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Simulation substation batteries often work under discontinuous operation conditions, which will result in capacity regeneration of batteries during their performance degradation. The degradation of batteries shows nonstationary and random characteristics, leading to a low prediction accuracy for the remaining useful life(RUL). Aimed at the problem of RUL prediction of batteries with capacity regeneration, a prediction method is proposed based on variational mode decomposition(VMD) and bat optimized kernel extreme learning machine(Bat-KELM). First, VMD is employed to decompose the battery state-of-health(SOH) time series into overall degradation components and capacity regeneration components. Then, Bat-KELM is used to construct prediction models of each component, so that the prediction accuracy of component trend is improved. At last, the prediction results of all components are blended together to yield the accurate battery SOH prediction results as well as the RUL results. The proposed method is applied to the analysis of battery degradation instance data, and results show its superiority in terms of prediction accuracy compared with the KELM and VMD-KELM models.
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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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