Estimation method for side slip angle and tire lateral force of four-wheel independent drive electric automobile

Abstract

The invention discloses a four-wheel independent drive electric vehicle side slip angle and tire lateral force estimation method which comprises the following steps: calculating tire longitudinal force according to a wheel kinetic equation; according to a longitudinal dynamic balance equation of the vehicle, estimating the mass of the whole vehicle based on a least square method with a forgetting factor; the robust volume Kalman estimation module is used for establishing a four-wheel drive electric vehicle dynamic model comprising three degrees of freedom of vehicle longitudinal, lateral and yawing and a semi-experience magic tire model for reflecting instantaneous mechanical characteristics of tires; and estimating the side slip angle and the tire lateral force based on the established robust volume Kalman filtering module. The method effectively improves the perturbation of filtering to model parameters and the anti-interference capability of unmodeled noise under the complex working condition, improves the accuracy, robustness and anti-interference performance of a joint estimation algorithm under different working conditions, and solves the problem of joint estimation of the side slip angle and the tire lateral force of the four-wheel-drive electric vehicle under the composite working condition.

Description

technical field [0001] The invention relates to a method for estimating the sideslip angle of the center of mass and tire lateral force of a four-wheel independently driven electric vehicle, and belongs to the field of new energy vehicle related and manufacturing. Background technique [0002] The four-wheel drive electric vehicle uses the hub motor as the power unit, and the electric vehicle that integrates independent drive and braking has been considered by experts in the international automotive field as one of the structures with the most development potential. With the continuous increase of car ownership, a series of problems such as frequent road traffic accidents, intensified urban traffic congestion and environmental pollution have become increasingly prominent. The auto industry has ushered in the challenge of "new four modernizations" , Networking, intelligence, and sharing are the future trends of the development of the automotive industry and important breakthr...

AI Technical Summary

Problems solved by technology

The extended Kalman filter is based on the first-order approximation of the Taylor expansion, and the estimation accuracy is not ideal; the unscented Kalman filter has a second-order accuracy approximation; compared with the standard volumetric Kalman filter, which has a third-order accuracy, the estimation accuracy is higher. However, because the parameter perturbation and unknown input of the model are not considered, the estimation results are greatly affected by the perturbation of the model parameters, resulting in low estimation accuracy.

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