The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques
Open accessAbstract
State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.
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Cites
Double-slit photoelectron interference in strong-field ionization of the neon dimer
Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-07882-8 · Cited by 8,162
Chai, T., & Draxler, R. R. (2014) · Geoscientific Model Development · doi:10.5194/gmd-7-1247-2014 · Cited by 7,178 · cited 1×
“In this study, RMSE was chosen as the objective function because of the large number of sample variables and randomness behavior of SOC errors 27 .”
MethodA few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326
Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.001 · Cited by 2,005
Self-citationLong 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
Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.06.104 · Cited by 818
Zheng, F., Xing, Y., Jiang, J., Sun, B., Kim, J., & Pecht, M. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.09.010 · Cited by 574
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
A new neural network model for the state-of-charge estimation in the battery degradation process
Kang, L., Zhao, X., & Ma, J. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.01.066 · Cited by 325
A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries
Xiong, R., Zhang, Y., He, H., Zhou, X., & Pecht, M. G. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2733475 · Cited by 285
Cited by
Hannan, M. A., Wali, S. B., Ker, P. J., Rahman, M. S. A., Mansor, M., Ramachandaramurthy, V. K., Muttaqi, K. M., Mahlia, T. M. I., & Dong, Z. Y. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103023 · Cited by 676
Self-citationSolar photovoltaic energy optimization methods, challenges and issues: A comprehensive review
Al-Shahri, O. A., Ismail, F. B., Hannan, M. A., Lipu, M. S. H., Al-Shetwi, A. Q., Begum, R. A., Al-Muhsen, N. F. O., & Soujeri, E. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.125465 · Cited by 518
Self-citationCui, Z., Wang, L., Li, Q., & Wang, K. (2022) · International Journal of Energy Research · doi:10.1002/er.7545 · Cited by 384
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
Self-citationMa, L., Hu, C., & Cheng, F. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102440 · Cited by 308
Metadata
- Authors
- Hannan, M. A., Lipu, M. S. H., Hussain, A., Ker, P. J., Mahlia, T. M. I., Mansor, M., Ayob, A., Saad, M. H., & Dong, Z. Y.
- Venue
- Scientific Reports
- Published
- 2020-03-13
- Volume
- 10
- Issue
- 1
- Pages
- 4687-4687
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W3012309884
- Cited by
- 244
- References
- 14
- FWCI
- 13.49
- Type
- journal article
- Language
- en
- Open access
- gold
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- Hannan, M. A., Lipu, M. S. H., Hussain, A., Ker, P. J., Mahlia, T. M. I., Mansor, M., Ayob, A., Saad, M. H., & Dong, Z. Y. (2020). Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques. Scientific Reports. https://doi.org/10.1038/s41598-020-61464-7
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