A two-stage integrated method for early prediction of remaining useful life of lithium-ion batteries

Ma, Guijun; Wang, Zidong; Liu, Weibo; Fang, Jingzhong; Zhang, Yong; Ding, Han; Yuan, Ye

doi:10.1016/j.knosys.2022.110012

Cited by 34 publications

(4 citation statements)

References 54 publications

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“…Notable among these adaptations is the kernel ELM (KELM) [40,41], which introduces a kernel-based approach in order to augment the ELM's capabilities, enabling it to tackle complex patterns in battery data. Additionally, the emergence of the multiple-kernel ELM (MKELM) [42] signifies a stride towards accommodating varying data representations, thus contributing to the refinement of RUL estimates. This study introduced a new model for predicting the RUL of batteries named the particle filter-temporal attention mechanismbidirectional gated recurrent unit (PF-BiGRU-TSAM).…”

Section: The Rul Based On Machine Learning Modelsmentioning

confidence: 99%

A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles

Jafari,

Byun

2023

Computers

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Predicting the remaining useful life (RUL) is a pivotal step in ensuring the reliability of lithium-ion batteries (LIBs). In order to enhance the precision and stability of battery RUL prediction, this study introduces an innovative hybrid deep learning model that seamlessly integrates convolutional neural network (CNN) and gated recurrent unit (GRU) architectures. Our primary goal is to significantly improve the accuracy of RUL predictions for LIBs. Our model excels in its predictive capabilities by skillfully extracting intricate features from a diverse array of data sources, including voltage (V), current (I), temperature (T), and capacity. Within this novel architectural design, parallel CNN layers are meticulously crafted to process each input feature individually. This approach enables the extraction of highly pertinent information from multi-channel charging profiles. We subjected our model to rigorous evaluations across three distinct scenarios to validate its effectiveness. When compared to LSTM, GRU, and CNN-LSTM models, our CNN-GRU model showcases a remarkable reduction in root mean square error, mean square error, mean absolute error, and mean absolute percentage error. These results affirm the superior predictive capabilities of our CNN-GRU model, which effectively harnesses the strengths of both CNNs and GRU networks to achieve superior prediction accuracy. This study draws upon NASA data to underscore the outstanding predictive performance of the CNN-GRU model in estimating the RUL of LIBs.

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Section: The Rul Based On Machine Learning Modelsmentioning

confidence: 99%

A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles

Jafari,

Byun

2023

Computers

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“…Next, we compare the cubic polynomial function with four other typical nonlinear functions, which are denoted as Λ(t; θ) = t b , Λ(t; θ) = exp(bt) − 1, Λ(t; θ) = exp(bt) + c exp(dt) [30][31][32], and Λ(t; θ) = exp(bt) + ct 2 [35]. In order to visually compare the fitting effect of these nonlinear degradation models, the lithium-ion batteries' degradation data CS235 from University of Maryland were selected for experimental verification, and the results are shown in Figure 1.…”

Section: Nonlinear Degradation Model Based On a Cubic Polynomial Func...mentioning

confidence: 99%

“…The first aspect was selecting different nonlinear models and fitting the degradation process in order to improve the fitting accuracy. Currently, there are various types of nonlinear models for modeling lithium-ion batteries, such as the double exponential function [30][31][32], the combination of the exponential function and the linear function [33,34], the combination of the exponential function and the quadratic function [35], the combination of the power function and the exponential function [21], the combination of the logarithmic function and the polynomial [36], and polynomial functions (quadratic polynomial [37,38], cubic polynomial [39], quintic polynomial [40]). However, current research focuses mostly on analyzing the effects of different nonlinear functions from the perspective of model fitting of data, and there are few comparisons of typical performance degradation characteristics of lithium-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Cubic Polynomial Degradation Model and Envelope Extraction

Su,

Deng,

Tang

et al. 2023

Batteries

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Remaining useful life (RUL) prediction has become one of the key technologies for reducing costs and improving safety of lithium-ion batteries. To our knowledge, it is difficult for existing nonlinear degradation models of the Wiener process to describe the complex degradation process of lithium-ion batteries, and there is a problem with low precision in parameter estimation. Therefore, this paper proposes a method for predicting the RUL of lithium-ion batteries based on a cubic polynomial degradation model and envelope extraction. Firstly, based on the degradation characteristics of lithium-ion batteries, a cubic polynomial function is used to fit the degradation trajectory and compared with other nonlinear degradation models for verification. Secondly, a subjective parameter estimation method based on envelope extraction is proposed that estimates the actual degradation trajectory by using the average of the upper and lower envelope curves of the degradation data of lithium-ion batteries and uses the maximum likelihood estimation (MLE) method to estimate the unknown model parameters in two steps. Finally, for comparison with several typical nonlinear models, experiments are carried out based on the practical degradation data of lithium-ion batteries. The effectiveness of the proposed method to improve the accuracy of RUL prediction for lithium-ion batteries was demonstrated in terms of the mean square error (MSE) of the model and MSE of RUL prediction.

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“…However, numerical predictions often fail to adapt to different practical application scenarios. Moreover, battery degradation trajectories are complex and highly nonlinear, posing challenges in developing accurate RUL prediction models [12]. Then the RUL prediction model should not only accurately determine the future degradation of LIBs but also possess flexibility and adaptability to the specific conditions and provide an assessment of the uncertainty [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Battery Based on Adaptive Fractional Lévy Stable Motion with Capacity Regeneration and Random Fluctuation Phenomenon

Song,

Chen,

Wang

et al. 2023

Fractal Fract

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The capacity regeneration phenomenon is often overlooked in terms of prediction of the remaining useful life (RUL) of LIBs for acceptable fitting between real and predicted results. In this study, we suggest a novel method for quantitative estimation of the associated uncertainty with the RUL, which is based on adaptive fractional Lévy stable motion (AfLSM) and integrated with the Mellin–Stieltjes transform and Monte Carlo simulation. The proposed degradation model exhibits flexibility for capturing long-range dependence, has a non-Gaussian distribution, and accurately describes heavy-tailed properties. Additionally, the nonlinear drift coefficients of the model can be adaptively updated on the basis of the degradation trajectory. The performance of the proposed RUL prediction model was verified by using the University of Maryland CALEC dataset. Our forecasting results demonstrate the high accuracy of the method and its superiority over other state-of-the-art methods.

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A two-stage integrated method for early prediction of remaining useful life of lithium-ion batteries

Cited by 34 publications

References 54 publications

A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles

A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Cubic Polynomial Degradation Model and Envelope Extraction

Remaining Useful Life Prediction of Lithium-Ion Battery Based on Adaptive Fractional Lévy Stable Motion with Capacity Regeneration and Random Fluctuation Phenomenon

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