CN110395266A - A kind of evaluation method decoupled about bus mass change and road grade - Google Patents
A kind of evaluation method decoupled about bus mass change and road grade Download PDFInfo
- Publication number
- CN110395266A CN110395266A CN201910749846.8A CN201910749846A CN110395266A CN 110395266 A CN110395266 A CN 110395266A CN 201910749846 A CN201910749846 A CN 201910749846A CN 110395266 A CN110395266 A CN 110395266A
- Authority
- CN
- China
- Prior art keywords
- estimation
- mass
- acceleration
- vehicle
- road gradient
- 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.)
- Granted
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/076—Slope angle of the road
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
- B60W40/13—Load or weight
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
- B60W2520/105—Longitudinal acceleration
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/30—Wheel torque
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Mathematical Physics (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
- Hybrid Electric Vehicles (AREA)
Abstract
The invention discloses a kind of evaluation methods decoupled about bus mass change and road grade, are related to field of intelligent transportation technology, are considered by the segmentation to acceleration, carry out decoupling estimation to quality and road ramp.The gradient is tabled look-up according to the predeterminated position of GPS positioning or website, obtains the grade information of website；By the judgement to door switch signal and acceleration, start quality estimation algorithms；Driving torque, speed and acceleration in a period of time is sampled, estimate complete vehicle quality using the method for least square and is normalized；If converging on the threshold value less than setting, algorithm terminates and exports estimation result, otherwise persistently exports, until vehicle stops traveling.Estimation for road grade, in the case where quality has exported estimation result, the present invention uses the blending algorithm based on the dynamic method with forgetting factor least square and the kinematic method based on acceleration transducer, to ensure that estimation on line precision.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to an estimation method for decoupling mass change of a bus and road gradient.
Background
The mass of the whole vehicle and the gradient of a road are important parameters in a vehicle dynamic model, and the mass and the gradient can be accurately estimated in real time, so that the dynamic property and the economical efficiency of the vehicle can be effectively improved. The driving and braking control strategy is adjusted timely according to the change of the whole vehicle mass, so that the dynamic property and the economical efficiency can be improved to the maximum extent, the control feeling of a driver on the vehicle is enhanced, and the driving smoothness is improved. The accurate estimation of the road gradient can more accurately calculate the axle load transfer of the vehicle, and the minimum output torque required under the current working condition is calculated according to the axle load transfer, so that the driving feeling is improved, and meanwhile, the dynamic property and the economical efficiency can be improved. However, if the mass and the gradient are estimated by a dynamic method, the mass and the gradient are strongly coupled, so that it is necessary to explore a decoupling estimation algorithm of the mass and the gradient.
Disclosure of Invention
The invention provides an estimation method for decoupling mass change of a bus and road gradient, and mainly aims to solve the problems in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
an estimation method for decoupling mass change of a bus and road gradient comprises the following steps: (1) acquiring gradient information of a station; (2) judging whether the current working condition of the vehicle meets the enabling condition of using a quality estimation algorithm; (3) if the enabling condition is met and the holding time is longer than the time threshold t, sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method, normalizing the mass variance, if the mass variance is smaller than the set threshold, terminating the mass estimation algorithm and outputting an estimation result, and if the mass variance is smaller than the set threshold, continuously outputting the estimation result until the vehicle stops running; (4) and under the condition of the output quality estimation result, estimating the road gradient by adopting a road gradient estimation algorithm based on the combination of a dynamics method with least square forgetting factor and a kinematics method based on an acceleration sensor.
Further, the enabling conditions in the step (2) are: when the vehicle door is closed, the driving torque is larger than a torque threshold value F or the longitudinal acceleration of the whole vehicle is larger than an acceleration threshold value a.
Further, the torque threshold F is 1000Nm, and the acceleration threshold a is 0.5m/s2And the time threshold t is 0.3 s.
Further, in the step (2), the estimation formula of the vehicle mass is as follows:
。
further, in the step (4), the estimation formula of the road gradient is:
wherein τ is a time constant; s is a weighting coefficient;
an estimated value of a slope angle based on a kinematic method;
the road surface gradient angle estimation value is based on a dynamic method.
From the above description of the structure of the present invention, compared with the prior art, the present invention has the following advantages:
the invention carries out decoupling estimation on the mass and the road ramp by considering the acceleration in a subsection way. The slope is subjected to table lookup according to GPS positioning or a preset position of the station, and slope information of the station is obtained; starting a quality estimation algorithm through judging a vehicle door opening and closing signal and acceleration; sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method and carrying out normalization processing; if the vehicle speed is less than the set threshold value, the algorithm is stopped and the estimation result is output, otherwise, the output is continued until the vehicle stops running. For the estimation of the road gradient, under the condition that the quality outputs an estimation result, the method adopts a fusion algorithm based on a dynamics method with least square of forgetting factors and a kinematics method based on an acceleration sensor, thereby ensuring the online estimation precision.
Drawings
FIG. 1 is a flow chart of a mass and road grade estimation algorithm.
Fig. 2 is a schematic view of the road surface gradient.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings.
Vehicle quality identification
As shown in fig. 1 and 2, the longitudinal force analysis is performed on the vehicle:
wherein,F xin order to obtain the resultant force of the longitudinal forces of the wheels,F gin order to provide the resistance for climbing the slope,F win order to be the air resistance,T ifor the drive torque of each wheel，J wFor rotation of wheelsInertia, wiAs the angular velocity of each wheel of the vehicle,Ris the wheel radius, μrIs the rolling resistance coefficient, theta is the slope angle,C din order to obtain the wind resistance coefficient,Athe area of the wind-facing surface is,v xthe longitudinal speed of the whole vehicle is set,Mthe weight of the whole vehicle is measured,ρis the air density.
Is finished to obtain
。
And because the acceleration sensor can measure the gravity acceleration component along the measuring axis, the measured acceleration is as follows:
。
so that there are
。
Wherein order
。
Then there is
。
Estimating by least square method
。
M is the number of sampling points, and M is obtained by the least square method to satisfy the following formula to obtain the minimum value, namely
。
The formula of the estimated value of the vehicle mass is solved as follows:
。
considering that the above formula contains a resistance variable, and the resistance is related to the road gradient, the road gradient needs to be determined in advance to estimate the vehicle mass. That is to say, the mass of the whole vehicle and the gradient of the road have a strong coupling relation. Considering the influence of acceleration on estimation error, a decoupling estimation method of mass and gradient based on acceleration segmentation is provided, namely, when the acceleration is lower than a certain small acceleration, the estimation error of mass is larger, and conversely, the estimation precision is much higher.
The research object of the invention is a bus which runs under the special working condition, and the bus has a fixed running route, fixed road conditions and bus stops, so that offline table lookup can be carried out through GPS positioning or the preset position of the stop to obtain the slope information of the stop and obtain the slope of the road; and then, starting a mass estimation module by judging a vehicle door opening and closing signal, longitudinal acceleration and driving torque, sampling the driving torque, vehicle speed and vehicle acceleration within a period of time, estimating the mass of the whole vehicle by adopting a least square method, normalizing the mass variance, terminating the mass estimation algorithm and outputting an estimation result if the mass variance is converged to be less than a set threshold value, and otherwise, continuously outputting until the vehicle stops running.
In the starting process of the vehicle, the acceleration is inevitably increased to a larger value from 0, and the estimation of the mass is started under the larger acceleration, so that in the initial stage (namely the acceleration is less than or equal to the acceleration threshold value a), the half-load mass of the whole vehicle can be used as a predicted value, and the overlarge or undersize deviation of the true mass can not be caused; and when the acceleration is larger than the acceleration threshold value a, the estimation value formula of the whole vehicle mass is used for estimation.
Specifically, a recursive least square method with a forgetting factor is adopted for estimation, that is, an estimation value of a last sampling moment is corrected by using a measurement value of a current sampling moment. The algorithm is described as follows:
wherein,
。
after the start of the quality estimation, every othertsThe quality estimate is sampled in seconds. Taking the last in time series when calculatingnThe variance of the estimated value is found from the data of the sampling points, and the variance is normalized:
wherein:as an estimate of mass (time of sampling)tsSecond)； Is the most recentnMean of the mass estimates of the individual samples. When the algorithm is not converged, the quality estimation value is output in real time to reduce the use of the predicted valueMThe resulting error; when the value of the variance σ is smaller than σ0When the vehicle is stopped, the algorithm is considered to be stable, estimation is stopped, the estimated value at the time is used as the vehicle quality and is input into other control systems and identification algorithms, and the algorithm resets the output value to the calibration value again until the vehicle stops and opens the door next timeM。
Therefore, whether the current working condition of the vehicle meets the enabling condition of the use quality estimation algorithm needs to be judged firstly; and if the enabling condition is met and the holding time is longer than the time threshold t, sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method, normalizing the mass variance, terminating the mass estimation algorithm and outputting an estimation result if the mass variance is converged to be smaller than the set threshold, and otherwise, continuously outputting until the vehicle stops running. The enabling conditions are: when the vehicle door is closed, the driving torque is larger than a torque threshold value F or the longitudinal acceleration of the whole vehicle is larger than an acceleration threshold value a.
Preferably, the threshold torque F is 1000Nm and the threshold acceleration a is 0.5m/s2The time threshold t is 0.3s, even if the enabling conditions are: under the condition that the vehicle door is closed, the driving torque is more than 1000Nm or the longitudinal acceleration of the whole vehicle is more than 0.5m/s2。
Thus, when the door is closed and the acceleration of the vehicle is less than 0.5m/s2The half-load mass of the whole vehicle can be used as a predicted value; and when the enabling condition is met and the retention time of the enabling condition is more than 0.3s, starting the mass estimation module, and estimating the mass by using the estimation value formula of the vehicle mass by using the mass estimation module.
(4) And under the condition of the output quality estimation result, estimating the road gradient by adopting a road gradient estimation algorithm based on the combination of a dynamics method with least square forgetting factor and a kinematics method based on an acceleration sensor.
Second, road slope estimation
(1) Kinetic method
As shown in fig. 2, using a vehicle dynamics model, let
Then
Wherein,ythe driving force is a longitudinal driving force and can be accurately obtained through feedback signals of the distributed driving motor;uas a function of mass and velocity, available with known mass;bthe mass is known as a function of mass and grade angle, so the grade angle value is obtained.
Since the slope is time-varying, it is possible to reduce the time required for the slope to be constantbThe value adopts a minimum of two with a forgetting factorAnd obtaining the multiplication estimation.
The time of each moment can be estimated by the above formulabThe value is further used for obtaining a road surface slope angle estimated value theta based on a dynamic method by using the following formula d 。
(2) Kinematic method
The acceleration sensor is fixed on the vehicle body and measures the valuea x In addition to the running acceleration of the vehicle itself, the road surface gradient is also affected. Slope angle estimation value theta based on kinematics method k ：
(3) Fusion method
As shown in fig. 1, the kinetic method relies more on the accuracy of the model parameters, whereas the kinematic method is more susceptible to the quality of the sensor signal. Therefore, the invention adopts a weighted average method to fuse the two methods, and finally obtains the estimated value theta of the subtended slope angle.
Wherein τ is a time constant, preferably of the order of 10-2A constant of (d); s is a weighting coefficient.
In conclusion, the invention performs a decoupling estimation of the mass and the road slope by taking the acceleration into account in sections. The slope is subjected to table lookup according to GPS positioning or a preset position of the station, and slope information of the station is obtained; starting a quality estimation algorithm through judging a vehicle door opening and closing signal and acceleration; sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method and carrying out normalization processing; if the vehicle speed is less than the set threshold value, the algorithm is stopped and the estimation result is output, otherwise, the output is continued until the vehicle stops running. For the estimation of the road gradient, under the condition that the quality outputs an estimation result, the method adopts a fusion algorithm based on a dynamics method with least square of forgetting factors and a kinematics method based on an acceleration sensor, thereby ensuring the online estimation precision.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.
Claims (5)
1. A method for estimating decoupling of mass change of a bus and road gradient is characterized by comprising the following steps: (1) acquiring gradient information of a station; (2) judging whether the current working condition of the vehicle meets the enabling condition of using a quality estimation algorithm; (3) if the enabling condition is met and the holding time is longer than the time threshold t, sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method, normalizing the mass variance, if the mass variance is smaller than the set threshold, terminating the mass estimation algorithm and outputting an estimation result, and if the mass variance is smaller than the set threshold, continuously outputting the estimation result until the vehicle stops running; (4) and under the condition of the output quality estimation result, estimating the road gradient by adopting a road gradient estimation algorithm based on the combination of a dynamics method with least square forgetting factor and a kinematics method based on an acceleration sensor.
2. An estimation method as claimed in claim 1 regarding the decoupling of mass change of the bus and road gradient, characterized in that: the enabling conditions in the step (2) are as follows: when the vehicle door is closed, the driving torque is larger than a torque threshold value F or the longitudinal acceleration of the whole vehicle is larger than an acceleration threshold value a.
3. According to claim 2The estimation method for decoupling the mass change of the bus and the road gradient is characterized by comprising the following steps of: the torque threshold value F is 1000Nm, and the acceleration threshold value a is 0.5m/s2And the time threshold t is 0.3 s.
4. An estimation method as claimed in claim 1 regarding the decoupling of mass change of the bus and road gradient, characterized in that: in the step (2), the estimation formula of the vehicle mass is as follows:
。
5. an estimation method as claimed in claim 1 regarding the decoupling of mass change of the bus and road gradient, characterized in that: in the step (4), the estimation formula of the road gradient is as follows:
wherein τ is a time constant; s is a weighting coefficient;
an estimated value of a slope angle based on a kinematic method;
the road surface gradient angle estimation value is based on a dynamic method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910749846.8A CN110395266B (en) | 2019-08-14 | 2019-08-14 | Estimation method for decoupling mass change of bus and road gradient |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910749846.8A CN110395266B (en) | 2019-08-14 | 2019-08-14 | Estimation method for decoupling mass change of bus and road gradient |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110395266A true CN110395266A (en) | 2019-11-01 |
| CN110395266B CN110395266B (en) | 2020-08-04 |
Family
ID=68328333
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910749846.8A Active CN110395266B (en) | 2019-08-14 | 2019-08-14 | Estimation method for decoupling mass change of bus and road gradient |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110395266B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110920625A (en) * | 2019-11-27 | 2020-03-27 | 北京交通大学 | Decoupling and continuous estimation method for whole vehicle mass and road resistance of electric vehicle |
| CN111559380A (en) * | 2020-05-21 | 2020-08-21 | 南京晓庄学院 | A vehicle active safety control method and device |
| CN111806449A (en) * | 2020-06-23 | 2020-10-23 | 西安法士特汽车传动有限公司 | Method for estimating total vehicle mass and road surface gradient of pure electric vehicle |
| CN113147768A (en) * | 2021-05-13 | 2021-07-23 | 东北大学 | Multi-algorithm fusion prediction-based automobile road surface state online estimation system and method |
| CN113264056A (en) * | 2021-05-25 | 2021-08-17 | 三一汽车制造有限公司 | Vehicle weight estimation method, device, vehicle and readable storage medium |
| CN114379568A (en) * | 2022-01-24 | 2022-04-22 | 广州瑞立科密汽车电子股份有限公司 | Vehicle control method, device, medium and vehicle based on vehicle mass estimation |
| CN114701508A (en) * | 2022-04-19 | 2022-07-05 | 上海前晨汽车科技有限公司 | Vehicle mass estimation method, apparatus, device, storage medium, and program product |
| CN114771547A (en) * | 2022-06-21 | 2022-07-22 | 北京清研宏达信息科技有限公司 | Weight estimation method and device for automatically driving bus, bus and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102486400A (en) * | 2010-12-06 | 2012-06-06 | 罗伯特·博世有限公司 | Vehicle mass identification method and device |
| CN102700551A (en) * | 2012-05-31 | 2012-10-03 | 清华大学 | Method for estimating gradient of road surface in real time in vehicle running process |
| JP2015013517A (en) * | 2013-07-03 | 2015-01-22 | 本田技研工業株式会社 | Vehicle controller |
| CN105485326A (en) * | 2014-10-02 | 2016-04-13 | 福特全球技术公司 | Vehicle system and method for controlling torque delivery during transmission engagements with road grade and mass estimation |
| CN109910905A (en) * | 2019-03-01 | 2019-06-21 | 同济大学 | Multi-condition speed estimation method for distributed driving vehicles based on slope estimation correction |
-
2019
- 2019-08-14 CN CN201910749846.8A patent/CN110395266B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102486400A (en) * | 2010-12-06 | 2012-06-06 | 罗伯特·博世有限公司 | Vehicle mass identification method and device |
| CN102700551A (en) * | 2012-05-31 | 2012-10-03 | 清华大学 | Method for estimating gradient of road surface in real time in vehicle running process |
| JP2015013517A (en) * | 2013-07-03 | 2015-01-22 | 本田技研工業株式会社 | Vehicle controller |
| CN105485326A (en) * | 2014-10-02 | 2016-04-13 | 福特全球技术公司 | Vehicle system and method for controlling torque delivery during transmission engagements with road grade and mass estimation |
| CN109910905A (en) * | 2019-03-01 | 2019-06-21 | 同济大学 | Multi-condition speed estimation method for distributed driving vehicles based on slope estimation correction |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110920625B (en) * | 2019-11-27 | 2021-04-06 | 北京交通大学 | Decoupling and continuous estimation method for whole vehicle mass and road resistance of electric vehicle |
| CN110920625A (en) * | 2019-11-27 | 2020-03-27 | 北京交通大学 | Decoupling and continuous estimation method for whole vehicle mass and road resistance of electric vehicle |
| CN111559380A (en) * | 2020-05-21 | 2020-08-21 | 南京晓庄学院 | A vehicle active safety control method and device |
| CN111559380B (en) * | 2020-05-21 | 2021-06-01 | 南京晓庄学院 | Vehicle active safety control method and device |
| CN111806449A (en) * | 2020-06-23 | 2020-10-23 | 西安法士特汽车传动有限公司 | Method for estimating total vehicle mass and road surface gradient of pure electric vehicle |
| CN113147768B (en) * | 2021-05-13 | 2024-02-23 | 东北大学 | Automobile road surface state online estimation system and method based on multi-algorithm fusion prediction |
| CN113147768A (en) * | 2021-05-13 | 2021-07-23 | 东北大学 | Multi-algorithm fusion prediction-based automobile road surface state online estimation system and method |
| CN113264056A (en) * | 2021-05-25 | 2021-08-17 | 三一汽车制造有限公司 | Vehicle weight estimation method, device, vehicle and readable storage medium |
| CN114379568A (en) * | 2022-01-24 | 2022-04-22 | 广州瑞立科密汽车电子股份有限公司 | Vehicle control method, device, medium and vehicle based on vehicle mass estimation |
| CN114379568B (en) * | 2022-01-24 | 2024-06-04 | 广州瑞立科密汽车电子股份有限公司 | Vehicle control method and device based on vehicle quality estimation, medium and vehicle |
| CN114701508A (en) * | 2022-04-19 | 2022-07-05 | 上海前晨汽车科技有限公司 | Vehicle mass estimation method, apparatus, device, storage medium, and program product |
| CN114771547A (en) * | 2022-06-21 | 2022-07-22 | 北京清研宏达信息科技有限公司 | Weight estimation method and device for automatically driving bus, bus and storage medium |
| CN114771547B (en) * | 2022-06-21 | 2022-09-23 | 北京清研宏达信息科技有限公司 | Weight estimation method and device for automatically driving bus, bus and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110395266B (en) | 2020-08-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110395266B (en) | Estimation method for decoupling mass change of bus and road gradient | |
| CN111806449A (en) | Method for estimating total vehicle mass and road surface gradient of pure electric vehicle | |
| CN110095635B (en) | Longitudinal speed estimation method of all-wheel drive vehicle | |
| CN100545595C (en) | A kind of automotive quality estimation system and method | |
| CN112613253B (en) | Joint Adaptive Estimation Method of Vehicle Mass and Road Slope Considering Environmental Factors | |
| CN103946679B (en) | Vehicle mass identification method and system | |
| CN102486400B (en) | Vehicle mass identification method and device | |
| CN108482379A (en) | Wheel-hub motor driven vehicle coefficient of road adhesion and road gradient synchronize real-time estimation system and method | |
| CN109664774B (en) | Drive anti-skid control method and system for distributed drive electric automobile | |
| CN110987470B (en) | Model iteration-based automobile quality online estimation method | |
| CN110239554A (en) | A kind of vehicle-mounted road surface head fall real-time identification method and its device | |
| CN107933562B (en) | Method and system for calculating road friction estimates | |
| CN110053431B (en) | An error compensation method and device for measuring vehicle tire pressure | |
| CN113859253B (en) | A real-time estimation method of vehicle mass during driving | |
| CN108819950B (en) | Vehicle speed estimation method and system of vehicle stability control system | |
| WO2012138286A1 (en) | Estimation of road inclination | |
| CN101655504A (en) | Vehicle speed estimation method of motor vehicle self-adaption cruise system | |
| CN111605559B (en) | Vehicle mass estimation method, torque control method and device | |
| BR102016023026A2 (en) | method for determining a change in air resistance perceived by a motor vehicle | |
| CN107229801B (en) | On-line Identification Method of Tire Rolling Resistance Coefficient | |
| CN115626166A (en) | Climbing capability prediction method in automatic vehicle driving | |
| CN109190153A (en) | A kind of Calculation Method of Energy Consumption and its system | |
| CN114771546A (en) | Driving-stroke-oriented vehicle load estimation method | |
| CN116588119B (en) | A vehicle state estimation method based on tire model parameter adaptation | |
| CN116834756A (en) | Vehicle mass and road gradient online estimation method, device and equipment |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |