A Sequential Clustering Method With Improved Iteration and Its Application to Plug-In Hybrid Electric Vehicle: Theoretical Design and Experiment Implementation

This study proposes a sequential clustering quadratic programming (SCQP) method for the energy management strategies of plug-in hybrid electric vehicles (PHEVs). In this method, the clustering algorithm is introduced to gather up the points with a smaller iteration step size in the iteration process. The clustering results are utilized to design the termination criterion based on the distance between the cluster centers of various iteration domains. In the case that the distance varies within the preset range, it indicates that the current iteration point is sufficiently close to the optimal point. So that the criterion turns to terminate the computation to reduce unnecessary iteration steps. To analyze the convergence of the method with the designed criterion, the mathematical illustrations are proposed. In the mathematical illustrations, the monotonicity of the clustering objective function is firstly given. Then, the theorem of feasibility for the solution obtained by the designed criterion is proved. On the basis of aforementioned conclusions, the convergence of the SCQP method is obtained. Finally, the performance of the proposed method is validated both in simulation test and hardware-in-loop (HIL) test. The simulation results reveal that the PHEV achieves 8.81% and 7.74% less fuel consumption under two driving cycles. And the average iteration number of the proposed method is obviously reduced compared with the conventional SQP. The HIL results reveal that the proposed strategy exhibits similar performance in both real controller and simulation. The energy saving and real-time performance can be verified.