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
References	[1] (). Global EV Outlook 2023: Trends in Batteries. Available: https://www.iea.org/reports/global-ev-outlook-2023/trends-in-batteri... [2] M. K. Al-Alawi, J. Cugley and H. Hassanin, "Techno-economic feasibility of retired electric-vehicle batteries repurpose/reuse in second-life applications: A systematic review," Energy and Climate Change, vol. 3, pp. 100086, 2022. Available: https://www.sciencedirect.com/science/article/pii/S2666278722000162. DOI: 10.1016/j.egycc.2022.100086. [3] L. Wu et al, "Physics-based battery SOC estimation methods: Recent advances and future perspectives," Journal of Energy Chemistry, vol. 89, pp. 27-40, 2024. Available: https://dx.doi.org/10.1016/j.jechem.2023.09.045. DOI: 10.1016/j.jechem.2023.09.045. [4] (Dec 14,). The Financial Implications of Inaccurate SOC in LFP Batteries. Available: https://www.accure.net/battery-knowledge/lfp-soc-estimation-challeng... [5] S. Wang et al, Multidimensional Lithium-Ion Battery Status Monitoring. (First edition ed.) Boca Raton ; London ; New York: CRC Press, 2023. [6] M. Naguib, P. Kollmeyer and A. Emadi, "Lithium-Ion Battery Pack Robust State of Charge Estimation, Cell Inconsistency, and Balancing: Review," Access, vol. 9, pp. 50570-50582, 2021. Available: https://ieeexplore.ieee.org/document/9386065. DOI: 10.1109/ACCESS.2021.3068776. [7] Z. Cui et al, "A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network," International Journal of Energy Research, vol. 46, (5), pp. 5423-5440, 2022. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/er.7545. DOI: 10.1002/er.7545. [8] M. A. Hannan et al, "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable & Sustainable Energy Reviews, vol. 78, pp. 834-854, 2017. Available: https://dx.doi.org/10.1016/j.rser.2017.05.001. DOI: 10.1016/j.rser.2017.05.001. [9] S. D.V.S.R., C. Badachi and R. C. Green II, "A review on data-driven SOC estimation with Li-Ion batteries: Implementation methods & future aspirations," Journal of Energy Storage, vol. 72, pp. 108420, 2023. Available: https://dx.doi.org/10.1016/j.est.2023.108420. DOI: 10.1016/j.est.2023.108420. [10] K. S. Ng et al, "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, vol. 86, (9), pp. 1506-1511, 2009. Available: https://dx.doi.org/10.1016/j.apenergy.2008.11.021. DOI: 10.1016/j.apenergy.2008.11.021. [11] H. He et al, "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy (Oxford), vol. 39, (1), pp. 310-318, 2012. Available: https://dx.doi.org/10.1016/j.energy.2012.01.009. DOI: 10.1016/j.energy.2012.01.009. [12] W. S. Rui Xiong, Advanced Battery Management Technologies for Electric Vehicles. (1st ed.) Newark: John Wiley & Sons, Ltd, 2019. [13] E. Almaita et al, "State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network," Journal of Energy Storage, vol. 52, pp. 104761, 2022. Available: https://dx.doi.org/10.1016/j.est.2022.104761. DOI: 10.1016/j.est.2022.104761. [14] F. Feng et al, "Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model," Journal of Power Sources, vol. 455, pp. 227935, 2020. Available: https://dx.doi.org/10.1016/j.jpowsour.2020.227935. DOI: 10.1016/j.jpowsour.2020.227935. [15] I. B. Espedal et al, "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies (Basel), vol. 14, (11), pp. 3284, 2021. Available: https://search.proquest.com/docview/2539699219. DOI: 10.3390/en14113284. [16] S. V. Kishore N and V. S. Sravan Kumar, "Comparative analysis of model-based approaches for state-of-charge estimation in batteries," in Nov 24, 2022, pp. 1-6. [17] S. Sunil, B. Balasingam and K. R. Pattipati, "State-of-charge estimation of batteries using the extended kalman filter: Insights into performance analysis and filter tuning," in Dec 9, 2022, pp. 1-6. [18] Y. Zeng, Y. Li and T. Yang, "State of Charge Estimation for Lithium-Ion Battery Based on Unscented Kalman Filter and Long Short-Term Memory Neural Network," Batteries (Basel), vol. 9, (7), pp. 358, 2023. Available: https://doaj.org/article/33466da870d64b1f8996e2764d055080. DOI: 10.3390/batteries9070358. [19] W. Zhou et al, "Review on the Battery Model and SOC Estimation Method," Processes, vol. 9, (9), pp. 1685, 2021. Available: https://search.proquest.com/docview/2576497406. DOI: 10.3390/pr9091685. [20] M. S. Hossain Lipu et al, "Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends," Journal of Cleaner Production, vol. 277, pp. 124110, 2020. Available: https://dx.doi.org/10.1016/j.jclepro.2020.124110. DOI: 10.1016/j.jclepro.2020.124110. [21] F. Yang et al, "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy (Oxford), vol. 175, pp. 66-75, 2019. Available: https://dx.doi.org/10.1016/j.energy.2019.03.059. DOI: 10.1016/j.energy.2019.03.059. [22] Q. Gong, P. Wang and Z. Cheng, "A novel deep neural network model for estimating the state of charge of lithium-ion battery," Journal of Energy Storage, vol. 54, pp. 105308, 2022. Available: https://dx.doi.org/10.1016/j.est.2022.105308. DOI: 10.1016/j.est.2022.105308. [23] X. Fan et al, "SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture," Energy (Oxford), vol. 256, pp. 124612, 2022. Available: https://dx.doi.org/10.1016/j.energy.2022.124612. DOI: 10.1016/j.energy.2022.124612. [24] B. Fu et al, "An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system," Green Energy and Intelligent Transportation, vol. 2, (2), pp. 100067, 2023. Available: https://dx.doi.org/10.1016/j.geits.2023.100067. DOI: 10.1016/j.geits.2023.100067. [25] S. D.V.S.R., C. Badachi and R. C. Green II, "A review on data-driven SOC estimation with Li-Ion batteries: Implementation methods & future aspirations," Journal of Energy Storage, vol. 72, pp. 108420, 2023. Available: https://dx.doi.org/10.1016/j.est.2023.108420. DOI: 10.1016/j.est.2023.108420. [26] Z. Zhang et al, "A state-of-charge estimation method based on bidirectional LSTM networks for lithium-ion batteries," in Dec 13, 2020, pp. 211-216. [27] C. Hu et al, "State of Charge Estimation for Lithium-Ion Batteries Based on TCN-LSTM Neural Networks," Jes, vol. 169, (3), pp. 30544, 2022. Available: https://iopscience.iop.org/article/10.1149/1945-7111/ac5cf2. DOI: 10.1149/1945-7111/ac5cf2. [28] J. Hao et al, "Short-term power load forecasting for larger consumer based on TensorFlow deep learning framework and clustering-regression model," in Oct 2018, pp. 1-6. [29] J. Liu et al, "Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion," Reliability Engineering & System Safety, vol. 214, pp. 107807, 2021. Available: https://dx.doi.org/10.1016/j.ress.2021.107807. DOI: 10.1016/j.ress.2021.107807. [30] H. Yu et al, "Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning," Access, vol. 9, pp. 143824-143835, 2021. Available: https://ieeexplore.ieee.org/document/9576102. DOI: 10.1109/ACCESS.2021.3120379. [31] A. R. Khan et al, "Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification," Microscopy Research and Technique, vol. 84, (7), pp. 1389-1399, 2021. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/jemt.23694. DOI: 10.1002/jemt.23694. [32] P. J. Kollmeyer et al. (). Tesla Model 3 2170 Li-ion Cell Dataset and Battery SOC Estimation Blind Modeling Tool. Available: V1 https://doi.org/10.5683/SP3/ZVTR4B. DOI: 10.5683/SP3/ZVTR4B. [33] R. N. Vieira et al, "Feedforward and NARX neural network battery state of charge estimation with robustness to current sensor error," in Jun 21, 2023, pp. 1-6. [34] V. Pendyala and F. Nishanth, "Development of a machine learning technique to accurately estimate battery state of charge," in Dec 9, 2022, pp. 1-6. [35] A. E. Ezugwu et al, "A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects," Engineering Applications of Artificial Intelligence, vol. 110, pp. 104743, 2022. Available: https://dx.doi.org/10.1016/j.engappai.2022.104743. DOI: 10.1016/j.engappai.2022.104743. [36] E. B. Osunwoke et al, "A machine learning-enabled clustering approach for large-scale classification of solar data," in Nov 14, 2021, pp. 1. [37] C. Sánchez-Rebollo et al, "Detection of jihadism in social networks using big data techniques supported by graphs and fuzzy clustering," Complexity, vol. 2019, 2019. [38] O. OZDEMİR and A. KAYA, "Effect of Parameter Selection on Fuzzy Clustering," Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, vol. 2, (1), pp. 22-33, 2018. . DOI: 10.31200/makuubd.348688. [39] F. Kratzert et al, "Rainfall-runoff modelling using long short-term memory (lstm) networks," Hydrology and Earth System Sciences, vol. 22, (11), pp. 6005-6022, 2018. Available: https://search.proquest.com/docview/2136490956. DOI: 10.5194/Hess-22-6005-2018. [40] B. Leibe et al, "Spatio-temporal LSTM with trust gates for 3D human action recognition," in Computer Vision - ECCV 2016Anonymous Switzerland: Springer International Publishing AG, 2016, pp. 816-833. [41] C. Han and X. Fu, "Challenge and Opportunity: Deep Learning-Based Stock Price Prediction by Using Bi-Directional LSTM Model," Frontiers in Business, Economics and Management, vol. 8, (2), pp. 51-54, 2023. Available: https://explore.openaire.eu/search/result?id=doi_________::ef993f05447450f19f358a6de8764d72. DOI: 10.54097/fbem.v8i2.6616. [42] H. Jiang et al, "Construction and Analysis of Emotion Computing Model Based on LSTM," Complexity (New York, N.Y.), vol. 2021, pp. 1-12, 2021. Available: https://dx.doi.org/10.1155/2021/8897105. DOI: 10.1155/2021/8897105. [43] F. Yang et al, "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy (Oxford), vol. 201, pp. 117664, 2020. Available: https://dx.doi.org/10.1016/j.energy.2020.117664. DOI: 10.1016/j.energy.2020.117664. [44] J. Chen et al, "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy (Oxford), vol. 262, pp. 125375, 2023. Available: https://dx.doi.org/10.1016/j.energy.2022.125375. DOI: 10.1016/j.energy.2022.125375. [45] Z. Chen et al, "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy (Oxford), vol. 228, pp. 120630, 2021. Available: https://dx.doi.org/10.1016/j.energy.2021.120630. DOI: 10.1016/j.energy.2021.120630. [46] S. Montaha et al, "TimeDistributed-CNN-LSTM: A Hybrid Approach Combining CNN and LSTM to Classify Brain Tumor on 3D MRI Scans Performing Ablation Study," Access, vol. 10, pp. 60039-60059, 2022. Available: https://ieeexplore.ieee.org/document/9786658. DOI: 10.1109/ACCESS.2022.3179577. [47] F. Karim, S. Majumdar and H. Darabi, "Insights Into LSTM Fully Convolutional Networks for Time Series Classification," Access, vol. 7, pp. 67718-67725, 2019. Available: https://ieeexplore.ieee.org/document/8713870. DOI: 10.1109/ACCESS.2019.2916828. [48] N. S. Ranawat et al, "Performance evaluation of LSTM and Bi-LSTM using non-convolutional features for blockage detection in centrifugal pump," Engineering Applications of Artificial Intelligence, vol. 122, pp. 106092, 2023. Available: https://dx.doi.org/10.1016/j.engappai.2023.106092. DOI: 10.1016/j.engappai.2023.106092.

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Hassanin, H., Ullah, Z., Arshad, Cugley, J. and Al-Alawi, M. 2022. Planning, operation, and design of market-based virtual power plant considering uncertainty. Energies. 19 (15), p. 7290. https://doi.org/10.3390/en15197290

The epistemic insight digest: Issue : Autumn 2022

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