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
Authors | Al-Alawi, M., Jaddoa, A., Cugley, J. and Hassanin, H. |
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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 |
Publication dates | |
Online | 16 Jul 2024 |
Publication process dates | |
Deposited | 10 Jul 2024 |
Accepted author manuscript | License File Access Level Restricted |
Publisher's version | License 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... |
https://repository.canterbury.ac.uk/item/98571/a-novel-enhanced-soc-estimation-method-for-lithium-ion-battery-cells-using-cluster-based-lstm-models-and-centroid-proximity-selection
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