A single-stage SOC reference trajectory prediction method for series PHEV based on GRNN

In this paper, a single-stage SOC reference trajectory (SOC-RT) prediction method is proposed. Different from the two-stage prediction method mostly used in previous studies, the proposed strategy predicts the SOC-RT slope directly based on the operating condition, instead of predicting the vehicle speed first and then solving it based on a global optimization algorithm. Therefore, the proposed method can significantly reduce the computational load and has high application value. A generalized regression neural network (GRNN) is employed as the prediction model for its strong nonlinear fitting ability. Meanwhile, to solve the problem of error accumulation due to the slope prediction, the prediction trajectory is corrected by the linear trajectory, and the correction factor is determined based on particle swarm optimization algorithm (PSO). Finally, the trajectory is followed by equivalent consumption minimization strategy (ECMS) and the performance of the proposed strategy is verified. The results show that the prediction accuracy is improved by 52.10 % and 3.73 % compared to the linear trajectory under the two tested cycles, respectively. Meanwhile, the proposed strategy achieves the best fuel economy, with fuel consumption decreasing by 2.61 % and 6.40 %, respectively, compared to the rule-based control strategy. Finally, its real-time control performance in a real hardware environment is verified through the hardware-in-the-loop test.