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Remaining useful life prediction for Lithium-ion batteries using fractional Brownian motion and Fruit-fly Optimization Algorithm

  • Published:2020-09 Issue: Volume:161 Page:107904
  • ISSN:0263-2241
  • Container-title:Measurement
  • language:en
  • Short-container-title:Measurement

Author:

Wang Haiyang,Song Wanqing,Zio Enrico,Kudreyko Aleksey,Zhang Yujin

Funder

Natural Science Foundation of Shanghai

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference34 articles.

1. Prognostics and health management: a review of vibration based bearing and gear health indicators;Wang;Ieee Access,2018

2. A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm;Khumprom;Energies,2019

3. Remaining useful life estimation: review;Ahmadzadeh;Int. J. System Assurance Eng. Manage.,2014

4. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks;Li;J. Energy Storage,2019

5. An integrated probabilistic approach to lithium-ion battery remaining useful life estimation;Liu;Ieee Trans. Instrument. Measur.,2015
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