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Vehicle roll state estimation method based on double extended Kalman filtering

An extended Kalman, state estimation technology, applied in design optimization/simulation, special data processing applications, instruments, etc., can solve the problems of time-varying suspension roll stiffness, achieve good real-time performance, broad engineering application prospects, cost low effect

Pending Publication Date: 2022-03-04
TSINGHUA UNIV
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  • Abstract
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Problems solved by technology

[0006] In view of the time-varying problem of suspension roll stiffness in the above-mentioned vehicle roll state estimation process, the purpose of the present invention is to provide a method for estimating vehicle roll state based on double extended Kalman filter

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  • Vehicle roll state estimation method based on double extended Kalman filtering
  • Vehicle roll state estimation method based on double extended Kalman filtering
  • Vehicle roll state estimation method based on double extended Kalman filtering

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Embodiment Construction

[0066] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0067] Such as figure 1 As shown, the present invention provides a vehicle roll state estimation method based on double extended Kalman filter, in particular, considering the necessity of commercial vehicle roll state observation and rollover risk warning, a method suitable for commercial vehicles is established A dynamic model that takes into account both roll motion (both cabin roll motion and unsprung mass roll motion) and yaw motion. Specifically, the following steps are included:

[0068] 1) Establish a five-degree-of-freedom vehicle dynamics model considering the roll motion and yaw motion according to the moment balance, and obtain the state equation and output equation of the system.

[0069] Specifically, the following steps are included:

[0070] 1.1) If figure 2 As shown, a five-degree-of-freedom vehicle dynamics model suitable for com...

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Abstract

The invention relates to a vehicle roll state estimation method based on double extended Kalman filtering, which comprises the following steps of: establishing a five-degree-of-freedom vehicle dynamic model considering roll motion and yaw motion according to moment balance to obtain a state equation and an output equation of a system; and solving the state equation and the output equation by using a dual-extended Kalman filtering method based on the collected related data of the vehicle to be estimated to obtain an estimation result of the roll state of the vehicle to be estimated. The obtained roll angle estimation value can be used for anti-rollover control of the commercial vehicle, and the method has the advantages of being high in estimation precision, good in real-time performance, low in cost and the like. The method can be widely applied to the field of automobile electronic control.

Description

technical field [0001] The invention belongs to the field of automobile electronic control, and in particular relates to a vehicle roll state estimation method based on double extended Kalman filter. Background technique [0002] The rolling motion of the vehicle during driving will cause the load on the left and right wheels to change, and at the same time aggravate the vibration of the vehicle body, which is extremely detrimental to ensuring the active safety of the vehicle and the ride comfort of the passengers. Especially for commercial vehicles, due to their high center of mass and high risk of rollover, it is more important to control the roll state. [0003] In recent years, people have realized the lateral stability control of the vehicle by equipping the vehicle with an anti-roll control system. In order to ensure the control performance of the anti-roll control system, it is necessary to obtain the roll state of the vehicle in real time as the input of the control...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F119/14
CPCG06F30/20G06F2119/14
Inventor 李婧媛刘亚辉刘玉龙徐涛
Owner TSINGHUA UNIV
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