Prognosis of Lithium-Ion Batteries Considering Cycle and Storage Conditions
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Lithium-ion batteries are used as a major power source for electric vehicles (EV) due to their high energy density. When the battery is repeatedly charged and discharged during the operation of the vehicle, the capacity is gradually deteriorated, which is divided into cycle degradation and storage degradation. In order to predict the accurate life of the battery, combination of the two degradations must be considered reflecting the actual usage conditions. However, composite (i.e., both cycle and storage conditions) tests take relatively longer than the tests for each conditions. In this study, we proposed a method for predicting the remaining useful life (RUL) of a complex situation using the results of respective tests on cycle and storage conditions, which require relatively short test times. Based on the results of respective tests, the model-based approach using particle filter is applied to predict RUL of a lithium-ion battery, which was derived as a probability distribution. By modeling the joint probability distribution using copula, RUL of composite condition considering both cycle and storage degradations. It was verified by comparison with the distribution of the RUL derived using the composite tests results.
How to Cite
##plugins.themes.bootstrap3.article.details##
Lithium-ion battery, Particle filter, remaining useful life (RUL), Probability distribution, Copula
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.