State‐of‐health estimation and remaining useful life for lithium‐ion battery based on deep learning with Bayesian hyperparameter optimization

Kong, De‐Xing; Wang, Shuhui; Ping, Ping

doi:10.1002/er.7548

Cited by 38 publications

(21 citation statements)

References 46 publications

(48 reference statements)

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“…Therefore, it is important to establish the simple and effective health indicator. Some studies have shown that trend of degeneration is closely related to interval of charging saturation voltage 2,36,37 . Nevertheless, it is difficult to obtain the curve of complete charging saturation voltage.…”

Section: Health Indicator Extractionmentioning

confidence: 99%

“…Some studies have shown that trend of degeneration is closely related to interval of charging saturation voltage. 2,36,37 Nevertheless, it is difficult to obtain the curve of complete charging saturation voltage. Therefore, the ECVT is established for extraction health indicator.…”

Section: Health Indicator Reconstructionmentioning

confidence: 99%

“…Lithium-ion batteries are widely applied in electric vehicles (EVs), energy storage system (ESS), and consumer electronics due to the advantages of stable voltage, high energy density, and low cost. 1,2 However, upon long-term cycling, the capacity degradation, and power fading can cause the failure of battery system functionalities or even serious safety problems. [3][4][5] Therefore, it is important to obtain the accurate and reliable RUL prediction for maintenance the battery management system (BMS).…”

Section: Introductionmentioning

confidence: 99%

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A data‐driven approach with error compensation and uncertainty quantification for remaining useful life prediction of lithium‐ion battery

Wei

Wang

et al. 2022

Intl J of Energy Research

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Summary To accurately predict the remaining useful life (RUL) of lithium‐ion battery, this paper presents a novel machine‐learning method using gated recurrent unit (GRU) with error compensation (EC). Moreover, the dropout Monte Carlo technology is selected for reliable RUL prediction with uncertainty quantification. First, the equal charging voltage time is established and the variational mode decomposition is introduced to reduce the influence of capacity regeneration phenomenon. Then, the phase space reconstruction with C‐C technology is adopted to obtain the optimal input sequence, and the GRU with EC is established for RUL prediction. Finally, the probability distribution and 95% confidence interval are obtained based on dropout Monte Carlo technology. Compared with the existing methods, the proposed method can not only obtain a higher accuracy with a mean absolute error below 1.55% but also achieve reliable RUL prediction with probability distribution.

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Section: Health Indicator Extractionmentioning

confidence: 99%

Section: Health Indicator Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A data‐driven approach with error compensation and uncertainty quantification for remaining useful life prediction of lithium‐ion battery

Wei

Wang

et al. 2022

Intl J of Energy Research

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“…Kong et al [15] proposed a framework combining deep convolutional neutral network (DCNN) and two-layer LSTM, used for lithium-ion battery state-of-health (SOH) estimation and RUL prediction. Zheng et al [16] combined a multilayer LSTM unit with a standard feedforward layer to form a novel LSTM-based prediction model.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification

Yang

Peng

Xie

et al. 2022

Sensors

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To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in real-time. Meanwhile, external uncertainties significantly impact bearing degradation. Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. First, a fusion metric related to runtime (or degradation) is proposed to reflect the latent degradation process. Then, an improved dropout method based on nonparametric kernel density is developed to improve estimation accuracy of RUL. The PHM2012 dataset is adopted to verify the proposed method, and comparison results illustrate that the proposed prediction model can accurately obtain the point estimation and probability distribution of the bearing RUL.

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“…The rapid decrease in the cycle performance of high‐Ni LNCM materials is related to unwanted side reactions between the transition metal ions (especially Ni 4+ ) and the electrolyte: the highly activated Ni 4+ ions can accelerate the electrolyte decomposition, which not only leads to electrolyte depletion and formation of thick solid‐electrolyte interface (SEI) layers but is also associated with a phase transformation to inert rock salt structures (NiO) or spinel structures at the particle surface. In addition, because the ionic radius of Ni 2+ is similar to that of Li + , cation mixing occurs in which Ni ions are located in the Li layer 7‐11 . For this reason, Li that is not located in the Li layer reacts with CO 2 present in the air during the heat treatment process to form Li 2 CO 3 or reacts with water to form LiOH, which are referred to as residual Li compounds.…”

Section: Introductionmentioning

confidence: 99%

Design of a hydrolysis‐supported coating layer on the surface of Ni‐rich cathodes in secondary batteries

Park

Lee

Kim

et al. 2022

Intl J of Energy Research

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Summary The formation of Li4SiO4 (LSO) coating layer on LiNi0.88Co0.05Mn0.07O2 (LNCM) was achieved by incorporating a new tetraethyl orthosilicate (TEOS)‐dropping coating method. This concept includes the hydrolysis reaction of TEOS on the surface of Ni0.88Co0.05Mn0.07(OH)2 and the subsequent calcination process with lithium source to obtain LSO‐coated LNCM cathode materials successfully during the calcination process. This method provides the driving force for the formation of a more uniform and thin coating layer compared with the traditional wet‐chemical coating method. The bare LNCM and LSO‐coated LNCM showed similar capacity retention rates during room temperature (25°C) cycling, but the capacity retention of LSO‐LNCM (81.6% after 100 cycles) for the cycling test at elevated temperature was significantly increased compared with bare LNCM (63.69% after 100 cycles). Additionally, the Li‐ion accessibility of the coated LNCM electrode was improved by the existence of the Li‐containing coating materials, and the results of analysis by the galvanostatic intermittent titration technique (GITT) confirmed the better Li‐ion diffusivity of the coated sample. These results indicate the high possibility of this novel coating method for application in various materials for developing secondary batteries.

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State‐of‐health estimation and remaining useful life for lithium‐ion battery based on deep learning with Bayesian hyperparameter optimization

Cited by 38 publications

References 46 publications

A data‐driven approach with error compensation and uncertainty quantification for remaining useful life prediction of lithium‐ion battery

A data‐driven approach with error compensation and uncertainty quantification for remaining useful life prediction of lithium‐ion battery

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification

Design of a hydrolysis‐supported coating layer on the surface of Ni‐rich cathodes in secondary batteries

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