首页|Researcher from Zhejiang University Details New Studies and Findings in the Area of Machine Learning (A Machine-Learning-Based Approach to Analyse the Feature I mportance and Predict the Electrode Mass Loading of a Solid-State Battery)
Researcher from Zhejiang University Details New Studies and Findings in the Area of Machine Learning (A Machine-Learning-Based Approach to Analyse the Feature I mportance and Predict the Electrode Mass Loading of a Solid-State Battery)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting out of Hangzhou, People' s Republic of China, by NewsRx editors, research stated, "Solid-state batteries are currently developing into one of the most promising battery types for both t he electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour." The news journalists obtained a quote from the research from Zhejiang University : "The performance of solid-state batteries is largely determined by the manufac turing process, particularly in the production of electrodes. However, efficient ly analysing the effects of key manufacturing features and predicting the mass l oading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effe ctively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key feature s during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machinelearning- based regression methods, includ ing decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in t he mixing and coating stages. The comparative results show that the developed ma chine-learning-based approach is able to provide a satisfactory prediction of th e electrode mass loading of a solid-state battery with 0.995 R2 while successful ly quantifying the importance of four key features in the early manufacturing st ages." According to the news reporters, the research concluded: "Due to the advantages of its datadriven nature, the developed machine-learning-based approach can eff iciently assist engineers in monitoring/ predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batterie s for further energy storage applications."
Zhejiang UniversityHangzhouPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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