## 5 Conclusions  
This paper has described the online energy management of PHEVs based on an offline optimization algorithm. The minimum strategy was analyzed, and the optimal strategy for the approximately equivalent fuel consumption was derived. Combined with the DP algorithm and taking the equivalent factor as the control variable, the A-ECMS was nested in the DP algorithm, and the DP-A-ECMS algorithm was formed to calculate the global optimal sequence of the equivalent factor through DP global optimization.  
Taking a single-axis parallel CVT PHEV as the research object, a Simulink simulation model of the vehicle was established, and an off-line simulation calculation was carried out under the DP-A-ECMS strategy. The results show that, under different working conditions and different initial SOC conditions, the SOC curve under the DP-A-ECMS strategy fluctuates near the straight line formed by the initial SOC and the final SOC, and the battery can be discharged smoothly. Compared with the DP strategy, although the fuel consumption is slightly improved, the calculation cost is significantly reduced.  
A BP neural network was used to extract the nonlinear relationship between the optimal equivalent factor and real-time operating conditions and vehicle states. The control strategy rules suitable for specific operating conditions were extracted, and an online control strategy based on a BP neural network was established. The simulation results show that the neural network control strategy based on optimal equivalent factor recognition has a good robustness, and the change in battery SOC can track the reference curve well. Compared with a direct method of identifying the motor torque and CVT speed ratio, the fuel economy is improved by 2.46%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Control Strategies**} & \multicolumn{2}{c}{**Rule-Based**} & \multicolumn{2}{c}{**Optimization-Based**} & \multicolumn{2}{c}{**BP Neural Network**} \\ \cline{2-7}  & **CD-CS** & **Fuzzy Logic** & **A-ECMS** & **MPC** & **Based on DP** &
\begin{tabular}{c} **Based on** \\ **DP-A-ECMS** \\ \end{tabular} \\ \hline Fuel consumption (L) & 5.519 & 5.487 & 4.318 & 4.324 & 4.437 & 4.342 \\ \hline Calculating time (ms) & 0.67 & 1.2 & 3.5 & 6.2 & 7.5 & 8.3 \\ \hline Robustness & very good & very good & very poor & very poor & relatively poor & relatively good \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of online control strategies.  
**Author Contributions:** Conceptualization, S.W.; methodology, S.W.; software, S.W.; validation, S.W.; formal analysis, S.W.; investigation, S.W.; resources, D.Q.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, D.Q.; visualization, S.W.; supervision, D.Q.; project administration, D.Q.; funding acquisition, D.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (No. 2016 YFB0101402).  
**Conflicts of Interest:** We declare that we have no financial and personal relationships with other people or organizations that could have inappropriately influenced our work, and there is no professional or other personal interest of any nature or kind in any product, service, and/or company.