Citation :

Asma Parkar, Parshuram Sonawane, Arun Bhosale, Shriramshastri Chavali, Pravin Nitnaware, Vivek Ghulaxe, "Comprehensive Investigation of Lithium Ion Battery Life under Abuse Testing Across Various Form Factors Using Machine Learning Approach," International Journal of Mechanical Engineering, vol. 13, no. 8, pp. 74-92, 2026. Crossref, https://doi.org/10.14445/23488360/IJME-V13I8P108

Abstract

Present work, focused on prediction of lithium-ion batteries life during abuse tests in cell form factors with the help of a machine learning approach. Mechanical, thermal and electrical abuse test results are evaluated to learn the behavior of degradation and the mechanism of failures in prismatic, cylindrical and pouch cell. Form factors as structural arrangement and material distribution vary greatly between them and affect electrochemical stability and safety in harsh conditions of operation profoundly. A sophisticated machine learning approach, such as deep learning models and ensemble models is used to model the nonlinear behavior of degradation and detect essential parameters that influence battery life. Stress-induced variations in the voltage, temperature, inner resistance and mechanical deformations are analyzed as features to create an effective predictive model that can be used in estimating the remaining useful life of batteries. The comparative analysis of the various form factors can be used to retrieve unique failure signatures and structural strength properties. The suggested framework can help to improve the early detection of faults and optimize the precision of battery life prediction. The findings would help in enhancing the lithium-ion battery safety, reliability, and lifecycle materials of electric vehicles, consumer electronics, and large-scale energy storage systems.

Keywords

Machine learning methods, Battery Life Estimation, Battery form factors, Abuse testing, Electric Vehicles.

References

1. Lei Yao et al., “A Comprehensive Review of Lithium-Ion Battery Safety Issues and Fault Diagnosis Strategies Throughout the Entire Lifecycle,” Journal of Energy Storage, vol. 136, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Ang Yang et al., “A Comprehensive Investigation of Lithium-ion Battery Degradation Performance at Different Discharge Rates,” Journal of Power Sources, vol. 443, pp. 1-12, 2019.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Seyed Saeed Madani et al., “A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis,” Batteries, vol. 11, no. 4, pp. 1-68, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Alireza Valizadeh, and Mohammad Hossein Amirhosseini, “Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends,” SN Computer Science, vol. 5, no. 6, pp. 1-17, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Rui Cao et al., “Model-Constrained Deep Learning for Online Fault Diagnosis in Li-ion Batteries Over Stochastic Conditions,” Nature Communications, vol. 16, pp. 1-11, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Chunhui Ji, Guang Jin, and Ran Zhang, “Comprehensive Fault Diagnosis of Lithium-Ion Batteries: An Innovative Approach based on Hybrid Coding and Genetic Search,” Engineering Applications of Artificial Intelligence, vol. 141, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Mohammad I. Alfraheed, “A Review of Measurement Methods for Lithium-based Battery Defect and Degradation Analysis,” International Journal of Modelling and Simulation, pp. 1-19, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Prakash Venugopal et al., “Analysis of Optimal Machine Learning Approach for Battery Life Estimation of Li-Ion Cell,” IEEE Access, vol. 9, pp. 159616-159626, 2021.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Qiying Wang et al., “Application of Machine Learning in Ultrasonic Diagnostics for Prismatic Lithium-Ion Battery Degradation Evaluation,” Frontiers in Energy Research, vol. 12, pp. 1-16, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Wensheng Huang et al., “Questions and Answers Relating to Lithium-Ion Battery Safety Issues,” Cell Reports Physical Science, vol. 2, no. 1, pp. 1-12, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Binghe Liu et al., “Safety Issues and Mechanisms of Lithium-ion Battery Cell Upon Mechanical Abusive Loading: A Review,” Energy Storage Materials, vol. 24, pp. 85-112, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Junting Bao et al., “Critical Review of Temperature Prediction for Lithium-Ion Batteries in Electric Vehicles,” Batteries, vol. 10, no. 12, pp. 1-31, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Sangheon Lee et al., “Diagnosing Various Failures of Lithium-Ion Batteries using Artificial Neural Network Enhanced by Likelihood Mapping,” Journal of Energy Storage, vol. 40, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Adam Thelen et al., “Probabilistic Machine Learning for Battery Health Diagnostics and Prognostics—Review and Perspectives,” npj Materials Sustainability, vol. 2, pp. 1-33, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Youngrok Choi, and Pangun Park, “Thermal Runaway Diagnosis of Lithium-Ion Cells Using Data-Driven Method,” Applied Sciences, vol. 14, no. 19, pp. 1-12, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Manh-Kien Tran, and Michael Fowler, “A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges,” Algorithms, vol. 13, no. 3, pp. 1-18, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Alice V. Llewellyn et al., “Understanding the Degradation Mechanisms of Lithium Ion Batteries Using in-Situ Multi-Scale Diffraction Techniques,” ECS Meeting Abstracts, vol. MA2022-01, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Takafumi Ogawa et al., “Point-Defect Chemistry for Ionic Conduction in Solid Electrolytes with Isovalent Cation Mixing,” Journal of Materials Chemistry A, vol. 12, no. 45, pp. 31173-31184, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Zeyu Chen et al., “Thermal Runaway in Lithium-Ion Batteries: A Review of Mechanisms, Prediction Approaches, and Mitigation Strategies,” Batteries, vol. 12, no. 3, pp. 1-34, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Wenyang Zhao et al., “Investigation on Overcharge Cycling-Induced Degradation of Lithium-ion Batteries and Mechanical Deterioration of Components,” Journal of Power Sources, vol. 654, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Caisheng Li et al., “Electrochemical-Thermal Behaviors of Retired Power Lithium-ion Batteries during High-Temperature and Overcharge/Over-Discharge Cycles,” Case Studies in Thermal Engineering, vol. 61, pp. 1-16, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
22. Robert Schröder, Muhammed Aydemir, and Günther Seliger, “Comparatively Assessing Different Shapes of Lithium-Ion Battery Cells,” Procedia Manufacturing, vol. 8, pp. 104-111, 2017.
    [CrossRef] [Google Scholar] [Publisher Link]
23. Bingzeng Song et al., “Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM,” Batteries, vol. 11, no. 3, pp. 1-17, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
24. Li-Hua Ye et al., “Remaining Useful Life Prediction of Lithium-ion Battery based on Chaotic Particle Swarm Optimization and Particle Filter,” International Journal of Electrochemical Science, vol. 18, no. 5, pp. 1-10, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]