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Vehicle mass and road gradient iterative joint estimation method based on MMRLS and SH-STF

A technology of SH-STF and vehicle quality, which is applied in the field of iterative joint estimation of vehicle quality and road gradient, can solve problems such as system parameters that do not take into account the slow change of quality

Active Publication Date: 2020-08-07
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

The current quality slope identification algorithm basically estimates the quality and slope at the same time, and does not take into account the fact that the quality is a slowly changing system parameter, and the slope is a time-varying state variable.

Method used

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  • Vehicle mass and road gradient iterative joint estimation method based on MMRLS and SH-STF
  • Vehicle mass and road gradient iterative joint estimation method based on MMRLS and SH-STF
  • Vehicle mass and road gradient iterative joint estimation method based on MMRLS and SH-STF

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Embodiment approach

[0295] As a preferred implementation of the present invention, the vehicle speed and engine nominal torque values ​​in the first step can be obtained from the vehicle CAN bus information.

[0296] As a preferred embodiment of the present invention, in the step 4, in the gradient estimation algorithm, when the vehicle is running smoothly, the Sage-Husa algorithm is used to perform adaptive estimation of the noise, reduce the state estimation error of the system, and improve the filter When the driving state of the vehicle changes suddenly, the STF algorithm is used to improve the tracking estimation ability of the Kalman filter and enhance the robustness of the estimation algorithm. Therefore, the Sage-Husa algorithm can be combined with the STF algorithm to achieve In the period, combined with the Kusovkov HT filter convergence criterion, the Sage-Husa algorithm is used to estimate the slope when the filter converges, and the STF algorithm is used to estimate the slope when the...

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Abstract

The invention provides a vehicle mass and road gradient iterative joint estimation method based on MMRLS and SH-STF. The vehicle mass and road gradient iterative joint estimation method comprises thefollowing steps of: establishing a kinetic model considering steering, constructing an MMRLS / SH-STF iterative joint estimation algorithm architecture, and achieving improvement of a gradient estimation algorithm based on SH-STF. The vehicle mass and road gradient iterative joint estimation method based on MMRLS and SH-STF is reasonable in design, analyzes the slow change property of the vehicle mass and the time-varying property of the road gradient, calculates the vehicle mass by utilizing a system identification algorithm of multi-model fusion recursive least square and calculates the road gradient by using a state estimation algorithm of noise self-adaptive strong tracking filtering based on extended Kalman filtering according to the slow change and time-varying properties based on a vehicle longitudinal dynamic model and a steering monorail model, thus the algorithm can better adapt to estimation variables.

Description

technical field [0001] The invention relates to the technical field of quality estimation, in particular to an iterative joint estimation method for vehicle quality and road gradient based on MMRLS and SH-STF. Background technique [0002] With the development of the freight industry, the number of heavy vehicles is also increasing. Compared with passenger cars, the weight of heavy-duty vehicles varies greatly, even up to 400% from empty to fully loaded. The vehicle quality is the key parameter of the automatic transmission shift control system for gear decision-making, vehicle dynamics control and parameter estimation, and vehicle state monitoring. power, economy and safety; [0003] The degree of coupling between road slope and mass is high, so these two parameters need to be estimated simultaneously during the calculation process. Usually, the slope of the road can be indirectly measured by an inclination sensor or an acceleration sensor. However, due to the high cost...

Claims

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Application Information

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IPC IPC(8): G06F30/20G06F111/10G06F119/14
CPCG06F30/20G06F2111/10G06F2119/14Y02T90/00B60W40/06B60W40/13B60W2050/0028B60W2552/15B60W2050/0052B60W40/105
Inventor 王伟达杨超刘金刚张为张中国项昌乐
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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