## 4 Simulation Verification  
To verify the performance of proposed EMS compared to other strategies, the simulation framework is developed as shown in Figure 9, which includes FCHEV dynamic model, PEMFC model, battery model, UC model, and proposed EMS simulation platform. The characteristics of the FCHEV are listed in Table 3, and the simulation framework of online EMS includes FLC, levelization method based on ECMS, and proposed OMC strategy. According to Figure 9, the proposed EMS simulation platform consists of three consecutive stages. In the first step, FLC determines the output power of the fuel cell and the battery (\(P_{FC0}\), \(P_{Bat0}\)). The output power of the fuel cell and battery is levelized in the \begin{table}
\begin{tabular}{c c} \hline \hline
**VSM** & **Output Mode Function** \\ \hline \multirow{2}{*}{VSM = \(-1\)} & \(P_{F_{C,opt}}\)= 0 \\  & \(P_{Bat,opt}\)= \(P_{Bat1}\times u(0.5-SOC)\) \\  & \(P_{UC,opt}\)= \(P_{Demand}-P_{FC,opt}-P_{Bat,opt}\) \\ \hline \multirow{4}{*}{VSM = 0} & \(P_{FC,opt}\)= \(0.4\times u(0.8-SOC)\times u(98-VDR)\) \\  & \(P_{Bat,opt}\) = \(P_{Bat1}\times u(0.5-SOC)\) \\  & \(P_{UC,opt}\) = \(P_{Demand}-P_{FC,opt}-P_{Bat,opt}\) \\ \hline \multirow{4}{*}{VSM = 1} & \(P_{FC,opt}=P_{FC1}\times u(P_{FC1}-P_{Demand})\times u(0.5-SOC)\times u(56- VDR)\) \\  & \(+P_{FC1}\times u(P_{Demand}-P_{FC1})\) \\  & \(P_{Bat,opt}=P_{Bat1}\times u(P_{FC1}-P_{Demand})\times u(56- VDR)\) \\  & \(+P_{Bat1}\times u(P_{Demand}-P_{FC1})\) \\  & \(P_{UC,opt}=P_{Demand}-P_{FC,opt}-P_{Bat,opt}\) \\ \hline \multirow{4}{*}{VSM = 2} & \(P_{FC,opt}=P_{FC1}\times u(P_{Demand}-P_{FC1})\times u(95- VDR)\) \\  & \(+P_{PC1}\times u(P_{PC1}-P_{Demand})\times u(56- VDR)\) \\  & \(+P_{PC1}\times u(P_{PC1}-P_{Demand})\times u(56- VDR)\) \\  & \(P_{Bat,opt}=P_{Bat1}\times u(P_{PC1}-P_{Demand})\times u(56- VDR)\) \\  & \(+P_{Bat1}\times u(P_{Demand}-P_{FC1})\) \\  & \(P_{UC,opt}=P_{Demand}-P_{FC,opt}-P_{Bat,opt}\) \\ \hline \multirow{4}{*}{VSM = 3} & \(P_{FC,opt}=min(P_{FC1}(pu),0.4)\times u(P_{FC1}-P_{Demand})\times u( VDR-95)\) \\  & \(+max(P_{FC1}(pu),0.4)\times u(P_{FC1}-P_{Demand})\times u(95- VDR)\) \\ \cline{1-1}  & \(+max(P_{FC1}(pu),0.4)\times u(P_{Demand}-P_{FC1})\) \\ \cline{1-1}  & \(P_{Bat,opt}=P_{Bat1}\) \\ \cline{1-1}  & \(P_{UC,opt}=P_{Demand}-P_{FC,opt}-P_{Bat,opt}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Operational mode control rules. second stage (\(P_{FC1}\), \(P_{Bat1}\)). Finally, the operational mode control strategy in the third step calculates the desired output solutions (\(P_{FC,opt}\), \(P_{Bat,opt}\), \(P_{UC,opt}\)). To implement the proposed EMS in real-time conditions, the computational time of EMS running is an important subject. The running time of the fuzzy logic controller to provide a decision in the proposed EMS could be lengthy. Different driving scenarios and several input variables affect the output results of the FLC system. In order to solve this problem, the variable inputs are estimated for different scenarios and the fuzzy logic control system is executed offline for different scenarios at the first step, then the leveled output results for different scenarios are stored as a look-up table in the MATLAB/Simulink environment. The input variables of VSM, SOC, and VDR in FLC include various discrete modes of vehicle status such as movement, battery, and ultracapacitor charge. The main and anonymous variable of FLC in online execution mode is \(P_{Demand}\). According to the range of other input variables, the output scenarios of FLC in offline execution can be reduced. Accordingly, FLC output response evaluation is first investigated based on VSM discrete states, battery SOC, and UC VDR regions. In fact, for 60 different scenarios, the output response changes are simulated relative to the demanded power of the FCHEV. These output responses for 60 different scenarios can be classified. On the other hand, the proposed