## 3 Numerical and Experimental Results  
To verify the effectiveness of the global energy management for the plug-in hybrid vehicles based on a multi-scale hybrid traffic model, we tested the PHEV powertrain on a vehicle in the loop (VIL) testing system, as shown in Figure 5. A rapid prototype of a vehicle controller has been established on the dSPACE real-time simulation platform, which is used to send engine and motor load signals through the CAN bus. The engine and motor systems can be dynamically controlled independently or jointly, with great flexibility and reliability. All vehicle subsystems are connected to the CAN network. A high-performance parallel server computer was used to obtain target control sequences for engines, motors, and gearboxes under the multi-scale framework. The priority of motor speed is higher than that of other control variables, including engine torque, CVT speed, and motor torque, etc. Figure 4: Schematic diagram of local replanning scheme combined with reinforcement learning. Figure 5: Traffic environment simulation. (**a**) VIL test platform with AVL monitoring system and dSPACE control system and (**b**) physical vehicle components test system and data acquisition system (**c**). Numerical and experimental studies have been carried out to analysis the multi-scale energy management of PHEVs during the starting and braking processes in this paper, which are widely existing traffic situations. In all cases, the initial SOC is set to 60%, and the other initial conditions are as follows:  
\[k(x,0)=\left\{ \begin{array}{l}0.13,x\leq 10000\;\mathrm{m}\\ 0.04,x>10000\;\mathrm{m}\end{array} \right. \tag{24}\]  
\[u(x,0)=V(k(x,0)) \tag{25}\]  
The boundary conditions are as follows:  
\[k(0,t)=0.13 \tag{26}\]  
\[\frac{\partial k(x_{end},t)}{\partial t}=0,t\leq 1000\;\mathrm{s} \tag{27}\]  
\[k(x_{end},t)=0.04,t>1000\;\mathrm{s} \tag{28}\]  
\[u(x_{0},t)=0 \tag{29}\]  
\[u(x_{end},t)=V(k(x_{end},t)) \tag{30}\]  
where \(x_{0}=0\;\mathrm{m}\) and \(x_{end}=80000\;\mathrm{m}\) are the starting and ending coordinates of the road section, respectively. The first two cars in the lane queue are studied in this paper, i.e., the leading car and the following car, since the behavior of the leading car determines that of all subsequent vehicles. The driving behavior of the following vehicle is used to analyze novel global energy management based on multi-scale traffic model, while the driving behavior of the leading car is obtained according to the macroscopic traffic flow model alone. According to the macroscopic traffic flow model, the distribution of speed and density in the spatiotemporal domain can be obtained, as shown in Figure 6. It can be seen that, from a global perspective, the speed variation and the density distribution in the whole road section can be obtained by the macro traffic flow model. For this method, only the initial conditions and the traffic flow information of key road sections are needed, and thus it is easier to collect data and more consistent with the basic statistical assumptions in model calibration. The vehicle speed for any time and space can be obtained through this method, which makes it global energy planning possible. This method, with faster runtime, is quite suitable for the large transportation domain where a large number of data sets and calculation resources are needed. In contrast, using the car following model to calculate the driving behavior of each vehicle in the whole space-time domain requires too many computing resources and too much time. Figure 6: The distribution of speed and density in the time and space domain obtained from the macroscopic traffic flow model. The traffic system can be design by an understanding of a servomechanism when the vehicle of the future is to be automatic as well as automobile. Automated vehicles can achieve servo-control to intervene and optimize driving behavior through AICCS and ACCS, thereby reducing improper driving behavior or correcting unreasonable driving behavior.