A Power Preconditioning-Based Power Flow Predictive Control Strategy for Hybrid Electric Vehicle Using Fast Iteration Optimization Algorithm

In heavy-duty series hybrid electric vehicles (SHEVs), the driving power is provided by both the engine generator set (EGS) and the battery. Under urgent acceleration, deviations may occur in the power-following process because of the hysteretic response characteristic of the EGS to the high demand power. Furthermore, the computing speed of controllers may limit the real-time application of the control algorithm. Therefore, ensuring instantaneous response to a sudden increase in demand power and improving the operating efficiency of the control strategy are challenging technical problems. Thus, this study proposed a power preconditioning-based power flow predictive control strategy for SHEVs. First, a power preconditioning method based on demand power prediction is proposed to increase the generated output in advance. Second, the fast iteration sequential quadratic programming (SQP) algorithm is proposed in the receding horizon of model predictive control. In this algorithm, a constraint violation term, which can be described by the deviation of constraints between adjacent iteration points, is designed in the original value function. A projection matrix is introduced to modify the search direction of the optimization process in subproblems. The direction of gradient projection is applied as the steepest descent direction to replace the original descent direction. Finally, the performance of the proposed strategy is validated both in simulation and hardware-in-loop tests. The results reveal that the proposed strategy requires 5.39% less fuel consumption, whereas the lowest battery voltage is 40 V higher than that of the strategy without the power preconditioning method. The minimum computing step size of the proposed strategy is 20 ms less than that in the conventional SQP algorithm.