## 3 Numerical and Experimental Results
The results show that the experimental results are in good agreement with the optimized target values. \[k(0,t)=0.13 \tag{26}\]2 SOC trajectories of the original strategy and the replanning strategy for Mode 1. \[k(0,t)=0.13 \tag{26}\]3 SOC trajectories of the original strategy and the replanning strategy for Mode 2. \[k(0,t)=0.13 \tag{26}\]4 Target torque and speed of the engine and motor for both the original and the replanning strategies for Mode 1. In traffic planning, the traffic efficiency is closely related to the average speed of vehicles, which mainly involves a steady state or an equilibrium state. However, whether from the perspective of physical mechanism or data-based principal component analysis, the final energy consumption and emissions depend on the actual vehicle power, which  
\[k(0,t)=0.13 \tag{26}\]5 Experimental torque and speed of the engine and motor for both the original and the replanning strategies for Mode 2. \[k(0,t)=0.13 \tag{26}\]6 Experimental torque and speed of the engine and motor for both the original and the replanning strategies for Mode 1. \[k(0,t)=0.13 \tag{26}\]7 Target torque and speed of the engine and motor for both the original and the replanning strategies for Mode 2.  
mainly depends on the transient process away from equilibrium, so the replanning of instantaneous energy distribution needs to take the power factors into consideration [51]. Figures 17 and 18 show the experimental results of the power distribution of the engine and motor under the original strategy and the replanning strategy for Mode 1. For convenience in distinguishing the original and the RL based replanning strategies, Figure 19 shows the difference between them for Mode 1. Figures 20 and 21 show the experimental results of the power distribution of the engine and motor under the original strategy and the replanning strategy for Mode 2, and Figure 22 shows the difference between these two strategies for Mode 2. Global energy optimization can be achieved via the short-to-long time scale prediction of the driving behavior. The original strategy for global optimization is to distribute the HEV energy according to the  
\[k(0,t)=0.13 \tag{26}\]8 The difference between the original and the replanning strategies for Mode 1. \[k(0,t)=0.13 \tag{26}\]9 Experimental power of the engine and motor for the original strategy for Mode 2. For two typical types of speed deviation (i.e., Mode 1 and Mode 2), corresponding replanning is carried out, respectively. When significant deviation of speed occurs, the power threshold \(P_{\text{hi}}\), \(P_{lo}\) and the weight \(L_{e}\) are adjusted through reinforcement learning methods. For Mode 1, the power threshold \(P_{\text{hi}}\), \(P_{lo}\) and the weight \(L_{e}\) are increased when the actual power is higher than the predicted power of the original plan; but for Mode 2 the power threshold \(P_{\text{hi}}\), \(P_{lo}\) and the weight \(L_{e}\) are reduced when the actual power is lower than the predicted power of the original plan. This method further exploits the fuel saving potential of the engine at high power and reduces its inefficient operation at low power when the actual vehicle speed significantly deviates from the predicted speed of the original plan, not significantly affecting the original global optimization of the SOC trajectory. Vehicle power has a nonlinear function with its speed, and the frequency and amplitude of the speed fluctuation directly affect that of power fluctuation. The fluctuation frequency is related to the response time of the engine and motor, and the fluctuation amplitude is related to the fuel consumption gradient and motor efficiency. When replanning, it is necessary to deeply couple the dynamic characteristics (e.g., time constant) of the engine and motor with the speed fluctuation, in order to ensure that the replanning strategy can be executed by the subassemblies.