The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 29, 2024

Filed:

Dec. 14, 2023
Applicant:

Tongji University, Shanghai, CN;

Inventors:

Lin Zhang, Shanghai, CN;

Hong Chen, Shanghai, CN;

Haobo Sun, Shanghai, CN;

Hanghang Liu, Shanghai, CN;

Wei Pan, Shanghai, CN;

Assignee:

TONGJI UNIVERSITY, Shanghai, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
B60L 3/10 (2006.01); B60L 15/20 (2006.01);
U.S. Cl.
CPC ...
B60L 3/106 (2013.01); B60L 15/2036 (2013.01); B60L 2240/22 (2013.01); B60L 2240/24 (2013.01); B60L 2240/423 (2013.01); B60L 2240/461 (2013.01); B60L 2240/463 (2013.01); B60L 2240/465 (2013.01);
Abstract

The present disclosure relates to a multi-constraint optimal distribution method for a torque vectoring of a distributed drive electric vehicle, which includes: obtaining a vehicle real-time motion state, taking a pre-constructed tire rotation dynamics model as a control object of an optimal distribution problem, establishing an objective function for tracking a desired additional yaw moment, tracking a desired tire slip ratio, and suppressing a motor output energy consumption to a minimum value, establishing a corresponding system constraint, solving the optimal distribution problem, and obtaining an optimal distribution solution for a torque vectoring of each tire. Compared with the prior art, the present disclosure can effectively track a desired control target, can also effectively constrain an important state and a control input of the vehicle, and can avoid wrong solutions or no solution caused by an optimization problem.


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