Research on PHEV Multi-Objective Energy Management Strategy Based on Genetic Algorithm Optimized Feed-Forward-Neural Network

With the transformation and upgrading of the automotive industry, plug-in hybrid vehicles have become a research hotspot in the automotive field due to their advantages such as long range and environmental efficiency. Most of the previous studies on P13 plug-in hybrid vehicles have focused on their economy, ignoring the problem of the balance between fuel economy and emission performance. In this paper, the CS stage control strategy is optimized, and a control algorithm based on neural network-genetic algorithm multi-objective optimization is designed for the engine stable operation conditions. In this paper, the fuel consumption and NO_X emission of the engine are taken as the optimization objectives, and the engine series torque and speed are optimized by neural network fitting and genetic algorithm seeking, which improves the overall performance of the whole vehicle. Finally, the multi-objective optimal control strategy based on neural network-genetic algorithm proposed in this paper is simulated and verified. The simulation results show that the control strategy proposed in this paper has significantly improved the overall vehicle performance of the plug-in P13 hybrid vehicle.