Research On Energy Optimal Management Control Strategy For ISG Hybrid Electric Vehicle

Resource shortage and environment pollution are two big challenges in the world nowadays. Hybrid electric vehicle is considered as most promising kind of vehicle for industrialization because it has many advantages, such as low energy consumption, low emission and long drive distance. The design of energy management strategy of ISG HEV is one of important part for the vehicle development, and one of key techlonogy for the energy saving and emission reduction of ISG HEV. Therefore the energy management strategy becomes the research focus for ISG hybrid electric vehicle.This dissertation takes a ’national863program’ project as background and takes an ISG HEV as research object. The forward simulation model for ISG hybrid electric vehicle was founded. The research on energy optimal management strategy for ISG hybrid electric vehicle based on dynamic programming, model predictive control and stomastic model predictive control were studied separately. The rapid control prototype simulation test were implemented based on dSPACE real-time syetem. The main research work and conclusions were summarized as following:Based on Matlab/Simulink, the ISG HEV forward simulation model was developed to provide the simulation and verification foundation of energy management strategy. The engine model, ISG motor model and power battery model were founded by using the experimental data. The clutch model and gearbox model were founded based on physical logic of parameters.The energy optimal management was converted into multistage decision process by discretization of driving cycle. The cost function for each stage and the object function were founded by using the fuel consumption, the number of shift and SOC balance as optimal objection. The gear shift and motor torque were selected as control variable. The SOC and gear were selected as state variable. The optimal motro torque horizon and gear shift horizon were got by the optimization. The simulation results show that the energe management strategy has good fuel economy performance.A method of exponential function was proposed to predict torque of the wheel. The energy management strategy was established by the rolling optimization for the fuel consumption based on the combination of model predictive control algorithm and dynamic programming. The motor torque horizon was got by the rolling optimization. The influence of the prediction horizon on the optimization results was studied. The simulation for the energy management strategy was developed based on the ISG HEV model under the NEDC. The results show that it has good fuel economy and it is real-time implementable. An energy management strategy based on stochastic model predictive control (SMPC) was proposed. The driver model based on Markov chain was developed to predict the required power of driver. The optimization model was developed by combining the dynamic programming and model predictive control. The optimal toque distribution was got by rolling optimization. The simulation was developed and the results were compared to the results for two other predictive methods. The results show that the method of stochastic predictive is feasiable and the energy management strategy has good economy performance.The test bench based on dSPACE real-time system was build and the rapid prototype simulation test for the SMPC energy management stategy of ISG HEV was implemented. The energy management strategy was further verified by the test.