Energy Management Control Strategy Of Hybrid Electirc Logistics Truck Fleet Under Connected Environment

In the face of urgent environmental crisis and energy crisis,hybrid logistics trucks,as new energy commercial vehicles that usually complete transportation tasks in the form of a fleet,have the advantages of saving fossil energy and reducing carbon emissions.In recent years,the construction of intelligent transportation system and smart city infrastructure has developed rapidly.It is currently a challenge to develop energy management strategies that can be applied in real time for the fleet of integrated networked traffic information.This paper takes the hybrid logistics truck fleet under urban conditions as the research object,and studies the layered energy management control strategy of the upper speed planning and the lower adaptive energy management of the truck fleet under the networked environment.The control strategy is verified by the co-simulation experiment platform.The main research contents are as follows:Aiming at the problems such as traffic pressure and fuel loss caused by stopping at red lights when logistics trucks travel in urban road conditions,a vehicle speed planning strategy based on model predictive control was proposed by combining vehicle-to-vehicle and vehicle-to-infrastructure.This strategy is based on the longitudinal fleet model,and on the premise of meeting the traffic laws and regulations,the upper and lower limits of the target speed are designed to enable vehicles to pass the traffic lights without stopping as much as possible.Considering that the vehicles in the queue maintain a certain safe distance from each other and reduce the number of acceleration and deceleration,the research on fuel economy is added to the multi-objective optimization problem.By solving the multi-objective optimization function,the optimal speed curve suitable for the queue vehicles in the current traffic scene is obtained,which improves the traffic efficiency of the logistics fleet,alleviates traffic congestion and saves energy consumption.The challenge for the industry is how to maximize fuel economy while improving computational efficiency so that energy management strategies can be implemented online.In order to further improve the fuel economy of the whole fleet from the vehicle power system level,an energy management control strategy based on the adaptive equivalent fuel consumption minimum strategy is designed based on the optimal driving speed of the queue based on the upper-level planning.Based on pontryagin’s minimum principle,the relationship between covariate and equivalent factor is established.The control sequence and covariate are obtained to ensure the global optimal by using the optimization method of pontryagin’s minimum principle in historical driving cycle,which can be used to train BP neural network.The online application of network model that meets the performance requirements guides the online adjustment of equivalent factor.Finally,in order to reflect the superiority of convoy ecological driving,based on the co-simulation experiment platform,this paper verifies the ability of the truck fleet speed planning controller to safely and stably pass through the intersection and the efficiency of queue driving in the networked freight road with traffic lights,and compares the adaptive energy management strategy with the two control strategies based on global optimization and instantaneous optimization.The real-time performance and fuel economy of the adaptive energy management strategy proposed in this paper are verified.