A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

Abstract

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods.

State‐of‐health estimation for lithium‐ion batteries based on partial charging segment and stacking model fusion

“…Herein, the choice of health features (HFs) and the prediction framework are two primary points that influence the accuracy of remaining useful life estimation. 15 From the present research, BP neural network, 16 support vector regression (SVR), 17 and Gaussian process regression (GPR) 18 can realize battery capacity estimation combined with meaningful HFs. Different HFs have been extracted from charging and discharging curves to describe battery aging.…”

“…Data‐driven methods, primarily applying machine learning, have been extensively utilized for battery state prediction. Herein, the choice of health features (HFs) and the prediction framework are two primary points that influence the accuracy of remaining useful life estimation 15 . From the present research, BP neural network, 16 support vector regression (SVR), 17 and Gaussian process regression (GPR) 18 can realize battery capacity estimation combined with meaningful HFs.…”

A hybrid method for online cycle life prediction of lithium‐ion batteries

“…When the battery capacity reaches 80% of its initial capacity, the battery is deemed to have reached the end of its useful life. 2 Regarding Li-ion battery's online RUL predictions, most published methods can be categorized into datadriven and hybrid methods. Machine learning models, filtering-based algorithms, and statistical models all fall under the category of data-driven methods.…”

“…The period between the initial prediction point of equipment performance and failure threshold represents the RUL. When the battery capacity reaches 80% of its initial capacity, the battery is deemed to have reached the end of its useful life 2 …”

Remaining useful life prediction of lithium-ion batteries based on wavelet denoising and transformer neural network

“…Thanks to the advantages of high energy density, long storage life, high safety, and no pollution, lithium-ion batteries are widely applied in the field of electric vehicles (Yuan et al, 2015;Wang et al, 2021). However, with the use of electric vehicles starting, irreversible electrochemical reactions occur in the onboard lithium-ion batteries, which will increase their internal resistance and decrease their maximum available capacity, leading to the attenuation of their remaining useful life (RUL) and a serious reduction of the driving distances of electric vehicles Ansari et al, 2022).…”