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The master cylinder pressure estimation of the Electro-Hydraulic Brake (EHB) system is crucial to reduce the sensor dependence of EHB. In this paper, the master cylinder pressure is estimated based on BP neural network. First, a real-vehicle road test is carried out and data such as vehicle speed, master cylinder piston displacement, master cylinder piston speed and master cylinder pressure are collected. Second, a BP neural network is established using the master cylinder piston displacement and master cylinder piston speed as feature inputs and the real master cylinder pressure as target output. Third, the BP neural network is trained by the training dataset and gradient-descent algorithm. Finally, the pressure estimation performance is verified by the testing dataset. The results show that the proposed algorithm reduces the estimation error by 38% and 15%, compared with the dynamic pressure-displacement model and the LSTM-based estimation algorithm, respectively.
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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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