Research On Control Strategy Of Hybrid Power System And Optimization Calibration Of Engine

With the development of society and economy,automobile has become an indispensable part of our life.But the resource and environmental problems that follow are becoming more and more serious.In such a background,new energy vehicles emerge as the times require.However,due to such problems as battery life and technical cost,pure electric vehicles and fuel cell vehicles can not perfectly meet the needs of vehicles for various purposes,but hybrid vehicles with multiple power sources can balance the problem well.The thesis takes the development of vehicle's fuel-saving potential as the main goal,based on a medium-sized truck to carry out hybrid power system matching design,control strategy design and targeted optimization calibration of hybrid power system engine.The research content of the thesis includes the following aspects:By reading a lot of related literature and combining with actual vehicle use requirements,the most suitable single-axle parallel hybrid power system structure is selected.Based on this structure,according to the specified dynamic performance index,the main components of the hybrid power system are subjected to parameter matching calculation and selection.Based on the AVL-Cruise software,the simulation model of the whole vehicle is built and its dynamic performance is simulated.The results show that all the dynamic performance of the vehicle meet the requirements of the design index,which shows that the power system matching design is reasonable.Based on MATLAB/Simulink software,the drive control strategy with the lowest instantaneous equivalent fuel consumption and the energy recovery brake control strategy with high fuel-saving potential are designed and built,and co-simulated with the vehicle model.Based on the nine speed characteristic parameters,the clustering analysis of typical cycling driving conditions is carried out,which are divided into three categories: city,suburb and high speed,and the corresponding optimal control factors of each type of cycling conditions are obtained respectively.Based on the neural network,a condition recognition module is built to upgrade the control strategy,so that the vehicle can realize the control of the adaptive driving condition.The results show that,in terms of fuel consumption,the control effect of the instantaneous equivalent fuel consumption minimum control strategy is better than simple synchronous control,and the upgraded adaptive control has better control effect under changing driving conditions than the single equivalent factor control.In order to reduce fuel consumption from the level of power source,an optimized calibration study was carried out on the selected diesel engine.Based on the structural data and performance test data of the engine,a simulation model is built with AVLBOOST software,and the performance error is within the allowable range.The model is embedded in the adjusted vehicle model for simulation verification.The calculation result is not much different from the data input engine modeling method,indicating that the engine model is built successfully.Analyze the distribution of engine fuel consumption under the combination of three working conditions of city,suburb and high speed,and get the area with heavy engine fuel consumption,which is the area to be optimized.Based on Isight software and AVLBOOST software,an engine optimization calibration platform is built,and the fuel consumption reduction is taken as the main objective to optimize the fuel injection advance angle calibration points related to the optimization area.The engine model before and after optimization is embedded in the vehicle model for calculation.The results show that vehicle fuel consumption has been further improved.