Estimation of Remaining Useful Life and SOH of Lithium Ion Batteries (For EV Vehicles)
This addresses the need for reliable battery life prediction in electric vehicles to prevent failures and reduce costs, but it appears incremental as it builds on existing methods.
The paper tackles the problem of accurately estimating the remaining useful life of lithium-ion batteries for electric vehicles by proposing a machine learning approach that uses battery performance parameters, achieving effective results compared to state-of-the-art methods.
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.