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Comprehensive machine learning approaches for modelling the state of charge of lithium-ion batteries
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Abstract
The advancement of lithium-ion batteries (LIBs) is vital for achieving net-zero emissions because it enables renewable energy integration, supports electric vehicle (EV) adoption, and promotes cost-effective and sustainable solutions. The growing demand for EVs and portable electronics has amplified the need for reliable battery management systems to ensure safety and performance. Machine learning (ML) methods for modelling the state of charge (SOC) in batteries are gaining traction owing to their adaptability to diverse datasets and lower computational demands. However, the challenge lies in selecting the most suitable ML architecture for a specific application. This study evaluates three ML approaches for SOC modelling in LIBs: multilayer perceptron (MLP), long short-term memory (LSTM), and nonlinear autoregressive with exogenous input (NARX) neural networks. The models were tested using an experimental dataset with multiple input variables, including electrochemical impedance spectroscopy data, voltage, and capacity from commercial LIB cells. The results show that MLP and LSTM perform effectively with smaller training datasets (14 samples), whereas the NARX model requires more extensive data (34 out of 67 samples) for accuracy. Additionally, the NARX model showed greater sensitivity to learning rate adjustments and hidden layer configurations, whereas MLP and LSTM maintained robust performance across varying parameters.
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Description
Publisher
Elsevier
Citation
Journal of Power Sources 646, 236929
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Rae_2025_Comprehensive.pdf
Adobe PDF, 4 MB
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Funding Information
The Energy Conversion and Storage’’, funded as project no. CZ.02.01.01/00/22_008/0004617 by Programme Johannes Amos Commenius, call Excellent Research; specific graduate research of the Brno University of Technology No. FEKT-S-23-8286. D.C. acknowledges the EU Horizon 2023 research and innovation program under the Marie Sklodowska-Curie Postdoctoral Fellowship Grant no. 101152715 (SALSA Project). This work also received funding under the European Union’s Horizon 2022 Research and Innovation Program; grant agreement no. 101069738 (SiGNE Project)