State-of-Charge Estimation of Li-ion Battery Using Gated Recurrent Unit With One-Cycle Learning Rate Policy
| Autor: | S. Hossain Lipu, Kashem M. Muttaqi, Dickson Neoh Tze How, Pin Ker, Muhamad Mansor, Mahammad A. Hannan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Hyperparameter
Mean squared error Computer science business.industry 020209 energy Deep learning Computation Feed forward 02 engineering and technology FLOPS Industrial and Manufacturing Engineering State of charge Computer engineering Control and Systems Engineering Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | IEEE Transactions on Industry Applications. 57:2964-2971 |
| ISSN: | 1939-9367 0093-9994 |
| Popis: | 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 root mean squared error (RMSE) and mean absolute 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 model trained with a one-cycle policy learning rate scheduler. The proposed model achieves a minimum RMSE of 0.52% on the train dataset and 0.65% on the test dataset while maintaining a relatively low computation cost. Executing the proposed model in real-time takes up approximately 1 MB in disk space, 300K FLOPS, and 0.03 ms run-time per datapoint. This makes the proposed model feasible to be executed on lightweight battery management system processors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|