{"title":"为低温 PEM 燃料电池开发基于机器学习的模型","authors":"Aryan Madaan, Jay Pandey","doi":"10.1016/j.compchemeng.2024.108754","DOIUrl":null,"url":null,"abstract":"<div><p>Low-Temperature Proton Exchange Membrane Fuel Cells (LT-PEMFC) are favored as an alternative power source due to their high efficiency, rapid initialization, shut-down cycles, and zero emissions. Developing an effective model for LT-PEMFC is essential. In this study, machine learning models are created for LT-PEMFC, utilizing techniques such as Gradient Boosting Regression (GBR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) to predict cell voltage based on operating parameters. The dataset is generated using an in-house physics-based MATLAB model, complemented by experimental data from elsewhere. GBR exhibits superiority over XGBoost, LightGBM, and RF. These data-based models for LT-PEMFC, developed on generated datasets, achieve R<span><math><msup><mrow></mrow><mn>2</mn></msup></math></span> <span><math><mo>≥</mo></math></span> 0.99 and MAPE <span><math><mo>≤</mo></math></span> 0.06 during testing. These models are further validated on experimental data with R<span><math><msup><mrow></mrow><mn>2</mn></msup></math></span> <span><math><mo>≥</mo></math></span> 0.90 and MAPE <span><math><mo>≤</mo></math></span> 0.1. This underscores the ability to construct accurate data-based models and thus reducing reliance on extensive experimentation.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of machine learning based model for low-temperature PEM fuel cells\",\"authors\":\"Aryan Madaan, Jay Pandey\",\"doi\":\"10.1016/j.compchemeng.2024.108754\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Low-Temperature Proton Exchange Membrane Fuel Cells (LT-PEMFC) are favored as an alternative power source due to their high efficiency, rapid initialization, shut-down cycles, and zero emissions. Developing an effective model for LT-PEMFC is essential. In this study, machine learning models are created for LT-PEMFC, utilizing techniques such as Gradient Boosting Regression (GBR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) to predict cell voltage based on operating parameters. The dataset is generated using an in-house physics-based MATLAB model, complemented by experimental data from elsewhere. GBR exhibits superiority over XGBoost, LightGBM, and RF. These data-based models for LT-PEMFC, developed on generated datasets, achieve R<span><math><msup><mrow></mrow><mn>2</mn></msup></math></span> <span><math><mo>≥</mo></math></span> 0.99 and MAPE <span><math><mo>≤</mo></math></span> 0.06 during testing. These models are further validated on experimental data with R<span><math><msup><mrow></mrow><mn>2</mn></msup></math></span> <span><math><mo>≥</mo></math></span> 0.90 and MAPE <span><math><mo>≤</mo></math></span> 0.1. This underscores the ability to construct accurate data-based models and thus reducing reliance on extensive experimentation.</p></div>\",\"PeriodicalId\":286,\"journal\":{\"name\":\"Computers & Chemical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Chemical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0098135424001728\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0098135424001728","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Development of machine learning based model for low-temperature PEM fuel cells
Low-Temperature Proton Exchange Membrane Fuel Cells (LT-PEMFC) are favored as an alternative power source due to their high efficiency, rapid initialization, shut-down cycles, and zero emissions. Developing an effective model for LT-PEMFC is essential. In this study, machine learning models are created for LT-PEMFC, utilizing techniques such as Gradient Boosting Regression (GBR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) to predict cell voltage based on operating parameters. The dataset is generated using an in-house physics-based MATLAB model, complemented by experimental data from elsewhere. GBR exhibits superiority over XGBoost, LightGBM, and RF. These data-based models for LT-PEMFC, developed on generated datasets, achieve R 0.99 and MAPE 0.06 during testing. These models are further validated on experimental data with R 0.90 and MAPE 0.1. This underscores the ability to construct accurate data-based models and thus reducing reliance on extensive experimentation.
期刊介绍:
Computers & Chemical Engineering is primarily a journal of record for new developments in the application of computing and systems technology to chemical engineering problems.