Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 481-484 |
| Number of pages | 4 |
| Journal | Proceedings of International Conference on Artificial Life and Robotics |
| Volume | 2020 |
| DOIs | |
| State | Published - 2020 |
| Event | 25th International Conference on Artificial Life and Robotics, ICAROB 2020 - Beppu, Oita, Japan Duration: 13 Jan 2020 → 16 Jan 2020 |
Keywords
- Fault diagnosis system
- Internal resistance
- Lithium battery
- Multilayer neural network
- State of health
Fingerprint
Dive into the research topics of 'Neural network and internal resistance based soh classification for lithium battery'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver