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Mathematical Problems in Engineering - Volume 2014 2014, Article ID 564894, 10 pages -

Research Article

School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China

Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing 100191, China

Received 9 April 2014; Revised 10 July 2014; Accepted 14 July 2014; Published 23 July 2014

Academic Editor: Qingsong Xu

Copyright © 2014 Ye Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.

Autor: Ye Tian, Chen Lu, Zili Wang, and Laifa Tao



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