Prediction of Battery Cycle Life Using Early-Cycle Data, Machine Learning and Data Management
Abstract
:1. Introduction
2. Materials and Methods
- For batch “2017-05-12”, channels 1, 2, 3, 4, 5, 6, 8, 13, 19, 21, 22 and 31 were excluded. Channels 4 and 8 did not successfully start and thus did not have data. The cells in channels 1, 2, 3, 5 and 6 were stopped at the end of the test and resumed in the “2017-06-30” batch. This pause in cycling led to a rise in capacity upon resuming the tests. The tests in channels 13, 19, 21, 22 and 31 were terminated before the cells reached 80% of nominal capacity.
- For batch “2017-06-30”, channels 1, 2, 3, 5, 6 and 10 were excluded. Channels 1, 2, 3, 5 and 6 were not new experiments but a continuation of experiments from the “2017-05-12” batch. The cell on channel 10 was possibly defective as it died quickly.
- For batch “2018-04-12”, channels 26, 31, 33, 41 and 46 were excluded. No data were provided for channels 26 and 31. The tests in channels 33 and 41 were terminated before the cells reached 80% of the nominal capacity. The cell in channel 46 had noisy voltage profiles due to an electronic connection error.
3. Results and Discussion
3.1. Prediction of Life Cycle
3.2. Analysis of Input Variables
3.3. Prediction Error Comparison
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
SEI | Solid electrolyte interphase |
ML | Machine learning |
ANN | Artificial neural network |
DRF | Default random forest |
GBM | Gradient boosting models |
XRT | Extremely randomized trees |
DL | Deep learning |
GLM | Generalized linear model |
NASA | National Aeronautics and Space Administration |
NREL | National Renewable Energy Laboratory |
SoC | State of charge |
MAE | Mean absolute error |
MAPE | Mean absolute percentage error |
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Dataset | 1st: MAE (Cycles)/MAPE (%) | 2nd: MAE (Cycles)/MAPE (%) | 3rd: MAE (Cycles)/MAPE (%) |
---|---|---|---|
Control | 12.16/1.54 | 13.00/1.71 | 13.81/1.68 |
New | 7.78/0.88 | 7.06/0.87 | 4.65/0.57 |
Model | MAE (Cycles) | MAPE (%) |
---|---|---|
XRT | 1.49 | 0.19 |
DRF | 2.15 | 0.30 |
GBM | 3.61 | 0.49 |
Ref. | Number of Cells Analyzed from the Database | Number of Input Cycles | ML Method | Lowest MAPE (%) |
---|---|---|---|---|
This work | 121 | 100 | Extremely randomized trees | 0.19 |
[6] | 124 | 100 | Elastic net | 9.1 |
[37] | 124 | 110 | Neural Gaussian process | 8.8 |
[38] | 123 | 100 | Gaussian process regression | 8.2 |
[39] | 124 | 100 | Convolutional neural networks | 8.6 |
[40] | 95 | 100 | Deep neural network | 3.97 |
[41] | Less than 124 * | 100 | Linear support vector regression and Gaussian process regression | 8.2 |
[42] | 123 | 100 | Elastic net | 5.21 |
[43] | 124 | 100 | Bayesian sparse learning | 8.4 |
[44] | 123 | 80 | Random forest, artificial bee colony and general regression neural network | 6.3 |
[45] | 124 | 250 | Gradient boosting regression tree | 7.0 |
[46] | 124 | 100 | Support vector machine | 8.0 |
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Celik, B.; Sandt, R.; dos Santos, L.C.P.; Spatschek, R. Prediction of Battery Cycle Life Using Early-Cycle Data, Machine Learning and Data Management. Batteries 2022, 8, 266. https://doi.org/10.3390/batteries8120266
Celik B, Sandt R, dos Santos LCP, Spatschek R. Prediction of Battery Cycle Life Using Early-Cycle Data, Machine Learning and Data Management. Batteries. 2022; 8(12):266. https://doi.org/10.3390/batteries8120266
Chicago/Turabian StyleCelik, Belen, Roland Sandt, Lara Caroline Pereira dos Santos, and Robert Spatschek. 2022. "Prediction of Battery Cycle Life Using Early-Cycle Data, Machine Learning and Data Management" Batteries 8, no. 12: 266. https://doi.org/10.3390/batteries8120266