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To solve the problem that the traditional shift schedule of two-speed transmissions for battery electric vehicles cannot obtain the optimal shift performance under different conditions, this paper proposes a knowledge-based method for shift decision-making. Firstly, the dynamic model of battery electric vehicles was established, the optimal shift data was obtained by dynamic programming. The two-parameter shift schedule was extracted based on support vector machine. Secondly, the data of manual shift was collected to build an exclusive database. The intelligent shift decision model was built based on the long short-term memory network, and the shift decision model was updated online by over-the-air technology. Finally the proposed shift decision method was verified by simulation. The results show that long short-term memory network model has high shift decision accuracy, the proposed knowledge-based shift decision method has better shifting performance than traditional two-parameter shift schedule.
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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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