Adaptive Joint Multiobjective Operating Parameters' Optimization for Active Direct Methanol Fuel Cells

The operating parameters of the active direct methanol fuel cell (DMFC) are essential factors affecting its power delivery performance. Different operating parameters lead to variations in the amount of methanol crossover in a DMFC, which might cause overpotential and cathode catalyst poisoning. Due to the complexity of the DMFC system, changes in operating conditions, and correlations among these parameters, it is challenging to maintain output power density while reducing the negative effects of methanol crossover. This paper proposes an adaptive joint optimization method for fuel cell operating parameters. The principle operating parameters are selected by the orthogonal tests, which include an adaptive numerical simulation and multiobjective optimization regarding cell output power density and methanol crossover. The selected parameter combinations are verified by an evaluation model that quantifies the influences of operating parameters on the active DMFC power density and its methanol crossover, where the nonlinear mapping function for the two optimization objectives is obtained. The nondominated sorting genetic algorithm-II (NSGA-II) is applied to rapidly obtain the optimal combination. The results show that with the optimal parameters, the maximum power density is increased by 16.7% and the methanol crossover is reduced by 35.1%. An active direct methanol fuel cell (DMFC) test platform is constructed and 250 orthogonal experiments are designed. Two random forest models are developed to describe the relationship between the power density and methanol crossover. The nondominated sorting genetic algorithm-II (NSGA-II) is introduced to simultaneously optimize the two operating parameters. Experimental results confirmed the superior performance under the optimal parameters.image (c) 2024 WILEY-VCH GmbH