Journal ArticleDOI
An ensemble model for predicting the remaining useful performance of lithium-ion batteries
TLDR
An ensemble model fuses an empirical exponential and a polynomial regression model to track the battery’s degradation trend over its cycle life based on experimental data analysis and presents the limitations of the model.About:
This article is published in Microelectronics Reliability.The article was published on 2013-06-01. It has received 379 citations till now. The article focuses on the topics: Ensemble forecasting & Battery (electricity).read more
Citations
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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
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.
Journal ArticleDOI
Stochastic modelling and analysis of degradation for highly reliable products
Zhi-Sheng Ye,Min Xie +1 more
TL;DR: In this paper, degradation models are classified into three classes, that is, stochastic process models, general path models, and other models beyond these two classes.
Journal ArticleDOI
Battery Lifetime Prognostics
TL;DR: A timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches is presented, analyzed, and compared.
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An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks
Ji Wu,Chenbin Zhang,Zonghai Chen +2 more
TL;DR: In this article, the analysis of battery terminal voltage curves under different cycle numbers during charge process is utilized for RUL definition and the relationship between RUL and charge curve is simulated by feed forward neural network for its simplicity and effectiveness.
References
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The Elements of Statistical Learning
TL;DR: Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.
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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
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On sequential Monte Carlo sampling methods for Bayesian filtering
TL;DR: An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.
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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.