STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research Special issue

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

  1. R.A. Kapgate Department of Mechatronics Engineering, Sanjivani College of Engineering, Kopargaon
  2. Bhagyashree Ashok Tingare Department of Artificial Intelligence and Data Science, D Y Patil College of Engineering, Akurdi
  3. Prashant V. Thokal Department of Electrical Engineering, Sanjivani College of Engineering, Kopargaon
  4. Sandip R. Thorat Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon
  5. Laxmikant S. Dhamande Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon
  6. Jaikumar M. Patil Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon

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

References (20)

  1. Sahoo S, Timmann P. Energy Storage Technologies for Modern Power Systems: A Detailed Analysis of Functionalities, Potentials, and Impacts. IEEE Access. 2023;11:49689-49729. doi:10.1109/access.2023.3274504
  2. Yu X, Chen R, Gan L, Li H, Chen L. Battery Safety: From Lithium-Ion to Solid-State Batteries. Engineering. 2023;21:9-14. doi:10.1016/j.eng.2022.06.022
  3. Li L, Duan Y. Engineering Polymer-Based Porous Membrane for Sustainable Lithium-Ion Battery Separators. Polymers. 2023;15(18):3690. doi:10.3390/polym15183690
  4. Yazie N, Worku D, Gabbiye N, Alemayehu A, Getahun Z, Dagnew M. Development of polymer blend electrolytes for battery systems: recent progress, challenges, and future outlook. Materials for Renewable and Sustainable Energy. 2023;12(2):73-94. doi:10.1007/s40243-023-00231-w
  5. Kausthubharam, Vishnugopi BS, Alujjage ASJ, Premnath V, Tang WS, Jeevarajan JA, et al. Mechanistic Understanding of Thermal Stability and Safety in Lithium Metal Batteries. Chemical Reviews. 2025;126(1):404-447. doi:10.1021/acs.chemrev.5c00621
  6. He Q, Ning J, Chen H, Jiang Z, Wang J, Chen D, et al. Achievements, challenges, and perspectives in the design of polymer binders for advanced lithium-ion batteries. Chemical Society Reviews. 2024;53(13):7091-7157. doi:10.1039/d4cs00366g
  7. Badi N, Theodore AM, Alghamdi SA, Al-Aoh HA, Lakhouit A, Singh PK, et al. The Impact of Polymer Electrolyte Properties on Lithium-Ion Batteries. Polymers. 2022;14(15):3101. doi:10.3390/polym14153101
  8. Mahdy OS, Ali ABM, Mostafa L, Agarwal D, Dhawan A, Mabrouk A, et al. Quantitative evaluation of thermal runaway in lithium-ion batteries under critical heating conditions to enhance safety. Scientific Reports. 2025;15(1). doi:10.1038/s41598-025-07824-7
  9. Samy MM, Mohamed MG, Kuo SW. Conjugated Microporous Polymers Based on Ferrocene Units as Highly Efficient Electrodes for Energy Storage. Polymers. 2023;15(5):1095. doi:10.3390/polym15051095
  10. Xue X, Feng L, Ren Q, Tran C, Eisenberg S, Pinongcos A, et al. Interpenetrated Structures for Enhancing Ion Diffusion Kinetics in Electrochemical Energy Storage Devices. Nano-Micro Letters. 2024;16(1). doi:10.1007/s40820-024-01472-8
  11. Jones SD, Bamford J, Fredrickson GH, Segalman RA. Decoupling Ion Transport and Matrix Dynamics to Make High Performance Solid Polymer Electrolytes. ACS Polymers Au. 2022;2(6):430-448. doi:10.1021/acspolymersau.2c00024
  12. Ajibade H, Ujah CO, Nnakwo KC, Kallon DVV. Improvement in battery technologies as panacea for renewable energy crisis. Discover Applied Sciences. 2024;6(7). doi:10.1007/s42452-024-06021-x
  13. Aruchamy K, Ramasundaram S, Divya S, Chandran M, Yun K, Oh TH. Gel Polymer Electrolytes: Advancing Solid-State Batteries for High-Performance Applications. Gels. 2023;9(7):585. doi:10.3390/gels9070585
  14. Wang Y, Wang D. Advances in Porous Silicon Materials for Sensing, Energy Storage, and Microelectronics. Nanomaterials. 2026;16(4):257. doi:10.3390/nano16040257
  15. Machín A, Márquez F. The Next Frontier in Energy Storage: A Game-Changing Guide to Advances in Solid-State Battery Cathodes. Batteries. 2023;10(1):13. doi:10.3390/batteries10010013
  16. Czagany M, Hompoth S, Keshri AK, Pandit N, Galambos I, Gacsi Z, et al. Supercapacitors: An Efficient Way for Energy Storage Application. Materials. 2024;17(3):702. doi:10.3390/ma17030702
  17. Kopac T. Advancing Polymer Science and Energy Storage Solutions Through the Integration of Artificial Intelligence and Machine Learning: A Transformative Approach. Polymers. 2025;17(24):3267. doi:10.3390/polym17243267
  18. Huo H, Li P, Xin C, Wang Y, Zhou Y, Li W, et al. Life Cycle Cost Modeling and Multi-Dimensional Decision-Making of Multi-Energy Storage System in Different Source-Grid-Load Scenarios. Processes. 2025;13(8):2400. doi:10.3390/pr13082400
  19. Wang J, Guo Z, Miao X. Risk Assessment of Vehicle Battery Safety based on Abnormal Features and Neural Networks. Scalable Computing: Practice and Experience. 2024;25(6). doi:10.12694/scpe.v25i6.3347
  20. Kumar D, Sharma MK. Early prediction of battery swelling via delta resistance features and optimized machine learning models to avoid thermal runaway. Franklin Open. 2025;12:100389. doi:10.1016/j.fraope.2025.100389
Support