Energy prediction oriented SOC trajectory optimized multi-objective control strategy with equivalent factor corrected by PSO-fuzzy algorithm for a plug-in hybrid electric vehicle

The SOC trajectory is of great importance in fuel economy, emission reduction performance of plug-in hybrid electric vehicle (PHEV). This study proposes a energy prediction oriented SOC trajectory optimized multi-objective control strategy with equivalent factor corrected by particle swarm optimization fuzzy algorithm to improve the vehicle performance. First, a hybrid dynamic state transition mode including five different drive mode utilizing hybrid system theory is developed to obtain optimal mode switching. Next, the neural network algorithm is implemented to achieve demand energy prediction and the energy prediction oriented SOC trajectory is constructed. Moreover, equivalent factor (EF) based on fuzzy rules is corrected in real time combined with the PSO algorithm for tuning the PI factors to obtain the optimal EF. And the fuel consumption, the instantaneous HC emissions, the instantaneous CO emissions and the instantaneous NO x emissions are defined as the optimized objectives. Finally, a Fuzhou city driving condition based simulation research and HIL experiment are established to validate the effectiveness of the proposed strategy. The experiment results show that the proposed strategy has superior stability and significantly improves fuel economy and emission compared to traditional equivalent cost minimization strategy with constant EF.