A Comparison of Various Universally Applicable Power Distribution Strategies for Fuel Cell Hybrid Trains Utilizing Component Modeling at Different Levels of Detail: From Simulation to Test Bench Measurement

Abstract A comparative study of energy management strategies for fuel cell hybrid trains, which focuses merely on universally applicable power distribution strategies, is presented in this contribution. These scalable strategies include a load follower strategy, an adaptive rule-based strategy, and adaptive Pontryagin's minimum principle (APMP)-based strategies with or without considering the relaxation process in batteries. For the load follower strategy, information about the characteristic consumption curves of the fuel cell system and the equivalent circuit of the batteries is not required. The adaptive rule-based strategy exploits the fuel cell system's characteristic consumption curves to maintain the fuel cell power close to its mean value. For the APMP-based strategies, in addition to the fuel cell modeling, the battery modeling with and without relaxation process in batteries is considered separately. In order to fairly compare the different scalable energy management strategies regarding hydrogen efficiency, Pontryagin's minimum principle-based strategy is used as the reference strategy, which considers the relaxation process in batteries. The comparison of the four strategies in terms of fuel economy is firstly based on the simulative analysis. For the different strategies, the additional hydrogen consumption amounts to 1.6%, 0.7%, 0.6%, and 0.6% respectively, compared to the reference strategy under a typical all-day regional train driving cycle. Then, in the RWTH Aachen University's Center for Mobile Propulsion, the hydrogen consumption is measured experimentally using the different strategies for a short driving cycle. The APMP strategy considering the relaxation process in batteries, consumes the least hydrogen per kilometer travel of 161.9 g/km, with charge sustaining maintained, which is the most energy-efficient energy management strategy. The simulation and experimental results show that the strategy should be selected based on the level of detail to utilize the modeling accuracy of components. Furthermore, due to the scalability of the strategies, they can be further transferred to other applications without enormous tuning effort.