Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 447

The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Open access
Rivera-Barrera, Juan, Muñoz-Galeano, Nicolás, Sarmiento-Maldonado, Henry·2017·Electronics
DOI 10.3390/electronics6040102
Open this paper in CoChat — free account

Abstract

Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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 · 10.3390/electronics6040102

Cites

136 of 136 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 136 references

Research works printed but not linked: 17

  1. Yu, 2017, China’s energy storage industry: Develop status, existing problems and countermeasures, Renew. Sustain. Energy Rev., 71, 767, 10.1016/j.rser.2016.12.103 · doi:10.1016/j.rser.2016.12.103
  2. Beir, M.D., Calado, M.d.R.A., Pombo, J.A.N., and Mariano, S.J.P.S. (2016, January 7–10). Balancing management system for improving li-ion batteries capacity usage and lifespan. Proceedings of the 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy.
  3. Kim, M.Y., Kim, J.W., Kim, C.H., Cho, S.Y., and Moon, G.W. (June, January 30). Automatic charge equalization circuit based on regulated voltage source for series connected lithium-ion batteries. Proceedings of the 2011 IEEE 8th International Conference on Power Electronics and ECCE Asia (ICPE & ECCE), Jeju, Korea. · doi:10.1109/icpe.2011.5944448
  4. Abdul-Hak, M., and Al-Holou, N. (2009, January 7–10). Its based predictive intelligent battery management system for plug-in hybrid and electric vehicles. Proceedings of the 2009 IEEE Vehicle Power and Propulsion Conference, Dearborn, MI, USA. · doi:10.1109/vppc.2009.5289858
  5. Mahamadi, A., and Sastry, S. (2015, January 7–9). Model predictive controller for battery management systems. Proceedings of the 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), Khartoum, Sudan. · doi:10.1109/iccneee.2015.7381418
  6. Ali, D., Mukhopadhyay, S., and Rehman, H. (2016, January 25–29). A novel adaptive technique for li-ion battery model parameters estimation. Proceedings of the 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS), Dayton, OH, USA. · doi:10.1109/naecon.2016.7856769
  7. Savanth, P., and Shailesh, K.R. (2016, January 19). Reduction of parameters in a lithium ion cell model by experimental validation of relationship between ocv and soc. Proceedings of the 2016 Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, India. · doi:10.1109/get.2016.7916800
  8. Xia, B., Zhao, X., Callafon, R.D., Garnier, H., Nguyen, T., and Mi, C. (2016, January 18–22). Accurate lithium-ion battery parameter estimation with continuous-time system identification methods. Proceedings of the 2016 IEEE Energy Conversion Congress and Exposition (ECCE), Milwaukee, WI, USA. · doi:10.1109/ecce.2016.7854811
  9. Zhou, X., Cheng, L., Tang, Y., Pan, Z., and Sun, Q. (2016, January 20–22). Model identification of lithium-ion batteries in the portable power system. Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, ON, Canada. · doi:10.1109/icphm.2016.7542843
  10. Jiang, 2013, An optimal charging method for li-ion batteries using a fuzzy-control approach based on polarization properties, IEEE Trans. Veh. Technol., 62, 3000, 10.1109/TVT.2013.2252214 · doi:10.1109/tvt.2013.2252214
  11. Raszmann, E., Baker, K., Shi, Y., and Christensen, D. (2017, January 23–24). Modeling stationary lithium-ion batteries for optimization and predictive control. Proceedings of the 2017 IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL, USA. · doi:10.1109/peci.2017.7935755
  12. Jaguemont, 2017, Development of a 2d-thermal model of three battery chemistries considering entropy, IEEE Trans. Energy Convers., PP, 1
  13. Payyappilly, B., and John, V. (2016, January 14–17). A generic in-circuit modeling approach for electrochemical batteries. Proceedings of the 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Trivandrum, India. · doi:10.1109/pedes.2016.7914323
  14. Jansen, P., Gebel, T., Renner, D., Vergossen, D., John, W., and Götze, J. (2016, January 19–21). An approach to determine the state of charge of a lithium iron phosphate cell using classification methods based on frequency domain data. Proceedings of the 8th IET International Conference on Power Electronics, Machines and Drives (PEMD 2016), Glasgow, UK. · doi:10.1049/cp.2016.0312
  15. Zuchang, G., Chin, C.S., Wai Lok, W., Junbo, J., and Wei Da, T. (2015, January 15–17). Genetic algorithm based back-propagation neural network approach for fault diagnosis in lithium-ion battery system. Proceedings of the 2015 6th International Conference on Power Electronics Systems and Applications (PESA), Hong Kong, China. · doi:10.1109/pesa.2015.7398911
  16. Ismail, N.H.F., and Toha, S.F. (2013, January 25–27). State of charge estimation of a lithium-ion battery for electric vehicle based on particle swarm optimization. Proceedings of the 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), Kuala Lumpur, Malaysia. · doi:10.1109/icsima.2013.6717978
  17. Kim, T., Qiao, W., and Qu, L. (2013, January 16–19). Online state of charge and electrical impedance estimation for multicell lithium-ion batteries. Proceedings of the 2013 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA. · doi:10.1109/itec.2013.6574523

Cited by

Metadata

Authors
Rivera-Barrera, J., Muñoz-Galeano, N., & Sarmiento-Maldonado, H.
Venue
Electronics
Publisher
MDPI AG
Published
2017-11-23
Volume
6
Issue
4
Pages
102
ISSN
2079-9292 (ISSN-L)
DOI
10.3390/electronics6040102
OpenAlex
W2769684120
Cited by
447
References
136
FWCI
14.15
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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.