A Self-Triggered MPC Strategy With Adaptive Prediction Horizon for Series Hybrid Electric Powertrains

Automotive electrification is a major trend for environmentally friendly transportation. Hybrid electric vehicles are also gaining popularity as a key transitional technology. Coordination control is crucial in improving the operation efficiency for series hybrid electric powertrains (S-HEPs), which involves determining the output power of multiple units such as the engine-generator set (EGS), battery, and motor. However, due to nonlinearity and the electromechanical dynamic difference between each unit, the powertrain state, such as engine speed and direct current link voltage, is prone to fluctuation. So, a high-performance coordinated control strategy is urgently needed. To address this problem, this article proposes a self-triggered model predictive control (MPC) method with adaptive prediction horizon for S-HEPs. Unlike traditional unit-independent feedback control schemes, this article proposes a system-integrated control scheme by establishing a multi-input and multioutput control model that integrates the EGS, battery, and motor. The model is then translated into a linear optimization control problem with input constraints applied into MPC. To reduce the computing burdens of MPC, a mechanism of self-triggered with adaptive prediction horizon is designed by considering the state dispersion and future state deviation. Finally, a hardware-in-the-loop experiment and a simulation experiment are conducted to validate the efficiency of the proposed self-triggered MPC. The results show that the proposed control method achieves a more desirable powertrain state compared to the conventional stability-voltage strategy, and the proposed self-triggered MPC reduces about 60% computing burdens while maintaining similar tracking performance to normal MPC.