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Outline
To solve the problem of low model prediction accuracy caused by noise interference and capacity augmentation in the remaining useful life (RUL) prediction of lithium batteries, an RUL prediction method combining the parameter-optimized multivariate variational modal decomposition (MVMD) and bi-directional long and short-term memory (BiLSTM) network is proposed. Multiple health factors (HFs) are extracted from the charging and discharging processes of lithium batteries as model inputs, and the northern goshawk optimization (NGO) algorithm is introduced by combining the sine cosine algorithm and the whale spiral predation mechanism, which is used to optimize the MVMD parameters. Then, the optimized MVMD is used to decompose the HF into a number of smooth components. Finally, the optimized multilayer BiLSTM is used for prediction, and the stacked summation is the final RUL result. The results of a comparative analysis of the proposed method and other methods based on NASA and CALCE data sets show that this method has higher prediction accuracy and generalized adaptability.
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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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