NDLI: Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning

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Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Hu, Chao Jain, Gaurav Schmidt, Craig Strief, Carrie Sullivan, Melani
Copyright Year	2015
Abstract	Lithium-ion (Li-ion) rechargeable batteries are used as one of the major energy storage components for implantable medical devices. Reliability of Li-ion batteries used in these devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, patients and physicians. To ensure a Li-ion battery operates reliably, it is important to develop health monitoring techniques that accurately estimate the capacity of the battery throughout its life-time. This paper presents a sparse Bayesian learning method that utilizes the charge voltage and current measurements to estimate the capacity of a Li-ion battery used in an implantable medical device. Relevance Vector Machine (RVM) is employed as a probabilistic kernel regression method to learn the complex dependency of the battery capacity on the characteristic features that are extracted from the charge voltage and current measurements. Owing to the sparsity property of RVM, the proposed method generates a reduced-scale regression model that consumes only a small fraction of the CPU time required by a full-scale model, which makes online capacity estimation computationally efficient. 10 years’ continuous cycling data and post-explant cycling data obtained from Li-ion prismatic cells are used to verify the performance of the proposed method.
Sponsorship	Design Engineering Division Computers and Information in Engineering Division
File Format	PDF
ISBN	9780791857076
DOI	10.1115/DETC2015-46964
Volume Number	Volume 2A: 41st Design Automation Conference
Conference Proceedings	ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Language	English
Publisher Date	2015-08-02
Publisher Place	Boston, Massachusetts, USA
Access Restriction	Subscribed
Subject Keyword	Lithium-ion batteries Medical devices Regression models Lithium Batteries Secondary cells Reliability Machinery Energy storage
Content Type	Text
Resource Type	Article

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