Experience-Shared Variable-Step Predictive Control of Range-Extended Electric Vehicles Using Transferable Driver Model

Integrating range-extended electric vehicles (REEVs) in the automotive market is a key part of the drive toward environmental sustainability. This paper leverages an experience-shared approach to variable-step predictive control to improve REEV energy efficiency, where a transferable driver model is designed to accommodate varying driver experience levels via knowledge transfer. This model incorporates a confidence level factor to determine the effective length of speed prediction, ensuring a more accurate and reliable model predictive control system with lower requirement data. A grey wolf optimizer is employed as an advanced global solver in the model predictive control system of the studied REEV to seek better energy-saving performance. Experimental validation utilizes an industry-recognized driver-in-the-loop co-simulation platform to investigate the proposed approach’s performance. Compared to Gaussian mixture regression one, the transferable driver model achieves a 27.29% improvement in speed prediction accuracy. Incorporating the driver model, the proposed experience-shared variable-step predictive control approach helps a 3.9% reduction in fuel consumption compared to an LQR-driven MPC one.