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Neural Network and Internal Resistance based SOH classification for lithium battery
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- Lee Jong-Hyun
- School of Electronics Engineering, Kyungpook National University
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- Kim Hyun-Sil
- Naval Combat System PMO Agency For Defense Development
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- Lee In-Soo
- School of Electronics Engineering, Kyungpook National University
Description
This paper presents a battery state of health (SOH) monitoring system to diagnose fault in battery using a multilayer neural network state classifier (MNNSC) and an internal resistance state classifier (IRSC). In this system, the MNNSC utilizes discharge voltage data from operating the lithium battery at high temperatures. Whereas, the IRSC uses the open circuit voltage, terminal voltage, and current to calculate the internal resistance. From experimental results, it is noted that the proposed battery SOH monitoring method diagnoses the battery status very well.
Journal
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- Proceedings of International Conference on Artificial Life and Robotics
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Proceedings of International Conference on Artificial Life and Robotics 25 481-484, 2020-01-13
ALife Robotics Corporation Ltd.
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Keywords
Details 詳細情報について
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- CRID
- 1390846609806753792
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- ISSN
- 21887829
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed
