Performance enhancements of power density and exergy efficiency for high-temperature proton exchange membrane fuel cell based on RSM-NSGA III

Zhiqing Zhang, Hui Liu, Dayong Yang, Junming Li, Kai Lu, Yanshuai Ye and Dongli Tan

Energy, 2024, vol. 301, issue C

Abstract: In this study, a three-dimensional simulation model was developed and a multi-objective optimization of a single-channel high-temperature proton exchange membrane fuel cell (PEMFC) was carried out by the response surface methodology (RSM) and the non-dominated genetic algorithm III (NSGA III). Firstly, five main influencing factors including operating temperature (T), operating pressure (p), gas diffusion layer porosity (εGDL), proton exchange membrane (PEM) thickness (Mmem) and anode stoichiometry ratio (λa) were identified. The optimization objectives include three cell performance evaluation indexes: power density (wpow), system efficiency (ηsystem), and exergy efficiency (ηexergy). Secondly, the prediction accuracies of the RSM and artificial neural network (ANN) for the three performance evaluation indexes of high-temperature PEMFC were compared. Finally, the optimal solution from the Pareto solutions derived through RSM-NSGA III. The results show that the maximum wpow, maximum ηsystem and maximum ηexergy are 0.793 W cm−2, 22.31 % and 54.69 %, respectively. The optimal result obtained by combining EWM and VIKOR is wpow = 0.6726 W cm−2, ηsystem = 19.44 % and ηexergy = 54.01 %, respectively. The corresponding condition is T = 447.18 K, p = 1.15 atm, εGDL = 0.617, Mmem = 0.0412 mm, and λa = 1.26, respectively. Moreover, the suggestions are provided for the design of more efficient high-temperature PEMFC.

Keywords: NSGA III; Multi-objective optimization; RSM; High-temperature PEMFC; ANN (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:301:y:2024:i:c:s0360544224014609

DOI: 10.1016/j.energy.2024.131687

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