Journal ArticleDOI
Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model
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TLDR
In this article, a battery capacity prognostic method is developed to estimate the remaining useful life of lithium-ion batteries, which consists of a relevance vector machine and a conditional three-parameter capacity degradation model.About:
This article is published in Journal of Power Sources.The article was published on 2013-10-01. It has received 295 citations till now. The article focuses on the topics: Relevance vector machine & Prognostics.read more
Citations
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Journal ArticleDOI
Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
TL;DR: In this paper, the methods for monitoring the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications.
Journal ArticleDOI
A recurrent neural network based health indicator for remaining useful life prediction of bearings
TL;DR: A recurrent neural network based health indicator for RUL prediction of bearings with fairly high monotonicity and correlation values is proposed and it is experimentally demonstrated that the proposed RNN-HI is able to achieve better performance than a self organization map based method.
Journal ArticleDOI
Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
TL;DR: The developed method is able to predict the battery's RUL independent of offline training data, and when some offline data is available, the RUL can be predicted earlier than in the traditional methods.
Journal ArticleDOI
State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures
TL;DR: In this article, the unscented Kalman filtering (UKF) was applied to tune the model parameters at each sampling step to cope with various uncertainties arising from the operation environment, cell-to-cell variation, and modeling inaccuracy.
Journal ArticleDOI
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Yi Li,Yi Li,Yi Li,Kailong Liu,Aoife Foley,Alana Aragon Zulke,Alana Aragon Zulke,Maitane Berecibar,Elise Nanini-Maury,Joeri Van Mierlo,Harry E. Hoster,Harry E. Hoster +11 more
TL;DR: This review categorises data-driven battery health estimation methods according to their underlying models/algorithms and discusses their advantages and limitations, then focuses on challenges of real-time battery health management and discuss potential next-generation techniques.
References
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Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
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Sparse bayesian learning and the relevance vector machine
TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
Journal ArticleDOI
Data mining and knowledge discovery: making sense out of data
TL;DR: Without a concerted effort to develop knowledge discovery techniques, organizations stand to forfeit much of the value from the data they currently collect and store.
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Remaining useful life estimation - A review on the statistical data driven approaches
TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.
Book
Prognostics and health management of electronics
Nikhil M. Vichare,Michael Pecht +1 more
TL;DR: The state-of-the-art in the area of electronics prognostics and health management can be found in this article, where four current approaches include built-in-test (BIT), use of fuses and canary devices, monitoring and reasoning of failure precursors, and modeling accumulated damage based on measured life-cycle loads.