Lithium-ion Battery’s State of Health Prediction: A Deep Learning Framework

The growing adoption of electric vehicles (EVs) has increased the need for reliable and efficient battery health monitoring systems. Lithium-ion batteries, widely used in EVs, undergo performance degradation over time, making the prediction of their health status a crucial task for ensuring safety, longevity, and operational efficiency. Traditional model-based approaches often face challenges due to their reliance on complex physical models and sensitivity to varying operational conditions. To overcome these limitations, data-driven methods are increasingly preferred for predictive maintenance. In this study, a data-driven framework is proposed for estimating the State of Health (SoH) of lithium-ion batteries. The framework is developed using data collected from multiple battery cells under diverse operating scenarios. Key steps such as data preprocessing, feature engineering, and model development are carried out to build robust machine learning and deep learning models. A focus is placed on handling measurement noise, which commonly affects real-world battery data. Through advanced techniques, improved noise resilience and prediction accuracy are achieved. The results demonstrate the effectiveness of the proposed approach in enabling accurate and scalable battery health estimation, offering valuable insights for enhancing battery management systems in EV applications.