- Battery RUL prediction using data-driven method based on a hybrid deep model of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM).
- CNN and LSTM are used to extract features from multiple measurable data (Voltage, Current, Temperature, Capacity) in parallel.
- CNN extracts features of multi-channel charging profiles, whereas LSTM extracts features of historical capacity data of discharging profiles which related to time dependency.
- This repository provides the code for training in python.
- Voltage (V), Current (I), and Temperature (T) inputs will each get in the CNN layer separately.
- Feature V gets into a different CNN layer with features I and T, as well as a feature I get into a separate CNN layer with V and T, and so on.
- The output from the CNN layer for each feature, is then concatenated. Then they get the next CNN layer
- The extracted features in the last CNN layer is concatenated with the output of the LSTM layer.
| Model | RMSE | MAE | MAPE (%) |
|---|---|---|---|
| SC-LSTM | 0,0620 | 0,0549 | 3,6080 |
| MC-LSTM | 0,0403 | 0,0340 | 2,2847 |
| SC-CNN-LSTM | 0,0270 | 0,0215 | 1,3804 |
| MC-CNN-LSTM | 0,0359 | 0,0291 | 1,9346 |
| MC-SCNN-LSTM | 0,0276 | 0,0220 | 1,4207 |
- SC : Single Channel, MC : Multi Channel
- The performance of prediction models were compared using some evaluation metrics including root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).
- The hybrid model with excellent feature extraction helps to produce more accurate prediction.
- The MC-SCNNLSTM, MC-CNN-LSTM, and SC-CNN-LSTM model’s prediction results produce predictive values that are close to actual values and are better than the baseline model.
- Hybrid of CNN-LSTM model achieves 61%, 37%, and 15% performance improvements of MAPE in terms of SC-CNN-LSTM, MC-SCNN-LSTM, and MC-CNN-LSTM respectively, compared to using the single model
@inproceedings{10.1145/3575882.3575903,
author = {Hafizhahullah, Huzaifi and Yuliani, Asri Rizki and Pardede, Hilman and Ramdan, Ade and Zilvan, Vicky and Krisnandi, Dikdik and Kadar, Jimmy},
title = {A Hybrid CNN-LSTM for Battery Remaining Useful Life Prediction with Charging Profiles Data},
year = {2023},
isbn = {9781450397902},
publisher = {Association for Computing Machinery},
url = {https://doi.org/10.1145/3575882.3575903},
doi = {10.1145/3575882.3575903},
booktitle = {Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications},
pages = {106–110},
numpages = {5},
keywords = {Lithium-ion battery, remaining useful life, capacity prediction, CNN-LSTM, neural networks},
}
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