A novel enhanced SOC estimation method for lithium-ion battery cells using cluster-based LSTM models and centroid proximity selection

Journal article


Al-Alawi, M., Jaddoa, A., Cugley, J. and Hassanin, H. 2024. A novel enhanced SOC estimation method for lithium-ion battery cells using cluster-based LSTM models and centroid proximity selection. Journal of Energy Storage. 97 (B), p. 112866. https://doi.org/10.1016/j.est.2024.112866
AuthorsAl-Alawi, M., Jaddoa, A., Cugley, J. and Hassanin, H.
Abstract

In line with the global mission in achieving the net zero target through deployment of renewable energy technologies and electrifying the transportation sector; precise and adaptable State of Charge (SOC) estimation for Lithium-ion batteries has emerged as a critical need. The paper introduces a novel Cluster-Based Learning Model (CBLM) framework that integrates the strengths of K-Means and Fuzzy C-Means clustering with the predictive power of Long Short-Term Memory (LSTM) networks. This approach aims to enhance the precision and reliability of battery SOC estimations, adapting to the dynamic and complex operational conditions characteristic of Li-ion batteries. The key contributions of this study is the development and validation of the CBLM framework, which was proven to outperform state-of-art standalone deep learning techniques particularly under diverse operational conditions. Additionally, the introduction of a centroid proximity selection mechanism within the CBLM framework, which dynamically selects the most appropriate cluster model in real-time based on the proximity of the operational data to the cluster centroids. The performance of the proposed CBLM approach is evaluated using a Tesla Model 3 2170 Li-ion battery dataset. Results demonstrate the model's enhanced performance, with reductions in Root Mean Square Error (RMSE) to as low as 0.65% and Mean Absolute Error (MAE) to 0.51%, reducing state-of-art benchmark model errors by margins of 61.8% and 68.5% respectively. Additionally, the maximum error using CBLM was lower than benchmark, emphasising the model's reliability in worst-case-scenarios. The study also conducted comprehensive ablation tests on the proposed novel framework to further optimise its performance.

KeywordsState of charge (SOC) estimation; Lithium-ion batteries; LSTM; Battery management systems (BMS); Dynamic SOC estimation; Cluster-based learning model
Year2024
JournalJournal of Energy Storage
Journal citation97 (B), p. 112866
PublisherElsevier
ISSN2352-152X
Digital Object Identifier (DOI)https://doi.org/10.1016/j.est.2024.112866
Official URLhttps://www.sciencedirect.com/science/article/pii/S2352152X24024526?via%3Dihub
Publication dates
Online16 Jul 2024
Publication process dates
Deposited10 Jul 2024
Accepted author manuscript
License
File Access Level
Restricted
Publisher's version
License
File Access Level
Open
Output statusPublished
References

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Gordon, A., Shalet, D., Simpson, S., Hassanin, H., Lawson, F., Lawson, M., Litchfield, A., Thomas, C., Canetta, E., Manley, K. and Choong, C. Shalet, D. (ed.) 2022. The epistemic insight digest: Issue : Autumn 2022. Canterbury Canterbury Christ Church University.
Modeling, optimization, and analysis of a virtual power plant demand response mechanism for the internal electricity market considering the uncertainty of renewable energy sources
Ullah, Z., Arshad and Hassanin, H. 2022. Modeling, optimization, and analysis of a virtual power plant demand response mechanism for the internal electricity market considering the uncertainty of renewable energy sources. Energies. 15 (14), p. 5296. https://doi.org/doi.org/10.3390/en15145296
Interdisciplinary engineering education - essential for the 21st century
Gordon, A., Simpson, S. and Hassanin, H. 2022. Interdisciplinary engineering education - essential for the 21st century.
Multipoint forming using hole-type rubber punch
Hassanin, H., Tolipov, A., El-Sayed, M., Eldessouky, H., A. Alsaleh, N., Alfozan, A., Essa, K. and Ahmadein, M. 2022. Multipoint forming using hole-type rubber punch. Metals. 12 (3), p. 491. https://doi.org/10.3390/met12030491
Influence of bifilm defects generated during mould filling on the tensile properties of Al−Si−Mg cast alloys
El-Sayed, M., Essa, K. and Hassanin, H. 2022. Influence of bifilm defects generated during mould filling on the tensile properties of Al−Si−Mg cast alloys. Metals. 12 (1), p. e160. https://doi.org/10.3390/met12010160
Multistage Tool Path Optimisation of Single-Point Incremental Forming Process
Yan, Zhou, Hassanin, H., El-Sayed, M., Eldessouky, Hossam Mohamed, Djuansjah, Joy Rizki Pangestu, A. Alsaleh, N., Essa, K. and Ahmadein, M. 2021. Multistage Tool Path Optimisation of Single-Point Incremental Forming Process. Materials (Basel, Switzerland). 14 (22), p. e6794. https://doi.org/10.3390/ma14226794
How oviform is the chicken egg? New mathematical insight into the old oomorphological problem
Valeriy G. Narushin, Darren K. Griffin, James Cugley, Michael N. Romanov and Gang Lu 2021. How oviform is the chicken egg? New mathematical insight into the old oomorphological problem. Food Control. 119 (107484). https://doi.org/10.1016/j.foodcont.2020.107484
Effect of runner thickness and hydrogen content on the mechanical properties of A356 alloy castings
El-Sayed, M., Essa, K. and Hassanin, H. 2021. Effect of runner thickness and hydrogen content on the mechanical properties of A356 alloy castings . International Journal of Metalcasting. https://doi.org/10.1007/s40962-021-00753-x
Parts design and process optimization
Hassanin, Hany, Bidare, Prveen, Zweiri, Yahya and Essa, Khamis 2021. Parts design and process optimization. in: Salunkhe, S., Hussein, H. and Davim, J. (ed.) Applications of Artificial Intelligence in Additive Manufacturing USA IGI Global. pp. 25-49
Micro-additive manufacturing technologies of three-dimensional MEMS
Hassanin, H., Sheikholeslami, G., Pooya, S. and Ishaq, R. 2021. Micro-additive manufacturing technologies of three-dimensional MEMS . Advanced Engineering Materials. https://doi.org/10.1002/adem.202100422
Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications
Fan , W., Chen, Y., Li, J., Sun, Y., Feng, F., Hassanin, H. and Sareh, P. 2021. Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications. Structures. 33, pp. 3954-3963. https://doi.org/10.1016/j.istruc.2021.06.110
Porosity, cracks, and mechanical properties of additively manufactured tooling alloys: A review
Bidare, P., Jiménez, A., Hassanin, H. and Essa, K. 2021. Porosity, cracks, and mechanical properties of additively manufactured tooling alloys: A review. Advances in Manufacturing. https://doi.org/10.1007/s40436-021-00365-y
Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches
Hassanin, H., Zweiri, Y., Finet, L., Essa, K., Qiu, C. and Attallah, M. 2021. Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches. Materials. 14 (8), p. 2056. https://doi.org/10.3390/ma14082056
3DP printing of oral solid formulations: a systematic review
Brambilla, C., Okafor-Muo, O., Hassanin, H. and ElShaer, A. 2021. 3DP printing of oral solid formulations: a systematic review. Pharmaceutics. 13 (3), p. 358. https://doi.org/10.3390/pharmaceutics13030358
Powder-based laser hybrid additive manufacturing of metals: A review
Hassanin, H. 2021. Powder-based laser hybrid additive manufacturing of metals: A review. The International Journal of Advanced Manufacturing Technology.
Micro-fabrication of ceramics: additive manufacturing and conventional technologies
Hassanin, H., Essa, K., Elshaer, A., Imbaby, M. and El-Sayed, T. E. 2021. Micro-fabrication of ceramics: additive manufacturing and conventional technologies. Journal of Advanced Ceramics. 10, pp. 1-27. https://doi.org/10.1007/s40145-020-0422-5
A deep gated recurrent neural network for petroleum production forecasting
Raghad Al-Shabandar, Ali Jaddoa, Panos Liatsis and Abir Jaafar Hussain 2020. A deep gated recurrent neural network for petroleum production forecasting. Machine Learning with Applications . 3, p. 100013. https://doi.org/10.1016/j.mlwa.2020.100013
Dynamic decision support for resource offloading in heterogeneous Internet of Things environments
Ali Jaddoa, Georgia Sakellari, Emmanouil Panaousis, George Loukas and Panagiotis G. Sarigiannidis 2020. Dynamic decision support for resource offloading in heterogeneous Internet of Things environments. Simulation Modelling Practice and Theory. 101. https://doi.org/10.1016/j.simpat.2019.102019
A 2-D imaging-assisted geometrical transformation method for non- destructive evaluation of the volume and surface area of avian eggs
Valeriy G. Narushin, Gang Lu, Cugley, J., Michael N. Romanov and Darren K. Griffin 2020. A 2-D imaging-assisted geometrical transformation method for non- destructive evaluation of the volume and surface area of avian eggs. Food Control. 112 (107112). https://doi.org/10.1016/j.foodcont.2020.107112
Digital imaging assisted geometry of chicken eggs using Hügelschäffer's model
Valeriy G. Narushin, Michael N. Romanov, Gang Lu, James Cugkey and Darren K. Griffin 2020. Digital imaging assisted geometry of chicken eggs using Hügelschäffer's model. Biosystems Engineering. 197, pp. 45-55. https://doi.org/10.1016/j.biosystemseng.2020.06.008
4D Printing of origami structures for minimally invasive surgeries using functional scaffold
Langford, T, Mohammed, A., Essa, K., Elshaer, A. and Hassanin, H. 2020. 4D Printing of origami structures for minimally invasive surgeries using functional scaffold. Applied Sciences. 11 (1), p. 332. https://doi.org/10.3390/app11010332
Reconfigurable multipoint forming using waffle-type elastic cushion and variable loading profile
Hassanin, H., Mohammed, M., Abdel-Wahab, A. and Essa, K 2020. Reconfigurable multipoint forming using waffle-type elastic cushion and variable loading profile. Materials.
3D printing of solid oral dosage forms: numerous challenges with unique opportunities
Hassanin, H. 2020. 3D printing of solid oral dosage forms: numerous challenges with unique opportunities. Journal of Pharmaceutical Sciences. https://doi.org/10.1016/j.xphs.2020.08.029
Design optimisation of additively manufactured titanium lattice structures for biomedical implants
El-Sayed, M.A., Essa, K., Ghazy, M. and Hassanin, H. 2020. Design optimisation of additively manufactured titanium lattice structures for biomedical implants. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-020-05982-8
4D Printing of NiTi auxetic structure with improved ballistic performance
Hassanin, H., Abena, A., Elsayed, M.A. and Essa, K. 2020. 4D Printing of NiTi auxetic structure with improved ballistic performance. Micromachines. 11 (8), p. 745. https://doi.org/doi.org/10.3390/mi11080745
Advanced flame monitoring and emission prediction through digital imaging and spectrometry
Cugley, J. 2019. Advanced flame monitoring and emission prediction through digital imaging and spectrometry. PhD Thesis University of Kent School of Engineering and Digital Arts
Estimating the prevalence of problematic opiate use in Ireland using indirect statistical methods
Gordon Hay, Jaddoa, A., Jane Oyston, Jane Webster and Marie Claire Van Hout 2017. Estimating the prevalence of problematic opiate use in Ireland using indirect statistical methods. Dublin National Advisory Committee on Drugs and Alcohol.
String matching enhancement for snort IDS
S. O. Al-Mamory, Ali Hamid, A. Abdul-Razak and Z. Falah 2010. String matching enhancement for snort IDS. in: 5th International Conference on Computer Sciences and Convergence Information Technology IEEE. pp. 1020-1023