To improve a parallel hybrid electric vehicle's (HEV's) fuel economy, this study develops a real-time optimisation strategy with a learning-based method that predicts the driver's power demand under the connected environment. This demand is strongly constrained by the total power generated by the energy sources. Therefore, a key issue of solving the energy management problem in real time by model-based predictive optimisation is to predict the power demand of each receding horizon. The proposed optimisation strategy consists of two layers. The upper layer provides the prediction of the driver's torque demand. Gaussian process regression (GPR) is used to predict the driver's demand with the uncertain and stochastic estimation between the traffic environment and torque demand. Vehicle-to-vehicle and vehicle-to-infrastructure data are used as the inputs of the GPR model. The lower layer performs finite-horizon optimisation based on the cost function of energy consumption. A receding horizon control (RHC) problem is formulated, and optimisation is achieved by a sequential quadratic programming algorithm. To validate the proposed optimisation strategy, a powertrain control co-simulation platform with a traffic-in-the-loop environment is constructed, and results validation with the platform is demonstrated. The comparisons with the dynamic programming and no-prediction RHC results show that the proposed strategy can improve fuel economy.
Receding horizon optimal control of HEVs with on-board prediction of driver's power demand
IET Intelligent Transport Systems ; 14 , 12 ; 1534-1545
2020-10-05
12 pages
Article (Journal)
Electronic Resource
English
sequential quadratic programming , vehicle-to-infrastructure data , energy management systems , learning (artificial intelligence) , optimal control , real-time optimisation strategy , model-based predictive optimisation , traffic-in-the-loop environment , predictive control , vehicle-to-vehicle data , quadratic programming , Gaussian process regression , hybrid electric vehicles , receding horizon optimal control , control engineering computing , energy management problem , parallel HEV , finite-horizon optimisation , RHC , Gaussian processes , power system simulation , fuel economy , parallel hybrid electric vehicle , powertrain control cosimulation , learning-based method , optimisation , on-board prediction , driver power demand , power engineering computing , power system control , receding horizon control , regression analysis
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