Full
Outline
The remaining useful life can monitor the instantaneous state of the performance and safety of lithium battery. Therefore, controlling the remaining useful life of lithium battery is of significance for the safety of battery use. In response to the low predictability of high-frequency oscillation components in the initial decomposition of lithium battery capacity, a nonlinear and non-stationary signal processing method based on the secondary decomposition of high-frequency signals combined with machine learning prediction models is proposed. First, the original signal is decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm. Then, the high-frequency random disturbance signals are further decomposed using the variational mode decomposition algorithm, followed by the prediction of components using the long short-term memory (LSTM) network. Experimental verification based on four NASA datasets shows that the proposed method has high prediction accuracy. Compared with those obtained by the CEEMDAN-LSTM model for single decomposition prediction, the value of R2 of the sequence prediction is improved by 1.25%, while the values of MAE, RMSE and MAPE are reduced by 39.34%, 49.43% and 41.44%, respectively. The proposed method is robust for the effective prediction of high-frequency signals, as demonstrated by experiments on two CALCE datasets.
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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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