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

TL;DR

This paper reviews existing methods for estimating lithium-ion battery lifespan and introduces a new machine learning approach that improves prediction accuracy using battery performance data.

Contribution

It presents a novel machine learning-based method for more accurate remaining useful life estimation of lithium-ion batteries in EVs, compared to existing approaches.

Findings

The proposed method outperforms state-of-the-art techniques in accuracy.

It effectively utilizes voltage, current, and temperature data for predictions.

The approach demonstrates high reliability on battery cycle datasets.

Abstract

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.