Vehicle mass and road gradient joint adaptive estimation method considering environmental factors

Abstract

According to the method, a vehicle kinematics model and a vehicle longitudinal dynamics model are constructed, after a continuous system is discretized, the road slope is estimated in real time based on recursive Kalman filtering, and a tire rolling resistance coefficient and an air resistance coefficient are estimated in real time based on extended Kalman filtering; and the vehicle longitudinal kinetic model is corrected in real time by utilizing the above parameter estimation values, and the vehicle mass is estimated in real time based on a recursive least square method with a forgetting factor. Compared with the mode of directly adopting the calibration values of the parameters to estimate the vehicle mass, the sensitive parameters in the vehicle dynamics model constructed in the method can be adaptively corrected according to the change of the road environment, so that the error between the set values of the sensitive parameters in the model and actual values is reduced; the accuracy and stability of the slope and vehicle mass estimation algorithm are effectively improved, the range of application conditions is wide, and a reliable road slope and vehicle mass estimation result is provided for a vehicle control system.

Description

technical field [0001] The invention relates to the technical field of vehicle mass and slope calculation, in particular to a joint adaptive estimation method of vehicle mass and road slope considering environmental factors in the electronic control of new energy vehicles. Background technique [0002] With the development of electric vehicles, the chassis structure is more streamlined, and the control-by-wire technology is more in-depth, but the control system is more sensitive to changes in road gradient and vehicle quality, which poses challenges to the dynamic performance of the system. On the premise of accurately knowing the road slope and vehicle quality, the energy consumption calculation and energy management of electric vehicles can be better performed, and intelligent driving assistance systems such as slope descent and active braking can also be better developed and applied. Therefore, accurately knowing the road slope value and vehicle mass value in the current ...

AI Technical Summary

Problems solved by technology

However, in practical applications, the coefficient error of the least squares method will have a significant impact on the accuracy of parameter identification results. When the road environment changes, there is a large error between the calibration value of the sensitive parameter and the actual value, and there is a certain disturbance in the data. Based on The accuracy of the vehicle mass estimation results by the least squares method is significantly reduced

For example: Patent CN107247824A discloses a car mass-road gradient joint estimation method considering the impact of braking and turning. In this patent, the rolling resistance coefficient and the wind resistance coefficient are all regarded as known definite values, and the estimated Kalman of the gradient In the filtering method, the state quantity is only the slope and the vehicle speed, and the acceleration sensor is not introduced to further improve the slope estimation accuracy, and the slope is regarded as known when the vehicle mass is estimated.

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