Lithium-ion battery remaining useful life prediction under grey theory framework

Zhou, Zhenwei; Huang, Yun; Lu, Yudong; Shi, Zhichao; Liang-biao, Zhu; Wu, Jiliang; Li, Hui

doi:10.1109/phm.2014.6988182

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…To improve the prediction accuracy, in one study, the least-squares method with forgetting factors was employed to estimate and update the model parameters, and greater weights were given to the newly measured data. 123 In another study, a GM(1,1) and a residual GM(1,1) were used to describe battery degradation trend and prediction errors, respectively. 124,125 Moreover, the parameters of the above model were dynamically updated using online measurements to achieve high prediction accuracy.…”

Section: Statistical Approachmentioning

confidence: 99%

Battery Lifetime Prognostics

Lin

et al. 2020

Joule

794

244

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Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.

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Section: Statistical Approachmentioning

confidence: 99%

Battery Lifetime Prognostics

Lin

et al. 2020

Joule

794

244

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“…applicability and good prediction results. Many data-driven algorithms have been applied to the remaining life prediction of lithium batteries, such as support vector regression (SVR) [4], relevance vector machine (RVM) [5], gaussian process regression (GPR) [6], artificial neural network (ANN) [7], autoregressive integrated moving average (ARIMA ) [8], grey model (GM) [9], wiener process (WP) [10], etc. In recent years, hybrid methods combining model-based and data-driven methods to predict the RUL of LIBs have become a research hotspot, and hybrid methods attempt to exploit the advantages of both methods to achieve better prediction results.…”

Section: P P a A Xmentioning

confidence: 99%

Remaining useful life prediction of Lithium-ion battery using improved support vector regression and fuzzy information granulation

Chen

Gao

et al. 2022

J. Phys.: Conf. Ser.

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With the widespread use of lithium-ion batteries in various fields, battery Prognostics and Health Management (PHM) technologies are gaining more and more attention. Repeated use of batteries can lead to degradation of battery performance and thus affect battery life. Accurate prediction of the remaining useful life (RUL) of batteries is crucial and is the most central issue in battery PHM. In this paper, a method based on a combination of fuzzy information granulation (FIG) and support vector regression with artificial bee colony optimization (ABC-SVR) is proposed to estimate the RUL of Lithium-ion batteries. First, the capacity degradation data are divided into several windows using the FIG method. Second, the maximum and minimum values of each window are predicted separately using the ABC-SVR algorithm to obtain the information of the prediction window. Finally, the missing values of the prediction windows are complemented by the linear interpolation method to obtain the complete capacity prediction values, and the remaining useful life of the battery can be calculated according to the failure threshold. The results show that the proposed method obtains the RUL value with high accuracy.

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“…Thus, the GM weakens the randomness of the original data and improves the prediction accuracy. The gray prediction model predicts the growth trend of the data by establishing a differential equation model from the original data [32]. These advantages make it suitable to predict the long-term RUL of the lithium-ion battery.…”

Section: Gray Forecasting Theorymentioning

confidence: 99%

A Hybrid Method for Remaining Useful Life Estimation of Lithium-Ion Battery with Regeneration Phenomena

2019

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The lithium-ion battery has become the primary energy source of many electronic devices. Accurately forecasting the remaining useful life (RUL) of a battery plays an essential role in ensuring reliable operatioin of an electronic system. This paper investigates the lithium-ion battery RUL prediction problem with capacity regeneration phenomena. We aim to reduce the accumulation of the prediction error by integrating different capacity degradation models and thereby improve the prediction accuracy of the long-term RUL. To describe the degradation process more accurately, we decoupled the degradation process into two types: capacity regeneration and normal degradation. Then, we modelled two kinds of degradation processes separately. In the prediction phase, we predicted the battery state of health (SOH) by using the relevance vector machine (RVM) and the gray model (GM) alternately, updated the training dataset according to the prediction results, and then updated the RVM and GM. The RVM and GM correct each other’s prediction results constantly, which reduces the cumulative error of prediction and improves the prediction accuracy of the battery SOH. Experimental results with the National Aeronautics and Space Administration (NASA) battery dataset demonstrated that the proposed method can accurately establish the degradation model and achieve better performance for the RUL estimation as compared with the single RVM or GM methods.

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Lithium-ion battery remaining useful life prediction under grey theory framework

Cited by 7 publications

References 6 publications

Battery Lifetime Prognostics

Battery Lifetime Prognostics

Remaining useful life prediction of Lithium-ion battery using improved support vector regression and fuzzy information granulation

A Hybrid Method for Remaining Useful Life Estimation of Lithium-Ion Battery with Regeneration Phenomena

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