Full
Outline
Lithium-ion batteries are the most commonly used batteries in an energy storage system, and their state-of-health (SOH) is an important battery parameter monitored in a battery management system. In addition to the accuracy of prediction, the simulation of a user’s random charging behavior for realizingreal-time evaluation at any time is also very important. Based on the user’s random charging behavior and from the perspective of obtaining the data integrity, the SOH prediction methods are divided into three types, which are based on complete charging cycle data, segment of charging data and segment of charging data restored complete data, respectively. The mean and variance of battery capacity changes during the charging process of NASA battery aging experimental data are used as feature inputs of different prediction models, and the prediction results obtained using three methods are compared and analyzed. Results show that the complete charging data features have high prediction accuracy, and the quality of features extracted from segment data is related to the segment collection time. The complete data features can be obtained in a short time through the restoration network, but the prediction accuracy is affected by the restoration network. In addition, the actual prediction needs to take into account the randomness of charging behavior and the real-time performance of prediction results.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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