XGBoost-based prediction of electrical properties for anode aluminium foil

• Published in: Materials today communications Vol. 41; p. 110400
• Main Authors: Zhang, Yue, Pan, Sining
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Anode aluminium foil; Energy storage; Feature engineering; Properties prediction; XGBoost; Anode aluminium foil; Feature engineering; XGBoost; Properties prediction; Energy storage
• DOI: 10.1016/j.mtcomm.2024.110400
• ISSN: 2352-4928

Anode aluminium foil (AAF) shows advantages such as high electrical conductivity, high specific capacitance, and low cost, making it a high-quality electrode material for energy storage. However, the formation of AAF is a material process with multiple influencing factors interacted with each other, and it is difficult to analyze the influencing mechanism of each factor and predict the electrical properties. To solve the aforementioned problem, the process data of AAF is collected from a actual production line as the research subject firstly. The correlation coefficient between the process parameters and withstand voltage (WV) is used for feature selection of the data sets. XGBoost-based prediction model is established to predict the electrical properties of AAF, which is evaluated using R2 and root-mean-square error (RMSE). The results indicate that the process parameter X70 (Voltage of repair 3) shows the most significant effect on the WV. The magnitude of the WV shows a stepwise increase with the increase of X70. The result of prediction model is a R2 of 0.990 and a RMSE of 3.888. The XGBoost-based prediction model for the electrical properties (WV) of AAF shows excellent prediction characteristic, which contributes to the optimization of process parameters and the properties prediction. [Display omitted]