Predictive Control Strategy of PHEV Model Based on Sequential Quadratic Programming Algorithm

In order to improve the speed prediction accuracy and fuel economy of plug-in hybrid electric vehicle (PHEV), a model predictive control energy management strategy based on sequential quadratic programming (SQP) algorithm was proposed. Based on the speed prediction model constructed by convolutional neural network (CNN), three types of typical historical working conditions were selected as the training set of CNN speed prediction model. Whale optimization algorithm (WOA) was used to optimize CNN parameters, and the optimized WOA-CNN model was used to predict the future speed in the time domain. SQP algorithm was used to solve the model predictive control strategy, and the control results with the rule-based charge depleting and charge sustaining (CD-CS) control strategy and the global optimization based dynamic programming (DP) control strategy were compared and analyzed to verify the effectiveness of the proposed strategy. The results show that the prediction accuracy of vehicle speed can be improved by WOA-CNN model, which is 4.88%−8.39%. Compared with the DP control strategy, the fuel consumption of the proposed strategy is 1.98% higher, but the calculation time is reduced by 74.32%, and the real-time energy management is greatly improved. Compared with the CD-CS control strategy, the fuel saving rate of the proposed strategy is 20.37%. Overall, the energy consumption and calculation cost of the vehicle proposed in this paper are better, and the intelligent control of torque distribution in PHEV can be realized reasonably.