Research on prediction of energy density and power density of biomass carbon-based supercapacitors based on machine learning

文献类型： 外文期刊

第一作者： Lu, Xueying

作者： Lu, Xueying;Zhao, Chenxi;Tu, Huanyu;Wang, Siyu;Chen, Aihui;Zhang, Haibin

作者机构：

关键词： Supercapacitor; Machine learning; Biomass carbon; Energy density; Power density

期刊名称：SUSTAINABLE MATERIALS AND TECHNOLOGIES （ 影响因子：9.2； 五年影响因子：9.8 ）

ISSN： 2214-9937

年卷期： 2025 年 44 卷

页码：

收录情况： SCI

摘要： The advancement of computer technology has made machine learning models widely used to study the electrochemical performance of supercapacitors, thus accelerating material discovery and performance optimization. From the perspective of biomass raw material characteristics, this study innovatively predicts the energy density and power density of biomass carbon-based supercapacitors based on Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Deep-Learning Neural Network (DNN) models. The results show that the LightGBM model performs best in energy density prediction, with R2 reaching 0.922. The XGBoost model has the best effect on the power density prediction, and the R2 is as high as 0.984. At the same time, through the analysis of SHAP value, it is found that biomass raw materials' composition and activation conditions are important characteristics affecting energy density and power density, which is very important for optimizing the performance of carbon materials. Therefore, it is a feasible method to predict supercapacitors' energy and power density from the perspective of the characteristics of biomass raw materials. This provides a reliable and valuable method for optimizing the performance of supercapacitors and predicting other performance parameters.

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