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Accurately predicting the lifetime of lithium-ion batteries, especially in their early cycles, is a critical challenge. This paper introduces a comprehensive machine learning (ML) framework aimed at overcoming these challenges by focusing on accurate early-cycle predictions. The framework proposed consists of two major modules: feature extraction and feature selection, followed by a machine learning-based prediction model. The feature extraction module is designed to gather critical battery characteristics that have a significant impact on battery performance. Feature selection then narrows down the most relevant parameters, ensuring that the machine learning model processes only the most significant variables for prediction. Our model is powered by two state-of-the-art algorithms: Random Forest and XG Boost. These algorithms were selected for their robustness in handling complex datasets and their ability to generate highly accurate predictions. Random Forest offers a strong baseline with its ensemble approach, which minimizes overfitting by averaging multiple decision trees. Meanwhile, XG Boost introduces gradient boosting techniques, offering enhanced accuracy through iterative optimization. Unlike traditional models that rely on static power profiles, our model dynamically adapts to the individual usage characteristics of each user. This dynamic approach enables more personalized and accurate battery life predictions, making the model more practical for real-world applications.

Copyright © 2024 Sarvesh Warjurkar. This is an open access article distributed under the Creative Commons Attribution License.