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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/11/7/264 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849714378918592512 |
|---|---|
| author | Mojtaba Khakpour Komarsofla Kavian Khosravinia Amirkianoosh Kiani |
| author_facet | Mojtaba Khakpour Komarsofla Kavian Khosravinia Amirkianoosh Kiani |
| author_sort | Mojtaba Khakpour Komarsofla |
| collection | DOAJ |
| description | 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 features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R<sup>2</sup> of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. |
| format | Article |
| id | doaj-art-73dce95f1a5242c5bd8976b344952dfe |
| institution | DOAJ |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-73dce95f1a5242c5bd8976b344952dfe2025-08-20T03:13:43ZengMDPI AGBatteries2313-01052025-07-0111726410.3390/batteries11070264Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health ForecastingMojtaba Khakpour Komarsofla0Kavian Khosravinia1Amirkianoosh Kiani2Silicon Hall: Micro/Nano Manufacturing Facility, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaSilicon Hall: Micro/Nano Manufacturing Facility, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaSilicon Hall: Micro/Nano Manufacturing Facility, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaThe 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 features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R<sup>2</sup> of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability.https://www.mdpi.com/2313-0105/11/7/264supercapacitor degradationmachine learningmaterial characterizationlifetime predictionenergy storage systems |
| spellingShingle | Mojtaba Khakpour Komarsofla Kavian Khosravinia Amirkianoosh Kiani Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting Batteries supercapacitor degradation machine learning material characterization lifetime prediction energy storage systems |
| title | Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting |
| title_full | Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting |
| title_fullStr | Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting |
| title_full_unstemmed | Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting |
| title_short | Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting |
| title_sort | integrated machine learning framework combining electrical cycling and material features for supercapacitor health forecasting |
| topic | supercapacitor degradation machine learning material characterization lifetime prediction energy storage systems |
| url | https://www.mdpi.com/2313-0105/11/7/264 |
| work_keys_str_mv | AT mojtabakhakpourkomarsofla integratedmachinelearningframeworkcombiningelectricalcyclingandmaterialfeaturesforsupercapacitorhealthforecasting AT kaviankhosravinia integratedmachinelearningframeworkcombiningelectricalcyclingandmaterialfeaturesforsupercapacitorhealthforecasting AT amirkianooshkiani integratedmachinelearningframeworkcombiningelectricalcyclingandmaterialfeaturesforsupercapacitorhealthforecasting |