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In recent years, the burgeoning applications of hydrogen fuel cells have ignited a growing trend in their integration within the transportation sector, with a particular focus on their potential use in multi-rotor drones. The heightened mass-based energy density of fuel cells positions them as promising alternatives to current lithium battery-powered drones, especially as the demand for extended flight durations increases. This article undertakes a comprehensive exploration, comparing the performance of lithium batteries against air-cooled fuel cells, specifically within the context of multi-rotor drones with a 3.5kW power requirement. The study reveals that, for the specified power demand, air-cooled fuel cells outperform lithium batteries, establishing them as a more efficient solution.

The centrifugal compressor is one of the most commonly used air compressors for fuel cell air supply systems, and it has the small volume, high pressure ratio and low noise. However, surge in a centrifugal compressor severely limits its stable flow range. In this paper, a mathematical model of the compressor aerodynamic performance based on the energy transfer method was established, some parameters of model were identified by experimental data, and the model was validated through experiments. Then the dynamic model of the compression system was derived based on the compressor model and the Moore-Greitzer model. The stability analysis of the compression system was conducted, and it was strictly proved that when the compression system is unstable, there is the limit cycle in this nonlinear system, namely the surge cycle. Furthermore, the simulation of the compression system was conducted and the instability condition of the compression system was presented.

Since data processing methods could not completely eliminate the uncertainty of signals, it is a key issue for stable and robust decision-making for uncertainty tolerance of intelligent vehicles. In this paper, a decision-making for an Autonomous Emergency Braking (AEB) case considering the uncertainty of road adhesion coefficient estimation (RACE) is proposed. Firstly, the 3σ criterion is employed to classify the confidence in order to establish the decision-making mechanism considering the signal uncertainty of RACE. Secondly, the model for AEB with the uncertainty of the road adhesion coefficient estimated is designed based on the Seungwuk Moon model. Thirdly, a CCRs and CCRm scenario was designed to verify the feasibility in reference to the European New Car Assessment Programme (Euro NCAP) standard. Finally, the results of 10,000 cycles test illustrate that the proposed method is stable and could significantly improve the safety confidence both in the CCRs and CCRm scenarios.