The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks
Abstract
In this paper, an artificial neural network (ANN) based approach is proposed to estimate the capacity fade in lithium-ion (Li-ion) batteries for electric vehicles (EVs). Besides its robustness, stability, and high accuracy, the proposed technique can significantly improve the state-of-charge (SOC) estimation accuracy over the lifespan of the battery, which leads to more reliable battery operation and prolonged lifetime. In addition, the proposed technique allows accurate prediction of the battery remaining service time. Two identical 3.6-V/16.5-Ah Li-ion battery cells were repeatedly cycled with constant current and dynamic stress test current profiles at room temperature, and their discharge capacities were recorded. The proposed technique shows that very accurate SOC estimation results can be obtained provided enough training data are used to train the ANN models. Model derivation and experimental verification are presented in this paper.
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.
Integrity signals
Registry
Cites
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.
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
A review on the key issues for lithium-ion battery management in electric vehicles
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
A review on lithium-ion battery ageing mechanisms and estimations for automotive applications
Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.05.040 · Cited by 1,758
Cited by
Vidal, C., Malysz, P., Kollmeyer, P., & Emadi, A. (2020) · IEEE Access · doi:10.1109/access.2020.2980961 · Cited by 512
Reniers, J. M., Mulder, G., & Howey, D. A. (2019) · Journal of The Electrochemical Society · doi:10.1149/2.0281914jes · Cited by 462
SoC Estimation for Lithium-ion Batteries: Review and Future Challenges
Rivera-Barrera, J., Muñoz-Galeano, N., & Sarmiento-Maldonado, H. (2017) · Electronics · doi:10.3390/electronics6040102 · Cited by 447
Sui, X., He, S., Vilsen, S. B., Meng, J., Teodorescu, R., & Stroe, D.-I. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117346 · Cited by 409
An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery
Meng, J., Ricco, M., Luo, G., Swierczynski, M., Stroe, D.-I., Stroe, A.-I., & Teodorescu, R. (2018) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2017.2775179 · Cited by 393
Metadata
- Authors
- Hussein, A. A.
- Venue
- IEEE Transactions on Industry Applications
- Published
- 2015-05
- Volume
- 51
- Issue
- 3
- Pages
- 2321-2330
- ISSN
- 0093-9994 (ISSN-L)
- 1939-9367
- OpenAlex
- W2006918070
- Cited by
- 176
- References
- 3
- FWCI
- 8.99
- Type
- journal article
- Language
- en
- Open access
- closed
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: Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks. https://app.cochat.ai/papers/10.1109/tia.2014.2365152
- The paper
- Hussein, A. A. (2015). Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks. IEEE Transactions on Industry Applications. https://doi.org/10.1109/tia.2014.2365152
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.
