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Lithium-ion batteries are widely used in various applications, includingportable electronic devices, electric vehicles, and renewable energy storagesystems. Accurately estimating the remaining useful life of these batteries iscrucial for ensuring their optimal performance, preventing unexpected failures,and reducing maintenance costs. In this paper, we present a comprehensivereview of the existing approaches for estimating the remaining useful life oflithium-ion batteries, including data-driven methods, physics-based models, andhybrid approaches. We also propose a novel approach based on machine learningtechniques for accurately predicting the remaining useful life of lithium-ionbatteries. Our approach utilizes various battery performance parameters,including voltage, current, and temperature, to train a predictive model thatcan accurately estimate the remaining useful life of the battery. We evaluatethe performance of our approach on a dataset of lithium-ion battery cycles andcompare it with other state-of-the-art methods. The results demonstrate theeffectiveness of our proposed approach in accurately estimating the remaininguseful life of lithium-ion batteries.

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