Full
Outline
Capacity and impedance are usually used as direct characteristics to determine the health status of lithium-ion batteries, but they cannot be easily measured in a direct manner. Aimed at the problem of remaining useful life (RUL) prediction for lithium-ion batteries, an indirect prediction method based on expectile regression (ER) is proposed. First, several indirect health indicators which can characterize the performance decline of batteries are extracted from the charging process of batteries, and the correlations with capacity are analyzed using the Pearson and Spearman correlation analysis method. Second, a novel ER-extreme machine learning (ER-ELM) model is developed by combining with ELM. The BFGS quasi-Newton method is introduced to optimize the parameters of the ER-ELM model, and a lithium-ion battery RUL prediction model is established. Finally, the model is verified using the CALCE lithium-ion battery data set. Results show that compared with the standard ELM model, the ER-ELM model has stable output of prediction results, the maximum root mean square error of prediction results is 0.028 9, and all the values of goodness of fit are above 98%, indicating a better performance in terms of tracking capacity decline and regeneration.
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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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