A Supervisory Control Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Demand Prediction and Route Preview

This paper presents a supervisory control strategy for plug-in hybrid electric vehicles based on energy demand prediction and route preview. The aim is to minimize the fuel consumption in real-time operation. This strategy is realized through three successive steps. First, a neural network model is established to predict the energy demand of the vehicle. It reduces the complete traffic data to several statistical parameters, which contributes to ease the prediction process. Second, a mathematical model is proposed to translate the predicted energy demand into a state of charge (SOC) reference of the battery, which significantly simplifies the SOC-programming method. Finally, the adaptive equivalent consumption minimization strategy (ECMS) is used to track the SOC reference and determine the powertrain state. The proposed strategy can optimally distribute the energy between the engine and the motor on a global range and achieve an optimal torque split on a local range. Simulations are carried out on a power-split plug-in hybrid electric bus, and the proposed strategy shows substantial improvements in fuel economy and other indexes compared with the rule-based strategy and the ECMS.