Eco-Co-Optimization strategy for connected and automated fuel cell hybrid vehicles in dynamic urban traffic settings

In urban traffic settings, the dynamic changes of the preceding and rear vehicles state, road gradient, road coefficient as well as the possible traffic congestion at signal intersections contribute to the difficulty of real-time optimal energy management for connected and automated fuel cell hybrid vehicles. To address this problem, an eco-co-optimization strategy is developed to achieve velocity planning and the promotion of energy management in this paper. First, gradient-based model predictive control based on the fast projection gradient method is employed to obtain the real-time safe and optimal velocity according to the future information of driving conditions and signal lights state. Meanwhile, to achieve desirable velocity tracking and preferable power splitting, an energy management strategy based on model predictive control is designed, where a multi-objective performance function is leveraged to minimize the total cost, hydrogen consumption and extend battery service life. Additionally, an energy recovery strategy based on fuzzy logic control is executed to improve energy efficiency. The simulation results reveal that the developed strategy can obtain a real-time safe and optimal velocity sequence and enable the CAFCHV efficiently passes through the continuous signalized intersections. Simultaneously, compared with adaptive cruise control, the hydrogen consumption, SOC, global cost and battery degradation are reduced by 3.13%, 4.76%, 3.37%, and 14.48% in the planning state, respectively.