State of health prediction of medical lithium batteries based on multi-scale decomposition and deep learning

Liu, Changchun; Wu, Tao; He, Cheng

doi:10.1177/1687814020923202

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…The sum of the decomposed sequences is equal to the original sequence, whose formula 1 is shown below. Each IMF component obtained by decomposition contains the local characteristic signal of the original data, and R(t) is the margin [4].…”

Section: Decomposition Based On Eemd and Correlation Analysismentioning

confidence: 99%

Lithium Battery Life Prediction Based on DBN and LSTM

Ran

Yuan

2023

J. Phys.: Conf. Ser.

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Aiming at the low adaptability and inaccuracy of a single network in predicting the Remaining Useful Life (RUL) of lithium batteries, this paper proposed a prediction method based on the combination of Deep Belief Network (DBN) and Mogrifier Long Short-Term Memory network (LSTM). Firstly, the Ensemble Empirical Mode Decomposition (EEMD) method was used to preprocess the capacity data of lithium battery, and the feature information of the data was extracted by correlation analysis, which can effectively solve the regeneration of battery capacity. In addition, the decomposed high-frequency and low-frequency data are trained and predicted by DBN and Mogrifier LSTM networks respectively. The experimental results show that this method has high effectiveness and superiority.

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Section: Decomposition Based On Eemd and Correlation Analysismentioning

confidence: 99%

Lithium Battery Life Prediction Based on DBN and LSTM

Ran

Yuan

2023

J. Phys.: Conf. Ser.

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“…In 2020 [20], the author integrated the deep Boltzmann machines and LSTM for obtaining the health prediction of a medical Li-ion battery. The empirical results obtain a good of SOH prediction.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Network Method for Predicting the SOH and RUL of Lithium-Ion Batteries

Zraibi¹,

Mansouri²,

Loukili³

et al. 2022

Adv. sci. technol. eng. syst. j.

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The use of a battery to power an electrical or electronic system is accompanied by battery management, i.e. a set of measures intended to preserve it for preventative maintenance, thus the cost reduction. This management is generally based on two key parameters, the (remaining useful life) RUL and the (State-of-health) SOH, which relate respectively to the charge output and the aging of the Lithium-ion battery. The issue will be resolved and advances in production, battery utilization, and optimization will be made possible by accurate SOH determination and dependable RUL prediction. The CNN-BGRU-DNN hybrid strategy, which we suggest in this study, integrates Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BGRU), and Deep Neural Networks (DNN) to increase the precision of SOH and RUL estimates for Lithium-ion batteries. To that purpose, the performance of the prediction findings is assessed using the MAE, RMSE, AE, and RE as well as the NASA datasets of lithium-ion batteries for experimental validation. The verification tests' findings show that, in comparison to existing approaches in the literature, the suggested method may greatly reduce prediction error and achieve high estimation accuracy of the battery's state of health.

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