Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

By Bhaskar Saha, Kai Goebel, Scott Poll, Jon Christophersen. IEEE Transactions on Instrumentation and Measurement, 2009

769 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/2d5cd14ad3350afbb685263e65ac2d4019724fbe

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Citations in the corpus

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “The existing research on battery health prognostics includes the battery remaining useful lifetime (RUL) prediction [91] and capacity (fade) forecasting [92].”
Online Parameterization of Lumped Thermal Dynamics in Cylindrical Lithium Ion Batteries for Core Temperature Estimation and Health Monitoring (2013, background): “Parameterization of battery model and adaptive monitoring of the battery voltage and SOH have been explored previously in various seminal papers [15]–[17], but this paper is among the first to adaptively monitor the temperatures (especially the core temperature) of batteries and SOH from a thermal p”
Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error (2014, methodology): “To improve the accuracy of the PF algorithm, Rao-Blackwellized PF and unscented PF algorithm are used [16,17].”
Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks (2017, methodology): “More specifically, as underlined in [4], PHM methods address the crucial problems of whether the batteries are capable of supplying an adequate power level during any discharge cycle, what kind of degradation mechanisms the batteries are undergoing and how many more charge-recharge cycles the…”
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “In equivalent circuit model-based methods, electric models are established firstly, such as RC equivalent circuit models [9], fractionalorder equivalent circuit models [10], and impedance spectrum growth models [11].”
Stochastic modelling and analysis of degradation for highly reliable products (2015, background): “In many cases, these Jacobians are difficult to obtain [102].”
A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life (2021, methodology): “For example, a coupled numerical method was proposed in [12] for the life prediction of LIB for electric vehicles.”
A prognostics and health management roadmap for information and electronics-rich systems (2010, methodology): “For example, this approach to prognostics was demonstrated for lithium ion batteries [23] where a lumped parameter model was used along with extended Kalman filter and particle filter algorithms to estimate remaining useful life (RUL).”
Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life (2011, methodology): “In the literature, most engineering practices of health prognostics are based on various PHIs, such as the battery impedance [21], the magnitude of the vibration signal [45] and the radio frequency (RF) impedance [46].”