## 4 Conclusions  
In this paper, multi-scale physical processes management is proposed to investigate the collaborative physical effect between the micro vehicle system and the macro road transportation system, which can contribute to constraints in planning methods for the global optimization of energy consumption for intelligent transportation system. This novel model adopts a macro traffic flow model at the large time scale and a car following model at the micro scale, which combines the advantages of both the micro and macro models. Furthermore, energy trajectory replanning is carried out through human-like reinforcement learning once the expected driving behavior at the micro scale conflicts with the traffic behavior from the macro traffic model due to increase in uncertainty. This method, taking the intelligent transportation system as the object, is suitable for system management from the macro traffic scenario to micro mechanism levels, e.g., global energy management in the full temporal-spatial domain for plug-in hybrid electric vehicles. The experimental and numerical results show that:  
1. Based on the initial conditions and the traffic information for key road sections, the speed and density distribution in the whole road section can be determined according to the macro traffic flow model, especially when vehicles of the future are to be automatic. Furthermore, the macro driving behavior in the whole road section provides the possibility for large-scale traffic energy management specific to each (hybrid) vehicle, and improvement in driving behavior to reduce the speed fluctuation and dissipation of the traffic system. 2. The novel model takes into account errors in large macro planning induced by unknown disturbances, which is achieved by RL based replanning of energy management when there is a short term speed disturbance in the macro traffic flow. Thus, it can reduce the deviation in the global optimal solution and has good robustness when there are inevitable unknowns. 3. Since the relationship between speed fluctuation and power fluctuation is nonlinear, the frequency and amplitude of the speed fluctuation directly affect that of power fluctuation. Thus, it is necessary to map the vehicle speed and acceleration characteristics power characteristics, which directly determine energy efficiency and are suitable for energy utilization planning, instead of directly utilizing the traffic model characterized by speed and acceleration characteristics. Additionally, the dynamic characteristics of engine and motor should be coupled deeply with speed fluctuation, since the fluctuation frequency of speed fluctuation is related to the response time of engine and motor, which guarantees that the target value can be tracked correctly by vehicle components, and its fluctuation amplitude to the fuel consumption gradient and motor efficiency. 4. By analyzing the results of the RL-based replanning scheme, the following conclusions can be drawn. When significant speed deviation occurs and the vehicle's demand power is relatively higher (Mode 1) than the original plan, the engine power should \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Strategy**} & \multicolumn{2}{c}{**Accumulated Energy Consumption of Consumption (g)**} & \multicolumn{2}{c}{**Tested Fuel Consumption (g/kwh)**} & \multirow{2}{*}{**Fuel Saving Percentage (\%)**} \\  & 152 & 10.7 & 253 & - \\ Modified Mode 1 & 152 & 9.6 & 228 & 10.0 \\ Original Mode 2 & 142 & 10.3 & 260 & - \\ Modified Mode 2 & 142 & 9.3 & 236 & 9.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Energy consumption of the sum of areas A and B for the original strategy and the replanning strategy. be appropriately increased by adjusting the power threshold \(P_{\rm hi}\), \(P_{lo}\) and the weight \(L_{e}\); while the vehicle's demand power is relatively low (Mode 2), the engine power should be appropriately reduced in order to reduce the operating range for high fuel consumption.