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To achieve accurate prediction of electric vehicle remaining range，a method based on a three-layer weighted stacking model for predicting remaining range of electric vehicles is proposed in this paper. By combining the maximal information coefficient and Spearman correlation coefficient as criteria for variable evaluation，the minimum redundancy maximum relevance algorithm is employed to optimize and obtain the input feature set from the candidate features. A three-layer stacking model that incorporates the original training features is then constructed，and Bayesian optimization algorithm is used to determine the weights of the base models within the stacking model. Finally，the input feature set is used to train the three-layer weighted stacking model and realize electric vehicle remaining range prediction. The results show that the proposed three-layer weighted stacking model has high prediction accuracy and，compared to other models，with stronger generalization capabilities.
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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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