Abstract
In this paper, fuzzy entropy (FE), as a new feature, is applied for LiFeO4 battery state of health (SOH) estimation. Compared with sample entropy, FE introduces the exponential function to measure the similarity of voltage vectors and the mean of the match templates is removed. As a result, FE can capture the variation of voltage during the battery degradation more efficiently in terms of the parameter selection, data noise, and data size. Then the FE-SOH mapping is established by combining FE with support vector machine, and the effectiveness of the proposed method is verified by experimental results.
| Original language | English |
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| Title of host publication | 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC 2020-ECCE Asia) |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 2020 |
| Pages | 1417-1422 |
| Article number | 9368182 |
| ISBN (Print) | 978-1-7281-5302-5 |
| ISBN (Electronic) | 978-1-7281-5301-8 |
| DOIs | |
| Publication status | Published - 2020 |
| Event | 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia) - Nanjing, China Duration: 29 Nov 2020 → 2 Dec 2020 |
Conference
| Conference | 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia) |
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| Country/Territory | China |
| City | Nanjing |
| Period | 29/11/2020 → 02/12/2020 |
Keywords
- Lithium-ion batteries
- fuzzy entropy
- sample entropy
- state of health
- support vector machine