Research On Optimal Control Strategy For Hybrid Electric Vehicle Driving System

As a new type of multi-energy source traffic tools, the driving system in HEV is distinctive and complicated, so the control of HEV driving system is both the core technique of hybrid electric vehicle and the key technology to improve the power, economy and comfortability. To improve the control accuracy of the electric driving system and comfortability, the stabilizing control and H^ control of permanent mag-net synchronous motor(PMSM) is researched based on the Hamilton system theory. Besides the control of bottom execution units(e.g. the motor, the engine, the clutch and the transmission), the control of HEV driving system also comprises the coor-dination control between the motor, the engine and the clutch. In order to improve the comfortability, rapidity, and economy performance during the mode transition dynamic process of HEV driving system, the torque coordination control is studied based on the predictive control theory. The main contents of this paper are listed as follows:1. To improve the control performance and control precision of PMSM driving system, the stabilization control of PMSM considering iron loss based on Hamilton system theory is first proposed. At first, the whole dynamic mathematical model and the port-controlled Hamilton model of PMSM considering iron loss are established. Then, the Hamilton stabilization control of the PMSM driving system is realized by using the method of interconnection and damping assignment and energy-shaping. Finally, the damping parameter impact on the convergence speed of rotating speed and the iron loss impact on rotating speed control are analyzed. The simulation re-sults show that the proposed Hamilton control scheme can perform fast stabilization of the driving system. The controlled driving system can effectively inhibit the load disturbance.2. Aimed at inhibiting the load disturbance in permanent magnet synchronous motor(PMSM) under the actual working condition of HEV, a new type of distur- bance inhibition method is presented for the first time based on Hamilton system H∞ control theory. On one hand, a Hamilton H∞controller is designed on the basis of Hamilton realization and stabilization control of PMSM considering iron loss. On the other hand, the response of currents, rotating speed and electromagnetic torque are analyzed under different disturbance inhibition level when the load disturbance exists. The simulation results show that the proposed Hamilton H∞ control has strong anti-disturbance ability which is useful to the drivability and comfort of HEV.3. A novel torque coordination control strategy based on the combination of the model predictive control method and the model reference control method is proposed for the HEV mode transition dynamic process. Firstly, drawing on the experience of model reference control, the reference model is built for the mode transition dy-namic process from pure electric mode to compound driving mode. This reference model is used to generate the set-point signal of the model predictive controller. In the next place, the model predictive controller is designed for the mode transition dynamic process from pure electric mode to compound driving mode. Afterwards, the proposed strategy is compared with the traditional method which is based on the actual driving experience. The simulation results show that the proposed strategy can not only achieve smaller vehicle jerk, smaller driveline torque interruption, and less clutch frictional losses but also adapt different driving resistance and different driving style.4. A data-driven predictive control method is proposed for the first time to solve the torque coordination problem during the mode transition dynamic process of HEV. At first, the HEV model is constructed in the professional vehicle analysis software Cruise, and the system input data are designed to fully excitate the system characteristics of HEV driving system. Secondly, the system output and constrained output data are obtained after the designed input data act on the built HEV dynamics model. Then, the subspace predictor is directly identified based on the above input-output data and validated using another group of input-output data. Next, explicitly considering the input and output constraints, the torque coordination control prob- lem based on DDPC is converted into a quadratic programming, and the data-driven predictive torque coordination control strategy is developed in Matlab/Simulink en-vironment. Finally, the co-simulation results based on Cruise and Matlab/Simulink validate the effectiveness of the proposed torque coordination control strategy, and demonstrate that DDPC method can achieve less vehicle jerk, faster mode transition, and smaller clutch frictional losses compared with the traditional MPC method.