Abstract
Data-driven algorithms, such as the neural network ones, seem very appealing and accurate solutions to estimate the lithium-ion battery’s State of Charge. Their accuracy is strongly related to the amount of data used in their training phase. Therefore, huge experimental campaigns are needed to effectively train the neural network used to State of Charge estimation. The main idea behind this paper is to mitigate this drawback by training the algorithm with synthetic datasets generated from simulations of a model of the battery, instead of experimentally collected data. Two instances of the same Long-Short-Term-Memory neural network architecture designed for battery State of Charge estimation are trained, one with an experimental dataset, and the other with a synthetic one. The two neural network instances are then evaluated with the same test dataset derived from experimental data and their estimation accuracies are compared. Results show that the performances of the two networks are comparable. The experimental trained neural network scored a RMSE of only \(0.3\,\%\) lower than the RMSE of the synthetic trained one. These results suggest the possibility of fruitfully using a synthetic training dataset to speed up and reduce the complexity and cost of the training phase of neural network algorithm for battery state of charge estimation.
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Acknowledgements
This study was carried out within the MOST - Sustainable Mobility Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) - MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 - D.D. 1033 17/06/2022, CN00000023). Moreover, the project was partially funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3 - Call for tender No. 1561 of 11.10.2022 of Ministero dell’Università e della Ricerca (MUR); funded by the European Union - NextGenerationEU. The work was partially supported by the Ministero dell’Università e della Ricerca (MUR) in the framework of the FoReLab project (Departments of Excellence). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.
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Hattouti, L.A. et al. (2024). Comparison of Lithium-Ion Battery SoC Estimation Accuracy of LSTM Neural Network Trained with Experimental and Synthetic Datasets. In: Bellotti, F., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023. Lecture Notes in Electrical Engineering, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-031-48121-5_58
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