Research on hierarchical eco-driving strategy for fuel cell hybrid electric vehicles based on traffic flow

With the rapid advancement of intelligent transportation technologies, fuel cell hybrid electric vehicles(FCHEVs) have emerged as a pivotal technology for advancing sustainable transportation systems, owing to their exceptional energy conversion efficiency and zero-emission properties. However, optimizing energy management for FCHEVs in complex urban traffic environments remains a critical unresolved challenge. To address the above-mentioned problem, this study proposes a hierarchical eco-driving strategy leveraging traffic flow information to holistically optimize traffic efficiency and energy consumption. At the upper layer, a spatiotemporal dynamic programming (DP) framework is developed to optimize fleet-level speed trajectories by incorporating traffic signal phase and timing (SPaT) data along with dynamic inter-vehicle interactions, thereby enhancing driving safety while significantly improving energy efficiency. Considering real-time computational efficiency, the lower layer introduces an Alternating Direction Method of Multipliers (ADMM) algorithm to provide a computational foundation. On this basis, a cyclic iterative strategy is further employed to regulate the fuel cell system's power variation rate, thereby extending its lifespan and enhancing overall system efficiency. The efficacy of this strategy was validated through comparative analysis with a modified Gipps model. Simulation results demonstrate that the dynamic programming-based speed planning strategy exhibits significant advantages in reducing fuel consumption and improving driving comfort. Simultaneously, the ADMM algorithm significantly reduces computational complexity, achieving a computational efficiency one order of magnitude higher than the DP method, while still maintaining comparable fuel economy performance.