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To ensure the safety of new energy vehicles during the entire period of use, it is necessary to conduct health monitoring for the full life cycle of lithium-ion batteries. Aimed at the low learning rate due to the small capacity of training data set for the remaining useful life (RUL) prediction model based on neural network and the duplicate collinearity of the extreme learning machine(ELM) method, a method for augmenting the training data set is proposed. In addition, based on the improved ELM, an RUL prediction model for the full life cycle of lithium-ion battery is built. First, the early operation data of battery is extracted to formulate health factors, and the Akima interpolation method is used to augment the amount of training data. Then, the salp swarm algorithm is used to improve the ELM network, and the RUL prediction model for the full life cycle of lithium battery is established. Finally, the NASA battery data set is used to validate the model. Experimental results show that the proposed method for augmenting the training data capacity is effective, the capacity tracking capability of the RUL prediction model in full life cycle is strong, and the prediction error is small.
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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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