Machine Learning for Sustainable Battery Optimization: A Data Driven Approach
Abstract
The escalating demand for batteries in portable devices and electric vehicles has underscored the necessity for effective and sustainable battery management. This research delves into applying Machine Learning (ML) techniques across various facets of battery research, encompassing performance prediction, materials discovery, and remaining useful life (RUL) estimation. Specifically, regression analysis, clustering algorithms, and materials informatics are employed to address these pivotal areas. The findings substantiate the efficacy of ML in accurately forecasting battery metrics such as state of charge (SOC) and state of health (SOH). The research introduces a novel clustering-based approach for RUL prediction, enhancing the precision of lifespan estimates for batteries. Furthermore, ML-driven materials discovery expedites identifying promising materials with superior properties, thereby contributing to the advancement of next-generation batteries. Integrating ML into battery research can substantially improve battery performance, reliability, and sustainability. The results underscore the potential of ML as a transformative tool for addressing the challenges associated with battery technology and paving the way for a more sustainable energy future.