Remaining useful life prediction for lithium-ion batteries based on an integrated health indicator

Sun, Yongquan; Hao, Xueling; Pecht, Michael; Zhou, Yapeng

doi:10.1016/j.microrel.2018.07.047

Cited by 55 publications

(32 citation statements)

References 17 publications

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“…In Algorithm 1, several model forms such as single exponential function [21], dual exponential function [22], power function [23], hybrid linear function [24], and polynomial function [25] are generally used for f (•), there also exists lots of algorithms to identify these models' parameters. Among these methods, MATLAB nonlinear curve fitting algorithm has been packed into the standard toolbox and widely used in different areas.…”

Section: A Conventional Empirical Model-based Predictionmentioning

confidence: 99%

“…Rather than using complex electrochemical models, empirical model based methods portray the battery capacity degradation over time with mathematic functions such as single exponential function [21], dual exponential function [22], power function [23], hybrid linear function [24], and polynomial function [25]. Then different data-fitting techniques such as particle filters [22], Kalman filters [26] or some offline optimization algorithms [27,28] are employed to identify the models' parameters.…”

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confidence: 99%

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Model Migration Neural Network for Predicting Battery Aging Trajectories

Tang

Liu

Wang

et al. 2020

IEEE Trans. Transp. Electrific.

143

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Section: A Conventional Empirical Model-based Predictionmentioning

confidence: 99%

mentioning

confidence: 99%

Model Migration Neural Network for Predicting Battery Aging Trajectories

Tang

Liu

Wang

et al. 2020

IEEE Trans. Transp. Electrific.

143

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“…The existing RUL prediction methods for lithium-ion batteries mainly include the adaptive filtering method, the artificial intelligence method, and the stochastic process modeling method. The adaptive filtering mainly includes Kalman filtering [14], extended Kalman filtering [15], unscented Kalman filtering [16,17], particle filtering [18][19][20][21][22][23], and unscented particle filtering [24,25], etc. Although the adaptive filtering has higher accuracy for RUL prediction, the accuracy can be easily influenced by time-varying current and ambient temperature [3].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Substituting (20) and (21) into log-likelihood function (17), and simplifying, gives the profile log-likelihood function for b in terms of the estimated (a, σ 2 r ) as:…”

Section: Parameters Estimationmentioning

confidence: 99%

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Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

et al. 2019

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Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.

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Machinelearning‐basedapproach for useful capacity prediction ofsecond‐lifebatteries employing appropriate input selection

Bhatt

Ongsakul²,

Madhu

et al. 2021

Intl J of Energy Research

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Electric vehicle-discarded second-life batteries still contain 80% of usable capacity and can serve as a low-cost alternative for microgrid storage applications where the battery storage capacity is flexible against transport applications. By accurately predicting the remaining useful capacity or state of health of these batteries, using the data from their first life operation, their costeffectiveness for microgrid energy management can be analyzed. For this purpose, three machine learning models are proposed here. The input parameters for the models are selected from the charging and discharging profiles of batteries, considering both the aging and regeneration phenomenon. Eight different input cases with and without K-fold (K = 10) cross-validation are used for training the proposed models. Based on the comparative analysis it is found that all the models trained with K-fold cross-validation, show minimum error as compared to without K-fold. The forecasting results from multiple approaches showed that the long short term memory model trained with battery discharging profile outperformed other models, quantified by multiple error indices, including root mean square error (0.009147), mean absolute error (0.005841), and R 2 (0.9713). The robustness of the model is validated with multiple battery datasets. Further, the study illustrates the importance of activation functions, in machine learning models used for forecasting.

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Remaining useful life prediction for lithium-ion batteries based on an integrated health indicator

Cited by 55 publications

References 17 publications

Model Migration Neural Network for Predicting Battery Aging Trajectories

Model Migration Neural Network for Predicting Battery Aging Trajectories

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

Machinelearning‐basedapproach for useful capacity prediction ofsecond‐lifebatteries employing appropriate input selection

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