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To achieve accurate prediction of EV battery energy information, this paper proposed a method for battery energy analysis and prediction based on big data of chargeable pure electric vehicles. Firstly, the big data of vehicles with the same battery model from different regions were obtained through a big data platform, and then the interval average method and Support Vector Regression (SVR) were used to fit the relationship between mileage and total energy for both the total data and typical regional data, to predict degradation of the battery total energy. Finally, the predicted results were compared with that obtained from Long Short-Term Memory (LSTM) neural network, and the accuracy of the proposed method was verified by vehicle test. The results show that: the SVR-based model can quantitatively fit the degraded battery capacity, which has high prediction 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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