## 5 Conclusions  
The driving pattern recognition results affect the EMS directly, and the driving condition is complex in NEDC, UDDS, etc. Some results are now shown in Figures 10 and 11, because the \"driving cycle block\" is defined differently under different conditions, as shown in Figure 8 for the definition of NEDC; however, the NNF-EMS is still efficient for other conditions. Although there are other existing EMS techniques in the literature that were not designed in this paper in comparison with our proposed method, such as dynamic programming, it can be used as a benchmark for evaluating the proposed method; however, it is not useable in real-time, and the focus of this article is how to improve current fuzzy control methods with high availability. This paper does not excessively emphasize and compare global and real-time optimization methods of energy management. Looking at the literature, the concept of using driving data and traffic information in an adaptive fuzzy inference system for energy management of HEV is not new. In this paper, by analyzing the characteristic parameters of the driving cycles and learning using neural network samples, the DCR is realized, and the recognition results are used as the reference input for the fuzzy control strategy. Therefore, the membership function in the fuzzy controller is optimized to solve the problem of poorly targeted driving cycles. The proposed method can adopt the optimized control strategy in different driving cycles, and its robust substitution and performance are better than the traditional fuzzy control strategy. The proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Different from intelligent algorithms such as global optimization and real-time optimization, from the perspective of real vehicle application, fuzzy control is more practical. Thus, it will have good practical application value in improving the fuel economy of hybrid electric vehicles. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance** & **F-EMS** & **NNF-EMS** & **Optimization Ratio** \\ \hline Actual travel distance (m) & 18,513.22 & 17,613.76 & / \\ Total power consumption (kWh) & 9.6751 & 9.3127 & / \\ Battery SOC variation (\%) & 43.57 & 43.89 & / \\ Total fuel consumption (kg) & 1.2404 & 0.8042 & / \\ fuel consumption (L/100 km) & 8.945 & 6.096 & 31.85\% \\ CO emission (g/km) & 8.003 & 5.100 & 36.27\% \\ NOx emission (g/km) & 2.777 & 1.673 & 39.76\% \\ HC emission (g/km) & 0.8421 & 0.4496 & 46.61\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of fuel economy and emission performance under FTP75 cycles. Figure 20: Comparison of engine optimized operating points in NEDC cycles: (**a**) F-EMS; (**b**) NNF-EMS. For electric vehicles, especially battery electric vehicles with limited energy, the optimization of energy management is crucial. With the vigorous development of electric vehicles toward unmanned vehicles and connected vehicles, on-board electronic devices such as sensors and high-performance computers that consume large power put forward higher requirements for vehicle energy management. Road condition information not only affects driving safety but also directly affects fuel and/or electricity consumption. Intelligent energy management strategies that take into account information prediction of road/operating condition will become a hot research topic in the area of vehicle energy management in the future. Artificial intelligence methods are also widely applied, such as deep learning, reinforcement learning, and convolutional neural networks, and further research is urgently needed. Conceptualization, Q.Z. and X.F. ; methodology, Q.Z. and X.F. ; software, Q.Z. and X.F. ; validation, Q.Z. ; formal analysis, X.F. ; writing--original draft preparation, Q.Z. ; writing--review and editing, X.F. ; project administration, Q.Z. and X.F. ; funding acquisition, Q.Z. and X.F. All authors have read and agreed to the published version of the manuscript.