Entire car mass estimation method based on high-frequency information extraction

Abstract

The invention relates to an entire car mass estimation method based on high-frequency information extraction, which comprises the following steps that: an entire car controller controls a signal collection module to collect the signals of an acceleration sensor, a driving force sensor and a steering wheel turning sensor in the car running process in real time, and to simultaneously send the collected signals to a running state judgment module; the running state judgment module judges the running state of the car according to the collected steering wheel turning signal; a high-frequency information extracting module analyzes the longitudinal kinetic model of the car according to the obtained signals, carries out differential calculation on the acceleration in the longitudinal kinetic model of the car and acquires the high-frequency information of longitudinal acceleration signal and driving force signal, so as to obtain the primary estimation expression of the mass of the entire car; approximation is performed on the true mass of the entire car by adopting a least square method estimation method; and after once entire car mass estimation, the signal collection module repeats the calculation steps according to the set sampling interval in real time, and the entire car mass at each sampling instant is estimated in real time until the car extinguishes. The method can be widely applied to the mass estimation of various cars.

Description

technical field [0001] The invention relates to a method for estimating the whole vehicle quality, in particular to a method for estimating the whole vehicle quality based on high-frequency information extraction which can estimate the whole vehicle quality of the vehicle in real time during the running of the vehicle. Background technique [0002] Modern vehicle control systems include multiple subsystems. The real-time control effect of the vehicle during driving not only depends on the vehicle's own controller, but also needs to know real-time vehicle parameters. The estimation of vehicle parameters usually needs to consider the vehicle loading and Vehicle driving conditions, if certain parameters of the vehicle can be accurately estimated in real time during vehicle driving, the control accuracy of the entire vehicle will be improved, and the control effect will be effectively improved. During the driving process of the vehicle, the vehicle mass estimation of the vehicle...

AI Technical Summary

Problems solved by technology

[0003] The existing vehicle estimation methods estimate the vehicle mass by collecting various parameters of the vehicle at the same time. This quality estimation method relies on the measurement accuracy of various parameters of the vehicle, such as drag coefficient, slope, windward area, and road resistance. If the above parameters cannot be obtained or the obtained accuracy is not high, the quality of the vehicle cannot be estimated in real time. However, in the actual driving process of the vehicle, the estimation of the above parameters itself is relatively difficult. Take the slope as an example: because the change of the slope is not independent It is coupled with other parameters of the vehicle, that is, when the slope changes, other parameters of the vehicle will also change. Therefore, in the process of estimating the vehicle mass, multiple parameters that change at the same time are used to estimate the vehicle mass. It will lead to low accuracy of real-time vehicle quality estimation. To sum up, the existing vehicle estimation methods cannot avoid the inaccurate estimation of various parameters of the vehicle, which will adversely affect the estimation of vehicle quality.

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