Lithium-ion batteries are widely used in various applications, including portable electronic devices, electric vehicles, and renewable energy storage systems. Accurately estimating the remaining useful life of these batteries is crucial for ensuring their optimal performance, preventing unexpected failures, and reducing maintenance costs. In this paper, we present a comprehensive review of the existing approaches for estimating the remaining useful life of lithium-ion batteries, including data-driven methods, physics-based models, and hybrid approaches. We also propose a novel approach based on machine learning techniques for accurately predicting the remaining useful life of lithium-ion batteries. Our approach utilizes various battery performance parameters, including voltage, current, and temperature, to train a predictive model that can accurately estimate the remaining useful life of the battery. We evaluate the performance of our approach on a dataset of lithium-ion battery cycles and compare it with other state-of-the-art methods. The results demonstrate the effectiveness of our proposed approach in accurately estimating the remaining useful life of lithium-ion batteries.

Estimation of Remaining Useful Life and SOH of Lithium Ion Batteries (For EV Vehicles)

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Ganesh Kumar's profile on Hashnode. Student CSE | ML and DL

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CSE Undergrad at Vellore Institute of Technology - Cited by 1 - Computer Science - Data Science - Machine Learning - Artificial Intelligence - Computer Vision

Ganesh Kumar

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Hi, I'm Ganesh, a Pre-Final year student at VIT (Vellore Institute of Technology) with a passion for AI and ML. I'm particularly fascinated by Generative AI, NLP, and Computer Vision. Excited to explore the endless possibilities of AI and contribute to its advancement. <333