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The current methods for predicting the state of health of lithium batteries often suffer from low accuracy. This paper introduces a method for state of health prediction using a seagull optimization algorithm optimized deep extreme learning machine. Key health feature parameters, such as constant voltage charging and discharging times during battery cycles, are selected and their correlation with the battery state of health is analyzed using Pearson correlation. The proposed model predicts subsequent state of health values by learning from samples. Experiments conducted with battery data compare the proposed method with single extreme learning machine, single deep extreme learning machine, and other literature. Evaluation metrics, including maximum absolute error and root mean square error, demonstrate that the seagull optimization algorithm optimized deep extreme learning machine model achieves higher accuracy and faster prediction times, with errors below 1.1%, indicating superior prediction accuracy and applicability.
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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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