{"title":"提高 PEM 燃料电池的动态性能:利用新型替代模型共同优化阴极催化剂成分和操作条件","authors":"","doi":"10.1016/j.renene.2024.120993","DOIUrl":null,"url":null,"abstract":"<div><p>Optimizing the cathode catalyst layer (CCL) composition and operating conditions to enhance the dynamic performance of proton exchange membrane fuel cells garners significant attention. Although machine learning surrogate models are efficient for fuel cell analysis and optimization, the varied voltage dynamic response patterns (e.g., loading failure, voltage undershoot, and voltage hysteresis) challenge regression surrogate models designed for steady-state performance predictions. In response, this study introduces a joint framework combining classification and regression models for dynamic performance prediction. For training, a transient, two-phase, non-isothermal fuel cell model with integrated catalyst agglomerate is developed. The dynamic voltage deviation (<span><math><mrow><msub><mi>σ</mi><mi>V</mi></msub></mrow></math></span>) is proposed as an index to characterize the dynamic performance of the fuel cell. This joint surrogate model achieves correlation coefficients of 0.9976 and 0.9961 for predicting <span><math><mrow><msub><mi>σ</mi><mi>V</mi></msub></mrow></math></span> in training and test sets, respectively. Through this model, sensitivity analyses of the CCL composition and operating conditions are conducted to quantify their impact and interactions on the fuel cell's dynamic performance. Besides, the analysis reveals a trade-off between dynamic performance and steady-state output. To balance these, a multi-objective optimization is conducted. The results indicate that, compared to the base case, dynamic and steady-state performance improved by 44 % and 8 %, respectively.</p></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":null,"pages":null},"PeriodicalIF":9.0000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing PEM fuel cell dynamic performance: Co-optimization of cathode catalyst layer composition and operating conditions using a novel surrogate model\",\"authors\":\"\",\"doi\":\"10.1016/j.renene.2024.120993\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Optimizing the cathode catalyst layer (CCL) composition and operating conditions to enhance the dynamic performance of proton exchange membrane fuel cells garners significant attention. Although machine learning surrogate models are efficient for fuel cell analysis and optimization, the varied voltage dynamic response patterns (e.g., loading failure, voltage undershoot, and voltage hysteresis) challenge regression surrogate models designed for steady-state performance predictions. In response, this study introduces a joint framework combining classification and regression models for dynamic performance prediction. For training, a transient, two-phase, non-isothermal fuel cell model with integrated catalyst agglomerate is developed. The dynamic voltage deviation (<span><math><mrow><msub><mi>σ</mi><mi>V</mi></msub></mrow></math></span>) is proposed as an index to characterize the dynamic performance of the fuel cell. This joint surrogate model achieves correlation coefficients of 0.9976 and 0.9961 for predicting <span><math><mrow><msub><mi>σ</mi><mi>V</mi></msub></mrow></math></span> in training and test sets, respectively. Through this model, sensitivity analyses of the CCL composition and operating conditions are conducted to quantify their impact and interactions on the fuel cell's dynamic performance. Besides, the analysis reveals a trade-off between dynamic performance and steady-state output. To balance these, a multi-objective optimization is conducted. The results indicate that, compared to the base case, dynamic and steady-state performance improved by 44 % and 8 %, respectively.</p></div>\",\"PeriodicalId\":419,\"journal\":{\"name\":\"Renewable Energy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.0000,\"publicationDate\":\"2024-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Renewable Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0960148124010619\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Renewable Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960148124010619","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Enhancing PEM fuel cell dynamic performance: Co-optimization of cathode catalyst layer composition and operating conditions using a novel surrogate model
Optimizing the cathode catalyst layer (CCL) composition and operating conditions to enhance the dynamic performance of proton exchange membrane fuel cells garners significant attention. Although machine learning surrogate models are efficient for fuel cell analysis and optimization, the varied voltage dynamic response patterns (e.g., loading failure, voltage undershoot, and voltage hysteresis) challenge regression surrogate models designed for steady-state performance predictions. In response, this study introduces a joint framework combining classification and regression models for dynamic performance prediction. For training, a transient, two-phase, non-isothermal fuel cell model with integrated catalyst agglomerate is developed. The dynamic voltage deviation () is proposed as an index to characterize the dynamic performance of the fuel cell. This joint surrogate model achieves correlation coefficients of 0.9976 and 0.9961 for predicting in training and test sets, respectively. Through this model, sensitivity analyses of the CCL composition and operating conditions are conducted to quantify their impact and interactions on the fuel cell's dynamic performance. Besides, the analysis reveals a trade-off between dynamic performance and steady-state output. To balance these, a multi-objective optimization is conducted. The results indicate that, compared to the base case, dynamic and steady-state performance improved by 44 % and 8 %, respectively.
期刊介绍:
Renewable Energy journal is dedicated to advancing knowledge and disseminating insights on various topics and technologies within renewable energy systems and components. Our mission is to support researchers, engineers, economists, manufacturers, NGOs, associations, and societies in staying updated on new developments in their respective fields and applying alternative energy solutions to current practices.
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