SOC Estimation of LiFePO4 Power Battery Based on OSELM
Aiming at the problem that the SOC estimation accuracy of LiMPO4 power battery is hardly acceptable, this paper presented a SOC predictive model based on online sequential extreme learning machine (OSELM). The fast training property of the OSELM is leveraged so that the generalization of the online...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2019-01-01
|
| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.05.100 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Aiming at the problem that the SOC estimation accuracy of LiMPO4 power battery is hardly acceptable, this paper presented a SOC predictive model based on online sequential extreme learning machine (OSELM). The fast training property of the OSELM is leveraged so that the generalization of the online sequential learning study is improved and the SOC estimate accuracy by self-adaption of battery working states is enhanced. A 5Ah lithium iron phosphate cylindrical battery was studied and tested in this paper, such as the relationship between voltage and the SOC of the battery at different temperatures and different discharge currents. Theoretical analysis and experimental results verify the effectiveness of this method. |
|---|---|
| ISSN: | 2096-5427 |