Smart Predictive Energy Management Strategy for Range-Extended Electric Vehicles Using SiC-based Power Electronics

This paper presents a novel predictive energy management framework for range-extended electric vehicles (REEVs) that optimizes both energy efficiency and driving experience while subject to emission restrictions. The proposed approach introduces a three-layer hierarchical strategy: route segmentation based on road characteristics, power demand prediction, and prioritized power distribution. The framework classifies route segments into normal power, high power, and emission-restricted zones, establishing segment-specific power allocation priorities. A dynamic update mechanism enables real-time adaptation to changing conditions, while the range extender operation is optimized using the engine's universal characteristic curve. Additionally, the integration of Silicon Carbide (SiC) power electronics enhances the system's performance by improving the efficiency of the powertrain components. Simulation results demonstrate that the framework effectively manages power distribution across different road types while maintaining fuel consumption below target thresholds in emission-restricted zones. The strategy successfully balances battery state of charge, vehicle performance, and emission constraints, achieving efficient energy management through predictive control and priority-based power allocation. The proposed framework shows particular effectiveness in preparing for emission-restricted zones by pre-charging the battery during normal operation segments, demonstrating its capability to implement sophisticated energy management strategies while adhering to local emission regulations.