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This investigation presents a data-driven Long-short Term Memory battery model for predicting State of Charge for lithium-ion batteries LiFePO4 for next-generation vehicle operations. Our modified algorithm builds and updates a model using multivariate inputs that include physical properties, voltage, current, and ambient temperature during operations. The primary research goal is to improve prediction performance on future values from multiple training examples using an online learning scheme. Initial results demonstrate excellent predictions that outperform results from literature and other neural network algorithms. Due to computing constraints in on-board vehicle systems, the authors develop online training with autonomous control of lag (window width). The control algorithm embeds in the model with rules that govern and adjust lag during training. This method ensures the minimization of computational cost and prediction errors with the use of standard computing equipment during driving conditions.

Online State of Charge Prediction in Next Generation Vehicle Batteries Using Deep Recurrent Neural Networks and Continuous Model Size Control