Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach

Cheng, Yujie; Lu, Chen; Tieying, Li; Tao, Laifa

doi:10.1016/j.energy.2015.07.022

Cited by 62 publications

(27 citation statements)

References 29 publications

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“…The inverse Gaussian process was used to analyze the accelerated degradation model and both the Jeffreys prior and reference prior were derived and compared [18]. Based on the functional principal component analysis (PCA) and the Bayesian method, a new prediction method for Li ion battery residual lifetime evaluation was presented [19].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

2019

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Residual life estimation is an important problem in reliability engineering. Traditional methods, which are based on time-to-failure distribution, have limitations for components of on-orbit satellites characterized as high reliability with small sample size. Various types of reliability information can be collected during test and operation, including historical lifetime data, degradation data, similar data, expert information, etc. Therefore, making full use of multi-source information is meaningful for improving estimation precision. However, research on residual life estimation by fusing multi-source information is rare. No study has examined the overall process of fusing all of the different kinds of information. In this paper, a Bayesian method is presented to estimate the residual life of Weibull-distributed components of on-orbit satellites by fusing all the collected information. Prior distributions are determined using different kinds of information. After fusing the field data, posterior distributions can be obtained corresponding to each prior distribution. Then, the joint posterior distribution is the weighted sum of these posterior distributions with weights calculated using the second Maximum Likelihood Estimation (ML-II) method. Consistency is tested to guarantee the safety of the information fusion. Furthermore, residual life is estimated by the proposed sample-based method including both the Bayesian estimate and credible interval (CI). A Monte Carlo simulation study is conducted to demonstrate the proposed methods and shows that the Bayesian method is satisfactory and robust. Finally, a published dataset of the momentum wheel in a satellite is analyzed to illustrate the application of the method.

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Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

2019

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“…| • stands for the conditional to all the variables related to it and it should include all the terms that have from (4). Also, N, G, B, and R refer to normal, gamma, beta, and Rayleigh PDFs, respectively.…”

Section: B Mathematical Expressionmentioning

confidence: 99%

“…The data-driven methods are mostly used for realtime applications and after training through a set of data, they use present measurement data from the battery to predict the battery state of health (SOH). Different types of Kalman filters (KF) [2], relevance vector machine (RVM) [3] and functional principal component analyses (FPCA) [4] are some of these methods. These methods need expert knowledge to relate the available data to the battery health condition.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian Model for Probabilistic Analysis of Electric Vehicle Battery Degradation

Jafari

Brown

Gauchía

2019

IEEE Trans. Transp. Electrific.

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This paper proposes a hierarchical Bayesian model for probabilistic estimation of the electric vehicle battery capacity fade. Since the battery aging factors such as temperature, current, and state of charge are not fixed, and they change in different times, locations and by the different users, deterministic models with constant parameters cannot accurately evaluate the battery capacity fade. Therefore, a probabilistic presentation of the capacity fade including uncertainties of the measurements/ observations of the variables can be a proper solution. We have developed a hierarchical Bayesian Network model for the electric vehicle battery capacity fade considering multiple external variables. The mathematical expression of the model is extracted based on Bayes' theorem, the probability distributions for all variables and their dependencies are carefully chosen where the Metropolis-Hastings Markov Chain Monte Carlo sampling method is applied to generate the posterior distributions. The model is trained with a subset of experimental data (85%) to obtain its unseen parameters and tested with other 15% of data to prove its accuracy. Also, three case studies for different drivers, different grid services' frequencies, and different climates are explored to show model's flexibility with different input data. The developed model needs training data for parameter tuning in different conditions. However, after training, it has more than 95% precision in estimating the battery capacity fade percentage. Index Terms-Bayesian Networks, Electric Vehicle Battery, Markov

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“…Unlike the model-based methods, the data-driven methods are based on large numbers of data, where the deep understanding of electrochemical principles is not necessary [18]. It mainly includes the support vector machine [19][20][21], neural network [22,23], Bayesian method [24,25], random forest [26] and so on. For data-driven methods, the effectiveness of features and the performance of regression algorithms can greatly affect the accuracy of the RUL prediction, so they have attracted the attention of many researchers.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction

et al. 2020

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Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) the potentials of features are not fully exploited, and (ii) the parameters of the prediction model are difficult to determine. To address this challenge, this paper proposes a new data-driven method using feature enhancement and adaptive optimization. First, the features of battery aging are extracted online. Then, the feature enhancement technologies, including the box-cox transformation and the time window processing, are used to fully exploit the potential of features. The box-cox transformation can improve the correlation between the features and the aging status of the battery, and the time window processing can effectively exploit the time information hidden in the historical features sequence. Based on this, gradient boosting decision trees are used to establish the RUL prediction model, and the particle swarm optimization is used to adaptively optimize the model parameters. This method was applied on actual lithium-ion battery degradation data, and the experimental results show that the proposed model is superior to traditional prediction methods in terms of accuracy.

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Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach

Cited by 62 publications

References 29 publications

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

Hierarchical Bayesian Model for Probabilistic Analysis of Electric Vehicle Battery Degradation

A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction

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