Remaining useful life prediction of lithium-ion batteries using CEEMDAN and WOA-SVR model

Meng, Xianmeng; Cai, Cuicui; Wang, Yueqin; Wang, Qijian; Liu, Tan

doi:10.3389/fenrg.2022.984991

Cited by 15 publications

(6 citation statements)

References 34 publications

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“…Therefore, CEEMDAN not only improves the mode mixing phenomenon of EMD but also solves the computational complexity issue of EEMD and CEEMD. CEEMDAN is widely applied in the field of signal denoising [23][24][25], such as in mechanical fault diagnosis and predictive maintenance [26][27][28]. It improves the quality and accuracy of signals by decomposing non-stationary vibration signals into multiple IMFs and noise and then removing noise through adaptive noise estimation.…”

Section: Ceemdan Principles and Improvementsmentioning

confidence: 99%

A Study of Fault Signal Noise Reduction Based on Improved CEEMDAN-SVD

Zhao,

Ma,

et al. 2023

Applied Sciences

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In light of the challenges posed by the complex structural characteristics and significant coupling of vibration signals in rotating machinery, this study proposes an adaptive noise reduction method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Additionally, an enhanced threshold screening Singular Value Decomposition (SVD) algorithm is introduced to address the issues pertaining to noise identification and feature extraction in the context of vibration signals from rotating machinery, which are subjected to complex noise interference. The effectiveness of the proposed approach is substantiated through the evaluation of key metrics, such as the signal-to-noise ratio (SNR), as well as the utilization of advanced signal analysis techniques, including Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). The experimental results validate the finding that the combination of the improved CEEMDAN and the enhanced threshold screening SVD algorithm effectively reduces noise interference in vibration signals from rotating machinery. This integrated denoising approach successfully preserves the informative characteristics of the vibration signals, thereby laying a foundation for the subsequent fault diagnosis of rotating machinery.

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Section: Ceemdan Principles and Improvementsmentioning

confidence: 99%

A Study of Fault Signal Noise Reduction Based on Improved CEEMDAN-SVD

Zhao,

Ma,

et al. 2023

Applied Sciences

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“…This allows the classification problem to be transformed into a constraint value problem. This is illustrated by function (1) [8][10]:…”

Section: Svr Modelmentioning

confidence: 99%

“…In reference [1], The whale optimization algorithm (WOA) was combined with variational mode decomposition (VMD) and short-term memory neural networks (LSTMs) to produce a prediction model. In reference [8], a new method for predicting LIBs RUL was developed, which incorporates ensemble empirical mode decomposition with adaptive noise (CEEDMAN), WOA, and support vector regression (SVR). According to reference [9], the state of health (SOH) and RUL of LIBs based on capacity were predicted using a temporal convolutional network model.…”

Section: Introductionmentioning

confidence: 99%

An Online Forecasting Method of Remaining Useful Life of Lithium-Ion Batteries Based on TPE-SVR

Deng,

Zhu,

Shen

et al. 2023

Advances in Transdisciplinary Engineering

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Lithium-ion batteries with potential safety hazards require accurate forecasts of their remaining useful life to be maintained and replaced. An online forecasting method is proposed, in which the training dataset before a pre-set operating time for the start of forecasting is automatically extended in order to train support vector regression model and then the key hyperparameters of the model is optimized by tree-structured parzen estimator algorithm to update the model used to forecast battery capacity for the next cycle. Verified by NASA lithium-ion batteries datasets, this method provides higher forecasting accuracy and generalization ability, which is suitable for the scenario of online accurate short-term forecasting based on a limited amount of historical data.

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“…CEEMDAN is an improved algorithm based on EEMD and EMD. It has the advantages of a good mode spectrum separation effect, fewer shielding iterations, and low calculation cost, and is often used to process non-stationary and nonlinear signals [38,39]. The specific steps of CEEMDAN are as follows:…”

Section: Complete Ensemble Empirical Mode Decomposition With Adaptive...mentioning

confidence: 99%

Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model

Tang

Wan

Wang³

et al. 2023

Sustainability

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Accurate prediction of the remaining useful life (RUL) is a key function for ensuring the safety and stability of lithium-ion batteries. To solve the capacity regeneration and model adaptability under different working conditions, a hybrid RUL prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a bi-directional gated recurrent unit (BiGRU) is proposed. CEEMDAN is used to divide the capacity into intrinsic mode functions (IMFs) to reduce the impact of capacity regeneration. In addition, an improved grey wolf optimizer (IGOW) is proposed to maintain the reliability of the BiGRU network. The diversity of the initial population in the GWO algorithm was improved using chaotic tent mapping. An improved control factor and dynamic population weight are adopted to accelerate the convergence speed of the algorithm. Finally, capacity and RUL prediction experiments are conducted to verify the battery prediction performance under different training data and working conditions. The results indicate that the proposed method can achieve an MAE of less than 4% with only 30% of the training set, which is verified using the CALCE and NASA battery data.

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Remaining useful life prediction of lithium-ion batteries using CEEMDAN and WOA-SVR model

Cited by 15 publications

References 34 publications

A Study of Fault Signal Noise Reduction Based on Improved CEEMDAN-SVD

A Study of Fault Signal Noise Reduction Based on Improved CEEMDAN-SVD

An Online Forecasting Method of Remaining Useful Life of Lithium-Ion Batteries Based on TPE-SVR

Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model

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