CN116022152A - Dynamic gradient estimation method and device - Google Patents
Dynamic gradient estimation method and device Download PDFInfo
- Publication number
- CN116022152A CN116022152A CN202310138779.2A CN202310138779A CN116022152A CN 116022152 A CN116022152 A CN 116022152A CN 202310138779 A CN202310138779 A CN 202310138779A CN 116022152 A CN116022152 A CN 116022152A
- Authority
- CN
- China
- Prior art keywords
- gradient
- current
- vehicle
- credibility
- initial value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000001133 acceleration Effects 0.000 claims abstract description 36
- 230000008859 change Effects 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 5
- 238000010276 construction Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
Landscapes
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The application provides a dynamic gradient estimation method and a device, wherein the method comprises the following steps: acquiring a vehicle body speed signal of a target vehicle; estimating a current vehicle body acceleration of the target vehicle according to the vehicle body speed signal; calculating a current gradient initial value according to the current vehicle body acceleration; calculating the credibility of the initial value of the current gradient; and calculating the current dynamic gradient according to the reliability and the current gradient initial value. Therefore, the method and the device can estimate the gradient of the vehicle under the full driving condition, and have lower requirement on vehicle mass estimation and good estimation effect.
Description
Technical Field
The application relates to the technical field of whole vehicle control, in particular to a dynamic gradient estimation method and a dynamic gradient estimation device.
Background
In order to realize the best performance of the automobile, the decision of the gear of the automobile has important significance in grasping the parameters of the automobile and road conditions, in particular the quality of the automobile and the gradient of the road. Existing slope estimation methods generally calculate the magnitude of the slope by subtracting the vehicle's absolute acceleration from the vehicle's acceleration to calculate the gravitational acceleration component produced by the current ramp. In practice, the existing method can only estimate the gradient with certain precision when the vehicle is in a stable state (namely, acceleration is stable and the vehicle body does not shake), and has unsatisfactory estimation effects under the working conditions of acceleration, starting, braking, slipping and the like of the vehicle. It can be seen that the existing method cannot estimate the gradient of the vehicle in the low speed section and at rest, and the requirement for vehicle mass estimation is high.
Disclosure of Invention
An object of the embodiment of the application is to provide a dynamic gradient estimation method and a dynamic gradient estimation device, which can estimate the gradient of a vehicle under the full driving condition, and have low requirements on vehicle mass estimation and good estimation effect.
An embodiment of the present application provides a dynamic slope estimation method, including:
acquiring a vehicle body speed signal of a target vehicle;
estimating a current vehicle body acceleration of the target vehicle according to the vehicle body speed signal;
calculating a current gradient initial value according to the current vehicle body acceleration;
calculating the credibility of the initial value of the current gradient;
and calculating the current dynamic gradient according to the reliability and the current gradient initial value.
In the implementation process, the method can obtain the vehicle body speed signal of the target vehicle preferentially; then, estimating the current vehicle body acceleration of the target vehicle according to the vehicle body speed signal; calculating the initial value of the current gradient according to the current vehicle body acceleration; then, the credibility of the initial value of the current gradient is calculated; and finally, calculating the current dynamic gradient according to the reliability and the current gradient initial value. Therefore, the method can estimate the gradient of the vehicle under the full driving condition, has lower requirement on vehicle mass estimation and has good estimation effect.
Further, the acquiring the vehicle body speed signal of the target vehicle includes:
determining drive configuration information of the target vehicle;
and acquiring a vehicle speed signal of the vehicle body according to the driving configuration information.
Further, the driving configuration information is two-drive configuration or four-drive configuration;
when the driving configuration information is the two-wheel configuration, the vehicle speed signal is a non-driving wheel speed, and when the driving configuration information is the four-wheel configuration, the vehicle speed signal is the reference vehicle speed of the target vehicle.
Further, the estimating the current vehicle body acceleration of the target vehicle according to the vehicle body speed signal includes:
constructing a state space equation according to the vehicle speed signal of the vehicle body;
constructing a standard Kalman filter based on the state space equation;
and estimating the current body acceleration of the target vehicle according to the standard Kalman filter.
Further, the calculating the credibility of the initial value of the current gradient includes:
calculating the gradient change rate credibility, the slip credibility, the sudden emergency subtracting credibility and the distance credibility according to the current gradient initial value;
and calculating the credibility of the initial value of the current gradient according to the credibility of the gradient change rate, the credibility of skidding, the credibility of sudden emergency subtraction and the credibility of the distance.
Further, the calculating the current dynamic gradient according to the reliability and the current gradient initial value includes:
acquiring a historical gradient signal at the previous moment, and calculating a historical gradient signal weighting coefficient according to the credibility;
performing weighted average processing according to the current gradient initial value, the historical gradient signal, the credibility and the historical gradient signal weighting coefficient to obtain a current dynamic gradient; the reliability is a current gradient initial value weighting coefficient.
A second aspect of the embodiments of the present application provides a dynamic gradient estimation device, including:
an acquisition unit configured to acquire a vehicle body speed signal of a target vehicle;
an estimation unit configured to estimate a current vehicle body acceleration of the target vehicle based on the vehicle body speed signal;
a first calculation unit for calculating a current gradient initial value according to the current vehicle body acceleration;
the second calculating unit is used for calculating the credibility of the initial value of the current gradient;
and the third calculation unit is used for calculating the current dynamic gradient according to the credibility and the current gradient initial value.
In the implementation process, the dynamic gradient estimation device can acquire a vehicle body speed signal of the target vehicle through the acquisition unit; estimating, by an estimation unit, a current vehicle body acceleration of the target vehicle from the vehicle body speed signal; calculating a current gradient initial value according to the current vehicle body acceleration by a first calculation unit; calculating the credibility of the initial value of the current gradient through a second calculating unit; and then the third calculating unit is used for calculating the current dynamic gradient according to the reliability and the current gradient initial value. Therefore, the device can estimate the gradient of the vehicle under the full driving condition, has lower requirement on vehicle mass estimation and has good estimation effect.
Further, the acquisition unit includes:
a determination subunit configured to determine drive configuration information of the target vehicle;
and the first acquisition subunit is used for acquiring a vehicle speed signal of the vehicle body according to the driving configuration information.
Further, the driving configuration information is two-drive configuration or four-drive configuration;
when the driving configuration information is the two-wheel configuration, the vehicle speed signal is a non-driving wheel speed, and when the driving configuration information is the four-wheel configuration, the vehicle speed signal is the reference vehicle speed of the target vehicle.
Further, the estimation unit includes:
the construction subunit is used for constructing a state space equation according to the vehicle speed signal of the vehicle body;
the construction subunit is further configured to construct a standard kalman filter based on the state space equation;
and the estimation subunit is used for estimating the current vehicle body acceleration of the target vehicle according to the standard Kalman filter.
Further, the second calculation unit includes:
the first calculating subunit is used for calculating the gradient change rate credibility, the slip credibility, the sudden emergency subtracting credibility and the distance credibility according to the current gradient initial value;
and the second calculating subunit is used for calculating the credibility of the initial value of the current gradient according to the credibility of the gradient change rate, the credibility of skidding, the credibility of sudden emergency subtraction and the credibility of the distance.
Further, the third calculation unit includes:
the second acquisition subunit is used for acquiring the historical gradient signal at the previous moment and calculating the weighting coefficient of the historical gradient signal according to the credibility;
the weighted average subunit is used for carrying out weighted average processing according to the current gradient initial value, the historical gradient signal, the credibility and the historical gradient signal weighting coefficient to obtain a current dynamic gradient; the reliability is a current gradient initial value weighting coefficient.
A third aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to perform the dynamic slope estimation method according to any one of the first aspect of the embodiments of the present application.
A fourth aspect of the embodiments of the present application provides a computer readable storage medium storing computer program instructions which, when read and executed by a processor, perform the method for estimating dynamic gradient according to any of the first aspect of the embodiments of the present application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a dynamic slope estimation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a dynamic slope estimation device according to an embodiment of the present application;
fig. 3 is a schematic diagram of a reliability summary example flow chart of a current slope initial value according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1
Referring to fig. 1, fig. 1 is a flowchart of a dynamic slope estimation method according to the present embodiment. The dynamic gradient estimation method comprises the following steps:
s101, determining driving configuration information of a target vehicle.
In this embodiment, the driving configuration information is two-drive configuration or four-drive configuration.
In this embodiment, when the drive arrangement information is the two-drive arrangement, the vehicle body speed signal is the non-drive wheel speed, and when the drive arrangement information is the four-drive arrangement, the vehicle body speed signal is the reference vehicle speed of the target vehicle.
S102, acquiring a vehicle speed signal of the vehicle body according to the driving configuration information.
S103, constructing a state space equation according to the vehicle speed signal of the vehicle body.
S104, constructing a standard Kalman filter based on a state space equation.
S105, estimating the current body acceleration of the target vehicle according to a standard Kalman filter.
In this embodiment, the method approximates the vehicle speed by using the average value of the non-driving wheel speeds for a two-wheel drive configuration vehicle. For a four-wheel drive configuration vehicle, the estimated reference vehicle speed is employed as the approximated vehicle body speed.
Specifically, the method can perform third-order Taylor expansion on the vehicle speed signal of the vehicle body:
then, after first-order and second-order derivation, discretization is performed to obtain the following state space equation:
it can be seen that the current estimated vehicle body acceleration can be obtained by constructing a standard kalman filter based on the above state space equation.
S106, calculating the initial value of the current gradient according to the current vehicle body acceleration.
In this embodiment, the method adopts a kinematic equation to estimate the initial value of the current gradient, and specifically includes the following steps:
wherein,,
for vehicle body acceleration, a x And acquiring absolute acceleration by adopting a longitudinal acceleration sensor, wherein qsin theta is the longitudinal component of the gravity acceleration, and theta is the initial value of the gradient estimated currently.
And S107, calculating the gradient change rate credibility, the slip credibility, the sudden emergency subtracting credibility and the distance credibility according to the initial value of the current gradient.
In this embodiment, the reliability of the gradient change rate may be reduced according to the change rate when the original estimated value is changed in a time domain rapid oscillation. The reliability is obtained through looking up a table according to the change rate of the current theta, and the specific parameters are determined according to the real vehicle test.
In this embodiment, the slip reliability may be a flag bit (Bool amount) of whether the current vehicle is slipping or not according to the current vehicle output by the vehicle speed calculation module, where the vehicle is slipping with a lower reliability T1, and the reliability gradually rises from T1 to 1 after slipping is completed. The value of T1 and the rate at which it rises are determined by real vehicle testing.
In this embodiment, the sudden-acceleration-deceleration reliability may be the acquisition of depth signals of the current vehicle accelerator and brake pedal from sensors. And the rate of change of both is calculated therefrom. When one of the current change rates exceeds A1 (corresponding to the accelerator) and A2 (corresponding to the brake), the driver is considered to be performing rapid acceleration and rapid deceleration operations, and at this time, the driver adopts a lower reliability T2, and the travel speed gradually rises from T2 to 1 after the rapid acceleration and deceleration is finished. The value of T2 and the rate at which it rises are determined by real vehicle testing.
In this embodiment, the distance reliability may be that when the wheel speeds of the vehicles are all 0, the vehicles are considered to be stationary, and at this time, the distance reliability is 1. After the vehicle starts to run, a lower reliability T3 is adopted, and the T3 to 1 are gradually increased according to the running distance. The value of T3 and the rate at which it rises are determined by real vehicle testing.
In this embodiment, the reliability of the current gradient initial value is the product of the above four kinds of reliability.
Referring to fig. 3, fig. 3 is a schematic flow chart showing an example of reliability summary of the initial value of the current gradient in the method.
S108, calculating the reliability of the initial value of the current gradient according to the reliability of the gradient change rate, the reliability of skidding, the reliability of sudden emergency subtraction and the reliability of the distance.
S109, acquiring a historical gradient signal at the last moment, and calculating a historical gradient signal weighting coefficient according to the reliability.
In this embodiment of the present application, the calculation formula of the weighting coefficient of the historical gradient signal is: historical grade signal weighting coefficient = 1-confidence.
And S110, carrying out weighted average processing according to the current gradient initial value, the historical gradient signal, the credibility and the weighting coefficient of the historical gradient signal to obtain the current dynamic gradient.
In this embodiment of the present application, the reliability is a current slope initial value weighting coefficient.
In this embodiment, the gradient after the fusion correction is weighted and averaged according to the reliability by the current initial gradient value and the gradient value at the previous time.
In the embodiment, the method can be applied to the scenes of hill assistance, anti-slip, ICV field berthing torque request and the like, so that smoothness and consistency of hill traveling can be improved.
In this embodiment, the method is different from a general gradient estimation algorithm. Specifically, the method can output gradient signals with certain precision under the full-driving working condition, thereby meeting the functional application requirements.
In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, which is not limited in this embodiment.
In this embodiment, the execution body of the method may be an intelligent device such as a smart phone or a tablet computer, which is not limited in this embodiment.
Therefore, by implementing the dynamic gradient estimation method described in the embodiment, the vehicle slip state, the running estimation, the driver pedal request information and the like can be fused, so that the gradient can be continuously estimated effectively when the vehicle is rapidly accelerated and decelerated, the vehicle is rocked and the ice and snow road is slipped, further, the output gradient signal is ensured not to oscillate and jump along with the extreme working condition, and certain estimation precision is provided, so that the engineering application requirements are met.
Example 2
Referring to fig. 2, fig. 2 is a schematic structural diagram of a dynamic slope estimation device according to the present embodiment. As shown in fig. 2, the dynamic gradient estimation device includes:
an acquisition unit 210 for acquiring a vehicle body speed signal of a target vehicle;
an estimating unit 220 for estimating a current vehicle body acceleration of the target vehicle based on the vehicle body speed signal;
a first calculation unit 230 for calculating a current gradient initial value from a current vehicle body acceleration;
a second calculating unit 240 for calculating the reliability of the current gradient initial value;
the third calculating unit 250 is configured to calculate the current dynamic gradient according to the reliability and the current gradient initial value.
As an alternative embodiment, the acquisition unit 210 includes:
a determination subunit 211 for determining drive configuration information of the target vehicle;
the first acquisition subunit 212 is configured to acquire a vehicle speed signal of the vehicle body according to the driving configuration information.
In this embodiment, the driving configuration information is two-drive configuration or four-drive configuration;
when the driving configuration information is in a two-drive configuration, the vehicle speed signal is the non-driving wheel speed, and when the driving configuration information is in a four-drive configuration, the vehicle speed signal is the reference vehicle speed of the target vehicle.
As an alternative embodiment, the estimation unit 220 includes:
a construction subunit 221, configured to construct a state space equation according to the vehicle speed signal of the vehicle body;
a construction subunit 221 further configured to construct a standard kalman filter based on the state space equation;
an estimation subunit 222 is configured to estimate the current body acceleration of the target vehicle according to the standard kalman filter.
As an alternative embodiment, the second computing unit 240 includes:
a first calculating subunit 241, configured to calculate a gradient change rate reliability, a slip reliability, an urgent sudden decrease reliability, and a distance reliability according to the current gradient initial value;
the second calculating subunit 242 is configured to calculate the reliability of the current slope initial value according to the slope change rate reliability, the slip reliability, the sudden-decrease reliability, and the distance reliability.
As an alternative embodiment, the third computing unit 250 includes:
a second obtaining subunit 251, configured to obtain a historical gradient signal at a previous time, and calculate a weighting coefficient of the historical gradient signal according to the reliability;
the weighted average subunit 252 is configured to perform weighted average processing according to the current slope initial value, the historical slope signal, the reliability and the weighting coefficient of the historical slope signal, so as to obtain a current dynamic slope; the reliability is a weighting coefficient of the initial value of the current gradient.
In this embodiment, the explanation of the dynamic gradient estimation device may refer to the description in embodiment 1, and the description is not repeated in this embodiment.
Therefore, the dynamic gradient estimation device described in the embodiment can use a dynamic gradient estimation algorithm to integrate a vehicle slip state, running estimation, driver pedal request information and the like, so that the gradient is continuously estimated effectively when the vehicle is accelerated and decelerated rapidly, the vehicle shakes and the ice and snow road slips, further the output gradient signal is ensured not to oscillate and jump along with an extreme working condition, and certain estimation precision is provided, and engineering application requirements are met.
An embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute a dynamic slope estimation method in embodiment 1 of the present application.
The present embodiment provides a computer readable storage medium storing computer program instructions that, when read and executed by a processor, perform the dynamic gradient estimation method of embodiment 1 of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners as well. The apparatus embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method of dynamic slope estimation, comprising:
acquiring a vehicle body speed signal of a target vehicle;
estimating a current vehicle body acceleration of the target vehicle according to the vehicle body speed signal;
calculating a current gradient initial value according to the current vehicle body acceleration;
calculating the credibility of the initial value of the current gradient;
and calculating the current dynamic gradient according to the reliability and the current gradient initial value.
2. The dynamic gradient estimation method according to claim 1, wherein the acquiring the vehicle body speed signal of the target vehicle includes:
determining drive configuration information of the target vehicle;
and acquiring a vehicle speed signal of the vehicle body according to the driving configuration information.
3. The dynamic gradient estimation method according to claim 2, wherein the drive configuration information is a two-drive configuration or a four-drive configuration;
when the driving configuration information is the two-wheel configuration, the vehicle speed signal is a non-driving wheel speed, and when the driving configuration information is the four-wheel configuration, the vehicle speed signal is the reference vehicle speed of the target vehicle.
4. The dynamic gradient estimation method according to claim 1, wherein the estimating the current vehicle body acceleration of the target vehicle from the vehicle body speed signal includes:
constructing a state space equation according to the vehicle speed signal of the vehicle body;
constructing a standard Kalman filter based on the state space equation;
and estimating the current body acceleration of the target vehicle according to the standard Kalman filter.
5. The dynamic gradient estimation method according to claim 1, wherein the calculating the reliability of the current gradient initial value includes:
calculating the gradient change rate credibility, the slip credibility, the sudden emergency subtracting credibility and the distance credibility according to the current gradient initial value;
and calculating the credibility of the initial value of the current gradient according to the credibility of the gradient change rate, the credibility of skidding, the credibility of sudden emergency subtraction and the credibility of the distance.
6. The dynamic gradient estimation method according to claim 1, wherein the calculating the current dynamic gradient from the reliability and the current gradient initial value includes:
acquiring a historical gradient signal at the previous moment, and calculating a historical gradient signal weighting coefficient according to the credibility;
performing weighted average processing according to the current gradient initial value, the historical gradient signal, the credibility and the historical gradient signal weighting coefficient to obtain a current dynamic gradient; the reliability is a current gradient initial value weighting coefficient.
7. A dynamic gradient estimation device, characterized in that the dynamic gradient estimation device comprises:
an acquisition unit configured to acquire a vehicle body speed signal of a target vehicle;
an estimation unit configured to estimate a current vehicle body acceleration of the target vehicle based on the vehicle body speed signal;
a first calculation unit for calculating a current gradient initial value according to the current vehicle body acceleration;
the second calculating unit is used for calculating the credibility of the initial value of the current gradient;
and the third calculation unit is used for calculating the current dynamic gradient according to the credibility and the current gradient initial value.
8. The dynamic gradient estimation device according to claim 7, wherein the acquisition unit includes:
a determination subunit configured to determine drive configuration information of the target vehicle;
and the first acquisition subunit is used for acquiring a vehicle speed signal of the vehicle body according to the driving configuration information.
9. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the dynamic slope estimation method according to any one of claims 1 to 6.
10. A readable storage medium, characterized in that the readable storage medium has stored therein computer program instructions, which when read and executed by a processor, perform the dynamic gradient estimation method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310138779.2A CN116022152A (en) | 2023-02-17 | 2023-02-17 | Dynamic gradient estimation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310138779.2A CN116022152A (en) | 2023-02-17 | 2023-02-17 | Dynamic gradient estimation method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116022152A true CN116022152A (en) | 2023-04-28 |
Family
ID=86091384
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310138779.2A Pending CN116022152A (en) | 2023-02-17 | 2023-02-17 | Dynamic gradient estimation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116022152A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116215547A (en) * | 2023-05-10 | 2023-06-06 | 广汽埃安新能源汽车股份有限公司 | Dynamic gradient estimation method and device |
-
2023
- 2023-02-17 CN CN202310138779.2A patent/CN116022152A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116215547A (en) * | 2023-05-10 | 2023-06-06 | 广汽埃安新能源汽车股份有限公司 | Dynamic gradient estimation method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Singh et al. | Estimation of tire–road friction coefficient and its application in chassis control systems | |
| US9701289B2 (en) | Method for determining a vehicle reference speed and vehicle controller having such a method | |
| JP5146542B2 (en) | Traveling route estimation device and traveling route estimation method used in the device | |
| CN116215547B (en) | Dynamic gradient estimation method and device | |
| JPH09188114A (en) | Tire identifying method and device | |
| KR102471005B1 (en) | Method and apparatus for ramp and weight estimation | |
| Vignati et al. | Force-based braking control algorithm for vehicles with electric motors | |
| JP5839104B2 (en) | Vehicle turning characteristic estimation device | |
| JP2004026073A (en) | Device for estimating turning characteristic of vehicle | |
| CN110217239A (en) | A kind of vehicle yaw velocity measurement method and apparatus | |
| CN116022152A (en) | Dynamic gradient estimation method and device | |
| US8473174B2 (en) | Method for determining the vehicle longitudinal velocity in a vehicle | |
| CN113954846B (en) | Estimation and system for ramp information in vehicle running | |
| CN112224213B (en) | Method for monitoring wheel friction force and estimating maximum friction force in real time | |
| JP2011037338A (en) | Vehicle slip determination device | |
| Kim et al. | Dual deep neural network based adaptive filter for estimating absolute longitudinal speed of vehicles | |
| CN118124592A (en) | Vehicle speed estimation method, system, equipment and medium for non-all-wheel drive vehicle | |
| CN114435377B (en) | Method and device for acquiring reference vehicle speed, electronic equipment and storage medium | |
| CN116443014B (en) | Anti-skid control method and device for vehicle driving | |
| Hwang et al. | Vehicle speed estimation based on Kalman filtering of accelerometer and wheel speed measurements | |
| US11731638B2 (en) | Driving assistance apparatus, driving assistance method, and non-transitory storage medium | |
| Chen et al. | Vehicle side-slip angle and lateral force estimator based on extended Kalman filtering algorithm | |
| US20220379897A1 (en) | Method for determining state of road surface | |
| US20240175679A1 (en) | Estimation apparatus for water film thickness on road surface | |
| CN118597159A (en) | Method, device and equipment for determining braking distance, readable storage medium and vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |