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Parameter-decoupling electric vehicle mass and gradient estimation method

A technology for electric vehicles and vehicle quality, applied in the field of intelligent transportation, which can solve the problems of increasing the estimation accuracy and the complexity of convergence speed of road conditions.

Pending Publication Date: 2020-01-24
JILIN UNIV
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Problems solved by technology

The difficulty of this method lies in the decoupling of the vehicle's own parameters (weight, etc.) and external resistance (slope), and the time-varying nature of the road conditions also increases the complexity of the estimation accuracy and convergence speed in the estimation process

Method used

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  • Parameter-decoupling electric vehicle mass and gradient estimation method
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Embodiment Construction

[0120] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0121] The embodiment of the present invention discloses a method for estimating the mass and slope of an electric vehicle with parameter decoupling. The method performs recursive least squares estimation on the slowly changing vehicle mass, and performs Romberg observer estimation on the rapidly changing road surface slope. , to achieve decoupling of parameter estimation. And correct and compensate the abnormal values ​​of vehicle mass and road slope in param...

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Abstract

The invention discloses a parameter-decoupling electric vehicle mass and gradient estimation method which is characterized by comprising the following specific steps: acquiring vehicle driving state data by using a data acquisition system, and calculating parameters required by a model; establishing a relation model of the whole vehicle mass, the road surface gradient and the vehicle driving statedata; constructing a recursive least square estimation model and a Runbeg observer estimation model based on the whole vehicle mass model and the road surface slope model respectively; inputting thecollected vehicle driving state data and related model parameters into a real-time estimation system, and estimating the real-time whole vehicle mass and the road surface gradient; and processing theabnormal data by adopting global K-means clustering and three-time Hermite interpolation to obtain a whole vehicle mass and pavement gradient estimation result. According to the method, recursive least square estimation is carried out on the slowly-changed whole vehicle mass, Runbeg observer estimation is carried out on the rapidly-changed road surface gradient, decoupling of parameter estimationis achieved, correction and compensation processing is carried out, and therefore the parameter estimation precision is improved.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, and more specifically relates to a method for estimating the mass and slope of an electric vehicle with parameter decoupling. Background technique [0002] Real-time estimation of vehicle dynamics parameters is the basis of the underlying control of intelligent vehicles. Vehicle mass and road gradient are key parameters in the vehicle dynamics model. Accurate and real-time estimation of vehicle mass and road gradient can significantly improve vehicle dynamics and economy. However, there is a strong coupling relationship between vehicle mass and road gradient in the dynamic model of vehicle driving, which will seriously affect the perception accuracy of vehicle mass and road gradient during actual driving. Therefore, it is of great significance to explore a parameter estimation method that decouples vehicle mass estimation and road slope estimation to improve the safety and econ...

Claims

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Application Information

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IPC IPC(8): G06F30/15G06F30/20
Inventor 朱冰张伊晗赵健李志轩靳万里倪海龙
Owner JILIN UNIV
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