Abstract:

The study proposes a dynamic inverse hierarchical optimization method for plug-in hybrid electric vehicles (PHEVs) to improve energy consumption and economic driving. The method incorporates traffic-signal phase, timing, and road data to plan economic velocity and optimize energy consumption. The shortest path faster algorithm is used in the upper layer, while particle swarm optimization and Pareto theory are used in the lower layer. This approach enhances energy efficiency and computational efficiency in PHEVs, with simulation results showing a 7.43% improvement in energy consumption economy and reduced calculation time compared to existing solutions. The real-time applicability of the proposed algorithm is validated through hardware-in-the-loop experiments.