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Advanced battery modeling using neural networks
| dc.creator | Arikara, Muralidharan Pushpakam | |
| dc.date.accessioned | 2012-06-07T22:30:25Z | |
| dc.date.available | 2012-06-07T22:30:25Z | |
| dc.date.created | 1993 | |
| dc.date.issued | 1993 | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/ETD-TAMU-1993-THESIS-A699 | |
| dc.description | Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item. | en |
| dc.description | Includes bibliographical references. | en |
| dc.description.abstract | Batteries have gained importance as power sources for electric vehicles. The main problem with the battery technology available today is that the design of the battery system has not been optimized for different applications. No comprehensive battery models are available today that can accurately predict the performance of the battery system. This thesis presents a modeling technique for batteries employing neural networks. The advantage of using neural networks is that the effect of any variable of the performance of the battery need not be known apriori. The neural network develops the model by corelating experimental data. A software model was developed and tested for lead acid batteries using this technique. The results obtained from the model when compared to experimental data showed that the technique was successful in modeling the performance of a lead acid battery module. | en |
| dc.format.medium | electronic | en |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en_US | |
| dc.publisher | Texas A&M University | |
| dc.rights | This thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use. | en |
| dc.subject | electrical engineering. | en |
| dc.subject | Major electrical engineering. | en |
| dc.title | Advanced battery modeling using neural networks | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | electrical engineering | en |
| thesis.degree.name | M.S. | en |
| thesis.degree.level | Masters | en |
| dc.type.genre | thesis | en |
| dc.type.material | text | en |
| dc.format.digitalOrigin | reformatted digital | en |
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