Research On Optimization Of Key Parameters Of Small Range Extender Engine

Global energy and environmental problems are becoming more and more serious,and there are still various problems of pure electric vehicles that cannot be broken through.In order to reduce the pressure of worsening oil energy and environment,the extended-range electric vehicles have become an ideal model in the transition stage from traditional fuel vehicles to pure electric vehicles.However,the current extended-range electric vehicles on the market are not mature enough,The research direction mainly focuses on the matching and optimization of the main components of the power system and the optimization of the vehicle energy management control strategy.Mainly using the existing finished engine as the auxiliary power source,the energy utilization rate is low,so the key parameter optimization research of the extended-range electric vehicle engine with miniaturization and low fuel consumption is especially important.Based on the experimental data and main structural parameters of the modified manifold EFI single-cylinder gasoline engine,an accurate one-dimensional performance simulation model is established in GT-Power.According to the point cloud of the intake-combustion chamber-exhaust which is scanned by the three-dimensional scanner,the three-dimensional model is established in CATIA.According to reasonable boundary conditions,initial conditions and related parameters,the three-dimensional numerical simulation model of engine is established in CONVERGE.The accuracy of the model is verified by comparing the simulated cylinder pressure data with the experimental value of cylinder pressure.Based on the original engine to increase the compression ratio,comparing the engine's knock index,torque and fuel consumption rate at different compression ratios and different speeds,the value of optimal compression ratio is determined.The experimental design is carried out by the optimal Latin hypercube method.The approximate model is established according to the RBF neural network model.With the engine power output to meet the power demand of the extended-range electric vehicle as constraint conditions,with the minimize fuel consumption rate and emission as optimization targets,the NSGA-II genetic algorithm is used to optimize the ignition advance angle and air-fuel ratio to obtain the optimal schemes of the ignition advance angle and air-fuel ratio corresponding to the original engine and the optimized compression ratio engine.According to the optimal solution of the original engine and the optimized compression ratio engine,the combustion chamber is simulated respectively,and the simulation results are compared and analyzed.The flow field,flame propagation conditions and main combustion parameters before and after optimization,such as heat release rate,average cylinder pressure,average cylinder temperature,and the quality of NO_x,HC,and CO emissions,are analyzed.The results show that the combustion of optimized engine is more concentrated,the thermal efficiency is improved,and the Simulink vehicle model built by our research group verifies that the fuel economy and emission of the optimized engine can be improved.