Multi-Scale Analysis of Polymer Based Energy Storage Systems for High Performance Battery Applications

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Year : 2026 | Volume : 14 | 03 | Page :
    By

    R.A. Kapgate,

  • Bhagyashree Ashok Tingare,

  • Prashant V. Thokal,

  • Sandip R. Thorat,

  • Laxmikant S Dhamande,

  • Jaikumar M. Patil,

  1. Professor, Department of Mechatronics Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  2. Assistant Professor, Department of Artificial Intelligence and Data Science, D Y Patil College of Engineering, Akurdi, Maharashtra, India
  3. Assistant Professor, Department of Electrical Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  4. Assistant Professor, Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  5. Assistant Professor, Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  6. Associate Professor, Department of Computer Science and Engineering, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, SGBAU, Amravati, Maharashtra, India

Abstract

The energy storage systems based on polymers are becoming promising materials for the next generation of high performance batteries because of their excellent mechanical flexibility, improved safety, and favorable electrochemical properties. Even with computational tools in Python, polymer-based energy storage systems remain plagued by poor ionic conductivity, complicated electrochemical reactions and potential thermal runaway. Therefore, a multi-scale model is proposed to improve battery performance, thermal stability, reliability, and large-scale deployment safety. The approach integrates material-level modeling with system-level evaluation and risk assessment. Raw data are preprocessed using min–max normalization for uniform scaling, followed by PCA for feature extraction and dimensionality reduction to retain essential characteristics while removing redundancy. At the microscopic level, ion transport and polymer chain dynamics are modeled using continuum transport based on non-equilibrium thermodynamics with electrochemical–mechanical coupling via free energy formulations. These insights are shared across scales to evaluate the macroscopic battery properties of efficiency, thermal stability and reliability. To better evaluate safety, the deep learning-based Archerfish Hunting Optimizer (AHO) driven Intelligent Deep Neural Network (IntDNN) model is added to the risk assessment module to predict thermal runaway and failure probability in large-scale battery systems. The proposed AHO-IntDNN shows better accuracy (0.99), precision (0.99), recall (0.99), F1 score (0.99) and ROC-AUC 0.97, indicating high reliability in the predictive performance. The model is based on multi-source simulation and operational data for capturing the nonlinear relationships in the prediction of risk. The unified multi-scale model allows for design, optimization and safe deployment of polymer-based energy storage systems, whereas the validated numerical results demonstrate improved performance.

Keywords: Electrochemical–mechanical coupling, battery safety, energy storage, Polymer Electrolytes, Lithium-Ion Batteries.

How to cite this article:
R.A. Kapgate, Bhagyashree Ashok Tingare, Prashant V. Thokal, Sandip R. Thorat, Laxmikant S Dhamande, Jaikumar M. Patil. Multi-Scale Analysis of Polymer Based Energy Storage Systems for High Performance Battery Applications. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
R.A. Kapgate, Bhagyashree Ashok Tingare, Prashant V. Thokal, Sandip R. Thorat, Laxmikant S Dhamande, Jaikumar M. Patil. Multi-Scale Analysis of Polymer Based Energy Storage Systems for High Performance Battery Applications. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=246704


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Ahead of Print Subscription Original Research
Volume 14
03
Received 27/05/2026
Accepted 10/06/2026
Published 15/06/2026
Publication Time 19 Days
60

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