Research on the Prediction Method of Vehicle Moment of Inertia Based on BP Neural Network and Big Data

With the development of the production technology of the automobile industry, the requirements for parameter accuracy in various motion states are becoming higher and higher in the process of automobile design. In order to improve the input accuracy of the moment of inertia value in the vehicle simulation, and then make the vehicle simulation get more accurate results, this paper proposes a vehicle moment of inertia prediction method based on BP neural network. Through the selection of the indicators affecting the moment of inertia, the sample data is determined, and the sample data is divided into training set and test set to train and verify the BP neural network prediction model. The results show that the accuracy of the moment of inertia predicted by the neural network is significantly higher than that calculated by the traditional empirical formula, and can be used in the process of automobile development. This paper uses big data and neural network to predict vehicle simulation input parameters, so as to obtain more accurate vehicle simulation results, which can be applied to other aspects of the vehicle simulation field.