Speedy Hierarchical Eco-Planning for Connected Multi-Stack Fuel Cell Vehicles via Health-Conscious Decentralized Convex Optimization

Abstract

Connected fuel cell vehicles (C-FCVs) have gained increasing attention for solving traffic congestion and environmental pollution issues. To reduce operational costs, increase driving range, and improve driver comfort, simultaneously optimizing C-FCV speed trajectories and powertrain operation is a promising approach. Nevertheless, this remains difficult due to heavy computational demands and the complexity of real-time traffic scenarios. To resolve these issues, this article proposes a two-level eco-driving strategy consisting of speed planning and energy management layers. In the top layer, the speed planning predictor first predicts dynamic traffic constraints using the long short-term memory (LSTM) model. Second, a model predictive control (MPC) framework optimizes speed trajectories under dynamic traffic constraints, considering hydrogen consumption, ride comfort, and traffic flow efficiency. A multivariable polynomial hydrogen consumption model is also introduced to reduce computational time. In the bottom layer, the decentralized MPC framework uses the calculated speed trajectory to figure out how to allocate the power optimally between the fuel cell modules and the battery pack. The objective of the optimization problem is to reduce hydrogen consumption and mitigate component degradation by focusing on targets such as the operating range of state of charge (SoC), as well as battery and fuel cell degradation. Simulation results show that the proposed decentralized eco-planning strategy can optimize the speed trajectory to make the ride much more comfortable with a small amount of jerkiness (−0.18 to 0.18 m/s3) and reduce the amount of hydrogen used per unit distance by 7.28% and the amount of degradation by 5.33%.