The state of health (SOH) of Li-ion batteries gradually decreases with the number of charges and discharges, whose features are hidden in the physical information such as battery current and voltage. A data-driven method based on wavelet packet transform (WPT) and sparrow search algorithm (SSA) is proposed for online estimation of SOH, and the feature indices of discharge voltage plateau time (DVPT) and constant current charging time (CCCT) are proposed. The algorithm is verified on the University of Maryland battery public dataset. The results show that the proposed DVPT characteristic metric has a strong correlation with SOH, with an average Pearson correlation coefficient of 98.38% across the four batteries; the model predicted the optimal results with RMSE of 0.019 1, MAE of 0.012 5, and R2 of 97.78%.
| 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 |