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

Publication dates
Authors	Al-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.
Keywords	State of charge (SOC) estimation; Lithium-ion batteries; LSTM; Battery management systems (BMS); Dynamic SOC estimation; Cluster-based learning model
Year	2024
Journal	Journal of Energy Storage
Journal citation	97 (B), p. 112866
Publisher	Elsevier
ISSN	2352-152X
Digital Object Identifier (DOI)	https://doi.org/10.1016/j.est.2024.112866
Official URL	https://www.sciencedirect.com/science/article/pii/S2352152X24024526?via%3Dihub
Online	16 Jul 2024
Publication process dates
Deposited	10 Jul 2024
Accepted author manuscript	License CC BY-NC-ND 4.0 File Access Level Restricted
Publisher's version	1-s2.0-S2352152X24024526-main.pdf License CC BY 4.0 File Access Level Open
Output status	Published
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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