基于深度学习的混合动力汽车预测能量管理

To optimize the dynamic energy distribution process, improve fuel economy and power battery state of charge (SOC) balance, and raise the robustness of hybrid electric vehicle (HEV) energy management strategies, corresponding dynamic energy management strategies were formulated based on the equivalent consumption minimization strategy (ECMS). Further, these strategies were integrated with predictive research concerning the energy demand of HEVs in the near future. Using the internet of vehicles communication technology, vehicle information using state and traffic information was collected, in real-time, as input variables for the vehicle's future road condition prediction model. A vehicle speed prediction model was then established based on the characteristics of data-driven and hybrid deep learning. The seasonal-trend decomposition using loess (STL) was used to decompose the periodic and trend features of each input variable. The hybrid deep learning network was, then, used to mine the relationship between the target vehicle speed, external information and historical data from the local and time-dependent features of the data, to complete the prediction of the future road condition of the vehicle. Based on the predicted road condition information, the change in the future driving demand energy of the vehicle was analyzed and was applied to the real-time dynamic adjustment of the equivalent factor of ECMS to realize the optimal control of the vehicle. The effectiveness of this method was verified by comparing it with traditional adaptive ECMS. The results show that the prediction accuracy of the hybrid deep learning model is 44.72% higher than that of the back propagation network model. The prediction of energy management is capable of dynamically adjusting the power output of the engine and motor in real-time, reducing fuel consumption, and maintaining the SOC balance of the battery.