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Online RNN Model for SOC Prediction in Next Generation Hybrid Car Batteries
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Online RNN Model for SOC Prediction in Next Generation Hybrid Car Batteries
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Journal Article
Online RNN Model for SOC Prediction in Next Generation Hybrid Car Batteries
2020
Overview
This investigation presents a data-driven Long-short Term Memory (LSTM) battery model for predicting State of Charge (SOC) for a lithium-ion battery (LiFePO4) during Electric Vehicle (EV) operation. The LSTM builds and updates a model using multivariate inputs that include physical properties, voltage, current, and temperature during operation. The goal of training is to accurately predict future SOC from multiple training examples using an online learning scheme. Initial results demonstrate excellent prediction with a Root Mean Square Error (RMSE) ranging from 0.372 < RMSE < 0.534 which outperforms results from literature that utilized other neural network algorithms.
