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

Tang, Xuliang; Wan, Heng; Wang, Weiwen; Gu, Mengxu; Wang, Linfeng; Gan, Linfeng

doi:10.3390/su15076261

Cited by 11 publications

(2 citation statements)

References 48 publications

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“…To further illustrate the superior performance of our proposed method, we conducted a comparison between it and approaches previously reported in the literature. Tang et al [36] proposed a hybrid model based on CEEMDAN-IGWO-BiLSTM to predict the RUL of batteries, while Hu et al [37] proposed an RUL prediction method for lithium-ion batteries based on DEGWO-MSVR. Tables 12 and 13 compare the prediction results of the aforemen-tioned two algorithms and the proposed hybrid method (unavailable data in references are denoted by "-").…”

Section: Databasementioning

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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Section: Databasementioning

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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show abstract

“…Compared to the single channel-based method, this method significantly reduces prediction errors. Tang [17] introduced a hybrid RUL prediction model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a bi-directional gated recurrent unit, along with genetic algorithm-based factor improvement and dynamic population weighting to accelerate algorithm convergence. Danniel [18] proposed a deep learning-based method trained on the widely used Oxford battery degradation dataset, utilizing Generative Adversarial Networks (GANs).…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning-Based Remaining Useful Life Prediction Method for Lithium-Ion Batteries Considering Individual Differences

Gu,

Liu

2024

Applied Sciences

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With the wide utilization of lithium-ion batteries in the fields of electronic devices, electric vehicles, aviation, and aerospace, the prediction of remaining useful life (RUL) for lithium batteries is important. Considering the influence of the environment and manufacturing process, the degradation features differ between the historical batteries and the target ones, and such differences are called individual differences. Currently, lithium battery RUL prediction methods generally use the characteristics of a large group of historical samples to represent the target battery. However, these methods may be vulnerable to individual differences between historical batteries and target ones, which leads to poor accuracy. In order to solve the issue, this paper proposes a prediction method based on transfer learning that fully takes individual differences into consideration. It utilizes an extreme learning machine (ELM) twice. In the first stage, the relationship between the capacity degradation rate and the remaining capacity is constructed by an ELM to obtain the adjusting factor. Then, an ELM-based transfer learning method is used to establish the connection between the remaining capacity and the RUL. Finally, the prediction result is adjusted by the adjusting factor obtained in the first stage. Compared with existing typical data-driven models, the proposed method has better accuracy and efficiency.

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Accurate remaining useful life estimation of lithium-ion batteries in electric vehicles based on a measurable feature-based approach with explainable AI

Jafari,

Byun

2023

J Supercomput

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

Cited by 11 publications

References 48 publications

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

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

Transfer Learning-Based Remaining Useful Life Prediction Method for Lithium-Ion Batteries Considering Individual Differences

Accurate remaining useful life estimation of lithium-ion batteries in electric vehicles based on a measurable feature-based approach with explainable AI

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