Journal ArticleDOI
Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method
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TLDR
In this article, a new method for state of health (SOH) and remaining useful life (RUL) estimations for lithium-ion batteries using Dempster-Shafer theory (DST) and the Bayesian Monte Carlo (BMC) method is proposed.About:
This article is published in Journal of Power Sources.The article was published on 2011-12-01. It has received 586 citations till now. The article focuses on the topics: Dempster–Shafer theory & Prognostics.read more
Citations
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Machinery health prognostics: A systematic review from data acquisition to RUL prediction
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
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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.
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A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
TL;DR: Experimental results demonstrate the effectiveness of the proposed hybrid prognostics approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.
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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.
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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.
References
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Book
A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
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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.
Journal ArticleDOI
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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Ageing mechanisms in lithium-ion batteries
Jens Vetter,Petr Novák,Markus Robert Wagner,Claudia Veit,Kai-Christian Möller,Jürgen Besenhard,Martin Winter,Margret Wohlfahrt-Mehrens,C Vogler,Abderrezak Hammouche +9 more
TL;DR: In this article, the mechanisms of lithium-ion battery ageing are reviewed and evaluated, and the most promising candidate as the power source for (hybrid) electric vehicles and stationary energy storage.
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Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation
TL;DR: In this article, extended Kalman filtering (EKF) is used to estimate battery state-of-charge, power fade, capacity fade, and instantaneous available power for hybrid-electric-vehicle battery packs.