Smart Estimation of Battery Health Leveraging LSTM and DNNs for Accurate State of Health Predictions in Lithium-Ion Cells

Accurate estimation of the State of Health (SoH) of lithium-ion batteries is crucial for enhancing battery longevity, optimizing energy storage systems, and ensuring safety in applications such as electric vehicles and renewable energy grids. This paper presents a machine learning-based approach to predict battery SoH using historical data from NASA’s Li-ion Battery Aging Dataset. The data set comprises multiple charge-discharge cycles, capturing key parameters such as voltage, current, temperature, and impedance. Feature engineering techniques are used to extract relevant information and machine learning models are trained to establish predictive relationships between battery parameters and SoH degradation over time. Through detailed graphical analysis and correlation studies, the most influential factors affecting battery health have been identified. The proposed approach demonstrates high accuracy in SoH prediction, contributing to the development of intelligent battery management systems. This research provides a foundation for future advancements in predictive maintenance strategies, extending battery lifespan and improving energy efficiency in real-world applications.