Power optimization-based coordinated control of air management for automobile fuel cell system

The net power of the automotive fuel cell system varies with air mass flow and pressure. To enhance net power across different operating conditions, this study introduces an adaptive control strategy that integrates operational parameter optimization. Initially, the two-dimensional particle swarm optimization (TDPSO) method is proposed to select appropriate reference signals for the oxygen excess ratio (OER) and pressure to maximize net power. Furthermore, to coordinate the air compressor and flow valve, a feedback-linearization decoupling controller is designed to achieve independent control of air flow and cathode pressure. Additionally, to address time-varying and nonlinear interference, an active disturbance rejection control (ADRC) method has been developed to enhance dynamic response and stability. Through a series of simulation and hardware-in-the-loop (HIL) tests, the proposed control strategy is demonstrated to optimize net power under various operating conditions, showing superior transient and steady-state performance compared to traditional methods. Quantitative comparisons further illustrate the advantages of optimization algorithms and adaptive control techniques in improving power and control system performance.