METHOD AND SYSTEM FOR ESTIMATE WEIGHT-RELATED PARAMETERS OF VEHICLES

Abstract

Methods and systems for estimating weight-related parameter values ​​of a vehicle are provided. The system comprises a sensor system, a display device, and a control unit configured to receive static sensor data from the sensor system via a processor. This data is acquired while the vehicle is stationary and coupled to a trailer, and dynamic sensor data is acquired while the vehicle is in motion and uncoupled. The static sensor data includes a deflection value associated with the vehicle's suspension system. One or more weight-related parameter values ​​are determined using a suspension model and the static sensor data. The deterioration of the suspension system over time is determined using a suspension model and the dynamic sensor data while the vehicle is stationary.to estimate one or more weight-related parameter values ​​using a suspension model and static sensor data, to determine deterioration of the suspension system over time using a suspension sag estimation model and dynamic sensor data, to update the suspension model to account for the deterioration of the suspension system, and to display the one or more weight-related parameter values ​​on the optical display.

Description

[0001] The technical field relates generally to vehicles and trailers towed by them, and more particularly to systems and methods for estimating various weight-related parameter values ​​of a vehicle coupled to a trailer while the vehicle is stationary.

[0002] When loading and towing a trailer with a vehicle, various weight-related parameter values ​​can be useful. For example, the gross vehicle weight, tongue weight, and the recovery value for front axle loads or rear axle loads can be used to ensure that the trailers are loaded and coupled to the vehicle safely. Various modern vehicles have systems capable of estimating one or more weight-related parameter values ​​to assist in these activities. However, these systems can lose accuracy over time due to various vehicle-specific aspects. For example, many of these systems rely on suspension displacement measurements to estimate the weight-related parameter values.Over time, various components of the vehicle's suspension system, such as shock absorbers, struts, springs, bushings, and ball joints, can become worn or damaged due to driving stress, harsh weather conditions, and other factors. This wear can lead to inaccurate estimation of weight-related parameter values.

[0003] DE 10 2019 121 716 A1 discloses a method and a device for determining vehicle weight information based on ground clearance. The device includes a weight calculation unit for determining a relationship between ground clearance information and a weight of a vehicle, and a compensation calculation unit for adjusting the relationship based on compensation factors and calculating vehicle weight information for the vehicle based on the adjusted relationship. The compensation factors are based on vehicle sensor data. Furthermore, the device includes a calculation adjustment unit for adjusting the vehicle weight information calculation of the weight calculation unit or the compensation calculation unit based on driver behavior.

[0004] DE 10 2022 116 115 A1 discloses a method and a device for calibrating a weight estimate. The device comprises a memory containing stored instructions and a processor for executing the instructions to determine, via a first sensor, a first load estimate of a vehicle corresponding to when the vehicle is in a loaded state, to determine, via a second sensor, a second load estimate corresponding to when the vehicle is moving and in the loaded state, and to calibrate an error of the first load estimate based on a difference between the second load estimate and the first load estimate.

[0005] DE 10 2022 201 560 A1 discloses a method for determining the stiffness of a chassis of a vehicle, which, in addition to the chassis, has a vehicle drive and a vehicle body. A driving force is exerted on the vehicle by means of the vehicle drive. The vehicle is accelerated by means of the driving force. A force signal characterizing the driving force is determined by measurement. An acceleration signal characterizing the acceleration of the vehicle is determined by measurement. A mass signal characterizing the current mass of the vehicle is generated from the acceleration signal and the force signal. When the vehicle is stationary, at least one height signal characterizing the height of the vehicle is determined by measurement, and a value characterizing the current stiffness of the chassis is formed from the height signal and the mass signal.

[0006] DE 10 2019 114 259 A1 discloses a method for calibrating a load-displacement characteristic curve of a mechanically sprung vehicle axle in a control unit of a vehicle, in which a mechanical spring device is arranged on at least one vehicle axle and at least one displacement sensor is assigned to this spring device. The control unit stores the values ​​of a load-displacement characteristic curve in which the axle load is related to the spring deflection of the spring device. The deflection is measured using the displacement sensor, and an axle load acting on the vehicle axle is calculated using an algorithm stored in the control unit from the respective measured value of the displacement sensor and the load-displacement characteristic curve. During an unbraked journey of the unloaded vehicle, deflection measured values ​​are continuously determined within a specified distance or duration.An arithmetic mean is calculated from the measured deflection values. This average deflection value is compared with a deflection value contained in the stored load-displacement characteristic curve. If a significant deviation is detected, the previously stored first load-displacement characteristic curve is adjusted to a second load-displacement characteristic curve based on the deviation, or a switch is made to such a second stored load-displacement characteristic curve for future load determination, which takes into account any wear on the mechanical spring device.

[0007] Accordingly, it is desirable to provide systems and methods capable of accurately estimating weight-related parameter values ​​relevant to trailer loading and towing. Furthermore, it is desirable to promote accurate results from these systems and methods by compensating for any suspension degradation. Furthermore, other desirable features and characteristics of the present invention will become apparent from the following detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.

[0008] A method for estimating weight-related parameter values ​​of a vehicle is provided. In one embodiment, the method comprises receiving static sensor data from a sensor system of the vehicle, collected while the vehicle is stationary and coupled to a trailer, and dynamic sensor data while the vehicle is being driven and not coupled to the trailer, wherein the static sensor data includes a deflection value associated with a suspension system of the vehicle; estimating one or more weight-related parameter values ​​with a processor operatively coupled to the sensor system and, while the vehicle is stationary, estimating one or more weight-related parameter values ​​using a suspension model and the static sensor data;with the processor, determines deterioration of the suspension system over time using a model for estimating suspension sag and dynamic sensor data, with the processor, updates the suspension model to account for the deterioration of the suspension system, and displays the one or more weight-related parameter values ​​on a display device of the vehicle.

[0009] In various embodiments, the weight-related parameter values ​​include a value for the total weight of the vehicle, a value for the tongue weight of the trailer, and a value for the recovery of the front axle loads or rear axle loads.

[0010] In various embodiments, the suspension model uses a nonlinear representation of the load deflection with variable switching points.

[0011] According to the invention, the method comprises estimating a static estimated weight value of the vehicle with the processor using the suspension model, estimating a dynamic estimated weight value of the vehicle with the processor using a dynamic model and the dynamic sensor data, wherein the dynamic sensor data includes an acceleration value of the vehicle, and determining a fused estimated weight value of the vehicle with the processor by fusing the static estimated weight value and the dynamic estimated weight value.

[0012] In various embodiments, the step of determining deterioration of the suspension system over time using the suspension sag estimation model includes comparing the fused estimated weight value and the static estimated weight value with the processor.

[0013] In various embodiments, the method includes receiving one or more weight-related values ​​sensed directly at the vehicle from the sensor system and training the dynamic model with the processor using the one or more weight-related values.

[0014] In various embodiments, the method comprises receiving a first longitudinal acceleration value of the vehicle, a first driving force value of the vehicle, and a first braking pressure value of the vehicle from the sensor system, all of which are sensed at a first time while the vehicle is being driven, receiving a second longitudinal acceleration value of the vehicle, a second driving force value of the vehicle, and a second braking pressure value of the vehicle from the sensor system, all of which are sensed at a second time, wherein a speed of the vehicle is the same at the first time and the second time while the vehicle is being driven, and estimating the dynamic estimated weight value of the vehicle with the processor using the dynamic model and the first longitudinal acceleration value, the first driving force value, the first braking pressure value, the second longitudinal acceleration value,the second driving force value and the second brake pressure value, wherein the dynamic model comprises a variation-based vehicle longitudinal model.,

[0015] In various embodiments, the variation-based longitudinal vehicle model uses only a subset of the dynamic sensor data acquired while the vehicle speed is greater than a threshold speed and while the vehicle is traveling in a straight forward direction.

[0016] In various embodiments, the method includes receiving one or more external weight values ​​from the sensor system that are coupled to objects separate from and carried by the vehicle, wherein the dynamic model is configured to account for the one or more external weight values.

[0017] A system for estimating weight-related parameter values ​​of a vehicle is provided. In one embodiment, the system comprises a sensor system having one or more sensors configured to sense measurable conditions inside and outside the vehicle, a display device configured to generate a visual display, a controller operatively coupled to the sensor system and the display device, wherein the controller is configured to receive, through a processor, from the sensor system: static sensor data sensed while the vehicle is stationary and coupled to a trailer, and dynamic sensor data while the vehicle is being driven and not coupled to the trailer, wherein the static sensor data includes a deflection value associated with a suspension system of the vehicle,estimate one or more weight-related parameter values ​​using a suspension model and the static sensor data while the vehicle is stationary, determine deterioration of the suspension system over time using a model for estimating suspension sag and the dynamic sensor data, update the suspension model to account for the deterioration of the suspension system, display the one or more weight-related parameter values ​​on the visual display.

[0018] In various embodiments, the weight-related parameter values ​​include a value for the total weight of the vehicle, a value for the tongue weight of the trailer, and a value for the recovery of the front axle loads or rear axle loads.

[0019] In various embodiments, the suspension model uses a nonlinear representation of load deformation with variable switching points.

[0020] According to the invention, the control unit is configured to estimate, by the processor, a static estimated weight value of the vehicle using the suspension model, estimate a dynamic estimated weight value of the vehicle using a dynamic model and the dynamic sensor data, wherein the dynamic sensor data includes an acceleration value of the vehicle, and determine a fused estimated weight value of the vehicle by fusing the static estimated weight value and the dynamic estimated weight value.

[0021] In various embodiments, the suspension sag estimation model is configured to compare the fused estimated weight value and the static estimated weight value.

[0022] In various embodiments, the controller is configured to receive, through the processor, one or more weight-related values ​​measured directly on the vehicle from the sensor system and to train the dynamic model using the one or more weight-related values.

[0023] In various embodiments, the controller is configured to receive, through the processor, a first longitudinal acceleration value of the vehicle, a first driving force value of the vehicle, and a first braking pressure value of the vehicle, all sensed at a first time, from the sensor system while the vehicle is being driven, a second longitudinal acceleration value of the vehicle, a second driving force value of the vehicle, and a second braking pressure value of the vehicle, all sensed at a second time, from the sensor system, wherein a speed of the vehicle is the same at the first time and at the second time, while the vehicle is being driven, and to calculate the dynamic estimated weight value of the vehicle using the dynamic model and the first longitudinal acceleration value, the first driving force value, the first braking pressure value, the second longitudinal acceleration value,of the second driving force value and the second brake pressure value, wherein the dynamic model includes a variation-based vehicle longitudinal model.,

[0024] In various embodiments, the variation-based longitudinal vehicle model is configured to use only a subset of the dynamic sensor data acquired while the vehicle speed is greater than a threshold speed and while the vehicle is traveling in a straight forward direction.

[0025] In various embodiments, the controller is configured to receive, through the processor, from the sensor system one or more external weight values ​​coupled to objects detached from and carried by the vehicle, and the dynamic model is configured to take into account the one or more external weight values.

[0026] A vehicle is provided having a body, a suspension system configured to support the body, a sensor system having one or more sensors configured to sense measurable conditions inside and outside the body, a display device configured to generate a visual display, a controller operatively coupled to the sensor system and the display device, the controller configured to receive, through a processor, from the sensor system static sensor data sensed while the vehicle is stationary and coupled to a trailer and dynamic sensor data while the vehicle is being driven and not coupled to the trailer, the static sensor data including a deflection value associated with the suspension system while the vehicle is stationary,estimate one or more weight-related parameter values ​​using a suspension model and the static sensor data, determine deterioration of the suspension system over time using a suspension sag estimation model and the dynamic sensor data, update the suspension model to account for the deterioration of the suspension system, and display the one or more weight-related parameter values ​​on the visual display, wherein the weight-related parameter values ​​include a value for the gross vehicle weight, a value for the trailer tongue weight, and a value for the recovery of the front axle loads or rear axle loads.

[0027] In various embodiments, the vehicle control unit is configured to estimate, by the processor, a static estimated weight value of the vehicle using the suspension model, estimate a dynamic estimated weight value of the vehicle using a dynamic model and the dynamic sensor data, wherein the dynamic sensor data includes an acceleration value of the vehicle, and determine a fused estimated weight value of the vehicle by fusing the static estimated weight value and the dynamic estimated weight value. The suspension sag estimation model is configured to compare the fused estimated weight value and the static estimated weight value.

[0028] The exemplary embodiments are described below in conjunction with the following drawings, wherein like numerals indicate like elements and wherein: Fig. 1 is a functional block diagram of a vehicle including a weight estimation system according to various embodiments; Fig. 2 is a data flow diagram showing elements of the vehicle’s system from Fig. 1 in accordance with various embodiments; Fig. Figure 3 is a flowchart of a method for estimating weight-related parameter values ​​as used by the system of the vehicle of the Fig. 1 and Fig. 2 is performed in accordance with exemplary embodiments; and Fig. 4 is a flowchart of a method for activating a dynamic vehicle model as used by the system of the vehicle of the Fig. 1 and Fig. 2 in accordance with exemplary embodiments.

[0029] Fig. 1 shows a vehicle 10 according to an exemplary embodiment that includes a weight estimation system 100. Generally, the system 100 estimates one or more weight-related parameter values ​​coupled to the vehicle 10 in a manner that compensates for suspension degradation over time. In various embodiments, the estimated weight-related parameter values ​​include a gross vehicle weight value, a trailer tongue weight value, and / or a front axle load recovery value or rear axle load recovery value. As used herein, the gross vehicle weight value, also referred to as gross vehicle weight, refers to a measurement of the total weight of the vehicle 10 as well as external objects separate from the vehicle 10 and carried by the vehicle 10, such as passengers and cargo.The tongue load value, also referred to as the vertical load, refers to the static force exerted by a coupling device (e.g., the tongue load) of the trailer 13 on the vehicle 10 (e.g., the tow ball). The value used here for the recovery of the front axle loads or rear axle loads refers to the percentage recovery of a reduction in the weight distributed on a front axle of the vehicle 10 caused by a load shift due to a weight exerted by the trailer 13 on the coupling device of the vehicle 10 (i.e., the tongue load value).

[0030] In certain embodiments, vehicle 10 comprises an automobile. In various embodiments, vehicle 10 may be any of a number of different types of automobiles, such as a sedan, a station wagon, a truck, or a sport utility vehicle (SUV), and may be two-wheel drive (2WD) (i.e., rear-wheel drive or front-wheel drive), four-wheel drive (4WD), or all-wheel drive (AWD), and / or various other types of vehicles in certain embodiments. Furthermore, in various embodiments, vehicle 10 may also include any number of other types of mobile platforms.

[0031] In various embodiments, the vehicle 10 may be coupled to a trailer 13 capable of transporting a load. As can be appreciated, the trailer 13 may be any type of towable application with one or more wheels and is not limited to a particular embodiment. The vehicle 10 is configured to be coupled and connected to the trailer 13 via a connecting device 11 and is configured to tow the trailer 13. In various embodiments, the connecting device 11 includes a trailer hitch.In various other embodiments, the connecting device 11 includes one or more other types of systems, such as a gooseneck for a semi-trailer, and so on. In various embodiments, the connecting device 11 further includes a wiring harness configured to transmit power and / or communication signals to and from components of the trailer 13.

[0032] As in Fig. 1, the example vehicle 10 generally includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is disposed on the chassis 12 and substantially encloses components of the vehicle 10. The body 14 and the chassis 12 may together form a frame. Wheels 16-18 are each rotatably coupled to the chassis 12 near a corner of the body 14.

[0033] The vehicle 10 further comprises a drive system 20, the transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one control unit 34, a suspension system 50, and a display system 52. The drive system 20 comprises an engine 21, for example a gasoline- or diesel-powered internal combustion engine, an electric motor, or a hybrid engine.

[0034] The transmission system 22 is configured to transmit the power of the drive system 20 to the wheels 16-18 according to selectable speed ratios based on a range selection received from a human-machine interface, for example, a range selection device (e.g., gear selector, shift lever, PRNDL, and so on) configured to select an operating range (e.g., a gear ratio).

[0035] The steering system 24 influences the position of the wheels 16-18. Although a steering wheel 24a is shown for illustrative purposes, the steering system 24 may not include a steering wheel in some embodiments contemplated by the present disclosure.

[0036] The braking system 26 is configured to apply braking torque to the wheels 16-18. In various embodiments, the braking system 26 may include friction brakes, cable brakes, a regenerative braking system such as an electric machine, and / or other suitable braking systems. In one example, the vehicle 10 includes a brake pedal 31 that can be moved by the operator from a released position to a depressed position to activate the braking system 26 and apply the braking torque.

[0037] The suspension system 50 connects the frame to the wheels 16 and 18, for example, via axles, and is configured to absorb shocks and vibrations from the road and minimize the transmission of these forces to the vehicle body and passengers. The suspension system 50 may include various components, including, but not limited to, springs (e.g., coil springs, leaf springs, torsion bars, and so on), dampers (i.e., shock absorbers), stabilizer bars, control arms, bushings, and so on. Typically, the suspension system 50 is coupled to the frame at four locations, each at a corner of the frame and / or one of the wheels 16 and 18.

[0038] The display system 52 may include various components, including a screen configured to display a visual display. In various embodiments, the display system 52 may include a human-machine interface (HMI), such as a touchscreen display.

[0039] The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external environment, the internal environment, and / or a status or condition of a corresponding component of the vehicle 10 and communicate such condition and / or status to other systems of the vehicle 10, such as the controller 34. It is understood that the vehicle 10 may include any number of sensing devices 40a-40n. The sensor devices 40a-40n may include, but are not limited to, inertial measurement sensors, vehicle speed sensors, weight sensors, torque sensors, wheel speed sensors, engine torque sensors, suspension deflection sensors, ultrasonic sensors, laser displacement sensors, infrared light sensors, camera vision sensors, steering wheel angle sensors, brake pressure sensors, pedal position sensors, strain gauges, seat sensors, fuel level sensors, and so on.

[0040] The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle functions, such as, but not limited to, the drive system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle 10 may also include interior and / or exterior vehicle features that are Fig. 1 are not shown, such as various doors, a trunk, and cabin features such as air, music, lighting, touchscreen display components, and the like.

[0041] The data storage device 32 stores data for controlling the vehicle 10 and / or its systems and components. As can be appreciated, the data storage device 32 may be part of the controller 34, separate from the controller 34, or part of the controller 34 and part of a separate system. The data storage device 32 may be any suitable type of storage device, including various types of random access memory and / or other storage devices. In an exemplary embodiment, the data storage device 32 includes a program product from which a computer-readable storage device can receive a program that performs one or more embodiments of one or more processes of the present disclosure, such as the steps of the process described below in connection with the Fig. 3-7. In another exemplary embodiment, the program product may be stored and / or otherwise accessed directly on the storage device and / or one or more other disks and / or other storage devices.

[0042] The control unit 34 includes at least one processor 44, a communications bus 45, and a computer-readable storage device or medium 46. The processor 44 performs the computation and control functions of the control unit 34. The processor 44 may be any custom or off-the-shelf processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the control unit 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer-readable storage devices or media 46 may include volatile and non-volatile memory in read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM).For example, KAM is a persistent or non-volatile memory that can be used to store various operating variables while the processor 44 is off. The computer-readable storage device(s) 46 can be implemented using any number of known storage devices such as PROMs (programmable read-only memory), EPROMs (electrically erasable PROMs), EEPROMs (electrically erasable PROMs), flash memory, or other electrical, magnetic, optical, or combination storage devices capable of storing data, some of which are executable instructions used by the controller 34 in controlling the vehicle 10. The bus 45 is used to transfer programs, data, status, and other information or signals between the various components of the vehicle 10.Bus 45 may be any suitable physical or logical means for interconnecting computer systems and components. These include, but are not limited to, direct, hard-wired connections, fiber optics, infrared, and wireless bus technologies.

[0043] The instructions may comprise one or more separate programs, each containing an ordered collection of executable instructions for implementing logical functions. When executed by processor 44, the instructions receive and process signals from sensor system 28, perform logic, calculations, methods, and / or algorithms, and generate data based on the logic, calculations, methods, and / or algorithms. Although in Fig. 1 only one controller 34 is shown, embodiments of the vehicle 10 may include any number of controllers 34 communicating via any suitable communication medium or combination of communication media that cooperate to process the sensor signals, perform logic, calculations, methods and / or algorithms, and generate data.

[0044] It is obvious that the control unit 34 is Fig. 1. For example, the controller 34 may be coupled to or otherwise utilize one or more remote computer systems and / or other control systems, for example, as part of one or more of the above-mentioned vehicle devices and systems. While this exemplary embodiment is described in the context of a fully operational computer system, those skilled in the art will recognize that the mechanisms of the present disclosure may be distributed as a program product having one or more types of non-transitory, computer-readable, signal-bearing media used to store the program and its instructions and to facilitate its distribution, such asa non-transitory, computer-readable medium carrying the program and including computer instructions stored thereon for causing a computer processor (such as processor 44) to perform and execute the program. Such a program product may take a variety of forms, and the present disclosure applies equally regardless of the particular type of computer-readable signal-bearing medium used to effect distribution. Examples of signal-bearing media include writable media such as floppy disks, hard disks, memory cards, and optical discs, as well as transmission media such as digital and analog communication links. In certain embodiments, cloud-based storage and / or other technologies may also be used. It will also be appreciated that the computer system of controller 34 may otherwise differ from that shown in FIG. Fig. 1, for example, in that the computer system of the controller 34 may be coupled to or otherwise utilize one or more remote computer systems and / or other control systems.

[0045] With reference to Fig. 2 and with continued reference to Fig. 1 illustrates a data flow diagram of the elements of the weight estimation system 100 of Fig. 1 in accordance with various embodiments. As can be appreciated, various embodiments of the system 100 according to the present disclosure may include any number of modules embedded within the controller 34, which may be combined and / or further subdivided to implement the systems and methods described herein in a similar manner. Furthermore, inputs to the system 100 may be received from other control modules (not shown) coupled to the vehicle 10 and / or determined / modeled by other sub-modules (not shown) within the controller 34. Furthermore, the inputs may also be subjected to preprocessing, such as subsampling, noise reduction, normalization, feature extraction, missing data reduction, and the like.In various embodiments, the system 100 includes a static estimation module 110, a dynamic estimation module 112, a fusion module 120, a suspension sag module 122, and a saturation module 124.

[0046] In various embodiments, the static estimation module 110 receives as input sensor data 130 generated by the sensor system 28. The sensor data 130 includes various data indicative of observable conditions of environments inside and / or outside the vehicle 10. In various embodiments, the sensor data 130 includes static sensor data collected while the vehicle 10 is stationary. In various embodiments, the sensor data 130 includes deflection measurements associated with the suspension system 50.

[0047] The static estimation module 110 estimates weight-related parameter values ​​for the vehicle 10 based on the sensor data 130. In various embodiments, the static estimation module 110 includes a suspension model configured to estimate weight-related parameter values ​​based on deflection measurements associated with the suspension system 50. In various embodiments, the estimated weight-related parameter values ​​include a value for the gross vehicle weight, a value for the tongue weight of the trailer 13, and a value for the recovery of the front axle loads or rear axle loads. The static estimation module 110 generates static data 132 containing various data indicative of the estimated weight-related parameter values.In various embodiments, the static estimation module 110 transmits the static data 132 to the display device of the display system 52 so that the estimated weight-related parameter values ​​are displayed on the visual display.

[0048] In various embodiments, the dynamic estimation module 112 receives as input the sensor data 130 generated by the sensor system 28. In various embodiments, the sensor data 130 includes dynamic sensor data collected while the vehicle 10 was being driven. In various embodiments, the sensor data 130 includes longitudinal acceleration measurements, drive force measurements, and brake pressure measurements.

[0049] The dynamic estimation module 112 performs estimates of weight-related parameter values ​​coupled to the vehicle 10 based on the sensor data 130. In various embodiments, the dynamic estimation module 112 includes a vehicle model 114, various conditions and enablers 116, and a variation-based vehicle mass estimator 118. The vehicle model 114 may include a longitudinal vehicle model, a transverse vehicle model, and / or a longitudinal and transverse vehicle model to perform the dynamic weight-related parameter estimates. In various embodiments, the dynamic estimation module 112 is configured to estimate weight-related parameter values ​​based on longitudinal acceleration measurements, drive force measurements, and brake pressure measurements. In various embodiments, the estimated weight-related parameter values ​​include a value for the gross vehicle weight.The dynamic estimation module 112 generates dynamic data 134 containing various data indicating the estimated weight-related parameter values.

[0050] In various embodiments, the fusion module 120 receives as input the static data 132 generated by the static estimation module 110 and the dynamic data 134 generated by the dynamic estimation module 112. The fusion module 120 performs a fusion process to fuse the static data 132 and the dynamic data 134. The fusion module 120 may use various fusion methods, including, but not limited to, simple value averaging, Bayesian fusion, time-based fusion, and the Dempster-Shafter proof theory. The fusion module 120 generates fusion data 136 that includes various data indicative of the fused static data 132 and dynamic data 134.

[0051] In various embodiments, the suspension sag module 122 receives as input the static data 132 generated by the static estimation module 110 and the fusion data 136 generated by the fusion module 120. The suspension sag module 122 performs an analysis of the static data 132 and the fusion data 136 to estimate degradation of the suspension system 50. In various embodiments, the suspension sag module 122 may determine a difference in suspension deflection between the static data 132 and the fusion data 136 and attribute some or all of the deflection difference to suspension degradation.The suspension deflection module 122 generates suspension deflection data 138 that includes various data indicative of the estimated deterioration of the suspension system 50, for example, an amount of deflection coupled with deterioration over time.

[0052] In various embodiments, the saturation module 124 receives as input the suspension sag data 138 generated by the suspension sag module 122. The saturation module 124 is configured to determine if and when the static estimation module 110 should be updated using the suspension sag data 138. In various embodiments, the saturation module 124 may periodically update the static estimation module 110 after the vehicle 10 has traveled a predetermined distance (e.g., every 2,000 km). In various embodiments, the saturation module 124 may periodically update the static estimation module 110 after the suspension sag module 122 has estimated a predetermined amount of deterioration (e.g., every 1 mm).The saturation module 124 generates saturation data 140 containing various data indicating that the static estimation module 110 should be updated, as well as the associated deterioration values ​​contained in the suspension sag data 138. If it is determined that the static estimation module 110 should be updated, the saturation module 124 may transmit the saturation data 140 to the static estimation module 110.

[0053] With reference to Fig. 3 and with continued reference to Fig. 1-2, a flowchart illustrates a method 200 for estimating various weight-related parameter values ​​for the vehicle 10, as performed by the weight estimation system 100 according to exemplary embodiments. As will be appreciated in view of the disclosure, the order of operations within the method 200 is not limited to that described in Fig. 3, but may be performed in one or more different orders as applicable and consistent with the present disclosure. In various embodiments, method 200 may be scheduled to run based on one or more predetermined events and / or may run continuously during operation of vehicle 10.

[0054] In one example, method 200 may begin at 210. At 212, method 200 includes receiving static sensor data collected by sensor system 28 of vehicle 10 while the vehicle 10 is stationary both before and after coupling trailer 13, and dynamic sensor data while the vehicle 10 is driven both with and without the trailer 13 coupled. The static sensor data includes a deflection value coupled to a suspension system (e.g., suspension system 50) of vehicle 10. The deflection value may include a measured dimension indicative of a difference in the position of a component of suspension system 50 when loaded and unloaded. For example, a spring may compress a certain amount when coupling trailer 13 to vehicle 10.

[0055] At 214, the method 200 includes estimating one or more weight-related parameter values ​​with a processor (e.g., processor 44) operatively coupled to the sensor system 28 while the vehicle 10 is stationary using a suspension model and the static sensor data. For example, a comparison of the data before and after the trailer 13 is coupled to the vehicle 10 may be used to estimate the tongue weight of the trailer. At 216, the method 200 includes determining the deterioration of the suspension system 50 over time with the processor using a suspension sag estimation model and the dynamic sensor data. In various embodiments, the dynamic sensor data obtained while the trailer 13 is coupled to the vehicle 10 may be used to estimate the no-stick load.In various embodiments, the dynamic sensor data obtained while the trailer 13 is not coupled to the vehicle 10 may be used along with the fusion model to update the suspension model. At 218, the method 200 includes updating the suspension model with the processor to account for degradation of the suspension system 50. At 220, the method 200 includes displaying the one or more weight-related parameter values ​​on a display device (e.g., the display device of the display system 52) of the vehicle 10. At 222, the method 200 may end.

[0056] With reference to Fig. 4 and with continued reference to the Fig. 1-3 shows a flowchart of a method 300 for filtering sensor data, for example with the conditions and enabling features 116 of Fig. 2, for the vehicle 10, as performed by the weight estimation system 100 according to exemplary embodiments. During performance of the method 300, the system 100 determines whether the sensor data was collected under certain conditions coupled with various preprogrammed filter criteria. At 310, the method 300 includes determining whether the speed of the vehicle 10 was greater than a threshold speed when the sensor data was collected. The speed threshold may be, for example, 10 m / s, and the method 300 may exclude any sensor data collected while the vehicle 10 was traveling at 10 m / s or less.

[0057] If it is determined at 310 that the speed was below the speed threshold, the method 300 continues to monitor the speed of the vehicle 10. If the speed of the vehicle 10 was above the speed threshold at 310, the method 300 includes determining an initial weight of the vehicle at 312. In various embodiments, the method 300 may compensate for the weight of external objects (e.g., the contents of a fuel tank) at 314. At 316, the method 300 includes determining whether a trailer (e.g., trailer 13) is coupled to the vehicle 10. If the trailer is coupled to the vehicle 10 at 316, the method 300 returns to monitoring the speed of the vehicle 10 at 310.

[0058] If the trailer is not coupled to the vehicle 10 (316), the method 300 includes determining whether the vehicle 10 is traveling in a straight forward direction and accelerating (i.e., not decelerating). If the vehicle 10 is not traveling in a straight forward direction and accelerating (318), the method 300 returns to monitoring the speed of the vehicle 10 (310).

[0059] When the vehicle 10 is traveling in a straight forward direction and accelerating at 318, the method 300 includes storing a plurality of collected data points at 319 based on the sensor data obtained from the sensor system 28. At 320, the method 300 includes determining whether a set of data points stored at 319 exceeds a minimum data point threshold. If the data points of the sensor data do not exceed the minimum data point threshold at 320, the method 300 returns to monitoring the speed of the vehicle 10 at 310 and continues collecting sensor data and monitoring the set of data points therefrom. If the data points of the sensor data exceed the minimum data point threshold at 320, the method 300 includes performing a variation-based vehicle weight estimation, for example, with the variation-based vehicle mass estimator 118 of Fig. 2.

[0060] In particular, the method 300 may include fewer or more filter criteria. In various embodiments, the method 300 includes filtering the sensor data based on a tilt angle of the vehicle 10, a slope of the surface on which the vehicle 10 is traveling, a status of the sensor system 28, and so on.

[0061] In various embodiments, the estimated weight-related parameter values ​​may be used by various autonomous and semi-autonomous driving systems of the vehicle 10.

[0062] As a non-limiting example, the vehicle model 114 may include collecting dynamic sensor data at two separate times while the vehicle 10 is traveling in a straight forward direction and at the same speed. The dynamic sensor data collected at these two times includes measurements of longitudinal acceleration, drive force, and brake pressure. The vehicle model 114 may implement a model based on longitudinal variations to determine the value for the gross vehicle weight, for example, using the following equation (1). M(Δax)=ΔFdrive−KbΔPb, where, Δax=axA−axB ΔPb=PbA−PbB where M is the total weight of the vehicle, a x the vehicle's longitudinal acceleration, F drive the driving force, K b the flat-rate braking coefficient and P b the brake pressure is.

[0063] The variation-based vehicle mass estimator 118 may use the gross vehicle weight (M) determined by the longitudinal variation-based model, for example, using the following equations (2)-(5).

[0064] From the model based on longitudinal variation: ϕM^D=Z

[0065] Output signal Z: Z=ΔF={−Fb,B−Fd,AAccel→DecelFb,B+Fd,ADecel→Accel−Fd,AAccel→Coasting

[0066] Regression signal ϕ: ϕ=Δax

[0067] Estimation method (recursive least squares): x^k+1=x^k+Kk+1(Zk+1−ϕk+1x^k)Kk+1=Pkϕk+1λ+ϕk+1Pkϕk+1Pk+1=(1−Kk+1ϕk+1)pk / λ

[0068] Although the above examples refer to dynamic sensor data acquired at two different time points, the same or similar methods can be used for dynamic sensor data acquired at multiple time points.

[0069] The systems and methods described herein provide various benefits for vehicles configured to tow trailers. In particular, providing the driver with weight-related parameter values ​​can promote the safety and ease of loading and towing. For example, providing the gross vehicle weight value promotes proper trailer loading and can be an important factor in determining vehicle operation. Another example is providing the trailer tongue weight value to keep the trailer load within a safe range. In particular, underweighting the trailer tongue can cause the trailer to sway from side to side during towing and be difficult to control.Conversely, excessive trailer tongue weight can overload the towing vehicle's rear tires and push the rear end of the vehicle around, potentially negatively impacting handling (e.g., poor steering and braking). Another example: specifying the recovery value for front or rear axle loads can promote the correct distribution of weight across the axles. Furthermore, accurately estimating weight-related parameter values ​​can promote the functionality of various autonomous and semi-autonomous driving systems and / or optimize power distribution in specific vehicles.

Claims

[1] Method (200) for estimating weight-related parameter values ​​of a vehicle (10), the method comprising: Receiving (212) from a sensor system (28) of the vehicle (10), static sensor data collected while the vehicle (10) is stationary and coupled to a trailer (13) and dynamic sensor data while the vehicle (10) is being driven and not coupled to the trailer (13), the static sensor data including a deflection value associated with a suspension system (50) of the vehicle (10); Estimating (214), with a processor (44) operatively coupled to the sensor system (28) and while the vehicle (10) is stationary, one or more weight-related parameter values ​​using a suspension model and the static sensor data; Determining (216), with the processor (44), degradation of the suspension system (50) over time using a model for estimating suspension sag and the dynamic sensor data; Updating (218), with the processor (44), the suspension model to account for degradation of the suspension system (50); Displaying (220) the one or more weight-related parameter values ​​on a display device of the vehicle (10); and Estimating, with the processor (44), a static estimated weight value of the vehicle (10) using the suspension model; Estimating, with the processor (44), a dynamic estimated weight value of the vehicle (10) using a dynamic model and the dynamic sensor data, wherein the dynamic sensor data includes an acceleration value of the vehicle (10); and Determining, with the processor (44), a fused estimated weight value of the vehicle (10) by fusing the static estimated weight value and the dynamic estimated weight value, wherein the step of determining the deterioration of the suspension system (50) over time using the suspension sag estimation model comprises comparing the fused estimated weight value and the static estimated weight value with the processor (44). [2] The method (200) of claim 1, wherein the one or more weight-related parameter values ​​comprise a value for the total weight of the vehicle (10), a value for the tongue weight of the trailer, and a value for the restoration of the front axle loads or rear axle loads. [3] The method (200) of claim 1, further comprising: Receiving, from the sensor system (28), one or more weight-related values ​​that are detected directly on the vehicle (10); and Training, with the processor (44), the dynamic model using the one or more weight-related values. [4] The method (200) of claim 1, further comprising: Receiving, from the sensor system (28), a first longitudinal acceleration value of the vehicle (10), a first driving force value of the vehicle (10), and a first braking pressure value of the vehicle (10), all of which are sensed at a first time while the vehicle (10) is being driven; Receiving, from the sensor system (28), a second longitudinal acceleration value of the vehicle (10), a second driving force value of the vehicle (10), and a second braking pressure value of the vehicle (10), all detected at a second time, wherein a speed of the vehicle (10) is the same at the first time and the second time while the vehicle (10) is being driven; and Estimating, with the processor (44), the dynamic estimated weight value of the vehicle (10) using the dynamic model and the first longitudinal acceleration value, the first driving force value, the first brake pressure value, the second longitudinal acceleration value, the second driving force value and the second brake pressure value, wherein the dynamic model comprises a variation-based vehicle longitudinal model, wherein the variation-based longitudinal vehicle model uses a subset of the dynamic sensor data obtained while the speed of the vehicle (10) is greater than a threshold speed and while the vehicle (10) is traveling in a straight forward direction. [5] A system (100) for estimating weight-related parameter values ​​of a vehicle (10), the system (100) comprising: a sensor system (28) having one or more sensors (40a-40n) configured to detect measurable conditions inside and outside the vehicle (10); a display device configured to generate a visual display; and a controller (34) operatively coupled to the sensor system (28) and the display device, the controller (34) being configured by a processor (44): receive from the sensor system (28) static sensor data collected while the vehicle (10) is stationary and coupled to a trailer (13) and dynamic sensor data while the vehicle (10) is being driven and not coupled to the trailer (13), the static sensor data comprising a deflection value associated with a suspension system (50) of the vehicle (10); estimate one or more weight-related parameter values ​​using a suspension model and the static sensor data while the vehicle (10) is stationary; determine deterioration of the suspension system (50) over time using a model for estimating suspension sag and dynamic sensor data; update the suspension model to account for the deterioration of the suspension system (50); and display the one or more weight-related parameter values ​​on the visual display; and wherein the control unit (34) is configured by the processor (44): to estimate a static estimated weight value for the weight of the vehicle (10) from the suspension model; to estimate a dynamic estimated weight value of the vehicle (10) using a dynamic model and the dynamic sensor data, wherein the dynamic sensor data includes an acceleration value of the vehicle (10); and to determine a fused estimated weight value of the vehicle (10) by fusing the static estimated weight value and the dynamic estimated weight value, wherein the suspension sag estimation model is configured to compare the fused estimated weight value and the static estimated weight value. [6] The system (100) of claim 5, wherein the one or more weight-related parameter values ​​comprise a value for the total weight of the vehicle (10), a value for the tongue weight of the trailer (13), and a value for the restoration of the front axle loads or rear axle loads. [7] The system (100) of claim 5, wherein the controller (34) is configured by the processor (44): to receive one or more weight-related values ​​measured directly on the vehicle (10) from the sensor system (28); and to train the dynamic model using the one or more weight-related values. [8] The system (100) of claim 5, wherein the controller (34) is configured by the processor (44): receive from the sensor system (28) a first longitudinal acceleration value of the vehicle (10), a first driving force value of the vehicle (10) and a first braking pressure value of the vehicle (10), all of which are detected at a first time while the vehicle (10) is being driven; to receive a second longitudinal acceleration value of the vehicle (10), a second driving force value of the vehicle (10), and a second braking pressure value of the vehicle (10), all detected at a second time, from the sensor system (28), wherein a speed of the vehicle (10) is the same at the first time and at the second time while the vehicle (10) is being driven; and to estimate the dynamic estimated weight value of the vehicle (10) using the dynamic model and the first longitudinal acceleration value, the first driving force value, the first brake pressure value, the second longitudinal acceleration value, the second driving force value and the second brake pressure value, wherein the dynamic model comprises a variation-based vehicle longitudinal model, wherein the variation-based vehicle longitudinal model is configured to use a subset of the dynamic sensor data obtained while the speed of the vehicle (10) is greater than a threshold speed and while the vehicle (10) is traveling in a straight forward direction.

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