Machine Learning-Based Performance Prediction of Nanomaterial-Enhanced Energy Storage Devices

P. Senthil Pandian, M. Suresh, G. Suresh Kumar, A. Narendra Kumar, N. Vinothkumar and C. Ramachandran
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P. Senthil Pandian: Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India
M. Suresh: Department of Computer Science and Engineering Dhanalakshmi Srinivasan University Trichy, Tamil Nadu, India
G. Suresh Kumar: Department of AIML, Sethu Institute of Technology, Pullur, Kariyapatti Tamil Nadu, India
A. Narendra Kumar: Department of Bio Medical, Sethu Institute of Technology, Pullur, Kariyapatti Tamil Nadu, India
N. Vinothkumar: Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India
C. Ramachandran: Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 7, 330-336

Abstract: The growing demand for sustainable and efficient energy storage systems has driven interest in nanomaterial-enhanced devices due to their superior electrochemical properties. Predicting the performance of such devices is a complex task due to the non-linear and multi-parametric nature of nanostructures and their electrochemical behavior. In this study, we present a machine learning (ML)-based framework to predict the performance metrics of energy storage devices enhanced with Co-Fe N nanoparticles embedded in N,S-doped carbon matrices. Various ML models, including Random Forest, Support Vector Regression (SVR), and Gradient Boosting Machines, were trained on a curated dataset comprising material composition, synthesis conditions, and electrochemical output parameters. The proposed framework achieves over 92% accuracy in predicting specific capacitance, energy density, and cycling stability. Our results demonstrate the potential of ML for accelerating the design and development of next-generation nanomaterial-based energy storage systems.

Date: 2025
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