Receding horizon control based energy management strategy for PHEB using GRU deep learning predictive model

Abstract

As an important method to achieve clean public transportation, plug-in hybrid electric bus (PHEB) has been gradually utilized in the transport system. The energy consumption performance of PHEB is mainly determined by its energy management strategy (EMS). Aiming at improving fuel economy of PHEB and making better use of battery, a receding horizon control (RHC) based EMS is proposed in this paper. Under the real driving cycles, the gated recurrent units (GRU) based model is utilized to predict the velocity sequence over receding horizon, and two other deep neural network prediction models are introduced for comparison. Moreover, the trip distance-based state of charge (SOC) constraint method is designed. Combining the velocity predictor and the SOC constraints, the power distribution of PHEB is described as a rolling optimization problem in the prediction horizon. The simulation and hardware-in-loop (HIL) experiments demonstrate that this strategy can improve fuel economy while ensuring the rational battery discharging under different initial SOC and traveling distance.