Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery
The parameters of a well predicted model can be used as health characteristics for Lithium-ion battery. This article reports a parallelized parameter identification of the thermal-electrochemical model, which significantly reduces the time consumption of parameter identification. Since the P2D model...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2013-01-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1155/2013/754653 |
| _version_ | 1819265681822056448 |
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| author | Liqiang Zhang Chao Lyu Lixin Wang Jun Zheng Weilin Luo Kehua Ma |
| author_facet | Liqiang Zhang Chao Lyu Lixin Wang Jun Zheng Weilin Luo Kehua Ma |
| author_sort | Liqiang Zhang |
| collection | DOAJ |
| description | The parameters of a well predicted model can be used as health characteristics for Lithium-ion battery. This article reports a parallelized parameter identification of the thermal-electrochemical model, which significantly reduces the time consumption of parameter identification. Since the P2D model has the most predictability, it is chosen for further research and expanded to the thermal-electrochemical model by coupling thermal effect and temperature-dependent parameters. Then Genetic Algorithm is used for parameter identification, but it takes too much time because of the long time simulation of model. For this reason, a computer cluster is built by surplus computing resource in our laboratory based on Parallel Computing Toolbox and Distributed Computing Server in MATLAB. The performance of two parallelized methods, namely Single Program Multiple Data (SPMD) and parallel FOR loop (PARFOR), is investigated and then the parallelized GA identification is proposed. With this method, model simulations running parallelly and the parameter identification could be speeded up more than a dozen times, and the identification result is batter than that from serial GA. This conclusion is validated by model parameter identification of a real LiFePO 4 battery. |
| first_indexed | 2024-12-23T20:49:15Z |
| format | Article |
| id | doaj.art-e97e36671e5544e68c003288dd2b01c8 |
| institution | Directory Open Access Journal |
| issn | 1687-8132 |
| language | English |
| last_indexed | 2024-12-23T20:49:15Z |
| publishDate | 2013-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-e97e36671e5544e68c003288dd2b01c82022-12-21T17:31:42ZengSAGE PublishingAdvances in Mechanical Engineering1687-81322013-01-01510.1155/2013/75465310.1155_2013/754653Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion BatteryLiqiang ZhangChao LyuLixin WangJun ZhengWeilin LuoKehua MaThe parameters of a well predicted model can be used as health characteristics for Lithium-ion battery. This article reports a parallelized parameter identification of the thermal-electrochemical model, which significantly reduces the time consumption of parameter identification. Since the P2D model has the most predictability, it is chosen for further research and expanded to the thermal-electrochemical model by coupling thermal effect and temperature-dependent parameters. Then Genetic Algorithm is used for parameter identification, but it takes too much time because of the long time simulation of model. For this reason, a computer cluster is built by surplus computing resource in our laboratory based on Parallel Computing Toolbox and Distributed Computing Server in MATLAB. The performance of two parallelized methods, namely Single Program Multiple Data (SPMD) and parallel FOR loop (PARFOR), is investigated and then the parallelized GA identification is proposed. With this method, model simulations running parallelly and the parameter identification could be speeded up more than a dozen times, and the identification result is batter than that from serial GA. This conclusion is validated by model parameter identification of a real LiFePO 4 battery.https://doi.org/10.1155/2013/754653 |
| spellingShingle | Liqiang Zhang Chao Lyu Lixin Wang Jun Zheng Weilin Luo Kehua Ma Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery Advances in Mechanical Engineering |
| title | Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery |
| title_full | Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery |
| title_fullStr | Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery |
| title_full_unstemmed | Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery |
| title_short | Parallelized Genetic Identification of the Thermal-Electrochemical Model for Lithium-Ion Battery |
| title_sort | parallelized genetic identification of the thermal electrochemical model for lithium ion battery |
| url | https://doi.org/10.1155/2013/754653 |
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