Optimal control of power-split hybrid electric powertrains with minimization of energy consumption

This paper presents a real-time optimization strategy that targets on short-term energy consumption for the power-split hybrid electric vehicles (HEV) by focusing the mechanical motion of the powertrains. Instead of long-term energy optimization, which is usually investigated in the literatures on state of charge (SoC) management problem of HEVs, a short-term behavior of energy consumption is focused under the assumption that SoC of the battery is enough to provide the electric power required by the optimization. To this end, the transient mechanical motion of the powertrain is considered in the optimization problem instead of the battery SoC. The proposed strategy consists of two layers: the prediction of driver’s power demand based on the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication and the optimization of energy consumption along the predicted power demand. To deal with the stochastic uncertainties in the driver’s power demand, Gaussian process regression model is developed for the prediction, and the optimization is formulated as a model predictive control problem with the mechanical model of the powertrain dynamics forced by the predicted driver’s demand. Finally, the simulation results are demonstrated where the driver’s demand is generated by a professional simulator under randomly eliminated traffic environment.