers.