Fuel efficiency through co-optimization of speed planning and energy management in intelligent fuel cell electric vehicles

Fuel cell electric vehicles (FCEVs) offer a promising path to sustainable transportation, but their full potential depends on an effective energy management strategy (EMS). Intelligent driving offers an opportunity to integrate speed planning with EMS for improved efficiency. This study proposes a co-optimization framework that jointly optimizes speed planning and EMS for intelligent FCEVs. An energy model for the Toyota Mirai is developed and validated using real-world test data. The formulated optimal control problem (OCP) minimizes hydrogen consumption, effectively avoids power peaks, and maximizes regenerative braking by considering battery and electric motor constraints when planning deceleration. The proposed method is evaluated against widely studied sequential optimization approaches. On a flat road with a 1000-m look-ahead window, it achieves a 25% reduction in fuel consumption. Under realistic conditions with varying speed limits and slopes, it reduces hydrogen consumption from 39 g to 24.46 g, achieving a 36% improvement and a smoother power profile.