Fuzzy Adaptive Control Strategy with Improved PSO Algorithm for Parallel Hybrid Electric Vehicle

In order to further improve the whole vehicle economy of a novel plug-in hybrid electric vehicle (PHEV), considering two main factors, which are the driving working conditions and the driving distance, influencing the whole vehicle economy, a control strategy based on fuzzy self-adaptive online recognition is provided. A fuzzy working condition recognition algorithm is designed to online recognize the working condition types of actual driving of the vehicle. According to a minimum equivalent fuel consumption control algorithm and a battery electric quantity balance control method, corresponding optimal control parameters are called combining with a working condition recognition result, and real-time optimizing calculation is carried out on power distribution on an engine and a battery to control the whole vehicle. Finally, simulation analysis is carried out on an energy management control strategy of the whole vehicle. The result shows that under the new European driving cycle (NEDC) working condition at an equal driving distance, compared with a fixed parameter energy management strategy, the fuzzy self-adaptive online recognition energy management strategy can lower the whole vehicle equivalent fuel consumption per hundred kilometers by above 5%, and thus the whole vehicle economy of the PHEV is improved.