Battery Life-time Optimization Model (BLO) using Machine Learning Methods for Electric Vehicles

Effective and sustainable battery management is essential, as seen by the growing demand for batteries in electric cars and portable electronics. This study explores the use of machine learning approaches in battery research in a number of areas, including materials discovery, performance prediction, and remaining usable life (RUL) calculation. The rapid development of electric vehicle (EV) technology highlights the critical importance of lithium-ion batteries and intensifies the emphasis on sustainable energy storage. Using refined Machine Learning (ML) techniques like Random Forest (RF) and Support Vector Machine (SVM), this study mainly demonstrates how to predict the Remaining Useful Life (RUL) of lithium-ion batteries. Here, a new clustering-based method for predicting RUL is used to propose the Battery Life-Time Optimisation Model (BLO), which improves the accuracy of battery lifetime projections. Additionally, the development of next-generation batteries is aided by machine learning-driven materials discovery, which speeds up the identification of promising materials with exceptional attributes. Battery performance, sustainability, and dependability may all be significantly increased by using machine learning into battery research. The findings highlight machine learning's promise as a significant technique for resolving battery technology issues and opening the door to a more sustainable energy future.