This project has been executed via google colab.
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Download the code from github as a zip file.
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Extract the contents of the zip file and upload it into google drive.
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The code to be executed in google colab can be found in the
Battery_parameter_estimation_using_Contrastive_learning.ipynbfile. -
Make sure to set the runtime to GPU before running the code.
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Make the necessary changes mentioned in the code to get the result.
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The dataset to be used has to be uploaded in the
datasets/folder(if not already present). -
The horizon values can be edited in
datautils.pyscript and the number of epochs can be varied in the main code before running it.
To train and evaluate TS2Vec on a dataset the following command is used in the google colab notebook :
!python -u train.py <dataset_name> <run_name> --epochs <epochs> --loader <loader> --max-threads <max-threads> --seed <seed> --eval
The detailed descriptions about the arguments are as following:
| Parameter name | Description of parameter |
|---|---|
| dataset_name | The name of the dataset to be used |
| run_name | The folder name used to save model, output and evaluation metrics. This can be set to any word |
| epochs | The number of epochs the code has to run for (defaults to None) |
| loader | The data loader used to load the experimental data. This can be set to UCR, UEA, forecast_csv, forecast_csv_univar, anomaly, or anomaly_coldstart |
| max-threads | The maximum allowed number of threads used by this process (defaults to None) |
| seed | The random seed (defaults to None) |
| eval | Whether to perform evaluation after training |
After training and evaluation, the trained encoder, output and evaluation metrics can be found in training/DatasetName__RunName_Date_Time/
Any other changes such as batch-size or max-train-length can be done in the train.py script.
We appreciate the following github repos a lot for their valuable code base and datasets:
https://github.com/yuezhihan/ts2vec
Diao, W., Saxena, S., Pecht, M. Accelerated Cycle Life Testing and Capacity Degradation Modeling of LiCoO2 -graphite Cells. J. Power Sources 2019, 435, 226830.
Kollmeyer, Phillip (2018),"Panasonic 18650PF Li-ion Battery Data", Mendeley Data, V1, doi: 10.17632/wykht8y7tg.1