Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction
Joint Authors
Wang, Zili
Tao, Laifa
Tian, Ye
Lu, Chen
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-23
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries based on artificial fish swarm algorithm (AFSA) and particle filter (PF), which is an integrated approach combining model-based method with data-driven method.
The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF.
AFSA-PF aims to improve the performance of the basic PF.
By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence.
Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF.
AFSA-PF is shown to be more accurate and precise.
American Psychological Association (APA)
Tian, Ye& Lu, Chen& Wang, Zili& Tao, Laifa. 2014. Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-481175
Modern Language Association (MLA)
Tian, Ye…[et al.]. Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction. Mathematical Problems in Engineering No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-481175
American Medical Association (AMA)
Tian, Ye& Lu, Chen& Wang, Zili& Tao, Laifa. Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-481175
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-481175