The estimation performance of state-of-charge (SOC) of lithium battery plays a vital role in abattery management system. The recursive least squares method with forgetting factor was used to identify the parameters of a battery model. Aimed at the problem that the sampling of a filter in the existing SOC estimation algorithm cannot well simulate the probability distribution of the system state, a rank sampling method was introduced, and an adaptive square root rank filter algorithm was proposed. The covariance matrix of the system state was updated iteratively in the form of square root, and the observation noise was adjusted adaptively according to the innovation in each iteration, so as to improve the SOC estimation accuracy and its adaptive capability with respect to noise. The performance of the proposed algorithm was verified by simulation experiments under FUDS and DST conditions. Results show that the proposed algorithm can improve the convergence speed and estimation accuracy compared with extended Kalman filter, unscented Kalman filter and rank filter.
| 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 |