Abstract：In complex operating environments, the power generation system of fuel cell unmanned aerial vehicles (UAVs) encounters challenges, including insufficient oxygen supply at high altitudes and reduced overall system efficiency. To address these issues, a dynamic pressure ratio control strategy for the air compressor based on the particle swarm optimization (PSO) algorithm is proposed. A multi-parameter coupled system efficiency model is employed as the optimization objective function to achieve comprehensive efficiency enhancement. Firstly, a voltage output characteristic model of the proton exchange membrane fuel cell stack, a supply and exhaust pipeline model, and a system efficiency model are established. For the centrifugal air compressor under variable altitude conditions, a hybrid modeling approach combining mechanistic principles with data-driven techniques is developed. This method constructs a data-driven framework by transforming parameter identification into a nonlinear optimization problem subject to boundary constraints, solved efficiently using the sequential quadratic programming (SQP) algorithm. The optimized parameters are then integrated into the mechanistic model. Validation results demonstrate strong agreement between the hybrid compressor model and experimental data, with an average relative error of approximately 0.90% and a maximum relative error of 5.62%. As altitude increases, the compressor's maximum outlet flow rate and pressure decrease, while the maximum pressure ratio increases, leading to a narrowed operational range and a leftward shift of the surge line. According to gas partial pressure theory, the efficiency of the fuel cell system exhibits a unimodal relationship with reactant gas pressure. By adjusting the compressor speed and back-pressure valve opening, the system can maintain gas pressure at this optimal level, thereby maximizing efficiency. To meet engineering requirements for rapid dynamic response across a wide operating range, the PSO algorithm is used to design a dynamic pressure ratio control strategy. This strategy designed with the multi-parameter coupled system efficiency model as the objective function, with UAV altitude and load current serving as disturbance inputs. Within the dynamic operational constraints of the compressor, the optimal pressure ratios are computed over an altitude range of 0~6 000 meters and a load current range of 80~200 A using Matlab's built-in particleswarm function. A penalty term is incorporated into the optimization process to eliminate infeasible solutions and ensure convergence toward global optimality. The proposed strategy achieves average efficiency improvements of 5.62%, 1.53%, and 0.91% compared to conventional constant boost ratio strategies (1.2×, 1.6×, and 2.0×, respectively). Through dynamic compensation within a PID dual-loop control structure, cathode-side gas parameters rapidly converge to the optimal operating point. Simulations confirm that the proposed strategy consistently maintains peak system efficiency and outperforms alternative approaches under varying environmental and load conditions. The compressor's operating trajectory under variable altitudes remains within the safe region to the right of the surge line, closely approaching it while maintaining adequate margins (flow margin≥0.002 kg/s, pressure ratio margin≥0.1). Thus, the compressor operates continuously within its high-efficiency zone, achieving simultaneous “surge risk mitigation” and “efficient operating range retention”. The following conclusions can be drawn from the simulation analysis: (1) Under specific flight altitude and load conditions, there exists a globally optimal output pressure value for the air compressor that enables the system to operate at maximum efficiency. (2) The proposed strategy avoids the surge risk caused by the low flow rate and high pressure of the air compressor. (3) The proposed strategy can enhance the flight stability and endurance of unmanned aerial vehicles in high-altitude environments compared to the constant boost method.
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2026, Vol. 41 
