Investigation of MPC-based Engine Torque Prediction Control Strategy for a Parallel Hybrid Electric Vehicle

Abstract

To improve the fuel economy of a parallel hybrid power vehicle, the torque distribution strategy based on engine prediction is investigated. The proposed approach combines the rule logic and optimization algorithm. The 'state-of-charge’ and acceleration rate are selected as the control parameters for the mode transition.When the system is working in the torque-split mode, the model predictive control (MPC) of the optimization algorithm is implemented to establish the receding horizon predictive control optimization strategy. The fuel consumption is defined as the target cost function and the engine torque serves as the control variable. The orthogonal simulation experiment is carried out to examine the two MPC parameters, which are the prediction horizon and weight coefficient. Specifically, the prediction time domain variable tradeoff factor was determined. The prediction time domain <italic>S</italic> was chosen as 3 <italic>s</italic>, and the tradeoff factors were 0.0001, 0.00001, and 0.000001, respectively.In the prediction time domain of constant tradeoff variable, the tradeoff factor is 0.0001, and the prediction time domain <italic>S</italic> are 3 <italic>s</italic>, 5 <italic>s</italic>, and 10 <italic>s</italic> respectively. The results indicate that the best solution is the group consisting of the prediction horizon <italic>S</italic> = 3 <italic>s</italic> and weight coefficient <italic>λ</italic> = 0.0001; the equivalent integrated fuel consumption for 100 km is 6.179 L/100 km. In addition, comparison of the proposed strategy and the rule-based strategy showed that the former increased fuel economy by 7.8%. Through the above comparative experiments, the effectiveness of the MPC algorithm for improving fuel economy is further demonstrated.