## 3 Numerical and Experimental Results
Simultaneously, unlike human driving behavior, it has less randomness and can improve the predictability, orderliness and regularity of the system. In addition, with the support of vehicle-to-everything (V2X) communication [50], it is possible to predict macro traffic flow and induce vehicle driving in practical application scenarios. The prediction of driving behavior in the future can be used for energy management from the global perspective. Longer time scale prediction of driving behavior has the advantage of achieving longer time scale energy optimization. The method in this paper makes full use of the macro scale advantages of the macro traffic flow model and provides the possibility of long-distance and large-scale traffic energy management specific to each (hybrid) vehicle. Whether it is near or far away from the charging station, one can make a reasonable overall plan in advance. This can improve the overall efficiency of the engine while ensuring reasonable charging and discharging (e.g., SOC not too low) of the battery. Furthermore, the overall optimization control, from the overall system point of view, can ensure the smooth overall density of macro traffic flow and reduce the fluctuation and corresponding dissipation of the traffic system. The disordered physical processes at the micro scale will present a certain order at the macro scale. A large number of driving behaviors in the whole transportation system are obtained by the car following model at the micro scale level, which show disorder characteristics and thus make it difficult to achieve global optimization. However, the order of the macro scale level obtained by the macro traffic flow model enables the overall optimization of the traffic system. Furthermore, by extending the prediction time of the micro car following behavior, it can become closer to the global prediction. The longer the prediction time domain, the closer the global optimal control achieved. In this paper, by adopting a unified model both at the macro level and the micro level, the micro driving behavior follows the macro traffic flow state smoothly, which avoids disturbance dissipation in the transmission process from macro variable to micro variable. For multi-scale physical processes management in this paper, the macro traffic flow model is used to realize large-scale planning, and the car following model at the vehicle level is adopted to describe specific driving behavior in detail. In this hierarchical control method, the micro car following behavior obeys the evolution process of the macro state of the traffic system. Figure 7 shows the car following behavior in macro traffic scenarios, where the driving behavior of the leading car is determined by the macro traffic flow model, but the rear car follows the leading car according to the micro car following model. The results show that the following car closely follows the leading car, indicating that the time scale of driving behavior obtained from the car following model is much smaller than that from the macro traffic flow model. This means that the time scale division of the multi scale model in Figure 1 is reasonable. In the initial process of smooth acceleration, the micro car following model can track the macro planning objectives well. However, in the later deceleration process, the difference between the micro car following and the macro traffic behavior is relatively significant. This is because the acceleration in the decelerating process is larger than that of the accelerating process, as well as the effect of faster speed variations, which is consistent with the actual vehicle characteristics and the physiological response characteristics of the driver. Improving the ordering of the macro system can reduce the disorder of its micro subsystems. For example, according to the overall fluctuation trend of macro traffic flow, the time series of micro driving behavior can be improved appropriately to reduce the fluctuations of instantaneous driving behavior.