Abstract
Plug-in hybrid electric vehicles (PHEVs) can be considered as a hybrid system (HS) which includes the continuous state variable, discrete event, and operation constraint. Thus, a model predictive control (MPC) strategy for PHEVs based on the mixed logical dynamical (MLD) model and short-term vehicle speed prediction is proposed in this paper. Firstly, the mathematical model of the controlled PHEV is set-up to evaluate the energy consumption using the linearized models of core power components. Then, based on the recognition of driving intention and the past vehicle speed data, a nonlinear auto-regressive (NAR) neural network structure is designed to predict the vehicle speed for known driving profiles of city buses and the predicted vehicle speed is used to calculate the total required torque. Next, a MLD model is established with appropriate constraints for six possible driving modes. By solving the objective function with the Mixed Integer Linear Programming (MILP) algorithm, the optimal motor torque and the corresponding driving mode sequence within the speed prediction horizon can be obtained. Finally, the proposed energy control strategy shows substantial improvement in fuel economy in the simulation results.
Highlights
Focusing on plug-in hybrid electric vehicles (PHEVs), which are a typical hybrid system (HS) [1], an accurate system model is mandatory to study their control strategy
Based on the analysis above, this paper focuses on a parallel plug-in hybrid electric city bus, and the powertrain model is set-up firstly
Combined with mixed integer linear programming (MILP) algorithm and aiming at the minimal equivalent fuel consumption, a mixed logic dynamical-model predictive control (MLD-MPC) strategy based on vehicle speed prediction is proposed
Summary
Focusing on plug-in hybrid electric vehicles (PHEVs), which are a typical hybrid system (HS) [1], an accurate system model is mandatory to study their control strategy. If the vehicle state information is predicted accurately beforehand, global optimization within this time could be applied to overcome the disadvantages of inadaptability This could form the basis of the Model Predictive Control (MPC) method for a hybrid electric vehicle. A vehicle speed prediction method using a NAR neutral network based on the combination of past speed data and driving intention data recognized by fuzzy inference is proposed. A vehicle speed prediction method using a NAR neutral network based on the combination of driving intention and the past speed data is proposed to predict the future short-term vehicle running state and calculate the required vehicle torque. Combined with MILP algorithm and aiming at the minimal equivalent fuel consumption, a mixed logic dynamical-model predictive control (MLD-MPC) strategy based on vehicle speed prediction is proposed. The simulation results are presented to validate the proposed energy control strategy
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