State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms
Abstract
:1. Introduction
2. Materials and Method
2.1. Batteries State of Charge Estimation
2.2. Artificial Neural Network (ANN)
2.3. Support Vector Machine (SVM)
- Step 1.
- Import the input features
- Step 2.
- Analyze the correlation and directivity of the data
- Step 3.
- Split the dataset into the train and validation test
- Step 4.
- Choose the kernel function out of (linear, polynomial, sigmoid, radial basis)
- Step 5.
- Train the model with training data
- Step 6.
- Evaluate the model performance
- Step 7.
- Test the model with testing data
- Step 8.
- Calculate the performance metrics for the tested data
2.4. Linear Regression (LR)
- Step 1.
- Get the input features
- Step 2.
- Analyze the correlation and directivity of the data
- Step 3.
- Estimate the model
- Step 4.
- Fit the best fitting line
- Step 5.
- Evaluate the model and
- Step 6.
- Test the model with testing data
- Step 7.
- Calculate the performance metrics for the tested data
2.5. Gaussian Process Regression (GPR)
- Step 1.
- Import the input features
- Step 2.
- Analyze the correlation and directivity of the data
- Step 3.
- Split the dataset into the train and validation test
- Step 4.
- Build the model for the Gaussian process regression model
- Step 5.
- Train the model with training data
- Step 6.
- Evaluate the model performance
2.6. Ensemble Bagging (EBa)
- Step 1.
- for i = 1 to K, do
- Step 2.
- Generate a bootstrap sample of the original data
- Step 3.
- Train an unpruned tree model on this sample
- Step 4.
- End
2.7. Ensemble Boosting (EBo)
- Step 1.
- Set (x) = 0 and = for all i in the training set
- Step 2.
- Compute the average response, , and use this as the initial predicted value sample
- Step 3.
- for i = 1 to K, do
- Step 4.
- Fit a tree with D splits (d + 1 terminal nodes) to the training data
- Step 5.
- Update (x) by adding in a shrunken version of the new tree:
- Step 6.
- Step 7.
- Update the residuals, -
- Step 8.
- End
3. Training and Testing Datasets
4. Performance Metrics
4.1. Root Mean Square Error (RMSE)
4.2. R2 Square
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Feature Parameter | Battery | Performance Index and Precision | Reference | |
---|---|---|---|---|
The energy of the signal (current, voltage) | NASA 18650 | MAE | <1.29% | [53] |
Temperature (min, max, average, area) | NASA 18650 | RMSE | <3.58% | [54] |
The slope of the charging voltage curve | NASA 18650 | RMSE | <3.45% | [55] |
The slope of the discharging voltage curve | NASA 18650 | RMSE | <3.84% | [56] |
Equal voltage drops in charging curve | NCM/ graphite | RMSE | 2% | [57] |
Equal voltage drops in discharging curve | NASA 18650 | MAE | <1.29% | [58] |
The characteristic of I.C. curves (peak, valley) | Prismatic Li-ion Battery | RMSE | 2.99% | [59] |
Algorithms Model | Empirical Equation |
---|---|
Artificial Neural Network | weight to neuron i from neuron j bias input vectors |
Support Vector Machine | = predicted output W = weights K = kernel trick support vectors B = bias |
Gaussian Process Regression | Test the model with testing data calculate the performance metrics for the tested data noise variance coefficient vector observtion |
Linear Regression | bias value input feature values |
Ensemble Bagging | Output the bagging model: |
Ensemble Boosting | The output of boosting tree: |
Dataset Splitting | ||
---|---|---|
Total Training Set = 43,355 | Testing Set | |
Training set (80%) | Validation set (20 %) | |
34,684 | 8671 | 25,416 |
Algorithm | MSE | RMSE | NRMSE | MAE | MAPE | Scatter Index | Variance | R2 |
---|---|---|---|---|---|---|---|---|
SVM | 0.01505 | 0.12266 | 0.17517 | 0.00752 | 0.000052 | 0.21 | 81.63 | 0.999 |
ANN | 0.00054 | 0.02329 | 0.03126 | 0.00027 | 0.000002 | 0.040 | 99.99 | 0.999 |
Linear | 0.00130 | 0.03610 | 0.04829 | 0.00065 | 0.000004 | 0.062 | 99.95 | 0.979 |
GPR | 0.00170 | 0.04118 | 0.05507 | 0.00085 | 0.000006 | 0.071 | 99.83 | 1.000 |
Ensemble boosting | 0.05245 | 0.22902 | 0.32122 | 0.02623 | 0.000186 | 0.39 | 90.32 | 1.000 |
Ensemble bagging | 0.04231 | 0.04118 | 0.28576 | 0.02115 | 0.000149 | 0.35 | 85.25 | 0.979 |
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Chandran, V.; Patil, C.K.; Karthick, A.; Ganeshaperumal, D.; Rahim, R.; Ghosh, A. State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms. World Electr. Veh. J. 2021, 12, 38. https://doi.org/10.3390/wevj12010038
Chandran V, Patil CK, Karthick A, Ganeshaperumal D, Rahim R, Ghosh A. State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms. World Electric Vehicle Journal. 2021; 12(1):38. https://doi.org/10.3390/wevj12010038
Chicago/Turabian StyleChandran, Venkatesan, Chandrashekhar K. Patil, Alagar Karthick, Dharmaraj Ganeshaperumal, Robbi Rahim, and Aritra Ghosh. 2021. "State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms" World Electric Vehicle Journal 12, no. 1: 38. https://doi.org/10.3390/wevj12010038