A COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERY
This work presents a comparative analysis of state of charge (SOC) estimation for lithium-ion battery using neural network algorithms. The lithium-ion battery has been operating successfully in the automotive industry due to the long-life cycles, low memory effect, high voltage, and high energy dens...
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Penerbit Universiti Teknikal Malaysia Melaka
2023
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my.uniten.dspace-252912023-05-29T16:07:55Z A COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERY Lipu M.S.H. Ayob A. Hussain A. Hannan M.A. Salam M.A. 36518949700 26666566900 57208481391 7103014445 57220489228 This work presents a comparative analysis of state of charge (SOC) estimation for lithium-ion battery using neural network algorithms. The lithium-ion battery has been operating successfully in the automotive industry due to the long-life cycles, low memory effect, high voltage, and high energy density. As such, numerous research works have been conducted on lithiumion battery towards estimating SOC. The conventional and model-based SOC estimation approaches have shortcomings including heavy computational calculation and inaccurate battery model parameters determination. Therefore, neural network algorithms based SOC estimation have received huge attention since they have the adaptively to adjust the network parameters automatically without battery model. Three prominent neural network algorithms including backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and recurrent nonlinear autoregressive with exogenous inputs neural network (RNARXNN) are used to compare the SOC estimation results. The three methods are validated by battery experimental tests and electric vehicle (EV) drive cycles. The results demonstrate that RNARXNN is dominant to BPNN and RBFNN algorithms in obtaining high SOC accuracy with the low computational cost. � 2020. All rights reserved. Final 2023-05-29T08:07:55Z 2023-05-29T08:07:55Z 2020 Article 2-s2.0-85097054057 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097054057&partnerID=40&md5=a7874f2297a6bc44d4a9cda1ffbdcc18 https://irepository.uniten.edu.my/handle/123456789/25291 14 2 89 100 Penerbit Universiti Teknikal Malaysia Melaka Scopus |
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This work presents a comparative analysis of state of charge (SOC) estimation for lithium-ion battery using neural network algorithms. The lithium-ion battery has been operating successfully in the automotive industry due to the long-life cycles, low memory effect, high voltage, and high energy density. As such, numerous research works have been conducted on lithiumion battery towards estimating SOC. The conventional and model-based SOC estimation approaches have shortcomings including heavy computational calculation and inaccurate battery model parameters determination. Therefore, neural network algorithms based SOC estimation have received huge attention since they have the adaptively to adjust the network parameters automatically without battery model. Three prominent neural network algorithms including backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and recurrent nonlinear autoregressive with exogenous inputs neural network (RNARXNN) are used to compare the SOC estimation results. The three methods are validated by battery experimental tests and electric vehicle (EV) drive cycles. The results demonstrate that RNARXNN is dominant to BPNN and RBFNN algorithms in obtaining high SOC accuracy with the low computational cost. � 2020. All rights reserved. |
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36518949700 |
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36518949700 Lipu M.S.H. Ayob A. Hussain A. Hannan M.A. Salam M.A. |
| format |
Article |
| author |
Lipu M.S.H. Ayob A. Hussain A. Hannan M.A. Salam M.A. |
| spellingShingle |
Lipu M.S.H. Ayob A. Hussain A. Hannan M.A. Salam M.A. A COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERY |
| author_sort |
Lipu M.S.H. |
| title |
A COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERY |
| title_short |
A COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERY |
| title_full |
A COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERY |
| title_fullStr |
A COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERY |
| title_full_unstemmed |
A COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERY |
| title_sort |
comparative performance evaluation of neural network algorithms based state of charge estimation for lithium-ion battery |
| publisher |
Penerbit Universiti Teknikal Malaysia Melaka |
| publishDate |
2023 |
| _version_ |
1806423961458704384 |
| score |
13.222552 |