Research On Energy-saving Control Of Plug-in Hybrid Logistics Vehicle Based On Network Information

With the low-carbon transformation of the automobile industry and the rapid development of the logistics industry,plug-in hybrid logistics vehicles have attracted widespread attention due to their long driving range and great potential for energy saving and emission reduction.Broad market demand and strict fuel consumption limits put forward higher requirements for the economy performance of plug-in hybrid logistics vehicles.At the same time,the rapid development of information interaction technology between the vehicle and the cloud provides a new idea for vehicle energy-saving control.Further tapping the energy-saving potential of plug-in hybrid logistics vehicles using networked information is of great significance for the green and sustainable development of the logistics industry.Therefore,research on economical vehicle speed planning algorithms and predictive energy management strategies based on network information is conducted for plug-in hybrid logistics vehicles in this paper.The main research contents of this paper are as follows:1)The models of control system and vehicle of the plug-in hybrid logistics vehicle are established.Firstly,the hierarchical control system architecture for plug-in hybrid logistics vehicles is built.Then the speed planning algorithm based on the Krauss carfollowing model is designed and the rule-based energy management strategy is formulated in MATLAB.Finally,the plug-in hybrid logistics vehicle model including modules such as vehicle longitudinal dynamics,engine,motor,battery and driver is established to provide a simulation basis for the subsequent verification of the effectiveness of energysaving control strategy.2)The economical vehicle speed planning algorithm based on network information is developed.For multi-signal intersections,based on the derivation of the reference time for single-signal intersections,the expected time interval to reach each signal intersection within the speed limit is solved forward,and then the reference time interval with the best traffic efficiency is reversely recursively deduced,and the corresponding reference speed is derived.Finally,the objective function is constructed under the framework of model predictive control algorithm by comprehensively considering vehicle comfort,economy and reference vehicle speed.The multi-target method is introduced to convert the optimal problem into a nonlinear programming problem,and the economic speed trajectory is solved using the sequential quadratic programming algorithm.3)A predictive energy management algorithm based on economical vehicle speed is designed.Firstly,the global target SOC trajectory is linearly solved based on the vehicle driving path and the target electric energy consumption,and then in the predictive horizon,the optimal torque allocation scheme is solved using the DP algorithm to obtain the reference SOC trajectory.Then aiming at the optimal SOC tracking effect and engine fuel consumption,the linear quadratic output tracking regulator is used to solve the optimal engine power.Finally,the required torque of each power source is calculated based on the rules,and the control quantity is sent to the corresponding controller to realize the torque distribution.4)The energy-saving control strategy for plug-in hybrid logistics vehicles is simulated and verified.The effectiveness of the economical vehicle speed planning algorithm and the predictive energy management algorithm is verified under the joint simulation platform of SUMO and MATLAB.The simulation results show that in the Benchmark challenge scenario,compared with the speed planning algorithm based on the Krauss car-following model,the economical vehicle speed planning algorithm can reduce the vehicle’s electric energy consumption and fuel consumption by 9.84% and 15.52%respectively.The predictive energy management strategy can further save 0.10% and 3.24%of vehicle electric energy and fuel consumption on the basis of rule-based energy management strategy.Finally,compared with the rule-based control strategy,the hierarchical energy-saving control strategy proposed in this paper can reduce electric energy consumption by 9.94% and fuel consumption by 18.76%.