A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries
The thermal dynamics of Li-ion batteries are very complicated, and the battery temperature is spatially distributed imbalanced from the battery interior to the surface. The thermal dynamics are commonly modeled by partial differential equations (PDEs); however, parameter identification for PDEs is d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9222173/ |
| _version_ | 1818734103180083200 |
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| author | Wenjing Shen Kangkang Xu Liming Deng Shupeng Zhang |
| author_facet | Wenjing Shen Kangkang Xu Liming Deng Shupeng Zhang |
| author_sort | Wenjing Shen |
| collection | DOAJ |
| description | The thermal dynamics of Li-ion batteries are very complicated, and the battery temperature is spatially distributed imbalanced from the battery interior to the surface. The thermal dynamics are commonly modeled by partial differential equations (PDEs); however, parameter identification for PDEs is difficult and time consuming. In this work, a Karhunen-Loeve Galerkin method is proposed to obtain a simple but effective low-order model for the distributed thermal dynamics of Li-ion batteries. The Karhunen-Loeve decomposition method is applied to capture the most representative spatial modes of the dynamics, while the Galerkin method is used to obtain the corresponding temporal modes. All the uncertain physical parameters in the temporal modes are identified by the Levenberg-Marquardt algorithm. The updated temporal modes synthesized with spatial modes can offer a fast estimation of the temperature distribution on the battery surface and thus has the potential to provide distributed temperature prediction for the battery management system. The proposed modeling scheme is tested on a 60Ah Li-ion battery cell, and the simulation result shows an excellent match in the temperature distribution and a faster computing speed than the rigorous physical model. |
| first_indexed | 2024-12-18T00:00:02Z |
| format | Article |
| id | doaj.art-653a7796599149b6af830e233d5d6c2c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:00:02Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-653a7796599149b6af830e233d5d6c2c2022-12-21T21:27:58ZengIEEEIEEE Access2169-35362020-01-01818789318790110.1109/ACCESS.2020.30307199222173A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion BatteriesWenjing Shen0https://orcid.org/0000-0003-2175-7179Kangkang Xu1https://orcid.org/0000-0003-3622-2134Liming Deng2https://orcid.org/0000-0002-6394-9112Shupeng Zhang3Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, ChinaSchool of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou, ChinaPing An Technology at Shenzhen, Shenzhen, ChinaCollege of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, ChinaThe thermal dynamics of Li-ion batteries are very complicated, and the battery temperature is spatially distributed imbalanced from the battery interior to the surface. The thermal dynamics are commonly modeled by partial differential equations (PDEs); however, parameter identification for PDEs is difficult and time consuming. In this work, a Karhunen-Loeve Galerkin method is proposed to obtain a simple but effective low-order model for the distributed thermal dynamics of Li-ion batteries. The Karhunen-Loeve decomposition method is applied to capture the most representative spatial modes of the dynamics, while the Galerkin method is used to obtain the corresponding temporal modes. All the uncertain physical parameters in the temporal modes are identified by the Levenberg-Marquardt algorithm. The updated temporal modes synthesized with spatial modes can offer a fast estimation of the temperature distribution on the battery surface and thus has the potential to provide distributed temperature prediction for the battery management system. The proposed modeling scheme is tested on a 60Ah Li-ion battery cell, and the simulation result shows an excellent match in the temperature distribution and a faster computing speed than the rigorous physical model.https://ieeexplore.ieee.org/document/9222173/Li-ion batteriesKarhunen-Loeve Galerkin methodspatiotemporal separationtemperature distributionparameter identification |
| spellingShingle | Wenjing Shen Kangkang Xu Liming Deng Shupeng Zhang A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries IEEE Access Li-ion batteries Karhunen-Loeve Galerkin method spatiotemporal separation temperature distribution parameter identification |
| title | A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries |
| title_full | A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries |
| title_fullStr | A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries |
| title_full_unstemmed | A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries |
| title_short | A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries |
| title_sort | karhunen loeve galerkin online modeling approach for the thermal dynamics of li ion batteries |
| topic | Li-ion batteries Karhunen-Loeve Galerkin method spatiotemporal separation temperature distribution parameter identification |
| url | https://ieeexplore.ieee.org/document/9222173/ |
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