EP-4741779-A1 - METHOD AND DEVICE FOR DETERMINING THE MASS OF A VEHICLE, DEVICE, VEHICLE AND MEDIUM

EP4741779A1EP 4741779 A1EP4741779 A1EP 4741779A1EP-4741779-A1

Abstract

The embodiments of the present application provide a method and a device for determining the mass of a vehicle, a device, a vehicle, and a medium. The method comprises: selecting multiple real-time mass estimation segments from a complete real-time mass estimation result; subsequently determining a vehicle mass estimation result for each real-time mass estimation segment; and finally, determining a mass estimation result for a single driving operation based on a road surface friction coefficient during the driving of the vehicle and the vehicle mass estimation result for each real-time mass estimation segment. The method is used to improve the accuracy and reliability of the mass estimation result in a single driving operation.

Inventors

WANG, XIANDONG
LUO, ZHENG
Fang, Gaoming
ORMANKIRAN, MESUT

Assignees

ZF Friedrichshafen AG

Dates

Publication Date: 20260513
Application Date: 20251014

Claims (19)

Method for determining the mass of a vehicle, characterized in that the method comprises: Selecting multiple real-time mass estimation segments from a complete real-time mass estimation result; Determining a vehicle mass estimation result for each real-time mass estimation segment; and Determining a mass estimation result for a single driving operation based on a road surface friction coefficient while driving the vehicle and the vehicle mass estimation result for each real-time mass estimation segment.
The method according to claim 1, characterized in that determining a mass estimation result for a single driving operation based on a road surface friction coefficient during driving the vehicle and the vehicle mass estimation result for each real-time mass estimation segment comprises: Determining a weight factor for each real-time mass estimation segment depending on the road surface friction coefficient while driving the vehicle for the real-time mass estimation segment; and Determining the mass estimation result for each individual driving operation depending on the vehicle mass estimation result and the weight factor for each real-time mass estimation segment.
The method according to claim 2, characterized in that determining the mass estimation result for the individual driving process comprises, depending on the vehicle mass estimation result and the weight factor for each real-time mass estimation segment: Weighted averaging of vehicle mass estimation results for multiple real-time mass estimation segments, depending on the weight factor for each real-time mass estimation segment, to obtain the mass estimation result for each individual driving operation.
The method according to claim 2, characterized in that determining a weight factor for the real-time mass estimation segment, depending on the road surface coefficient of friction when driving the vehicle, comprises: Determining a weight factor corresponding to a range of the road surface friction coefficient as the weight factor for the real-time mass estimation segment based on a preset mapping relationship between friction coefficient ranges and weight factors, as well as based on the road surface friction coefficient when driving the vehicle for the real-time mass estimation segment.
Method according to one of claims 1 to 4, characterized in that the selection of several real-time mass estimation segments from a complete real-time mass estimation result comprises: Selecting multiple real-time mass estimation segments from the complete real-time mass estimation result depending on a road surface type during the individual driving operation of the vehicle, where the number of selected real-time mass estimation segments varies depending on the road surface type.
The method according to claim 5, characterized in that the selection of several real-time mass estimation segments from the complete real-time mass estimation result depends on a Road surface type during each individual driving maneuver of the vehicle includes: Selecting multiple real-time mass estimation segments from the complete real-time mass estimation result depending on the road surface type during each vehicle journey and preset selection rules for real-time mass estimation segments. where the selection rules for real-time mass estimation segments include at least one limiting condition for vehicle driving data.
The method of claim 6, characterized in that the selection rules for real-time mass estimation segments comprise at least one of the following conditions: that the vehicle's speed is greater than a preset speed; that the vehicle's acceleration is greater than a preset acceleration; that the vehicle's drive torque is greater than a preset torque; and that the vehicle's traction control (TC), anti-lock braking system (ABS) and yaw stability control (YSC) functions are not activated.
The method according to claim 5, characterized in that the method further comprises: Classifying captured road surface images using a pre-trained algorithm for classifying road surfaces during a vehicle driving process to obtain the road surface type.
The method of claim 5, characterized in that the road surface type comprises one of the following types: snow-covered road surface; unpaved road surface; flooded road surface; and dry asphalted road surface.
Method according to one of claims 1 to 4, characterized in that determining a vehicle mass estimation result for each real-time mass estimation segment comprises: Calculating an average of mass estimation results from multiple sampling points in each real-time mass estimation segment to obtain a vehicle mass estimation result for the real-time mass estimation segment.
The method according to claim 10, characterized in that the method further comprises, prior to calculating an average of mass estimation results from several sampling points in each real-time mass estimation segment: Removing abnormal data from the mass estimation results of the multiple sampling points in the real-time mass estimation segment to obtain processed mass estimation results of the multiple sampling points, which accordingly includes calculating an average of mass estimation results from multiple sampling points in the real-time mass estimation segment: Calculating an average of the processed mass estimation results from multiple sampling points.
Method according to any one of claims 1 to 4, characterized in that the method further comprises: Performing a real-time mass estimation using a Kalman filter state estimation model during the vehicle's driving process, depending on the vehicle's baseline information, driving data, and environmental data, to obtain the complete real-time mass estimation result. where the Kalman filter state estimation model is derived from a four-dimensional state-space equation created on the basis of a dynamic equation of the vehicle in the longitudinal direction after optimization of parameters of a front wheel steering angle.
Method according to one of claims 2 to 4, characterized in that the method further comprises, prior to determining the mass estimation result for the individual driving process depending on the vehicle mass estimation result and the weight factor for each real-time mass estimation segment: Determining the vehicle's acceleration within a preset time period before each real-time mass estimation segment; and Correcting the weight factor for the real-time mass estimation segment depending on the acceleration to obtain a corrected weight factor; where, accordingly, determining the mass estimation result for the individual driving process includes, depending on the vehicle mass estimation result and the weight factor for each real-time mass estimation segment: Determining the mass estimation result for each individual driving operation depending on the vehicle mass estimation result and the corrected weight factor for each real-time mass estimation segment.
Method according to any one of claims 1 to 4, characterized in that the method further comprises: Re-acquiring a complete real-time mass estimation result upon detection that the vehicle is parked and the vehicle door is opened, and determining a mass estimation result for a single vehicle movement depending on from the recaptured full real-time mass estimation result; or Re-acquiring a full real-time mass estimation result upon detecting that the vehicle is parked and the parking duration is longer than a preset duration, and determining a mass estimation result for a single movement of the vehicle depending on the re-acquired full real-time mass estimation result.
Method according to any one of claims 1 to 4, characterized in that the method further comprises: Determining a road surface friction coefficient in real time during the vehicle's driving process based on data acquired by the vehicle's sensors.
Device for determining the mass of a vehicle, characterized in that the method comprises: a selection module for selecting multiple real-time mass estimation segments from a complete real-time mass estimation result; a determination module for determining a vehicle mass estimation result for each real-time mass estimation segment; and a determination module that is used to determine a mass estimation result for a single driving operation based on a road surface friction coefficient when driving the vehicle and the vehicle mass estimation result for each real-time mass estimation segment.
Electronic device characterized in that it comprises: a memory and a processor; where computer-executable instructions are stored in the memory, wherein the processor executes the computer-executable instructions stored in the memory to enable the processor to carry out a method according to any one of claims 1 to 14.
Vehicle characterized in that it comprises an electronic device according to claim 17.
A computer-readable storage medium, characterized in that the computer-readable storage medium contains computer-executable instructions which, when executed by a processor, are used to implement a method according to one of claims 1 to 14.

Description

Technical field The present application relates to the field of vehicle engineering, in particular to a method and a device for determining the mass of a vehicle, a device, a vehicle and a medium. State of the art A vehicle's mass is a crucial parameter in its dynamic control and can remain essentially stable throughout a single driving sequence. Accurate information about the vehicle's mass plays a vital role in performance optimization, fuel-saving strategies, fault diagnosis, and vehicle maintenance. In the current state of the art, a complete real-time mass estimation result consists of a set of data that fluctuates considerably due to road conditions. The problem is that differing road conditions lead to significant deviations in the vehicle mass estimation results. In conventional solutions, the vehicle's unladen weight or a typical mass is selected as the mass estimation result for a single driving maneuver. However, this specific mass estimation result applies to a single driving process low accuracy and poor reliability. Disclosure of the invention The embodiments of the present application provide a method and a device for determining the mass of a vehicle, a device, a vehicle and a medium to improve the accuracy and reliability of the determined vehicle mass estimation result for a single driving operation. In a first aspect, the embodiments of the present application provide a method for determining the mass of a vehicle, which includes: Selecting multiple real-time mass estimation segments from a complete real-time mass estimation result; Determining a vehicle mass estimation result for each real-time mass estimation segment; and Determining a mass estimation result for a single driving operation based on a road surface friction coefficient while driving the vehicle and the vehicle mass estimation result for each real-time mass estimation segment. In one possible embodiment, determining a mass estimation result for a single driving operation based on a road surface friction coefficient while driving the vehicle and the vehicle mass estimation result for each real-time mass estimation segment includes: Determining a weight factor for each real-time mass estimation segment depending on the road surface friction coefficient while driving the vehicle for the real-time mass estimation segment; and Determining the mass estimation result for each individual driving operation depending on the vehicle mass estimation result and the weight factor for each real-time mass estimation segment. In one possible embodiment, determining the mass estimation result for each driving operation includes, depending on the vehicle mass estimation result and the weight factor for each real-time mass estimation segment: Weighted averaging of vehicle mass estimation results for multiple real-time mass estimation segments, depending on the weight factor for each real-time mass estimation segment, to obtain the mass estimation result for each individual driving operation. In one possible embodiment, determining a weight factor for the real-time mass estimation segment, depending on the road surface coefficient of friction when driving the vehicle for the real-time mass estimation segment, includes: Determining a weight factor corresponding to a range of the road surface friction coefficient as the weight factor for the real-time mass estimation segment based on a preset mapping relationship between friction coefficient ranges and weight factors, as well as based on the road surface friction coefficient when driving the vehicle for the real-time mass estimation segment. In one possible embodiment, selecting multiple real-time mass estimation segments from a complete real-time mass estimation result includes: Selecting multiple real-time mass estimation segments from the complete real-time mass estimation result depending on a road surface type during a single driving operation. of the vehicle, with the number of selected real-time mass estimation segments varying depending on the road surface type. In one possible embodiment, selecting multiple real-time mass estimation segments from the complete real-time mass estimation result, depending on a road surface type during the individual driving operation of the vehicle, includes: Selecting multiple real-time mass estimation segments from the complete real-time mass estimation result depending on the road surface type during each vehicle journey and preset selection rules for real-time mass estimation segments. where the selection rules for real-time mass estimation segments include at least one limiting condition for vehicle driving data. In one possible embodiment, the selection rules for real-time mass estimation segments include at least one of the following conditions: that the vehicle's speed is greater than a preset speed; that the vehicle's acceleration is greater than a preset acceleration; that the vehicle's drive torque is greater than a preset torque; and that the