Three-dimensional multi-phase model of PEM fuel cell coupled with improved agglomerate sub-model of catalyst layer

© 2019 Elsevier Ltd An improved agglomerate sub-model of catalyst layer (CL) involving actual agglomerate size and oxygen local transport characteristics is developed and incorporated into a three-dimensional (3D) multi-phase model of proton exchange membrane (PEM) fuel cell. This makes it capable to consider the effect of platinum (Pt) loading on oxygen transport and fuel cell performance more accurately. Oxygen local transport resistance near the catalyst surface is divided into three parts caused by liquid water blockage, ionomer coverage and Pt/carbon agglomeration, respectively. The resistances caused by ionomer coverage and Pt/carbon agglomeration are two major sources of oxygen local transport resistance. They have opposite variation trends as Pt loading changes. However, the ionomer resistance increases dramatically when Pt loading is lower than 0.1 mg cm−2 because of the much harder transport process through a relatively heavier ionomer coating. The simulation results agree with the experimental data reasonably under different cathode Pt loadings (from 0.3 to 0.025 mg cm−2), for both polarization curves and local transport resistance. In addition, a transport dominance parameter is defined to judge whether the concentration loss predominates the electrochemical reaction. A value greater than 10% can be seen as a symbol of local oxygen starvation. Using this model, fine channel geometry with extremely small channel and rib widths is investigated, and the highest net output power in this study is corresponding to 0.2 and 0.6 mm for channel (rib) width and height

AI-based optimization of PEM fuel cell catalyst layers for maximum power density via data-driven surrogate modeling

© 2020 Elsevier Ltd Catalyst layer (CL) is the core electrochemical reaction region of proton exchange membrane fuel cells (PEMFCs). Its composition directly determines PEMFC output performance. Existing experimental or modeling methods are still insufficient on the deep optimization of CL composition. This work develops a novel artificial intelligence (AI) framework combining a data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variables global optimization for improving the maximum power density of PEMFCs. Simulation results of a three-dimensional computational fluid dynamics (CFD) PEMFC model coupled with the CL agglomerate model constitutes the database, which is then used to train the data-driven surrogate model based on Support Vector Machine (SVM), a typical AI algorithm. Prediction performance shows that the squared correlation coefficient (R-square) and mean percentage error in the test set are 0.9908 and 3.3375%, respectively. The surrogate model has demonstrated comparable accuracy to the physical model, but with much greater computation-resource efficiency: the calculation of one polarization curve will be within one second by the surrogate model, while it may cost hundreds of processor-hours by the physical CFD model. The surrogate model is then fed into a Genetic Algorithm (GA) to obtain the optimal solution of CL composition. For verification, the optimal CL composition is returned to the physical model, and the percentage error between the surrogate model predicted and physical model simulated maximum power densities under the optimal CL composition is only 1.3950%. The results indicate that the proposed framework can guide the multi-variables optimization of complex systems

Additional file 1 of Causal effects of gut microbiome on HIV infection: a two-sample mendelian randomization analysis

Supplementary Material 1

Supplementary information files for: Designing the next generation of proton-exchange membrane fuel cells

Supplementary files for article: Designing the next generation of proton-exchange membrane fuel cells.With the rapid growth and development of proton-exchange membrane fuel cell (PEMFC) technology, there has been increasing demand for clean and sustainable global energy applications. Of the many device-level and infrastructure challenges that need to be overcome before wide commercialization can be realized, one of the most critical ones is increasing the PEMFC power density, and ambitious goals have been proposed globally. For example, the short- and long-term power density goals of Japan's New Energy and Industrial Technology Development Organization are 6 kilowatts per litre by 2030 and 9 kilowatts per litre by 2040, respectively. To this end, here we propose technical development directions for next-generation high-power-density PEMFCs. We present the latest ideas for improvements in the membrane electrode assembly and its components with regard to water and thermal management and materials. These concepts are expected to be implemented in next-generation PEMFCs to achieve high power density.</div

Designing the next generation of proton-exchange membrane fuel cells

With the rapid growth and development of proton-exchange membrane fuel cell (PEMFC) technology, there has been increasing demand for clean and sustainable global energy applications. Of the many device-level and infrastructure challenges that need to be overcome before wide commercialization can be realized, one of the most critical ones is increasing the PEMFC power density, and ambitious goals have been proposed globally. For example, the short- and long-term power density goals of Japan's New Energy and Industrial Technology Development Organization are 6 kilowatts per litre by 2030 and 9 kilowatts per litre by 2040, respectively. To this end, here we propose technical development directions for next-generation high-power-density PEMFCs. We present the latest ideas for improvements in the membrane electrode assembly and its components with regard to water and thermal management and materials. These concepts are expected to be implemented in next-generation PEMFCs to achieve high power density