Prashik Sudhir Meshram

“Machine Learning-Based Analysis of Mechanical Testing Effects on Electrical Parameters of Lithium-Ion Batteries across Form Factors for Life Prediction and Safety Enhancement”

lithium-ion batteries (LIBs) plays a crucial role in their electrical performance, safety, and longevity, particularly in electric vehicle and energy storage applications. This study presents a machine learning (ML)-based framework to analyze the effects of mechanical testing on the electrical parameters of LIBs across different form factors. Experimental data from compression, vibration, drop and impact tests are utilized to evaluate key electrical characteristics, including voltage fluctuations, impedance variations, capacity degradation, and thermal response based on the mechanical action results of stress and strain to identify the novel analogy between mechanical test and its effects on electrical parameters of battery. Advanced ML algorithms, including regression models, support vector machines, and deep learning networks, are applied to identify degradation trends and predict battery failure modes. The proposed predictive model enhances battery life estimation.