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- Feature-enhanced machine learning prediction of supercapacitor cycle . . .
This study proposes a feature-enhanced framework for predicting the cycle life of EDLCs, enabling both regression and classification models with low data dependency and strong interpretability
- Feature-enhanced machine learning prediction of . . .
Data-driven approaches for predicting supercapacitor cycle life have received growing attention However, owing to their exceptionally long lifetimes and the scarcity of available aging data, these methods face more significant challenges than those applied to lithium-ion batteries
- Feature-enhanced machine learning prediction of supercapacitor cycle . . .
BatLiNet, a deep learning framework tailored to predict battery lifetime reliably across a variety of ageing conditions, is introduced, integrating an inter-cell learning mechanism to predict the lifetime differences between two battery cells
- Early prediction of cycle life of supercapacitors based on GA-LSTM
This paper introduces a novel cycle life prediction method for supercapacitors, employing a Genetic Algorithm-Long Short-Term Memory (GA-LSTM) approach Initially, a Moving Average Filter (MAF) is applied to the degradation data of 20 different supercapacitors for noise reduction
- Xu, Zhen; Zhu, Chun; Tang, Hongjian; Duan, Lunbo (2025) Feature . . .
Xu, Zhen; Zhu, Chun; Tang, Hongjian; Duan, Lunbo (2025) Feature-enhanced machine learning prediction of supercapacitor cycle life from limited early-cycle data
- Integrated Machine Learning Framework Combining Electrical Cycling and . . .
This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors
- Remaining Useful Life Prediction of Super-Capacitors in Electric . . .
In order to achieve the highest accuracy and anticipate the remaining usable life RUL of the SC battery, a variety of machine learning (ML) techniques have been used in this section
- Feature-enhanced machine learning prediction of . . .
Data-driven approaches for predicting supercapacitor cycle life have received growing attention However, owing to their exceptionally long lifetimes and the scarcity of available aging data, these methods face more significant challenges than those applied to lithium-ion batteries
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