Optimization of fuel cell bipolar plate using genetic algorithm and lumped parameter method: A novel approach for large-scale mobility applications

This study proposes an optimization design methodology for proton exchange membrane fuel cells for high-power applications. To achieve this, the PEMFC-LPM model, serving as an optimization design tool, integrates the lumped parameter method and genetic algorithm to optimize bipolar plate geometry and predict stack performance. Initially, the PEMFC-LPM model is initialized based on CFD analysis results, in which novel equations for the transfer coefficient profile and equivalent length coefficient are introduced to simulate nonlinear system characteristics. As a result, the initialized model predicts CFD results within a ±3 % error margin. Furthermore, using the weight and pressure drop of the bipolar plate as objective functions, the optimized design shows a 5.7 % weight reduction compared to the reference model, with pressure drop decreasing by 16.8 % and 32.2 % in the anode and cathode, respectively. Additionally, the maximum power density shows a 6 % increase with the expanded useable current density range. Consequently, the program can be utilized for designing prototypes with different levels of power output and establishing optimal control strategies. Furthermore, it effectively analyzes the relationship between stack weight and output for high-power applications, ultimately contributing to performance prediction and design optimization in mobility applications.