Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques
Abstract
:1. Introduction
1.1. Methods Used for RUL Prediction of Li-Ion Batteries
1.2. Literature Review
1.3. Contribution and Structure of the Work
2. RUL Prediction Methodology Using Machine Learning
2.1. The Li-Ion Battery RUL Prediction Using SVMR
2.2. The Li-ion Battery RUL Prediction Using MLR
2.3. The Li-Ion Battery RUL Prediction Using RF
2.4. Performance Evaluation of Regression Algorithms
3. Experimental Stand
4. Experimental Setup
5. Results
6. Conclusions
7. Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Feature No. | Input Data | Output Data (Labels) | ||
---|---|---|---|---|
I [mA] | U [V] | C [mAh] | Cycle No. | |
... | ... | ... | ... | ... |
i | Ikl | Ukl | Ckl | k -> C_totk |
... | ... | ... | ... | ... |
Algorithm | Hyperparameters | Tuned Value |
---|---|---|
SVMR | C | 0.1 |
kernel | poly | |
degree | 3.5 | |
gamma | scale | |
MLR | fit_intercept | True |
copy_X | bool | |
RF | n_estimators | 600 |
criterion | squared_error | |
random_state | 1 |
Performance Indicator | SVMR | MLR | RF |
---|---|---|---|
MAE | 1.02 | 67.21 | 1.53 |
MSE | 51.02 | 87.25 | 63.77 |
RMSE | 7.14 | 7614.10 | 7.98 |
R2 Score | 0.99 | 0.93 | 0.99 |
Year, Ref. | ML Method | Battery | Dataset | Performance |
---|---|---|---|---|
[21], 2021. | Hybrid convolutional neural network (CNN), which is based on a fusion of two-dimensional CNN and three-dimensional CNN. | Lithium Iron Phosphate (LFP)/graphite batteries (A123 Systems, model APR18650M1A). | Dataset for 124 commercial LFP/graphite batteries. | MAPE = 3.55, RMSE = 11, MAE = 9 for all test batteries and MAPE = 1.35, RMSE = 4, MAE = 3 for a single battery data. |
[48], 2023. | Linear Regression (LR), Cat Boost Regressor and Random Forest Regressor. | Li-ion, Battery Electric Vehicles (BEVs). | Dataset for 26 li-ion batteries. | The LR model had the best performance, RMSE = 7.53 and R2 score = 97.83. |
[49], 2019. | Elastic Net Regression. | LFP/graphite batteries (A123 Systems, modelAPR18650MA). | Dataset consisting of 124 commercial li-ion phosphate/graphite cells. | Primary test for ‘Discharge’ model has RMSE = 91. |
[50], 2023. | Robust linear regression (RLR) and Gaussian process regression (GPR). | Li-ion batteries. | Three datasets were used, summing up data from 61 li-ion batteries. | The GPR model had the best performance, RMSE = 2.95% and MAE = 1.82%. |
[51], 2017 | Multi-Layer Perceptron (MLP), Least Absolute Shrinkage and Selection Operator (LASSO), Gradient Boosted Trees (GBT) and Least Square Support Vector machines for Regression (LSSVR). | Li-ion batteries from UAVs. | Six datasets, from six different accumulators. | The GBT model had the best performance for the six Li-ion batteries, Average MAPER = 96.57 and Average Precision = 0.85. |
[52], 2019. | Non-Homogenous Hidden Semi-Markov model (NHHSMM). | Lithium-Polymer (Li-Po) from UAVs. | Data generated from flight monitoring for 10 Li-Po batteries from UAVs. | The estimated RUL is 95% of the actual RUL, for Battery 1 and Battery 9. |
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Andrioaia, D.A.; Gaitan, V.G.; Culea, G.; Banu, I.V. Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques. Computers 2024, 13, 64. https://doi.org/10.3390/computers13030064
Andrioaia DA, Gaitan VG, Culea G, Banu IV. Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques. Computers. 2024; 13(3):64. https://doi.org/10.3390/computers13030064
Chicago/Turabian StyleAndrioaia, Dragos Alexandru, Vasile Gheorghita Gaitan, George Culea, and Ioan Viorel Banu. 2024. "Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques" Computers 13, no. 3: 64. https://doi.org/10.3390/computers13030064