Physics-Informed Markov Chain Model to Identify Degradation Pathways of Lithium-Ion Cells

Kim, Jun Hyeong; Kwak, Eunji; Jeong, Jinho; Oh, Ki‐Yong

doi:10.1109/tte.2023.3302433

Cited by 1 publication

(1 citation statement)

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“…They analyze a large amount of experimental data obtained throughout the lifecycle of mechanical equipment, and then utilize mathematical statistics and probability theory to analyze and process the data, thereby obtaining statistical predictions of RUL based on reliability criteria. Kim et al [3] proposed a physics-based Markov chain model to identify degradation pathways of lithium-ion batteries. By analyzing the direct correlations between phenomena and states, they used capacity measurements to identify degradation pathways and predict the remaining useful life.…”

Section: Introductionmentioning

confidence: 99%

Turbofan Engine Health Assessment Based on Spatial–Temporal Similarity Calculation

Peng,

Hu,

Tang

2023

Sensors

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Aiming at the problem of the remaining useful life prediction accuracy being too low due to the complex operating conditions of the aviation turbofan engine data set and the original noise of the sensor, a residual useful life prediction method based on spatial–temporal similarity calculation is proposed. The first stage is adaptive sequence matching, which uses the constructed spatial–temporal trajectory sequence to match the sequence to find the optimal matching sample and calculate the similarity between the two spatial–temporal trajectory sequences. In the second stage, the weights of each part are assigned by the two weight allocation algorithms of the weight training module, and then the final similarity is calculated by the similarity calculation formula of the life prediction module, and the final predicted remaining useful life is determined according to the size of the similarity and the corresponding remaining life. Compared with a single model, the proposed method emphasizes the consistency of the test set and the training set, increases the similarity between samples by sequence matching with other spatial–temporal trajectories, and further calculates the final similarity and predicts the remaining use through the weight allocation module and the life prediction module. The experimental results show that compared with other methods, the root mean square error (RMSE) index and the remaining useful life health score (Score) index are reduced by 12.6% and 14.8%, respectively, on the FD004 dataset, and the RMSE index is similar to that in other datasets; the Score index is reduced by about 10%, which improves the prediction accuracy of the remaining useful life and can provide favorable support for the operation and maintenance decision of turbofan engines.

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Section: Introductionmentioning

confidence: 99%