Enhancing Lithium-Ion Battery Reliability Through Machine Learning: A CNN-Based Approach for RUL Estimation

With their high energy efficiency, extended lifespan, and minimal self-discharge, lithium-ion batteries have become the backbone of modern energy storage systems, particularly in renewable energy applications like solar and wind power. These batteries are essential for ensuring stable and sustainable energy distribution. Predicting their longevity is crucial for safety, operational planning, and performance optimization. The field of Prognostics and Health Management (PHM) provides effective strategies for monitoring battery degradation and estimating their remaining service life. This research introduces a deep learning framework based on convolutional neural networks (CNNs) to predict the Remaining Useful Life (RUL) of lithium-ion batteries. By analyzing battery parameters such as voltage, current, and temperature, the model identifies degradation patterns over multiple charge-discharge cycles. The proposed approach significantly enhances predictive accuracy, outperforming traditional methods when applied to real-world datasets. The findings demonstrate the potential of machine learning to revolutionize battery health estimation, ensuring more reliable and efficient energy storage solutions.