Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application
Battery management systems; Charging (batteries); Errors; Feature extraction; Ions; Lithium-ion batteries; Mean square error; Multilayer neural networks; Principal component analysis; Data training and testing; Mean squared error; Neural network model; Neural-networks; Principle components analysis;...
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International Journal of Renewable Energy Research
2023
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my.uniten.dspace-233642023-05-29T14:39:48Z Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application Hossain Lipu M.S. Hannan M.A. Hussain A. 36518949700 7103014445 57208481391 Battery management systems; Charging (batteries); Errors; Feature extraction; Ions; Lithium-ion batteries; Mean square error; Multilayer neural networks; Principal component analysis; Data training and testing; Mean squared error; Neural network model; Neural-networks; Principle components analysis; Root mean squared error; Root mean squared errors; States of charges; Training and testing; Neural network models This paper presents the estimation of the state of charge (SOC) for a lithium-ion battery using feature selection and an optimal NN algorithm. Principle component analysis (PCA) is used to select the most influencing features. Out of nine variables, five input variables are selected based on the value of eigenvectors. An optimal neural network (NN) is developed by selecting the hidden layer neurons and learning rate since these parameters are the most critical factors in constructing a NN model. The model is tested and evaluated by using US06 driving cycle at 25�C and 45�C respectively. In order demonstrate the effectiveness and accuracy of the proposed model, a comparative study is performed between proposed NN model and two different NN models (NN1 and NN2). The proposed NN model estimates SOC with lower mean squared error (MSE) and root mean squared error (RMSE) compared to two NN models which proves that the proposed model is competent and robust in estimating SOC. The simulation results show an improvement in proposed NN model accuracy over NN1 and NN2 models in minimizing RMSE by 26% and 22% and MSE by 45% and 39% respectively at 25�C. � Renewable Energy Research, 2017. Final 2023-05-29T06:39:48Z 2023-05-29T06:39:48Z 2017 Article 2-s2.0-85043487236 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043487236&partnerID=40&md5=1a00ac72c7500ef3380e6900789819b9 https://irepository.uniten.edu.my/handle/123456789/23364 7 4 1701 1708 International Journal of Renewable Energy Research Scopus |
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| description |
Battery management systems; Charging (batteries); Errors; Feature extraction; Ions; Lithium-ion batteries; Mean square error; Multilayer neural networks; Principal component analysis; Data training and testing; Mean squared error; Neural network model; Neural-networks; Principle components analysis; Root mean squared error; Root mean squared errors; States of charges; Training and testing; Neural network models |
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36518949700 |
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36518949700 Hossain Lipu M.S. Hannan M.A. Hussain A. |
| format |
Article |
| author |
Hossain Lipu M.S. Hannan M.A. Hussain A. |
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Hossain Lipu M.S. Hannan M.A. Hussain A. Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application |
| author_sort |
Hossain Lipu M.S. |
| title |
Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application |
| title_short |
Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application |
| title_full |
Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application |
| title_fullStr |
Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application |
| title_full_unstemmed |
Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application |
| title_sort |
feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application |
| publisher |
International Journal of Renewable Energy Research |
| publishDate |
2023 |
| _version_ |
1806423269077680128 |
| score |
13.188404 |