# Optimization of Mode-Switching Quality of Hybrid Tractor Equipped with HMCVT
## 3 Optimization of Control Policy for Driver Source Switching
### Hardware-in-the-Loop Simulation Test Verification  
Hardware-in-the-loop (HIL) testing is an advanced testing method that integrates real hardware into a simulation environment. It can provide a more realistic and accurate simulation of a system, effectively reducing the time and cost required for the actual system test and commissioning phase [27]. Figure 23 shows the hardware-in-the-loop test scheme for the pure electric to speed-coupling mode-switching process. It aims to evaluate the switching process of the system under different working modes, and it provides important experimental data support for system optimization and performance improvement. There is no interaction during the switching process from pure electric mode to speed-coupling mode. Therefore, when using orthogonal experimental variance analysis, it is necessary to arrange blank columns, and factor G is a blank column. L\({}_{27}\)(3\({}^{6}\)) orthogonal table was selected to conduct 27 HIL tests, and the test results were analyzed by variance using MINITAB 2017 software. As shown in Table 1, analysis of variance was performed on the test results. The analysis shows that factor A, factor C, and factor E had the greatest influence on the velocity  
Figure 23: HIL test scheme. drop amplitude, factor D had some influence, and factor B and factor F had no obvious influence. As shown in Table 2, analysis of variance was performed on the test results. The analysis shows that factor E had the greatest influence on the dynamic load coefficient. The influence of factor D on the dynamic load coefficient was in the middle. Factor C had little effect on dynamic load coefficient. The influence of factors A, B, and F on the dynamic load coefficient was not obvious. As shown in Table 3, variance analysis was performed on the test results, and the analysis showed that factors C, D, and E had a greater impact on impact strength, while the other factors had no obvious impact on impact strength. As shown in Table 4, the variance analysis of the test results shows that factors D and E had the greatest impact on the switching time, factor C had a moderate impact, and the other factors had no significant impact. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Origin** & **df** & **Adj SS** & **Adj MS** & **F Value** & **\(p\) Value** & **Eminence** \\ \hline Factor A & 2 & 177.36 & 88.678 & 7.19 & 0.009 & *** \\ Factor B & 2 & 16.26 & 8.129 & 0.66 & 0.535 & \\ Factor C & 2 & 396.97 & 198.486 & 16.09 & 0.000 & *** \\ Factor D & 2 & 130.74 & 65.368 & 5.30 & 0.022 & ** \\ Factor E & 2 & 392.59 & 196.296 & 15.91 & 0.000 & *** \\ Factor F & 2 & 14.04 & 7.019 & 0.57 & 0.581 & \\ Factor G & 2 & 2.75 & 1.374 & 0.11 & 0.896 & \\ Error e & 12 & 148.07 & 12.339 & & & \\ \hline \hline \end{tabular} Note: In the table: when \(p<0.01\), the significance is ***; when \(0.01\leq p<0.05\), the significance is **.  
\end{table} 
Table 1: Variance method used to analyze velocity drop amplitude. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Origin** & **df** & **Adj SS** & **Adj MS** & **F Value** & **\(p\) Value** & **Eminence** \\ \hline Factor A & 2 & 5.178 & 2.589 & 1.14 & 0.353 & \\ Factor B & 2 & 4.019 & 2.009 & 0.88 & 0.439 & \\ Factor C & 2 & 13.221 & 6.611 & 2.90 & 0.094 & * \\ Factor D & 2 & 19.715 & 19.875 & 4.33 & 0.038 & ** \\ Factor E & 2 & 38.712 & 19.356 & 8.50 & 0.005 & *** \\ Factor F & 2 & 6.630 & 13.315 & 1.46 & 0.272 & \\ Factor G & 2 & 17.337 & 8.722 & 3.83 & 0.052 & \\ Error e & 12 & 27.337 & 2.278 & & & \\ \hline \hline \end{tabular} Note: In the table: when \(p<0.01\), the significance is ***; when \(0.01\leq p<0.05\), the significance is **; when \(0.05\leq p\) < 0.1, the significance is *.  
\end{table} 
Table 2: Variance method used to analyze dynamic load coefficient.