Battery pack temperature change prediction for running BLDC 1500 W motor using artificial neural network
The study explores the prediction of battery temperature using an artificial neural network (ANN) model, trained with experimental data from a brushless DC (BLDC) motor setup. The ANN model, with a 15-14-1 architecture, successfully predicted battery temperature change based on various input paramet...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29331/1/Battery%20Pack%20Temperature%20Change%20Prediction%20for%20Running%20BLDC%201500%20W%20Motor%20using%20Artificial%20Neural%20Network.pdf http://eprints.utem.edu.my/id/eprint/29331/ http://ieeexplore.ieee.org/document/10845741 |
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| Summary: | The study explores the prediction of battery temperature using an artificial neural network (ANN) model, trained with experimental data from a brushless DC (BLDC) motor setup. The ANN model, with a 15-14-1 architecture, successfully predicted battery temperature change based on various input parameters, including RPM, load and voltage change of thirteen series of battery. The ANN predictions aligned closely with experimental results, demonstrating the model’s effectiveness in capturing the nonlinear behavior of battery temperature changes. These findings highlight the potential of deep learning techniques to improve real-time thermal management in BMS, offering a promising approach for extending battery life and optimizing performance in electric vehicles and energy storage systems. |
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