Feed-forward State of Charge estimation of LiFePO4 batteries using time-series machine learning prediction with autoregressive models
Open accessAbstract
This paper presents a Recurrent Neural Network (RNN) model for the State of Charge (SoC) estimation for an Electric Vehicle’s Lithium-Ion battery. A new Autoregressive modification is proposed to improve upon earlier published Machine Learning models to enhance the prediction accuracy and make results less sensitive to varying usage conditions. By building upon earlier published findings, assessing several models’ ability to estimate charge using a history window of sensory data, this work proposes, implements, and reports its methods of improving accuracy by introducing the State of Charge as part of sensory inputs in a Feed-Forward manner. The method is based on the Autoregressive implementation of neural network model training, where successively calculated charge values are used as input in the subsequent predictions, but the potential inaccuracies with those predictions that might otherwise cause significant and rapid divergence are accounted for within a modified recursive training procedure. As a result, with a slight increase in training time, the accuracy of the output prediction for three different realistic charge–discharge driving profiles doubled compared to the two best previously published models. Overall, the test results have demonstrated the usability of the current model in actual driving scenarios, making such models a viable replacement to estimation approaches used in electric vehicle battery management systems .
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Sadykov et al. showed that SRNN estimators based on recursive strategies had half the error rate of non-recursive models when handling untrained driving dynamics.
“Sadykov et al. showed that SRNN estimators based on recursive strategies had half the error rate of non-recursive models when handling untrained driving dynamics[77].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [77]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009) · Applied Energy · doi:10.1016/j.apenergy.2008.11.021 · Cited by 1,636 · see citing works
Xing, Y., He, W., Pecht, M., & Tsui, K. L. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.07.008 · Cited by 977 · see citing works
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
He, W., Williard, N., Chen, C., & Pecht, M. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.04.059 · Cited by 539 · see citing works
Ali, M. U., Zafar, A., Nengroo, S. H., Hussain, S., Alvi, M. J., & Kim, H. (2019) · Energies · doi:10.3390/en12030446 · Cited by 455 · see citing works
2013 IEEE Energy Conversion Congress and Exposition
(2013) · doi:10.1109/ecce20179.2013 · Cited by 386 · see citing works
Support vector based battery state of charge estimator
Hansen, T., & Wang, C.-J. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.09.020 · Cited by 347 · see citing works
A GRU-RNN based momentum optimized algorithm for SOC estimation
Jiao, M., Wang, D., & Qiu, J. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228051 · Cited by 324 · see citing works
Battery state-of-charge estimator using the SVM technique
Álvarez Antón, J. C., García Nieto, P. J., de Cos Juez, F. J., Sánchez Lasheras, F., González Vega, M., & Roqueñí Gutiérrez, M. N. (2013) · Applied Mathematical Modelling · doi:10.1016/j.apm.2013.01.024 · Cited by 211 · see citing works
We, Z., Li, X., & Li, X. (2020) · Measurement · doi:10.1016/j.measurement.2020.108052 · Cited by 202 · see citing works
A High Precision Study of the Coulombic Efficiency of Li-Ion Batteries
Smith, A. J., Burns, J. C., & Dahn, J. R. (2010) · Electrochemical and Solid-State Letters · doi:10.1149/1.3487637 · Cited by 172 · see citing works
Xiao, B., Liu, Y., & Xiao, B. (2019) · IEEE Access · doi:10.1109/access.2019.2913078 · Cited by 170 · see citing works
Fuzzy logic-based learning system and estimation of state-of-charge of lead-acid battery
Malkhandi, S. (2006) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2005.12.005 · Cited by 120 · see citing works
Lithium-Ion Battery Remaining Useful Life Prediction Based on GRU-RNN
Song, Y., Li, L., Peng, Y., & Liu, D. (2018) · International Conference on Reliability, Maintainability and Safety · doi:10.1109/icrms.2018.00067 · Cited by 75 · see citing works
Mamo, T., & Wang, F. (2020) · IEEE Access · doi:10.1109/access.2020.2995656 · Cited by 53 · see citing works
Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms
Yan, J., Xu, G., Qian, H., & Xu, Y. (2010) · Energies · doi:10.3390/en3101654 · Cited by 51 · see citing works
Juang, L. W., Kollmeyer, P. J., Zhao, R., Jahns, T. M., & Lorenz, R. D. (2014) · IEEE Energy Conversion Congress and Exposition (ECCE) · doi:10.1109/ecce.2014.6953745 · Cited by 28 · see citing works
Standards for electric vehicle batteries and associated testing procedures
Castillo, E. C. (2015) · Elsevier eBooks · doi:10.1016/b978-1-78242-377-5.00018-2 · Cited by 21 · see citing works
Adaptive Online Gated Recurrent Unit for Lithium-Ion Battery SOC Estimation
Javid, G., Basset, M., & Abdeslam, D. O. (2020) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon43393.2020.9254506 · Cited by 21 · see citing works
Sadykov, M., Haines, S., Broadmeadow, M. A. H., Walker, G. R., & Holmes, D. W. (2023) · Energies · doi:10.3390/en16041628 · Cited by 13 · see citing works
Self-citationYamin, R., & Rachid, A. (2014) · IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin) · doi:10.1109/icce-berlin.2014.7034282 · Cited by 10 · see citing works
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Wu, Y., Bai, D., Zhang, K., Li, Y., & Yang, F. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.84 · Cited by 20 · see citing works
Soni, D. B., Amirrezai, M., Schotten, H. D., & Goetz, S. M. (2025) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon58223.2025.11221042
Reddy, M. R. P., Ghouse, N. M., Babu, V. M., & Shiva, K. (2026) · International Conference on Communication, Computing and Electronics Systems (ICCCES) · doi:10.1109/iccces62661.2026.11436195
- Authors
- Sadykov, M., Haines, S., Walker, G., & Holmes, D. W.
- Affiliations
- Queensland University of Technology (AU)
- Corresponding author
- Holmes, David William
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 100
- Pages
- 113516
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W4401983812
- Cited by
- 3
- References
- 25
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 0.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Energy Storage (publishedVersion, cc-by)
- Funders
- Department of Industry and Science, Australian Government
- Australian Government Department of Industry Innovation and Science (AEGP000036)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- state of charge estimation, lithium iron phosphate batteries, time series forecasting, autoregressive models, battery management systems
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