Predictive Modeling of Charge Levels for Battery Electric Vehicles using CNN EfficientNet and IGTD Algorithm
Convolutional Neural Networks (CNN) have been a good solution for understanding a vast image dataset. As the increased number of battery-equipped electric vehicles is flourishing globally, there has been much research on understanding which charge levels electric vehicle drivers would choose to char...
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Zusammenfassung: | Convolutional Neural Networks (CNN) have been a good solution for
understanding a vast image dataset. As the increased number of battery-equipped
electric vehicles is flourishing globally, there has been much research on
understanding which charge levels electric vehicle drivers would choose to
charge their vehicles to get to their destination without any prevention. We
implemented deep learning approaches to analyze the tabular datasets to
understand their state of charge and which charge levels they would choose. In
addition, we implemented the Image Generator for Tabular Dataset algorithm to
utilize tabular datasets as image datasets to train convolutional neural
networks. Also, we integrated other CNN architecture such as EfficientNet to
prove that CNN is a great learner for reading information from images that were
converted from the tabular dataset, and able to predict charge levels for
battery-equipped electric vehicles. We also evaluated several optimization
methods to enhance the learning rate of the models and examined further
analysis on improving the model architecture. |
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DOI: | 10.48550/arxiv.2206.03612 |