An integrated approach to battery health monitoring using bayesian regression and state estimation

By Bhaskar Saha, Kai Goebel, Scott Poll, Jon Christophersen. 2007 IEEE Autotestcon, 2007

184 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/b1a642669fed63bfd593907ff34b1ecb5ca1b4c0

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Citations in the corpus

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “[121] found that the sum of impedance parameters exhibits a linear correlation with the battery capacity measured at 1 C-rate Development of a lifetime Model for Lithium-ion batteries and can be used as a health indicator for capacity prediction.”
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework (2009, methodology): “Several figures in this paper have been revised from [6] and [10].”
Machine learning pipeline for battery state-of-health estimation (2021, methodology): “Other Bayesian-based methods, such as the relevance vector machine (RVM) [34], have also been used to estimate battery capacity fade.”
The future of employment: How susceptible are jobs to computerisation? (2017, methodology): “Following the declining costs of digital sensing and actuation, ML approaches have successfully addressed condition monitoring applications ranging from batteries (Saha et al., 2007), to aircraft engines (King et al.”
Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression (2013, background): “To address this problem, prognostic algorithms with uncertainty representation have been developed [14].”
Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “Another work using an SVM to estimate SOH was presented in [67].”
A review on lithium-ion battery ageing mechanisms and estimations for automotive applications (2013, methodology): “[107, 108] used this method in order to learn a dependency model of internal battery parameters model to estimate and predict the SOH.”
An ensemble model for predicting the remaining useful performance of lithium-ion batteries (2013, background): “In the battery community, end of performance (EOP) is usually set as the time that the maximum available capacity is reduced to 80% of its initial value [7,8].”
Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach (2018, methodology): “Saha et al. [25] and Zhou et al. [26] uses the internal parameters of the battery to build a Relevance Vector Machine (RVM) model and uses Particle ﬁlter (PF) particle ﬁlter algorithm to determine the adaptive parameters of the RVM model to predict the decline of the lithium battery.”
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression (2018, methodology): “[29] used the relevance vector machine, which was a Bayesian form representing a generalized linear model of identical functional form of the SVM, for diagnosis as well as for model development.”