In order to enhance the vehicle braking energy recovery efficiency and maintain braking stability, this paper proposes a comprehensive energy recovery method that takes the drivers’ styles and road adhesion characteristics into account. Firstly, the driving style feature parameters are extracted from different drivers’ data, and the driving style recognition model is trained based on the Support Vector Machine (SVM). Then, the road images are preprocessed using the U-Net network, and the lightweight network MobileNet V3 is trained to recognize the road surface efficiently. Finally, combined with the recognition results of driving styles and road surfaces, the variable ratio of braking force of the front and rear axles of the vehicle is allocated, and a method is proposed to determine the regenerative braking force considering the weight of driving styles and road adhesion conditions, the braking energy recovery strategy is formulated on this basis. The simulation results show that the braking efficiency and stability are significantly improved for different road surfaces; the SOC of the battery is improved by 2.13 percentage points and 1.02 percentage points in the WLTC and NEDC cycle conditions respectively, further improving the overall braking stability and economy of vehicles.
| 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 |