Published Paper Details:

Battery health monitoring is a critical aspect of ensuring the longevity and reliability of lithium-ion batteries used in electric vehicles (EVs) and energy storage systems. Accurately predicting the Remaining Useful Life (RUL) of batteries and diagnosing faults are key components of effective battery management systems (BMS). This paper explores various machine learning (ML) methods employed for battery health monitoring, focusing on RUL estimation and fault detection. Supervised and unsupervised learning techniques are discussed, highlighting their applications and challenges. The paper also reviews the importance of data preprocessing and feature extraction in enhancing the accuracy of ML models. Despite the progress made in ML applications, challenges like data quality, model generalization, and real-time monitoring persist. Future advancements in machine learning and battery management systems will continue to improve the efficiency, safety, and sustainability of battery-dependent technologies.