## 4 Simulation Results and Analysis
### Results Analysis of Fuel Economy and Emission Performance  
Cycle economy tests of vehicle fuel economy and emission performance under NEDC and FTP75 were carried out through AVL Cruise. The experimental results are compared in Table 3. In comparison with the F-EMS, the NNF-EMS considers that the DCR is more sensitive to the driving conditions and the control fuzzy rules are more detailed; hence, the control is more sensitive and the vehicle performance of the HEV is further improved. Specifically, compared to the F-EMS, the fuel consumption of the NNF-EMS based on the cycle recognition under the NEDC cycle was reduced from 7.093 L/100 km to 5.576 L/100 km, and the fuel optimization rate reached 21.39%; CO emissions decreased from 5.362 g/km to 4.419 g/km, a 17.59% reduction; NOx emissions decreased from 1.910 g/km to 1.524 g/km, a 20.21% reduction; HC emissions decreased from 0.5702 g/km to 0.4054 g/km, a 28.90% reduction. In addition, the reduction in fuel consumption and the improvement in emission performance did not occur at the  
Figure 16: Vehicle velocity following under FTP75 cycle.  
Figure 17: Vehicle velocity following under FTP75 cycle (partial enlargement).  
Figure 18: Vehicle velocity following under NEDC cycle (partial enlargement).  
expense of consuming more battery power. The power consumption only increased from 6.0488 kWh to 6.0645 kWh. This shows that the NNF-EMS based on DCR better optimizes the working zone of the engine, improves fuel efficiency, and reduces emissions. The simulation output results of NNF-EMS in AVL Cruise under NEDC cycles are shown in Figure 19.  
Fuel consumption is ultimately caused by differences in engine operating points. Figure 20 shows the distribution of key operating points of the engine under the NEDC cycle. According to the comparison, in Figure 20a,b, under the F-EMS, the engine can work closer to the rated torque, with higher fuel efficiency, thereby significantly improving fuel economy and emissions. NNF-EMS further optimizes the engine operating points, thereby further reducing fuel consumption and improving emission performance.  
Table 4 shows the simulation results of fuel economy and emissions comparison under the FTP75 cycle. It can be seen that the performance is quite different under NEDC and FTP cycles, indicating that the driving conditions have a great impact, and the control strategy should be adjusted based on the driving conditions. The fuel economy and emission performance of the F-EMS are relatively stable under different NEDC and FTP75 cycles, indicating that the fuzzy control itself is robust to changes in driving conditions. The NNF-EMS performance of DCR-based HEV is always the best under different driving conditions, indicating that the impact changes in driving conditions on the EMS optimization of HEVs cannot be ignored.  
Simultaneously, it can be seen from Tables 3 and 4 that, compared to NEDC driving conditions, DCR-based NNF-BMS improves the fuel consumption and emission performance of the vehicle by more than 30% under the FTP75 driving cycles, but not the power consumption, which was reduced from 9.6751 kWh to 9.3127 kWh. It shows that the application of DCR-based NNF-EMS is more obvious under real transient conditions.  
Figure 19: Simulation results of neural network fuzzy energy management strategy (NNF-EMS) under NEDC cycles in AVL Cruise.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance** & **F-EMS** & **NNF-EMS** & **Optimization Rate** \\ \hline Actual travel distance (m) & 11,312.29 & 11,002.79 & / \\ Total power consumption (kWh) & 6.0488 & 6.0645 & / \\ Battery SOC variation (\%) & 22.39 & 23.87 & / \\ Total fuel consumption (kg) & 0.6010 & 0.4595 & / \\ Fuel consumption (L/100 km) & 7.093 & 5.576 & 21.39\% \\ CO emission (g/km) & 5.362 & 4.419 & 17.59\% \\ NOx emission (g/km) & 1.910 & 1.524 & 20.21\% \\ HC emission (g/km) & 0.5702 & 0.4054 & 28.90\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of fuel economy and emission performance under NEDC cycles.