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Outline
To address the critical issues of range anxiety among battery electric vehicle users and the insufficient accuracy of energy consumption prediction, this paper proposes a two-stage energy consumption prediction method considering driving styles. First, characteristic parameters strongly related to driving styles, such as speed, accelerator pedal depth, and brake pedal depth, are extracted and calculated, and a back propagation neural network is used to construct a driving style recognition model. Second, for different driving styles, decision tree models are respectively used for energy consumption prediction. Verified by real vehicle test data, the average absolute percentage error (MAPE) of the energy consumption prediction model proposed in this paper is 5.84%. Compared with the average energy consumption method, the Deep Neural Network (DNN) and decision tree energy consumption prediction methods that do not consider driving styles, the errors are reduced by 16.74%, 9.77% and 1.44% respectively. The research results demonstrate that the energy consumption prediction method incorporating user driving style can effectively alleviate user range anxiety and significantly improve the accuracy of energy consumption prediction.
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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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