科创中国

Vehicle mass is an important parameter in vehicle dynamics control systems.Although many algorithms have been developed for the estimation of mass,none of them have yet taken into account the different types of resistance that occur under different conditions.This paper proposes a vehicle mass estimator.The estimator incorporates road gradient information in the longitudinal accelerometer signal,and it removes the road grade from the longitudinal dynamics of the vehicle.Then,two different recursive least square method(RLSM)schemes are proposed to estimate the driving resistance and the mass independently based on the acceleration partition under different conditions.A 6 DOF dynamic model of four In-wheel Motor Vehicle is built to assist in the design of the algorithm and in the setting of the parameters.The acceleration limits are determined to not only reduce the estimated error but also ensure enough data for the resistance estimation and mass estimation in some critical situations.The modification of the algorithm is also discussed to improve the result of the mass estimation.Experiment data on a sphalt road,plastic runway,and gravel road and on sloping roads are used to validate the estimation algorithm.The adaptability of the algorithm is improved by using data collected under several critical operating conditions.The experimental results show the error of the estimation process to be within 2.6%,which indicates that the algorithm can estimate mass with great accuracy regardless of the road surface and gradient changes and that it may be valuable in engineering applications.This paper proposes a recursive least square vehicle mass estimation method based on acceleration partition.

The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating conditions.The existing investigations on robust identification of maximum road friction coefficient are unsatisfactory.In this paper,an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip ratio.The instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model,and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time,which are updated with time progressing.The current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road,and the minimum statistical error is used as the recognition principle to improve identification robustness.Once the road type is recognized,the maximum road friction coefficient and optimal slip ratio are determined.The numerical simulation tests are conducted on two typical road friction conditions(single-friction and joint-friction)by using CarSim software.The test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in CarSim.The proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations,and the identification results can be used for the adjustment of vehicle active safety control strategies.