Estimasi State-of-Charge Pada Baterai Lithium-Ion Menggunakan Deep Neural Network
DOI:
https://doi.org/10.28926/briliant.v9i3.1874Keywords:
State-of-Charge, Deep Neural Network, bateraiAbstract
As electric vehicles (EV) become increasingly popular in the automotive world, an accurate State-of-Charge (SoC) estimation is critical to optimizing energy utilization, increasing driving range and ensuring long-lasting battery system. This research focuses on the application of Deep Neural Networks (DNN) as an SoC estimation method in EV, exploiting the inherent capacity of DNN to learn complex relationships in vast data sets. The results of the performed simulations show that the proposed DNN-based SoC estimation method achieves a high level of accuracy, outperforming traditional estimation techniques, especially in scenarios involving rapid changes in driving conditions. This research also explores the impact of Neural Networks architecture and hyperparameter tuning on overall performance and provides insights for optimizing DNN-based SoC estimation systems. From the tests that have been carried out, an error value of 1.3% is obtained from the results of the training carried out on the DNN structure that has been prepared.
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