Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition

Zhu, Chunxiang; He, Zhiwei; Bao, Zhengyi; Sun, Changcheng; Gao, Mingyu

doi:10.3390/en16020803

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…Model capability [1] GRNN Type 1 [2] Residual network with attention mechanism Type 2 [3] TCN Type 2 [4] CNN-LSTM Type 2 [5], [6] LSTM Type 3 [7] Bi-directional GRU Type 3 [8] Bi-directional LSTM Type 3 [9] LSTM with attention Type 3 [10] Bi-directional LSTM with attention Type 3 [11] Transformer Type 3 [12] FFNN Type 3…”

Section: Paper Primary Architecturesmentioning

confidence: 99%

“…To further enhance their model, they utilized the complete ensemble empirical mode decomposition with Adaptive Noise to decompose the SOH prediction results. Similarly, Zhu et al [8] employed a bi-directional (Bi) LSTM for RUL prediction. They addressed the issue of capacity regeneration by utilizing variational mode decomposition.…”

Section: Paper Primary Architecturesmentioning

confidence: 99%

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A machine learning framework for remaining useful lifetime prediction of li-ion batteries using diverse neural networks

Lee,

Sun,

Liu

et al. 2023

Preprint

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Accurate prediction of the remaining useful life (RUL) of li-ion batteries (LIBs) is essential for enhancing the operational efficiency and safety of LIB-powered applications. It also facilitates improvements in the cell design process and the evolution of fast charging methodologies, thereby minimizing cycle testing time. While artificial neural networks (ANNs) have emerged as promising tools for this task, identifying the optimal architecture across diverse datasets and optimization strategies is non-trivial. To address this challenge, a machine learning framework was developed for a systematic evaluation of diverse ANN architectures. Utilizing just 30% of the training dataset from 124 li-ion batteries cycled under various charging policies, hyperparameter optimization is conducted within this framework. This ensures that each model is evaluated at its optimal configuration, facilitating a balanced comparison for RUL prediction tasks. Furthermore, the study examines the influence of varied cycle windows on model efficacy. Employing a stratified partitioning method highlights the importance of uniform dataset representation across different subsets. Notably, the top-performing model, using cycle-by-cycle features from just 40 cycles, achieves a mean absolute percentage error of 10.7%.

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Section: Paper Primary Architecturesmentioning

confidence: 99%

Section: Paper Primary Architecturesmentioning

confidence: 99%

A machine learning framework for remaining useful lifetime prediction of li-ion batteries using diverse neural networks

Lee,

Sun,

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, VMD has high decomposition efficiency and is highly resistant to noise. Therefore, in this paper we adopt VMD as the signal processing algorithm to process the capacity attenuation signal of lithium batteries [18][19][20]. Long-and short-term time-series networks (LSTNet) [21] have recently been widely adopted in diagnostic and predictive scenarios due to its excellent performance.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

Wang,

Ye,

et al. 2023

Sustainability

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Accurately predicting the remaining useful life (RUL) of lithium-ion batteries holds significant importance for their health management. Due to the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, a single model may exhibit poor prediction accuracy and generalization performance under a single scale signal. This paper proposes a method for predicting the RUL of lithium-ion batteries. The method is based on the improved sparrow search algorithm (ISSA), which optimizes the variational mode decomposition (VMD) and long- and short-term time-series network (LSTNet). First, this study utilized the ISSA-optimized VMD method to decompose the capacity degradation sequence of lithium-ion batteries, acquiring global degradation trend components and local capacity recovery components, then the ISSA–LSTNet–Attention model and ISSA–LSTNet–Skip model were employed to predict the trend component and capacity recovery component, respectively. Finally, the prediction results of these different models were integrated to accurately estimate the RUL of lithium-ion batteries. The proposed model was tested on two public lithium-ion battery datasets; the results indicate a root mean square error (RMSE) under 2%, a mean absolute error (MAE) under 1.5%, and an absolute correlation coefficient (R2) and Nash–Sutcliffe efficiency index (NSE) both above 92.9%, implying high prediction accuracy and superior performance compared to other models. Moreover, the model significantly reduces the complexity of the series.

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Prediction of total volatile basic nitrogen (TVB‐N) in fish meal using a metal‐oxide semiconductor electronic nose based on the VMD‐SSA‐LSTM algorithm

Li,

et al. 2024

J Sci Food Agric

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BACKGROUNDThe total volatile basic nitrogen (TVB‐N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB‐N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB‐N, in this article, a self‐developed electronic nose (e‐nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB‐N value was established to complete the freshness detection.RESULTSThe TVB‐N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short‐term memory (LSTM) models, and finally, the final TVB‐N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD‐LSTM model optimized by SSA in predicting TVB‐N. The coefficient of determination (R2), the root‐mean‐squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%, respectively.CONCLUSIONSThis method improves the performance of e‐nose in detecting the freshness of fish meal and provides a reference for the quality detection of e‐nose in other materials. © 2024 Society of Chemical Industry.

show abstract

Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition

Cited by 8 publications

References 35 publications

A machine learning framework for remaining useful lifetime prediction of li-ion batteries using diverse neural networks

A machine learning framework for remaining useful lifetime prediction of li-ion batteries using diverse neural networks

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

Prediction of total volatile basic nitrogen (TVB‐N) in fish meal using a metal‐oxide semiconductor electronic nose based on the VMD‐SSA‐LSTM algorithm

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