Online Control Strategy for Plug-In Hybrid Electric Vehicles Based on an Improved Global Optimization Algorithm

Neural networks are widely used in the learning of offline global optimization rules to reduce the fuel consumption and real-time performance of hybrid electric vehicles. Considering that the torque and transmission ratio are direct control variables, online recognition by a neural network of these two parameters is insufficiently accurate. In the meanwhile, the dynamic program (DP) algorithm requires huge computing costs. Based on these problems, a fusion algorithm combining a dynamic programming algorithm and an approximate equivalent fuel consumption minimum control strategy (A-ECMS) is proposed in this paper. Taking the equivalent factor as the control variable, the global optimal sequence of the factor is obtained offline. The back propagation (BP) neural network is used to extract the sequence to form an online control strategy. The simulation results illustrate that, compared with the traditional dynamic programming algorithm, although the fuel consumption increases slightly, the computational cost of the fusion algorithm proposed in this paper is significantly reduced. Moreover, because the optimal sequence of the equivalent factors is within a particular range, the online control strategy based on DP-A-ECMS has a high robustness. Compared with an online control strategy based on the torque and transmission ratio, the fuel economy is improved by 2.46%.