Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture

Li, Lingling; Wang, Pengchong; Chao, Kuei‐Hsiang; Zhou, Yatong; Xie, Yang

doi:10.1371/journal.pone.0163004

Cited by 43 publications

(18 citation statements)

References 23 publications

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“…And the short-term forecasting model and long-term forecasting model are used to forecast the health indicators. Li et al (2016) used a Gaussian mixture method to forecast cell life. In this method, different trajectory segments are predicted by using different Gaussian regression models.…”

Section: Literature Reviewmentioning

confidence: 99%

Life prediction of lithium-ion battery based on a hybrid model

Chen

Wun

Wang

et al. 2020

Energy Exploration & Exploitation

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Lithium battery is a new energy equipment. Because of its long service life and high energy density, it is widely used in various industries. However, as the number of uses increases, the life of the energy battery gradually decreases. Aging of battery will bring security risks to energy storage system. Through the life prediction of energy lithium battery, the health status of energy battery is assessed, so as to improve the safety of energy storage system. Therefore, a hybrid model is proposed to predict the life of the energy lithium battery. The lithium-ion battery capacity data are always divided into two scales, which are predicted by extreme learning machine and support vector machine model. The energy lithium-ion battery capacity attenuation data were obtained through experiments. The original signal is decomposed into five layers by using the wavelet basis function to denoise the signal. Finally, the denoised signal is synthesized. The noise reduction effect of each wavelet was analyzed. The analysis results show that the mean square error value of the Haar wavelet is 5.31e-28, which indicates that the Haar wavelet has the best noise reduction effect. Finally, the combined model was tested by using two sets of experiments. The prediction results of the combined model are compared with those of the single model. The test results show that the prediction results of the combined model are better than the single model for either experiment 1 or experiment 2. Experiment 1 indicated the root mean square error values are 29.58 and 79.68% smaller than the root mean square error values of extreme learning machine and support vector machine. The model proposed in this study has positive significance for the safety improvement of energy storage system and can promote the development and utilization of energy resources.

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Section: Literature Reviewmentioning

confidence: 99%

Life prediction of lithium-ion battery based on a hybrid model

Chen

Wun

Wang

et al. 2020

Energy Exploration & Exploitation

View full text Add to dashboard Cite

show abstract

“…Since the stochastic process can well describe the uncertainty of the degradation process [41,42], many scholars have used the stochastic process to model the degradation of lithium-ion batteries for RUL prediction [41,43]. The degradation model based on the stochastic process mainly includes the Gaussian process and the Wiener process [3,44]. These methods can obtain the probability distribution function (PDF) of RUL, which could well describe the uncertainty of the degradation process for lithium-ion batteries.…”

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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“…49 The ultimate lifetime of a battery is crucially determined by its mode of operation and is typically reported to vary within a large range between a few to above 10 years, and the prediction of remaining useful time of a battery is quite complex. 50 A BMS is usually put in place for ensuring battery packs only operate in a region of parameters of current, temperature, and voltage where degradation is minimal and lifetime is extended to the maximum. Clearly, the definition of the optimal region of operation needs to take also into account the ultimate purpose of the batteries (whether, e.g., high power is demanded) and their physical location.…”

Section: Lifetime and Safety Concernsmentioning

confidence: 99%