Energy Management Strategy of Extended-Range Electric Bus Based on Model Predictive Control

An energy management strategy based on model predictive control (MPC) was proposed for the hybrid bus. For the series configuration, MPC was used for power distribution among transmission components. Real-time optimization of the control strategy was achieved, which improved the fuel economy. First, a rule-based energy management strategy was proposed, and the logical thresholds of the stage of charge (SOC) and the demand power were formulated to underlie the subsequent study of the control strategy. Second, an energy management strategy based on global optimization was established where the dynamic programming algorithm was used to determine the SOC optimal reference curve and the limitation of fuel economy. In this way, the target and reference can be provided for the subsequent control strategy. Third, a radial basis function neural network speed prediction model based on wavelet transform was formulated. Also sequential quadratic programming was used to establish an energy management strategy based on MPC. Fourth, in the Simulink environment, the simulation results of the SOC curve and the fuel consumption were compared under different control strategies. The fuel economy of the MPC-based energy management strategy was better than the rule-based control strategy and was close to the control result based on global optimization. The real-time optimization of the control strategy was realized and its effectiveness was verified.