Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC

Zhang, Xin; Miao, Qiang; Liu, Zhiwen

doi:10.1016/j.microrel.2017.02.012

Cited by 114 publications

(50 citation statements)

References 14 publications

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“…According to the predictive lines, we can calculate the RUL by (24) and (26). The results are tabulated in Table 1, it can be seen that the method proposed in the paper is more accurate than BMC and GPR.…”

Section: Instance Studymentioning

confidence: 96%

“…We get the values of these parameters via sampling procedure as presented in section IV. It is shown that the model in (4) can fit the batteries' degradation trends in certain extent [16], [24]. However, due to various uncertainties mentioned above, the prediction error is always inevitable, even for those more complicated models.…”

Section: Model Update For Lithium-ion Battery Rul Predictionmentioning

confidence: 99%

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Simulator Assessment Theory for Remaining Useful Life Prediction of Lithium-Ion Battery Under Multiple Uncertainties

Zhao

Suo

2020

IEEE Access

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As Lithium-ion batteries become the main power source in various electronics, it is important to predict the remaining useful life (RUL) of these batteries, in order to make the maintenance strategy and avoid serious consequences caused by the failure of power supply. With the convenience in fitting field measurements, the model based methods are widely used in RUL prediction for lithium-ion batteries. However, these predictions are usually unreliable because of incomplete uncertainty quantification. This paper proposes a model update method for the RUL prediction of lithium-ion battery based on the Bayesian simulator assessment theory. With an empirical degradation model, the method quantifies the uncertainties in model parameters, model form and measurements error. It infers the reality prediction to battery failure threshold with a combination of multiple uncertainties. The main innovation of the proposed method is that it doesn't only adjust the model parameters, but also the bias function which accounts for the model form uncertainty. And a modular Markov chain Monte Carlo method is employed to implement the model update with multiple uncertain parameters. As uncertainties are considered systematically in the inference process, it can provide a reliable RUL prediction. We demonstrate the predictive capability of the method by the real life cycle dataset of lithium-ion batteries from NASA.

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Section: Instance Studymentioning

confidence: 96%

Section: Model Update For Lithium-ion Battery Rul Predictionmentioning

confidence: 99%

Simulator Assessment Theory for Remaining Useful Life Prediction of Lithium-Ion Battery Under Multiple Uncertainties

Zhao

Suo

2020

IEEE Access

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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%

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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“…An improved particle filter algorithm is proposed in the literature [15]. An improved method of unscented particle filtering (UPF) based on Markov chain Monte Carlo (MCMC) is proposed in literature [16]. Cui et al [17] and Son et al [18] propose a new switching noiseless Kalman filter algorithm respectively.…”

Section: New Faultmentioning

confidence: 99%

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

Yang

Sun

2019

IEEE Access

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Remaining useful life (RUL) is the premise and basis of the equipment health management plan. As accurate as possible life prediction is of great significance to reliability and economy of equipment maintenance. In this paper, a data-driven improved particle filter (PF) RUL prediction method is proposed. A health indicator extraction method based on multi-feature fusion is introduced for the RUL prediction, which can visually show the degradation trend of the healthy state of the equipment. The degradation model and observation model of equipment health indicators are established, and the PF algorithm is used to track parameters of the model. A quantum genetic algorithm is employed to improve the problem of particle degradation in PF. On the basis of filter tracking, long short term memory (LSTM) network is used to predict the trend of model coefficients, which further improves the accuracy of RUL prediction. The experiment using the C-MAPSS data set shows the proposed method has a better prediction accuracy than other methods. INDEX TERMS Remaining useful life, particle filter, quantum genetic algorithm, long short term memory.

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Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC

Cited by 114 publications

References 14 publications

Simulator Assessment Theory for Remaining Useful Life Prediction of Lithium-Ion Battery Under Multiple Uncertainties

Simulator Assessment Theory for Remaining Useful Life Prediction of Lithium-Ion Battery Under Multiple Uncertainties

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

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

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