Citation :

Kamlesh Sharadchandra Mahajan, Nitish Kumar Gautam, Ravikant Keshav Nanwatkar, Pranali Satish Mali, "Machine Learning-Based Life Cycle Prediction of Lithium-Ion Batteries under Mechanical Abuse across Multiple Form Factors," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 5, pp. 276-294, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I5P119

Abstract

Energy dissipated by mechanical abuse during manufacturing, transportation, and actual implementation has a large impact on the degradation and lifetime of lithium-ion batteries, especially among varying cell form factors. This work introduces a general Machine Learning (ML)-based methodology for life cycle prediction of Lithium-Ion Batteries under mechanical abuse by combining Drop, impact, and vibration loads over cylinder-shaped, prismatic, and pouch configurations. At both the cell and pack stages, a hybrid experimental and simulation-based testing strategy is employed to capture the mechanical responses that depend on form-factor as well as their effects on electrical performance. Important electrical parameters that are captured include voltage response, capacity fade, internal resistance evolution, and energy efficiency during and post mechanical loading. Finite element simulations are performed to extract mechanical indicators (stress, strain, displacement, and acceleration signatures) and correlate them with electrical degradation characterized experimentally. Comparison of different machine learning algorithms, such as support vector machines, random forests, gradient boosting, and deep learning models, which are used for remaining useful life and life cycle prediction. Results show that mechanically induced damage progresses in a battery form factor-dependent manner, leading to very different degrees of accuracy in prediction. When the training datasets are scarce and heterogeneous, advanced ensemble and recurrent models significantly outperform classical approaches. An explicit representation of the mechanical-electrical coupling effects enables a reliable life prediction and makes the framework suitable for decision support in both design optimization and safety assessment of the battery system and predictive maintenance plans in electric vehicles and energy storage.

Keywords

Lithium-Ion Batteries, Mechanical Abuse Testing, Machine Learning, Life Cycle Prediction, Remaining Useful Life (RUL), Battery Form Factors.

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