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Online State of Charge Prediction in Next Generation Hybrid Vehicle Batteries Using Deep Recurrent Neural Networks and Continuous Model Size Control
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Online State of Charge Prediction in Next Generation Hybrid Vehicle Batteries Using Deep Recurrent Neural Networks and Continuous Model Size Control
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Dissertation
Online State of Charge Prediction in Next Generation Hybrid Vehicle Batteries Using Deep Recurrent Neural Networks and Continuous Model Size Control
2020
Overview
This investigation presents a data-driven LSTM battery model for predicting SOC for lithium-ion batteries (LiFePO4) during next-generation vehicle operation. Using a large temporal multivariate dataset, feature selection is performed to quantify feature importance values. Then, an offline LSTM is built using multivariate inputs that include physical battery properties, voltage, current, and ambient temperature during operation. Results demonstrated excellent prediction with a RMSE ranging from 0.372
Publisher
ProQuest Dissertations & Theses
ISBN
9798557084260
