Soft measurement method for tire force of four-wheel drive electric automobile

Abstract

The invention discloses a soft measurement method for the tire force of a four-wheel drive electric automobile. The soft measurement method comprises the following steps: 1, acquiring the longitudinal speed, the sideslip angle, the longitudinal and transverse acceleration, the front wheel turning angle and the tire longitudinal force of the automobile; 2, inputting the acquired information of the longitudinal speed, the sideslip angle, the longitudinal acceleration, the transverse acceleration, the front wheel turning angle and the tire longitudinal force of the automobile into a nonlinear vehicle dynamics model, and conducting calculating through the vehicle dynamics model to obtain estimated longitudinal acceleration and transverse acceleration; and 3, inputting the acquired longitudinal speed, sideslip angle, longitudinal and transverse acceleration, front wheel rotation angle and tire longitudinal force information of the automobile and the longitudinal acceleration and transverse acceleration information estimated in the step 2 into an unscented Kalman filtering algorithm to obtain a model-based automobile tire force estimation value. The method is based on continuous optimization of the algorithm, the prediction precision is continuously improved, and the development of the automobile active safety control technology is promoted.

Description

technical field [0001] The invention relates to a method for measuring softness of tires of four-wheel drive electric vehicles based on digital twins, belonging to the field of intelligent vehicles. Background technique [0002] With the advancement and development of information and artificial intelligence technology, smart cars have attracted more and more attention. Among them, the safety control of the car during driving is one of the core issues. The primary issue of its control is to obtain the current tire force of the car and other important parameters; however, there is no way to directly measure the tire force, which can only be obtained by predicting methods, and the current automobile tire force prediction methods all assume that the measurement noise of the vehicle sensor is accurately known, but the car is affected by the environment during operation (such as high and low temperature, vibration) etc.) and the aging of the on-board sensors themselves, the existi...

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Problems solved by technology

[0002] With the advancement and development of information and artificial intelligence technology, smart cars have attracted more and more attention. Among them, the safety control of the car during driving is one of the core issues. The primary issue of its control is to obtain the current tire force of the car and other important parameters; however, there is no way to directly measure the tire force, which can only be obtained by predicting methods, and the current automobile tire force prediction methods all assume that the measurement noise of the vehicle sensor is accurately known, but the automobile is affected by the environment during operation (such as high and low temperature, vibration) etc.) and the aging of the on-board sensor itself, the existing tire force prediction method is too ideal and cannot be applied in practice.

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