Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting

Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural fe...

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Bibliographic Details
Main Authors: Mojtaba Khakpour Komarsofla, Kavian Khosravinia, Amirkianoosh Kiani
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Batteries
Subjects:
supercapacitor degradation
machine learning
material characterization
lifetime prediction
energy storage systems
Online Access:https://www.mdpi.com/2313-0105/11/7/264
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