Full
Outline
It is becoming increasingly important to carry out reliability evaluation on core devices in the electric vehicle charging equipment, so as to ensure the safe and stable operation of the charging equipment. An advanced prediction method is proposed to estimate the state of health of electrolytic capacitor in the power supply which is one of the most important components in the charging equipment, and its remaining useful life (RUL) is also predicted. According to the capacitance loss data, the Verhulst model and exponential model are combined as the final empirical model, and unscented Kalman filter (UKF) is used to generate a recommended distribution of particle filter (PF) to track the degradation path. Aimed at the problem of particle impoverishment, an improved krill herd (KH) algorithm is used to optimize the residual resampling step and improve the prediction accuracy. The results of experiments on the electrolytic capacitor degradation data show that compared with those of the traditional PF algorithm, the root mean square error (RMSE) of prediction result decreases from 2.79 to 0.37 at the prediction time point of 80 h, and the confidence interval width at the 95% level decreases from 34 h to 4 h. At the same time, compared with that of the KH-UKF-PF algorithm, the RMSE decreases by 27.5%, indicating that the proposed method can provide higher prediction accuracy of RUL.
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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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