Transverse stability control method and system for distributed driving electric vehicle

Abstract

The invention discloses a transverse stability control method and system for a distributed driving electric vehicle. The method comprises the steps of establishing a vehicle yaw velocity prediction model according to the historical data of the vehicle yaw velocity, the historical data of the vehicle direct yaw moment and the noise data; the model does not depend on changeable vehicle parameters and environment parameters which cannot be accurately obtained in advance; and based on the model, determining a parameter vector in the model by adopting a recursive least square method according to the current data of the yaw velocity of the vehicle; predicting the yaw velocity of the vehicle according to the parameter vector and the current data of the yaw velocity of the vehicle to obtain a predicted value of the yaw velocity of the vehicle, and estimating and updating the parameter vector in real time due to the fact that model parameters are time-varying, so that the vehicle model has self-adaptability and can be suitable for all working conditions of vehicle driving; the model has certain robustness on sensor measurement errors, has a certain filtering effect on noise in a vehicle yawvelocity measurement value, and can improve the yaw stability of a vehicle.

Description

technical field [0001] The invention relates to the technical field of electric vehicle stability control, in particular to a distributed drive electric vehicle lateral stability control method and system. Background technique [0002] The vehicle models adopted by the existing vehicle stability controllers are mainly divided into two categories: vehicle dynamics models and vehicle kinematics models. When the driver is in the linear control range, the vehicle parameters and road adhesion coefficient are accurate, and the tire parameters are at the nominal value, the vehicle dynamics model has higher accuracy. However, the working conditions during the driving process of the vehicle are complex, and it is impossible to stay in the driver's linear control range at all times, and even often in dangerous extreme working conditions. The finite-dimensional dynamic model cannot accurately describe the real dynamic characteristics of the vehicle, and the model mismatch Serious; at ...

AI Technical Summary

Problems solved by technology

However, the working conditions during the driving process of the vehicle are complex, and it is impossible to stay in the driver's linear control range at all times, and even often in dangerous extreme working conditions. The finite-dimensional dynamic model cannot accurately describe the real dynamic characteristics of the vehicle, and the model mismatch Serious; at the same time, the tire parameters greatly deviate from the nominal value, making the model error serious

In addition, many vehicle parameters cannot be accurately known in advance, and the road adhesion coefficients of different driving conditions are quite different, which greatly affects the accuracy of the model

Therefore, the above factors have a greater impact on the accuracy of the vehicle dynamics model

For the vehicle kinematics model, on the one hand, it is less robust to sensor measurement errors and sensor drift; on the other hand, it is easy to lose observability in steady-state conditions

To sum up, both the vehicle dynamics model and the vehicle kinematics model have limitations and cannot be applied to all driving conditions of the vehicle

Images