Road surface attachment coefficient and road surface gradient synchronous real-time estimation system and method for hub-motor-driven vehicle

Abstract

The invention provides a road surface attachment coefficient and road surface gradient synchronous real-time estimation system and method for a hub-motor-driven vehicle. The road surface attachment coefficient and road surface gradient synchronous real-time estimation system comprises a wheel trackslip rate calculating module, a wheel side-slip angle calculating module, a wheel vertical force calculating module, a Dugoff tire model module for deformation treatment, an air resistance calculating module, a fading memory UKF parameter estimation algorithm module, a wheel longitudinal lateral force calculating module, a wheel rotating dynamics module and a longitudinal force feedback and correction module. According to the road surface attachment coefficient and road surface gradient synchronous real-time estimation method, through fading memory weighting processing on a traditional UKF algorithm, obsolete measured data of the algorithm are rejected in time, the weight of newly measured data is increased, and accordingly the estimation precision of parameters is improved; and the advantage that the torque of all wheels of the hub-motor-driven vehicle can be measured precisely is fullycombined, and accurate wheel longitudinal force information obtained by the wheel rotating dynamics module is utilized to correct wheel longitudinal force information obtained by a Dugoff tire model,so that the accuracy of longitudinal normalized force is ensured, and accordingly the estimation precision of the road surface attachment coefficient is improved indirectly.

Description

technical field [0001] The invention belongs to the technical field of road surface identification for distributed driving electric vehicles, and in particular relates to a synchronous real-time estimation system and method for road adhesion coefficient and road gradient of a wheel hub motor-driven vehicle. Background technique [0002] Road adhesion coefficient: refers to the ratio of the force between the wheel and the ground to the normal force of the wheel. At present, the identification methods of road adhesion coefficient can be divided into two types according to the final identification results: one is based on hardware equipment, and the road is directly detected by sensors; the other is based on vehicle dynamics model. Estimated road surface identification method. The first method mainly uses hardware equipment, analyzes the physical factors that affect the adhesion coefficient of the road, and directly identifies it according to the existing empirical model. Str...

AI Technical Summary

Problems solved by technology

The first method is mainly to use hardware equipment. By analyzing the physical factors that affect the road surface adhesion coefficient, and based on the existing empirical model, it is directly identified. This method uses more sensor equipment, and the cost of related experimental instruments is high and the structure is complex. Strict requirements on the conditions of use, the accuracy of estimation is more dependent on the existing experience, it is difficult to be applied in real vehicles

The second method uses low-cost sensors to indirectly identify the adhesion coefficient of the road surface based on the vehicle dynamics model established by the change of the road surface adhesion coefficient in the motion response of the wheels or the vehicle body. Most scholars estimate the road surface based on the μ-s curve. A lot of research has been done on the adhesion coefficient, but this type of method requires a large amount of data for curve fitting, and has problems such as slow response and poor real-time performance, and the accuracy of the recognition results is too dependent on the model accuracy

The first two filtering algorithms are simple in form, but they are not suitable for strong nonlinear systems such as vehicles. Although the unscented Kalman filter takes into account the influence of nonlinear factors, it cannot know the noise characteristics of the system, nor can it predict the state of the system based on sensor measurement data. Variables are corrected in real time, which is prone to error accumulation, which leads to large errors in estimation results

In addition, the utilization rate of the algorithm for real-time estimation of the road surface adhesion coefficient based on the algorithm is usually relatively simple

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