Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework

By Bhaskar Saha, Kai Goebel. 2009

376 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6da6f559425e9b9f719be1089b3cf254dcc1f8d0

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Citations in the corpus

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error (2014, methodology): “For more details about how to extract the relaxation effect, see Reference [14].”
Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings (2015, unknown): “and predict degradation and RUL [23], [27].”
A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery (2021, background): “More details regarding the NASA test bench and CALCE test bench can be found in [27] and [20], respectively.”
Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks (2017, methodology): “…voltage variation during a discharge (in [V]), which is strictly related to the current and the SOC, is directly used as a diagnostic/prognostic indicator during the battery discharge phase, as for example done in [1] and in many other works of literature based on the NASA Ames database [6].”
Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (2016, unknown): “particle filter [30], etc.”
A review on prognostics and health monitoring of proton exchange membrane fuel cell (2017, methodology): “Applications of Prognostics to PEMFC Although prognostics is used in many engineering systems and products including batteries [70, 71, 72, 73, 74, 75, 76, 77], to assess the remaining useful 220”
Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation (2012, unknown): “Bayesian tracking algorithms like Kalman filter, extended Kalman fil ter, and particle filter are among the algorithms typically em ployed in a model-based progno tics methodology (Daigle & Goebel, 2011 ; Saha & Goebel, 2009; J. Celaya et al., 2011 ; J. Celaya, Saxena, Kulkarni, et aI., 2012).”
A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation (2010, methodology): “More recently, the particle filter with an empirical circuit model was used to predict the remaining useful lives for individual discharge cycles as well as for cycle life [9].”
Intelligent prognostics for battery health monitoring based on sample entropy (2011, background): “The recent paper was also presented by Saha and Goebel (2009) that present an empirical model to describe battery behavior during individual discharge cycles as well as for cycle life.”
Effect of Battery Degradation on Multi-Service Portfolios of Energy Storage (2016, methodology): “2) Cycle Characterization and Degradation: The degradation model is based on the structure proposed in [17], which corresponds to an extension of the model used in [18].”
A naive Bayes model for robust remaining useful life prediction of lithium-ion battery (2014, background): “A more comprehensive introduction to battery health management and Li-ion batteries is in [7].”