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In the realm of vehicle dynamics, the sideslip angle is a critical parameter. For the challenges posed by the current modelbased methods, which heavily rely on the accuracy of dynamic models, and the poor robustness of datadriven methods in unfamiliar operating conditions, in this paper a sideslip angle estimation method based on a hybrid of physics and datadriven approaches (DeepPhy) is proposed. The aim is to combine the strength of physical modeling and datadriven techniques to achieve reliable and accurate estimation of the sideslip angle. DeepPhy integrates prior values of the sideslip angle obtained from the lateral force model of the rear axle tires with a deep neural network, enabling the learning of nonlinear mapping relationship not captured by the physical model, thereby enhancing the model's reliability in unfamiliar conditions. The simulation results indicate that under continuous DLC conditions, the RMSE of the estimation results from DeepPhy is reduced by 93% compared to the physical model method and by 63% compared to the datadriven method, exhibiting robustness in scenarios with limited data. Realworld validation further confirms DeepPhy's exceptional generalization capabilities, as the models trained through simulation can be transferred to realworld conditions while maintaining highprecision estimation results.
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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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