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Deep learning has gained much traction in application to state-of-charge (SOC) estimation for Li-ion batteries in electric vehicle applications. However, with the vast selection of architectures and hyperparameter combinations, it remains challenging to design an accurate and robust SOC estimation model with a sufficiently low computation cost. Therefore, this study provides a comparative evaluation among commonly used deep learning models from the recurrent, convolutional and feedforward architecture benchmarked on an openly available Li-ion battery dataset. To evaluate model robustness and generalization capability, we train and test models on different drive cycles at various temperatures and compute the RMSE and MAE error metric. To evaluate model computation costs, we run models in real-time and record the model size, floating point operations per second (FLOPS), and run-time duration per datapoint. This study proposes a two-hidden layer stacked gated recurrent unit (GRU) model tr ....

Computational Modeling , Computer Architecture , Deep Learning , Eep Learning I , Gated Recurrent Unit , Eli Ion , Mathematical Model , State Of Charge , Tate Of Charge Estimation , கணக்கீட்டு மாடலிங் ,