Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 673

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

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
DOI 10.1016/j.microrel.2012.12.003
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.microrel.2012.12.003

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

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

Cited by

See all 673

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)
DOI
10.1016/j.microrel.2012.12.003
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)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.