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Outline
The accurate estimation of the state-of-health (SOH) of lithium-ion batteries is a basis for ensuring the safe, stable and efficient operation of battery systems. On this basis, a method for estimating the SOH of lithium-ion batteries was proposed based on health indicator extraction and improved Gaussian process regression (GPR). First, the health indicators were extracted from the charge/discharge current and voltage curves, and those with a high correlation with capacity were screened out by Pearson correlation analysis, which were further reduced by an autoencoder deep learning algorithm. Second, GPR was used to establish an SOH estimation model, and the kernel function of GPR was constructed by combinatorial kernel functions. Third, the African vultures optimization algorithm was used to optimize the hyperparameters in the combinatorial kernel function. Finally, the NASA lithium-ion battery dataset was used for model verification, and experimental results show that the proposed method has high accuracy.
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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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