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Capacitance Classification for Supercapacitors Using Machine Learning
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DOI：10.1109/ACCESS.2025.3553187.png)
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Abstract
En 中文
Supercapacitors are considered key components in many industrial and technological applications, due to their high ability to store energy and rapid charging and discharging. In this study, machine learning techniques were used for analyzing specific capacitance data of supercapacitors and developing a classification technique that helps to improve the efficiency of systems that rely on supercapacitors. Kernel Na & iuml;ve Bayes model was used in this study to classify the data of supercapacitors to improve understanding of their performances. MATLAB program was used to apply the model to a dataset of supercapacitors, taking into account specific capacitance, voltage window, electrode materials, and electrolyte used. Supercapacitors were divided into five classes according to the level of their specific capacitance values. The results showed high accuracy in classifying the data - more than 95% - as the model was able to distinguish between different categories effectively while displaying the percentage of correct and incorrect predictions for each class.
Keywords:
Supercapacitors
Mathematical models
Capacitance
Machine learning
Artificial neural networks
Predictive models
Kernel
Bayes methods
Graphene
Accuracy
kernel Na & iuml
ve Bayes
supercapacitors
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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