Official code and data repository of BatteryML: An Open-Source Tool for Machine Learning on Battery Degradation (ICLR 2024). Please star, watch, and fork BatteryML for the active updates! We appreciate any questions and suggestions!
Our paper is now available on Arxiv and ICLR 2024! This paper provides detailed introduction to our design, which we will be actively updating during the development of BatteryML.
The performance degradation of lithium batteries is a complex electrochemical process, involving factors such as the growth of solid electrolyte interface, lithium precipitation, loss of active materials, etc. Furthermore, this inevitable performance degradation can have a significant impact on critical commercial scenarios, such as causing 'range anxiety' for electric vehicle users and affecting the power stability of energy storage systems. Therefore, effectively analyzing and predicting the performance degradation of lithium batteries to provide guidance for early prevention and intervention has become a crucial research topic.
To this end, we open source the BatteryML tool to facilitate the research and development of machine learning on battery degradation. We hope BatteryML can empower both battery researchers and data scientists to gain deeper insights from battery degradation data and build more powerful models for accurate predictions and early interventions.
Data Source | Electrode Chemistry | Nominal Capacity | Voltage Range (V) | RUL dist. | SOC dist. (%) | SOH dist. (%) | Cell Count |
---|---|---|---|---|---|---|---|
CALCE | LCO/graphite | 1.1 | 2.7-4.2 | 566±106 | 77±17 | 48±30 | 13 |
MATR | LFP/graphite | 1.1 | 2.0-3.6 | 823±368 | 93±7 | 36±36 | 180 |
HUST | LFP/graphite | 1.1 | 2.0-3.6 | 1899±389 | 100±10 | 43±28 | 77 |
HNEI | NMC_LCO/graphite | 2.8 | 3.0-4.3 | 248±15 | 64±17 | 49±28 | 14 |
RWTH | NMC/carbon | 1.11 | 3.5-3.9 | 658±64 | 60±24 | 46±22 | 48 |
SNL | NCA,NMC,LFP/graphite | 1.1 | 2.0-3.6 | 1256±1321 | 86±7 | 45±27 | 61 |
UL_PUR | NCA/graphite | 3.4 | 2.7-4.2 | 209±50 | 89±6 | 41±33 | 10 |
For RUL (Remaining Useful Life) tasks, we also created combined datasets from the public sources to assess training efficacy when various battery data are combined. Notably:
For more detailed information on the data, please refer to the Appendix A of our paper.
Benchmark results for remaining useful life prediction. The comparison methods are split into four types, including 1) dummy regressor, a trivial baseline that uses the mean of training label as predictions; 2) linear models with features designed by domain experts; 3) traditional statistical models with QdLinear feature; 4) deep models with QdLinear feature.
For models sensitive to initialization, we present the error mean across ten seeds and attach the standard deviation as subscript.
Models | MATR1 | MATR2 | HUST | SNL | CLO | CRUH | CRUSH | MIX |
---|---|---|---|---|---|---|---|---|
Dummy regressor | 398 | 510 | 419 | 466 | 331 | 239 | 576 | 573 |
"Variance" model | 136 | 211 | 398 | 360 | 179 | 118 | 506 | 521 |
"Discharge" model | 329 | 149 | 322 | 267 | 143 | 76 | >1000 | >1000 |
"Full" model | 167 | >1000 | 335 | 433 | 138 | 93 | >1000 | 331 |
Ridge regression | 116 | 184 | >1000 | 242 | 169 | 65 | >1000 | 372 |
PCR | 90 | 187 | 435 | 200 | 197 | 68 | 560 | 376 |
PLSR | 104 | 181 | 431 | 242 | 176 | 60 | 535 | 383 |
Gaussian process | 154 | 224 | >1000 | 251 | 204 | 115 | >1000 | 573 |
XGBoost | 334 | 799 | 395 | 547 | 215 | 119 | 330 | 205 |
Random forest | 168±9 | 233±7 | 368±7 | 532±25 | 192±2 | 81±1 | 416±5 | 197±0 |
MLP | 149±3 | 275±27 | 459±9 | 370±81 | 146±5 | 103±4 | 565±9 | 451±42 |
CNN | 102±94 | 228±104 | 465±75 | 924±267 | >1000 | 174±92 | 545±11 | 272±101 |
LSTM | 119±11 | 219±33 | 443±29 | 539±40 | 222±12 | 105±10 | 519±39 | 268±9 |
Transformer | 135±13 | 364±25 | 391±11 | 424±23 | 187±14 | 81±8 | 550±21 | 271±16 |
pip install -r requirements.txt
pip install .
This will install the BatteryML into your Python environment, together with a convenient command line interface (CLI) batteryml
.
You may also need to install PyTorch for deep models.
Download raw files of public datasets and preprocess them into BatteryData
of BatteryML is now as simple as two commands:
batteryml download MATR /path/to/save/raw/data
batteryml preprocess MATR /path/to/save/raw/data /path/to/save/processed/data
If your data is measured by a cycler such as ARBIN, NEWARE, etc., you can use this command to process your data into BatteryData
of BatteryML.
batteryml preprocess ARBIN /path/to/save/raw/data /path/to/save/processed/data --config /path/to/config/yaml/file
Due to variations in software versions and configurations, the data format and fields exported by the same cycler may differ. Therefore, we have added default processing configurations in the /configs/cycler
directory to map raw data to target data fields. You can edit these default configurations as needed.
We currently support ARBIN
and NEWARE
data formats. Additionally, Biologic
, LANDT
, and Indigo
formats are being integrated. If you encounter any issues with our cycler processing your data, please submit an issue and attach a sample data file to help us ensure rapid compatibility with your data format.
BatteryML supports using a simple config file to specify the training and inference process. We provided several examples in configs
. For example, to reproduce the "variance" model for battery life prediction, run
batteryml run configs/baselines/sklearn/variance_model/matr_1.yaml ./workspace/test --train --eval
If you find this work useful, we would appreciate citations to the following paper:
@inproceedings{zhang2024batteryml,
title={Battery{ML}: An Open-source Platform for Machine Learning on Battery Degradation},
author={Han Zhang and Xiaofan Gui and Shun Zheng and Ziheng Lu and Yuqi Li and Jiang Bian},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}
By leveraging BatteryML, researchers can gain valuable insights into the latest advancements in battery prediction and materials science, enabling them to conduct experiments efficiently and effectively. We invite you to join us in our journey to accelerate battery research and innovation by contributing to and utilizing BatteryML for your research endeavors.