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An ensemble model for predicting the remaining useful performance of lithium-ion batteries

Xing, Yinjiao, Ma, Eden W.M., Tsui, Kwok-Leung, Pecht, Michael·2013·Microelectronics Reliability

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Research works printed but not linked: 19

  1. Rufus F, Lee S, Thakker A. Health monitoring algorithms for space application batteries; 2008. p. 1–8. · DOI
  2. Kim, 2010, A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer, Power Electron, IEEE Trans on, 25, 1013, 10.1109/TPEL.2009.2034966 · DOI · Paper page
  3. Verbrugge, 2006, Generalized recursive algorithm for adaptive multiparameter regression, J Electrochem Soc, 153, A187, 10.1149/1.2128096 · DOI
  4. Schmidt, 2010, Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries, J Power Sources, 195, 7634, 10.1016/j.jpowsour.2010.06.011 · DOI
  5. Spotnitz, 2003, Simulation of capacity fade in lithium-ion batteries, J Power Sources, 113, 72, 10.1016/S0378-7753(02)00490-1 · DOI · Paper page
  6. Saha B, Goebel K, Poll S, Christophersen J. An integrated approach to battery health monitoring using bayesian regression and state estimation; 2007. p. 646-53. · DOI · Paper page
  7. Bo, 2008, State of charge estimation based on evolutionary neural network, Energy Convers Manage, 49, 2788, 10.1016/j.enconman.2008.03.013 · DOI
  8. Santhanagopalan, 2010, State of charge estimation using an unscented filter for high power lithium ion cells, Int J Energy Res, 34, 152, 10.1002/er.1655 · DOI · Paper page
  9. Pattipati, 2011, System identification and estimation framework for pivotal automotive battery management system characteristics, IEEE Trans Syst Man and Cyber – Part C Appl Rev, 41, 869, 10.1109/TSMCC.2010.2089979 · DOI · Paper page
  10. Kozlowski JD. Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques; 2003. p. 3257–270. · DOI
  11. Micea, 2011, Online state-of-health assessment for battery management systems, Instrum Meas, IEEE Trans on, 60, 1997, 10.1109/TIM.2011.2115630 · DOI
  12. Bergveld, 2002, vol. 1
  13. Dai, 2012, Prognostics-based risk mitigation for telecom equipment under free air cooling conditions, Appl Energy, 10.1016/j.apenergy.2012.05.055 · DOI
  14. Sun, 2011, Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance, Mech Syst Signal Process
  15. Si, 2011, Remaining useful life estimation – a review on the statistical data driven approaches, Eur J Oper Res, 213, 1, 10.1016/j.ejor.2010.11.018 · DOI · Paper page
  16. Zhang, 2008, Capacity fade analysis of a lithium ion cell, J Power Sources, 179, 793, 10.1016/j.jpowsour.2008.01.028 · DOI · Paper page
  17. Spiess, 2010, An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach, BMC Pharmacol, 10, 6, 10.1186/1471-2210-10-6 · DOI · Paper page
  18. Arulampalam, 2002, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, Signal Process, IEEE Trans on, 50, 174, 10.1109/78.978374 · DOI · Paper page
  19. Doucet, 2000, On sequential Monte Carlo sampling methods for Bayesian filtering, Statist Comput, 10, 197, 10.1023/A:1008935410038 · DOI · Paper page

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Metadata

Authors
Xing, Y., Ma, E. W. M., Tsui, K.-L., & Pecht, M.
Venue
Microelectronics Reliability
Publisher
Elsevier BV
Published
2013-06
Volume
53
Issue
6
Pages
811-820
ISSN
0026-2714 (ISSN-L)
OpenAlex
W2144352195
Cited by
673
References
6
FWCI
30.08
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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CoChat. (2026). Citation check: An ensemble model for predicting the remaining useful performance of lithium-ion batteries. https://app.cochat.ai/papers/10.1016/j.microrel.2012.12.003
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Xing, Y., Ma, E. W. M., Tsui, K.-L., & Pecht, M. (2013). An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectronics Reliability. https://doi.org/10.1016/j.microrel.2012.12.003

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