Abstract
Sample entropy (SE) has been used as a feature to estimate the state of health (SOH) of batteries as it can capture the voltage variation during battery degradation. However, the SE shows an obvious change when the battery is aged at different temperatures, leading to a decrease in the estimation accuracy. Therefore, the fuzzy entropy (FE), is proposed as a feature in terms of temperature variation. The FESOH is used as the input-output data pair of support vector machine, then the single-temperature model, full temperature model, and partial-temperature model are established. Compared with the SE-based method, FE-based method not only has better estimation accuracy, but also decreases the dependence on the size of training data. Finally, the effectiveness of the proposed method is verified by the experimental results.
| Original language | English |
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| Title of host publication | 2020 IEEE Energy Conversion Congress and Exposition (ECCE) |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | Oct 2020 |
| Pages | 4401-4406 |
| ISBN (Print) | 978-1-7281-5827-3 |
| ISBN (Electronic) | 978-1-7281-5826-6 |
| DOIs | |
| Publication status | Published - Oct 2020 |
| Event | 2020 IEEE Energy Conversion Congress and Exposition (ECCE 2020) - Detroit, United States Duration: 11 Oct 2020 → 15 Oct 2020 https://www.ieee-ecce.org/2020/ |
Conference
| Conference | 2020 IEEE Energy Conversion Congress and Exposition (ECCE 2020) |
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| Country/Territory | United States |
| City | Detroit |
| Period | 11/10/2020 → 15/10/2020 |
| Internet address |
| Series | IEEE Energy Conversion Congress and Exposition |
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| ISSN | 2329-3721 |