• Naresh Kumar B
• Vinayak T Deshpande

This work provides a ground breaking analysis of the application of machine learning techniques for the accurate prediction of lithium-ion battery life cycles, with an emphasis on capacity degradation models. Understanding and anticipating the life cycles of lithium-ion batteries is crucial for ensuring sustainable energy solutions and optimizing performance, as these batteries are indispensable to several technological applications. The study begins with a thorough review of the literature, critically evaluating existing methods, and laying the groundwork for the introduction of machine learning models. The procedure comprises systematically gathering data in a variety of operational conditions, environmental factors, and charging-discharging cycles. Extensive pre-processing ensures the consistency and quality of the dataset for subsequent training of machine learning models. Many machine learning algorithms, such as regression models, support vector machines, and deep neural networks, are used to generate predictive models.

Keywords: Machine Learning; Lithium-ion Battery; Capacity Degradation; Life Cycle Prediction; Battery Management System

"RECHARGEABLE LITHIUM-ION BATTERY LIFE CYCLE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 2, page no.983-990, February-2020, Available :http://www.jetir.org/papers/JETIR2002547.pdf

"RECHARGEABLE LITHIUM-ION BATTERY LIFE CYCLE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 2, page no. pp983-990, February-2020, Available at : http://www.jetir.org/papers/JETIR2002547.pdf