  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.
