Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview
Open accessAbstract
In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the battery pack’s SOC. Notably, the advent of advanced microcontrollers and the availability of extensive datasets have contributed to the growing popularity and practicality of data-driven methodologies. This study examines the developments in SOC estimation over the past half-decade, explicitly focusing on data-driven estimation techniques. It comprehensively assesses the performance of each algorithm, considering the type of battery and various operational conditions. Additionally, intricate details concerning the models’ hyperparameters, including the number of layers, type of optimiser, and neuron, are provided for thorough examination. Most of the models analysed in the paper demonstrate strong performance, with both the MAE and RMSE for the estimation of SOC hovering around 2% or even lower.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Machine learning techniques use algorithms fed with big data to identify dependency between input parameters and SOC.
“Machine learning (ML) techniques entail use of algorithms that are fed with big data so as to identify dependency between input parameters and SOC, [38].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [38]
Cites
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2787586 · Cited by 906 · see citing works
Deng, Z., Hu, X., Lin, X., Che, Y., Xu, L., & Guo, W. (2020) · Energy · doi:10.1016/j.energy.2020.118000 · Cited by 423 · see citing works
Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Ayob, A., Saad, M. H. M., Karim, T. F., & How, D. N. T. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124110 · Cited by 378 · see citing works
Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions
Liu, D., Li, L., Song, Y., Wu, L., & Peng, Y. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.02.046 · Cited by 126 · see citing works
Bednarski, B. P., Singh, A. D., Zhang, W., Jones, W. M., Naeim, A., & Ramezani, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-25472-z · Cited by 41 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Transfer Learning Techniques for the Lithium-Ion Battery State of Charge Estimation
Eleftheriadis, P., Giazitzis, S., Leva, S., & Ogliari, E. (2024) · IEEE Access · doi:10.1109/access.2023.3337215 · Cited by 31 · see citing works
Self-citationReview Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement
Rehman, A. (2024) · Power System Technology · doi:10.52783/pst.906 · Cited by 12 · see citing works
- Authors
- Eleftheriadis, P., Giazitzis, S., Leva, S., & Ogliari, E.
- Affiliations
- Politecnico di Milano (IT)
- Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
- Corresponding author
- Eleftheriadis, Panagiotis
- Venue
- Forecasting
- Publisher
- MDPI AG
- Published
- 2023-09-14
- Volume
- 5
- Issue
- 3
- Pages
- 576-599
- ISSN
- 2571-9394 (ISSN-L)
- OpenAlex
- W4386742484
- Cited by
- 59
- References
- 5
- Citations by year
- 2026: 22 · 2025: 17 · 2024: 19 · 2023: 1
- FWCI
- 4.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Forecasting (publishedVersion, cc-by)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- state of charge estimation, lithium-ion batteries, data-driven methods, battery management systems, electric vehicles, neural network hyperparameters, deep learning models
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
