首页|Aalen University Researcher Adds New Study Findings to Research in Machine Learn ing (Swift Prediction of Battery Performance: Applying Machine Learning Models o n Microstructural Electrode Images for Lithium-Ion Batteries)
Aalen University Researcher Adds New Study Findings to Research in Machine Learn ing (Swift Prediction of Battery Performance: Applying Machine Learning Models o n Microstructural Electrode Images for Lithium-Ion Batteries)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news originating from Aalen, Germany , by NewsRx correspondents, research stated, "In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different poro sities." Financial supporters for this research include Carl Zeiss Foundation; Aalen Univ ersity of Applied Sciences And Deutsche Forschungsgemeinschaft. The news correspondents obtained a quote from the research from Aalen University : "The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (i.e., layer thickness and porosity) of its electrodes. Tail oring the microstructure to a specific application is a crucial process in batte ry development. However, unravelling the complex correlations between microstruc ture and rate performance using either experiments or simulations is time-consum ing and costly. Our approach provides a swift method for predicting the rate cap ability of battery electrodes by using machine learning on microstructural image s of electrode cross-sections. We train multiple models in order to predict the specific capacity based on the batteries' microstructure and investigate the dec isive parts of the microstructure through the use of explainable artificial inte lligence (XAI) methods."
NETL
NSTL
