Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge

Year : 2026 | Volume : 04 | Issue : 01 | Page : 1 5
    By

    Priyanka Sarang Patil,

  • Deepak Shankar Raskar,

  • Mukesh Kumar Gupta,

  • Amit Tiwari,

  1. Research Scholar, Department of Electronics and Communication Engineering, Suresh Gyan Vihar University , Jaipur, Rajasthan, India
  2. Professor, Department of Computer Science Engineering, Amit University, Mumbai, Maharashtra, India
  3. Professor, Department of ElectricalEngineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  4. Assistant Professor, Department of Mechancial Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India

Abstract

Lithium-ion battery materials used in modern energy storage systems are required to exhibit high reliability, safety, and long lifecycle performance under varying operational and environmental conditions. Accurate prediction of Remaining Useful Life (RUL) and State of Charge (SoC) is therefore essential for understanding material degradation behavior, improving manufacturing quality, and enabling effective lifecycle management. However, nonlinear electrochemical aging, load variability, and thermal uncertainty significantly complicate accurate estimation of these parameters. To address these challenges, this study proposes a hybrid predictive framework based on multimodal data fusion and advanced machine learning, integrating electrical, thermal, and degradation-related indicators to capture both short-term operational dynamics and long-term material aging characteristics. Multiple deep learning architectures, including LSTM, GRU, CNN, and CNN–LSTM, are evaluated, and a hybrid CNN–LSTM-based fusion model is developed to enhance feature representation and decision reliability. Validation using publicly available lithium-ion battery datasets demonstrates that the proposed approach achieves superior performance, with a classification accuracy of 94.8%, an R² value of 0.96 for SoC prediction, and 0.94 for RUL estimation. The results confirm that multimodal data fusion combined with hybrid learning architectures effectively reduces prediction uncertainty and improves generalization across different aging profiles. This work provides a data-driven tool for battery material degradation assessment, manufacturing quality control, and predictive maintenance of energy storage systems used in industrial, renewable, and electric mobility applications.

Keywords: Li-Ion Battery Prognostics, Multimodal Data Fusion, Hybrid Machine Learning, Remaining Useful Life Prediction, State of Charge Estimation.

[This article belongs to International Journal of Electro-Mechanics and Material Behaviour ]

How to cite this article:
Priyanka Sarang Patil, Deepak Shankar Raskar, Mukesh Kumar Gupta, Amit Tiwari. Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge. International Journal of Electro-Mechanics and Material Behaviour. 2026; 04(01):1-5.
How to cite this URL:
Priyanka Sarang Patil, Deepak Shankar Raskar, Mukesh Kumar Gupta, Amit Tiwari. Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge. International Journal of Electro-Mechanics and Material Behaviour. 2026; 04(01):1-5. Available from: https://journals.stmjournals.com/ijemb/article=2026/view=238772


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Regular Issue Subscription Original Research
Volume 04
Issue 01
Received 12/02/2026
Accepted 28/02/2026
Published 11/03/2026
Publication Time 27 Days
30

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