Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 277

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

State-of-health estimation for the lithium-ion battery based on support vector regression

Yang, Duo, Wang, Yujie, Pan, Rui, Chen, Ruiyang, Chen, Zonghai·2018·Applied Energy
DOI 10.1016/j.apenergy.2017.08.096
Open this paper in CoChat — free account

Abstract

Lithium-ion batteries have been widely used in many fields. The state-of-health is necessary and important for battery performance evaluation and lifetime prediction. A reliable state-of-health estimation is essential to help batteries work in a safe and suitable condition. In this paper, a novel state-of-health estimation approach is proposed for lithium-ion batteries based on statistical knowledge. An improved battery model, which combines the open-circuit-voltage modeling and the Thevenin equivalent circuit model, is proposed to improve the model accuracy and study the relation between internal parameters and states of the battery. The joint extended Kalman filter-recursive-least squares algorithm is employed to estimate battery state-of-charge and identify the model parameters and open-circuit-voltage simultaneously. Then a particle swarm optimization-least square support vector regression approach is employed to give a reliable state-of-health estimation result with high accuracy and good generalization ability, where the particle swarm optimization algorithm is used to improve the algorithm ability of global optimization. In order to verify the accuracy of the proposed method, static and dynamic current profile tests are carried out on lithium iron phosphate batteries in different aging levels. The experimental results indicate that the proposed method can present suitability for state-of-health estimation with high accuracy.

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.apenergy.2017.08.096

Cites

4 of 4 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

Show all 4 references

Research works printed but not linked: 27

  1. Yang, 2016, High capacity and cycle stability Rechargeable Lithium-Sulfur batteries by sandwiched gel polymer electrolyte, Electrochim Acta, 210, 71, 10.1016/j.electacta.2016.05.087 · doi:10.1016/j.electacta.2016.05.087
  2. Arai, 2005, Development of a high power lithium secondary battery for hybrid electric vehicles, J Power Sources, 146, 788, 10.1016/j.jpowsour.2005.03.087 · doi:10.1016/j.jpowsour.2005.03.087
  3. Wang, 2015, A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter, J Power Sources, 279, 306, 10.1016/j.jpowsour.2015.01.005 · DOI · Paper page
  4. Wang, 2014, A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries, Appl energy, 135, 81, 10.1016/j.apenergy.2014.08.081 · DOI · Paper page
  5. Cheng, 2011, Battery-management system (BMS) and SOC development for electrical vehicles, IEEE Trans Veh Technol, 60, 76, 10.1109/TVT.2010.2089647 · DOI · Paper page
  6. Ozkurt, 2016, Integration of sampling based battery state of health estimation method in electric vehicles, Appl Energy, 175, 356, 10.1016/j.apenergy.2016.05.037 · DOI
  7. Wang, 2016, On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis, Appl Energy, 168, 465, 10.1016/j.apenergy.2016.01.125 · DOI · Paper page
  8. Wang, 2017, On-line remaining energy prediction: a case study in embedded battery management system, Appl Energy, 194, 688, 10.1016/j.apenergy.2016.05.081 · DOI
  9. Remmlinger, 2011, State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation, J Power Sources, 196, 5357, 10.1016/j.jpowsour.2010.08.035 · DOI · Paper page
  10. Tsang, 2013, State of health detection for Lithium ion batteries in photovoltaic system, Energy Convers Manage, 65, 7, 10.1016/j.enconman.2012.07.006 · DOI
  11. Andre, 2013, Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries, J Power Sources, 224, 20, 10.1016/j.jpowsour.2012.10.001 · DOI · Paper page
  12. Kim, 2010, A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer, IEEE Trans Power Electron, 25, 1013, 10.1109/TPEL.2009.2034966 · DOI · Paper page
  13. Ouyang, 2016, A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery, Appl Energy, 165, 48, 10.1016/j.apenergy.2015.12.063 · DOI · Paper page
  14. Cheng, 2015, Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach, Energy, 90, 1983, 10.1016/j.energy.2015.07.022 · DOI
  15. Thein, 2014, Decision making model for lifecycle assessment of lithium-ion battery for electric vehicle–a case study for smart electric bus project in Korea, J Power Sources, 249, 142, 10.1016/j.jpowsour.2013.10.078 · DOI
  16. Feng, 2013, Using probability density function to evaluate the state of health of lithium-ion batteries, J Power Sources, 232, 209, 10.1016/j.jpowsour.2013.01.018 · DOI · Paper page
  17. He, 2014, Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks, J Power Sources, 267, 576, 10.1016/j.jpowsour.2014.05.100 · DOI
  18. Ng, 2014, A naive Bayes model for robust remaining useful life prediction of lithium-ion battery, Appl Energy, 118, 114, 10.1016/j.apenergy.2013.12.020 · DOI · Paper page
  19. Eddahech, 2012, Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks, Int J Electr Power Energy Syst, 42, 487, 10.1016/j.ijepes.2012.04.050 · DOI · Paper page
  20. You, 2016, Real-time state-of-health estimation for electric vehicle batteries: a data-driven approach, Appl Energy, 176, 92, 10.1016/j.apenergy.2016.05.051 · DOI · Paper page
  21. Patil, 2015, A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation, Appl Energy, 159, 285, 10.1016/j.apenergy.2015.08.119 · DOI · Paper page
  22. Klass, 2014, A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation, J Power Sources, 270, 262, 10.1016/j.jpowsour.2014.07.116 · DOI · Paper page
  23. He, 2012, Comparison study on the battery models used for the energy management of batteries in electric vehicles, Energy Convers Manage, 64, 113, 10.1016/j.enconman.2012.04.014 · DOI · Paper page
  24. Wang, 2017, On-line battery state-of-charge estimation based on an integrated estimator, Appl Energy, 185, 2026, 10.1016/j.apenergy.2015.09.015 · DOI
  25. Wang, 2015, A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy, Appl Energy, 137, 427, 10.1016/j.apenergy.2014.10.034 · DOI
  26. Bi, 2016, State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter, Appl Energy, 182, 558, 10.1016/j.apenergy.2016.08.138 · DOI · Paper page
  27. Dong, 2016, An online model-based method for state of energy estimation of lithium-ion batteries using dual filters, J Power Sources, 301, 277, 10.1016/j.jpowsour.2015.10.011 · DOI · Paper page

Cited by

See all 277

Metadata

Authors
Yang, D., Wang, Y., Pan, R., Chen, R., & Chen, Z.
Venue
Applied Energy
Publisher
Elsevier BV
Published
2018-10
Volume
227
Pages
273-283
ISSN
0306-2619 (ISSN-L)
DOI
10.1016/j.apenergy.2017.08.096
OpenAlex
W2748357818
Cited by
277
References
4
FWCI
11.73
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
National Natural Science Fund of China (61375079)

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: State-of-health estimation for the lithium-ion battery based on support vector regression. https://app.cochat.ai/papers/10.1016/j.apenergy.2017.08.096
The paper
Yang, D., Wang, Y., Pan, R., Chen, R., & Chen, Z. (2018). State-of-health estimation for the lithium-ion battery based on support vector regression. Applied Energy. https://doi.org/10.1016/j.apenergy.2017.08.096

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.