A Novel Nonlinear Optimal Control Approach for the Dynamic Process of a Hybrid Electric Vehicle Equipped with Electromechanical Transmission

Coordinatively controlling the engine and several motor/generators (MGs) during a dynamic process is a challenging problem because they are coupled together by the electromechanical transmission (EMT) system and all of them have strong nonlinear characteristics. We develop a novel nonlinear optimal control approach based on the multiobjective dynamic optimization model of the hybrid electric vehicle (HEV), which is equipped with an EMT system. In this approach, the current states of the components are obtained by using the state observation algorithm based on Kalman filtering; the future states of the components and the feasible region of the control variables are estimated by using the dynamic prediction algorithm based on the nonlinear model of the EMT system. Then, the control variables are achieved by using the optimal decision algorithm based on the hierarchical optimization and nonlinear programming, and the influence of the model error and the external disturbance are modified by using the feedback compensation algorithm. The simulation results illustrate the efficiency of the proposed control approach, and the test results verify its real-time performance.