Multiobjective Design Optimization of a PEMFC System Using Trust‐Region Adaptive Differential Evolution

This work couples a closure‐verified, physics‐based 50 kW automotive proton exchange membrane fuel cell model with a novel adaptive optimizer, Trust‐region Inspired Differential Evolution with Coordinated Expansion Control (TIDECEC), to address the net electrical power versus system efficiency trade‐off. The system energy, heat, and anode‐water balances are strictly enforced over the seven‐dimensional decision space, with the cell‐voltage model calibrated to production‐vehicle data. The proposed optimizer improves the L‐SHADE framework by adding an individual‐dependent geometric trust radius with asymmetric contraction–expansion dynamics, history‐driven local‐to‐global mutation switching, and a targeted bubble restart. The algorithm is tested on the CEC 2014 and CEC 2017 benchmarks at 30 and 50 dimensions, and it achieves a lower mean Friedman rank than 10 state‐of‐the‐art Differential Evolution variants. The multiobjective fuel cell design problem, solved with TIDECEC and benchmarked against NSGA‐II, NSGA‐III, and MOEA/D, yields a convex Pareto front from 2.80 kW at 56.94% efficiency to 42.47 kW at 29.63% efficiency. The adopted air‐supply model charges compressor work only above ambient pressure. The optimizer therefore selects near‐ambient cathode pressure and high air stoichiometry. As a result, radiator‐fan power accounts for 87% of the 6.92 kW peak parasitic power demand. However, multicriteria decision analysis and Monte Carlo uncertainty propagation show the maximum‐power and entropy‐weighted points are feasible in just 47% and 69% of samples, respectively, and otherwise violate the 368.15 K catalyst‐layer thermal ceiling. Thus, equal‐weight TOPSIS (39.38 kW, 34.27% efficiency) and fuzzy Bellman–Zadeh (24.44 kW, 44.38% efficiency) are proposed as robust, fully feasible design‐stage targets for rated and part‐load duties, respectively.