Fuel cells are considered to be the development direction for realizing green agricultural machinery due to their non-pollution and high efficiency advantages.Currently,the main challenge constraining the development of fuel cell tractors is to construct a strong link between power system energy distribution and unstable operating conditions.Therefore,in this paper,a hierarchical energy-saving optimization strategy for fuel cell tractors is designed to reduce the costs associated with tractor energy consumption and power system lifetime loss by predicting multi-layer future operating conditions.Specifically,the optimal power allocation sequence is solved based on real tractor operating conditions,and a deep learning-based mapping model of vehicle states and SOC reference trajectories is established to provide a reference for the power allocation strategy in the lower layers.The results show that the proposed strategy saves about 57.38％of the total equivalent hydrogen consumption,and about 8.74％of the total cost,compared with the strategy without working condition prediction.
维普
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