A novel health indicator for on-line lithium-ion batteries remaining useful life prediction

Zhou, Yapeng; Huang, Miaohua; Chen, Yupu; Tao, Ye

doi:10.1016/j.jpowsour.2016.04.119

Cited by 217 publications

(77 citation statements)

References 29 publications

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“…The artificial intelligence method uses machine learning to fit the degradation path based on the monitored degradation data for RUL prediction. The typical artificial intelligence method includes support vector machine [26], support vector regression [27][28][29], relevance vector machine [30][31][32], relevance vector regression [17], neural network [33][34][35], genetic algorithm [36], particle swarm optimization algorithm [37], artificial fish swarm algorithm [38,39], and deep learning [40], etc. The artificial intelligent method can predict the non-linear system by using a simple algorithm and has a better prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

et al. 2019

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Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.

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Section: Literature Reviewmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

et al. 2019

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“…In order to make the correlation analysis meaningful, the correlation analysis should be carried out after removing the data with the PE value of 0 and the corresponding capacity. Table 2 has shown the advantage of the proposed HI based on PE compared with MVF [14] and DVD_ETI [12]. The results show that the similarity between PE and battery capacity is higher than that of MVF and DVD_ETI.…”

Section: Verification Of Hi Extractionmentioning

confidence: 99%

“…Zhang and Guo [13] used six different extracted features as the HI of battery degradation. Zhou et al [14] proposed the mean voltage falloff (MVF) for on-line monitoring. Analysis of previous studies on HIs indicates that TIEDVD, DVDETI, and MVF all need to consider how to select the interval parameters.…”

Section: Introductionmentioning

confidence: 99%

Novel Approach for Lithium-Ion Battery On-Line Remaining Useful Life Prediction Based on Permutation Entropy

Chen

Yilin

2018

Energies

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The degradation of lithium-ion battery often leads to electrical system failure. Battery remaining useful life (RUL) prediction can effectively prevent this failure. Battery capacity is usually utilized as health indicator (HI) for RUL prediction. However, battery capacity is often estimated on-line and it is difficult to be obtained by monitoring on-line parameters. Therefore, there is a great need to find a simple and on-line prediction method to solve this issue. In this paper, as a novel HI, permutation entropy (PE) is extracted from the discharge voltage curve for analyzing battery degradation. Then the similarity between PE and battery capacity are judged by Pearson and Spearman correlation analyses. Experiment results illustrate the effectiveness and excellent similar performance of the novel HI for battery fading indication. Furthermore, we propose a hybrid approach combining Variational mode decomposition (VMD) denoising technique, autoregressive integrated moving average (ARIMA), and GM(1,1) models for RUL prediction. Experiment results illustrate the accuracy of the proposed approach for lithium-ion battery on-line RUL prediction.

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“…General requirements for all LIBs in the design of hybrid DEPCs are safety and ease of use, and full discharge of SB under cyclic mode, but do not work on schedule of weakest element. This is achieved by the introduction of the selective BMS of BMA in PVGS, which monitors the battery charge and protection from dangerous operating modes and provides information on its main parameters [47][48][49].…”

Section: Issn 2226-3780mentioning

confidence: 99%

Conceptualization of research of power hybrid electric power complexes

Budashko

Glazeva

Samonov

2016

TAPR

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A novel health indicator for on-line lithium-ion batteries remaining useful life prediction

Cited by 217 publications

References 29 publications

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

Novel Approach for Lithium-Ion Battery On-Line Remaining Useful Life Prediction Based on Permutation Entropy

Conceptualization of research of power hybrid electric power complexes

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