Hierarchical eco-driving control strategy for connected automated fuel cell hybrid vehicles and scenario-/hardware-in-the loop validation

A reliable and real-time eco-driving control strategy that incorporates vehicle connectivity is crucial for enhancing the fuel economy and mobility of fuel cell hybrid vehicles (FCHVs). However, the strong coupling of dynamic traffic, vehicle dynamics, and powertrain characteristics makes eco-driving challenging in terms of effectiveness and computational efficiency for co-optimizing velocity planning and energy management. This study proposes a hierarchical eco-driving predictive control framework for connected automated FCHVs, which improves comprehensive performance and is real-time applicable by incorporating an explicit dynamic traffic model (EDTM)-based velocity planning and an equivalent consumption minimization strategy (ECMS). The EDTM predicts the ego vehicle's state of proceeding through signalized intersections in dynamic traffic scenarios. The model predictive control is employed for multi-objective velocity planning, which balances energy savings, comfort, and traffic efficiency. At the powertrain level, a multi-horizon predictive ECMS (MhPECMS) is designed to incorporate both the optimized velocity (short-horizon) and the EDTM-based predictive velocity (mid-horizon). Dynamic traffic Scenario- and Hardware-in-the-Loop (S/HiL) validations show that the proposed strategy can make driving smoother in vehicle-following and through signalized intersections, and markedly improve fuel economy by 5.41% compared to the baseline controller. This study helps provide valuable insight into improving the efficiency and mobility of connected vehicles.