Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection
Backpropagation; Battery management systems; Charging (batteries); Electric batteries; Errors; Feature extraction; Ions; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Particle swarm optimization (PSO); Principal component analysis; Radial basis function networks;...
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2023
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my.uniten.dspace-230552023-05-29T14:37:36Z Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection Hossain Lipu M.S. Hannan M.A. Hussain A. Saad M.H.M. 36518949700 7103014445 57208481391 7202075525 Backpropagation; Battery management systems; Charging (batteries); Electric batteries; Errors; Feature extraction; Ions; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Particle swarm optimization (PSO); Principal component analysis; Radial basis function networks; Back-propagation neural networks; Electric vehicle drive cycles; Hidden layer neurons; Mean absolute error; Mean absolute percentage error; Radial basis function neural networks; Root mean square errors; State-of-charge estimation; Secondary batteries The state of charge (SOC) is the residual capacity of a battery, which indicates the available charge left inside a battery to drive a vehicle. Accurate SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. This paper presents an improved SOC estimation strategy for a lithium-ion battery using the back-propagation neural network (BPNN). Two algorithms, principal component analysis (PCA) and particle swarm optimization (PSO), are used to enhance the accuracy and robustness. PCA is utilized to select the most significant input features. The PSO algorithm is developed to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal BPNN model. The proposed model is tested and evaluated by using three electric vehicle drive cycles. The performance of the proposed model is compared with common BPNN and radial basis function neural network (RBFNN) models and verified based on the root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The validation results are very effective in predicting SOC with very narrow SOC error which demonstrates the model robustness. The results indicate that the proposed model computes RMSE to be 0.58%, 0.72%, and 0.47% for the Beijing Dynamic Stress Test (BJDST), Federal Urban Drive Schedule (FUDS), and US06, cycle, respectively. � 2017 Author(s). Final 2023-05-29T06:37:36Z 2023-05-29T06:37:36Z 2017 Article 10.1063/1.5008491 2-s2.0-85038446309 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038446309&doi=10.1063%2f1.5008491&partnerID=40&md5=e8e16fc8efaffcc5692af160fe4c4459 https://irepository.uniten.edu.my/handle/123456789/23055 9 6 64102 American Institute of Physics Inc. Scopus |
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Backpropagation; Battery management systems; Charging (batteries); Electric batteries; Errors; Feature extraction; Ions; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Particle swarm optimization (PSO); Principal component analysis; Radial basis function networks; Back-propagation neural networks; Electric vehicle drive cycles; Hidden layer neurons; Mean absolute error; Mean absolute percentage error; Radial basis function neural networks; Root mean square errors; State-of-charge estimation; Secondary batteries |
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36518949700 |
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36518949700 Hossain Lipu M.S. Hannan M.A. Hussain A. Saad M.H.M. |
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Article |
| author |
Hossain Lipu M.S. Hannan M.A. Hussain A. Saad M.H.M. |
| spellingShingle |
Hossain Lipu M.S. Hannan M.A. Hussain A. Saad M.H.M. Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection |
| author_sort |
Hossain Lipu M.S. |
| title |
Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection |
| title_short |
Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection |
| title_full |
Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection |
| title_fullStr |
Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection |
| title_full_unstemmed |
Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection |
| title_sort |
optimal bp neural network algorithm for state of charge estimation of lithium-ion battery using pso with pca feature selection |
| publisher |
American Institute of Physics Inc. |
| publishDate |
2023 |
| _version_ |
1806428290019229696 |