Full
Outline
To ensure the safe operation of energy storage power stations and new energy vehicles, aimed at the thermal runaway phenomenon caused by internal short-circuit faults in lithium-ion batteries, a fault diagnosis method based on sparrow search algorithm optimized variational mode decomposition (SSA-VMD) and random forest (RF) algorithm is proposed. First, SSA is used to find the optimal parameter combination of VMD decomposition layer K and penalty factor α, and the internal short-circuit fault signal and normal signal of lithium-ion batteries are decomposed into multiple intrinsic mode functions (IMF). Second, the sample entropy value of each IMF is calculated as an eigenvector. Finally, the eigenvectors are input into the support vector machine (SVM) fault diagnosis model and the RF fault diagnosis model for fault diagnosis, respectively. Results show that the fault diagnosis rate of the SVM model is 66.667 0% and that of the RF model is 96.666 7%, indicating that the internal short-circuit fault in lithium-ion batteries is effectively identified.
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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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