Abstract
Road freight transport on hilly routes represents a significant challenge for the advancement of fuel cell electric trucks because of the high-performance requirements for fuel consumption, vehicle lifetime, and battery charge control. Therefore, it is essential to optimize the vehicle design and energy management, which greatly influence the driving performance and total cost of ownership. This paper focuses on the cost-optimal design and energy management of fuel cell electric trucks, considering five key influencing factors: powertrain component sizing, driving cycle, vehicle weight, component degradation, and market prices. The cost optimization relies on a novel predictive energy management scheme based on dynamic programming and the systematic calibration of control parameters. The paper analyzes the simulation results to highlight three main findings for fuel cell electric trucks: 1) cost-optimal energy management is essential to define the best trade-off between fuel consumption and component degradation; 2) the total cost of ownership is significantly influenced by component sizing, driving cycles, vehicle weight, and market prices; 3) predictive energy management is highly beneficial in challenging road topographies for substantial cost-saving and lower component size requirements.
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