Predictive Energy Management of Plug-in Hybrid Electric Vehicles by Real-Time Optimization and Data-Driven Calibration

This article proposes a predictive energy management strategy of plug-in hybrid electric vehicles by real-time optimization and data-driven calibration. The powertrain modelling and physical constraints, including engine, battery, and generator, are simplified by polynomial fitting approximations, which reserve the system nonlinearities with acceptable accuracy. To mitigate the control complexity, the physical constraints of engine, generator, and battery, are merged into a unified one by methodical derivatives. The nonlinear model predictive control problem is established, and the continuation/ general minimal residual (C/GMRES) algorithm is proposed for real-time optimization. Since the original C/GMRES algorithm can only deal with the equality constraints, the external penalty method is adopted for inequality constraints handling. To tackle the parameters' tuning difficulties, the Bayesian optimization (BO) algorithm is proposed. Based on the prior knowledges of closed-loop experiments, the map between parameters and objective can be described by Gaussian process, and the control parameters can be optimized with few evaluations in BO. Moreover, owing to the real-time applicability of C/GMRES algorithm, the time of closed-loop experiments is reduced so that the calculation time of BO calibration can be saved, exhibiting the superior design for predictive energy management. Simulation and hardware-in-the-loop validations are carried out and verify the energy-saving effectiveness and real-time applicability for proposed approach.