CN111891133A - Vehicle mass estimation method and system adaptive to various road conditions - Google Patents
Vehicle mass estimation method and system adaptive to various road conditions Download PDFInfo
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- 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
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- 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
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- 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
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Abstract
The invention discloses a vehicle mass estimation method adaptive to various road conditions, which comprises the following steps: firstly, calculating the acceleration of a vehicle in real time; calculating corresponding vehicle speeds under a plurality of constant speed working conditions, and calculating corresponding vehicle resistance under each vehicle speed through a linear interpolation method; thirdly, the whole vehicle mass in vehicle acceleration; the overall vehicle mass during vehicle deceleration; and (4) the finished vehicle quality estimation value under the comprehensive road condition. The invention also discloses a whole vehicle mass estimation system adaptive to various road conditions, which comprises: an acceleration calculation module: calculating the acceleration of the vehicle in real time; a linear interpolation module: calculating corresponding vehicle speeds under a plurality of constant speed working conditions, and calculating the corresponding whole vehicle resistance under each vehicle speed by a linear interpolation method; the whole vehicle mass estimation module: the overall vehicle mass during vehicle acceleration; the overall vehicle mass during vehicle deceleration; and (4) the finished vehicle quality estimation value under the comprehensive road condition. The invention can obtain accurate finished automobile mass estimation value, greatly improves the adaptability to various roads and can be widely applied to the field of automobile control.
Description
Technical Field
The invention relates to the field of automobile control, in particular to a method and a system for estimating the quality of a whole automobile adaptive to various road conditions.
Background
In the automotive industry, technicians are constantly trying to ascertain quality parameters of vehicles in motion and many attempts have been made to do so. For example, the invention is a patent with publication number CN107901916A, entitled a method for acquiring vehicle load without adding a sensor, and three CAN (Controller area network) parameters of engine torque, transmission speed ratio and vehicle speed generated in real time are transmitted to a central server, and the load value of the vehicle CAN be calculated by combining the information of the tire radius of the vehicle, the rear axle speed ratio and the like, that is, a reverse model of a vehicle running equation; the invention is called as 201210105145.9, and the invention is named as a vehicle mass estimation method based on high-frequency information extraction, the vehicle mass is estimated according to the longitudinal acceleration and the driving force signal, the influence of wind resistance, rolling resistance and gradient on the mass estimation is ignored, the high-frequency component acquisition module is adopted to extract the high-frequency component of the longitudinal acceleration and the driving force signal, the relationship between the vehicle mass and the longitudinal acceleration and the driving force at each moment in the vehicle running is obtained, and the mass estimation error caused by the low-frequency signals such as gradient and the like is eliminated.
Although a lot of attempts are made in the technology, the methods still cannot complete the whole vehicle mass estimation work at a vehicle-mounted end, for example, the estimation work of the first invention mainly collects the whole vehicle data and completes calculation at a server, and different resistance coefficients and whole vehicle parameters need to be adopted according to different vehicle types, so that the problem is that the calculation mode cannot be transplanted to a vehicle-mounted controller, and the selection of various parameters causes difficulty in being pushed at a host computer factory, meanwhile, the invention is suitable for a high-speed and equal-level road surface, the gradient information of the road cannot be identified, and the biggest problem is that the calculated mass on an uphill road surface is larger due to the additional mass introduced by the gradient of the road, the calculated mass is opposite, and the calculated whole vehicle mass is smaller.
The second invention needs to add an acceleration sensor and a steering wheel angle sensor, and the cost is higher for any controller of the whole vehicle. In the invention, the steering working condition of the vehicle is filtered only according to the steering wheel rotation angle signal, and the whole vehicle mass estimation is not carried out under the working condition, the method defaults that the vehicle runs under the straight road condition without ascending and descending, but how to automatically identify the road condition is not explained, the large-scale mass production cannot be carried out on the actual vehicle, and the defects of larger ascending calculation quality and smaller descending calculation quality can be caused.
Meanwhile, the existing invention only focuses on the reality of an algorithm, and only considers the complex and severe running conditions of the vehicle under the application of the real vehicle, for example, the problem of sensor signal fluctuation caused by chassis vibration of the vehicle body is difficult to eliminate because the running road conditions of the vehicle are not always running on a straight road surface.
Disclosure of Invention
The invention aims to overcome the defects of the background technology and provides a finished automobile mass estimation method and a finished automobile mass estimation system adaptive to various road conditions, which can obtain accurate finished automobile mass estimation values and greatly improve the adaptability to various roads.
The invention provides a vehicle mass estimation method adaptive to various road conditions, which comprises the following steps: step one, calculating the acceleration of the vehicle in real time in the running process of the vehicle:
wherein, a-acceleration, V-terminal velocity, V--initial velocity, t-acceleration time; step two, in the running process of the vehicle, calculating the corresponding vehicle speeds under a plurality of constant speed working conditions through a formula (1), and calculating the corresponding whole vehicle resistance F under each vehicle speed through a linear interpolation methodfi(ii) a Step three, in the vehicle acceleration process, the whole vehicle mass calculation mode:
wherein:FD-vehicle driving force, m+Vehicle mass at acceleration, Ff-vehicle resistance, a-acceleration; in the vehicle deceleration process, the whole vehicle mass calculation mode is as follows:
wherein: m is-Vehicle mass at deceleration, Ff-vehicle resistance, a-acceleration; the estimated value of the mass of the whole vehicle under the comprehensive road condition is as follows:
0 < k < 1(4), wherein: m-estimated vehicle mass, m+Vehicle mass at acceleration, m--vehicle mass at deceleration, k-correction ratio.
In the above technical solution, in the third step, the selection of k is obtained by calibration in an actual vehicle test.
In the above technical solution, in the first step, before calculating the acceleration of the vehicle in real time, an ECU (electronic control Unit) obtains the driving force and the speed signal of the vehicle from the running whole vehicle, inputs the driving force and the speed signal into a first-in first-out queue, and then resynchronizes the inside of the ECU in the queue manner.
In the above technical solution, in the first step, when the ECU performs resynchronization in a queue manner, the time delay of signal synchronization is determined in the real vehicle test calibration process.
In the above technical scheme, in the second step, the corresponding vehicle speeds under 9 constant-speed working conditions are calculated through the formula (1), and the corresponding vehicle resistance F under 9 vehicle speeds is calculatedfiThe method comprises the following steps:
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Vehicle speed 9 |
| Ff1 | Ff2 | Ff3 | Ff4 | Ff5 | Ff6 | Ff7 | Ff9 | Ff9 |
Wherein the vehicle speed is 1, 2, 3, 4, 5, 6, 7, 8 and 9.
The invention also provides a whole vehicle mass estimation system adaptive to various road conditions, which comprises the following parts: an acceleration calculation module: and in the running process of the vehicle, calculating the acceleration of the vehicle in real time:
wherein, a-acceleration, V-terminal velocity, V--initial velocity, t-acceleration time; a linear interpolation module: in the running process of the vehicle, the pairs under a plurality of constant-speed working conditions are calculated by the formula (1)Calculating the corresponding vehicle resistance F under each vehicle speed by a linear interpolation methodfi(ii) a The whole vehicle mass estimation module: in the vehicle acceleration process, the whole vehicle mass calculation mode is as follows:
wherein: fD-vehicle driving force, m+Vehicle mass at acceleration, Ff-vehicle resistance, a-acceleration; in the vehicle deceleration process, the whole vehicle mass calculation mode is as follows:
wherein: m is-Vehicle mass at deceleration, Ff-vehicle resistance, a-acceleration; the estimated value of the mass of the whole vehicle under the comprehensive road condition is as follows:
0 < k < 1(4), wherein: m-estimated vehicle mass, m+Vehicle mass at acceleration, m--vehicle mass at deceleration, k-correction ratio.
In the technical scheme, in the whole vehicle mass estimation module, the selection of k is obtained by calibration in a real vehicle test.
In the technical scheme, the vehicle acceleration control system further comprises a queue synchronization module, wherein before the vehicle acceleration is calculated in real time, the ECU acquires vehicle driving force and vehicle speed signals from the running whole vehicle, the vehicle driving force and the vehicle speed signals are input into a first-in first-out queue, and then the ECU is internally resynchronized in a queue mode.
In the above technical solution, in the queue synchronization module, when the ECU performs resynchronization in a queue manner, the time delay of signal synchronization is determined in the real vehicle test calibration process.
In the above technical solution, in the linear interpolation module, the corresponding vehicle speeds under 9 constant speed conditions are calculated by formula (1), and the corresponding vehicle resistance F under 9 vehicle speeds is calculatedfiThe method comprises the following steps:
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Vehicle speed 9 |
| Ff1 | Ff2 | Ff3 | Ff4 | Ff5 | Ff6 | Ff7 | Ff8 | Ff9 |
Wherein the vehicle speed is 1, 2, 3, 4, 5, 6, 7, 8 and 9.
The invention discloses a vehicle mass estimation method and a vehicle mass estimation system adaptive to various road conditions, which have the following beneficial effects:
1. the defects generated on the scheme of replacing the calculated resistance with the driving force under the constant-speed working condition are corrected, the quality of the deceleration process is increased on the basis of the quality of the acceleration process in the conventional scheme, and the quality of the acceleration process and the quality of the deceleration process compensate each other, so that the estimated result of the mass of the whole vehicle approaches the true value.
2. And establishing a queue of input signals to ensure that the ECU acquires signals to be resynchronized, thereby greatly avoiding the deviation of quality estimation results caused by asynchronous signals.
3. The quality estimation result is independent of road conditions and environment, and the influence of the road conditions (uphill road surface and downhill road surface) on the result is not required to be considered.
Drawings
FIG. 1 is a schematic flow chart of a vehicle quality estimation method adapted to various road conditions according to the present invention;
FIG. 2 is a schematic diagram of a first-in first-out queue in a first step of the vehicle mass estimation method adapted to various road conditions according to the present invention;
FIG. 3 is a schematic diagram of a resynchronization queue in a first step of the vehicle mass estimation method adapted to various road conditions according to the present invention;
FIG. 4 is a schematic diagram showing a relationship between the interpolation resistance and the real resistance in the fourth step of the vehicle mass estimation method adapted to various road conditions;
fig. 5 is a schematic structural diagram of the entire vehicle quality estimation system adapted to various road conditions.
Detailed Description
The invention is described in further detail below with reference to the following figures and examples, which should not be construed as limiting the invention.
Referring to fig. 1, the method for estimating the vehicle mass adaptive to various road conditions of the present invention includes the following steps:
firstly, referring to fig. 2, before calculating the acceleration of the vehicle in real time, an ECU acquires the driving force and the speed signal of the vehicle from the running whole vehicle and inputs the driving force and the speed signal into a first-in first-out queue;
the torque input and the vehicle speed input are external input signals of the invention, namely the system input of a vehicle mass algorithm, in general, the torque is sent by an engine, the vehicle speed is sent by an instrument, two controllers can carry out operations such as filtering, debouncing and the like on the two signals before sending the two signals, the operations can inevitably cause that the currently sent signals have certain delay relative to the real torque vehicle speed, and the delay is different, in general, the delay can be ignored, but the algorithm is sensitive to the delay, so that special processing is needed to be carried out for synchronizing the delay time;
therefore, because the externally acquired signals are not synchronous, that is, the torque and speed signals acquired by the ECU at the same time do not reflect the real situation, referring to fig. 3, the ECU needs to be resynchronized internally by adopting a queue, the time delay of signal synchronization is determined in the real vehicle test calibration process, and the length of the time delay is not changed for the same mature ECU;
step two, calculating the acceleration of the vehicle in real time in the driving process of the vehicle:
wherein, a-acceleration, V-terminal velocity, V--initial velocity, t-acceleration time;
step three, in the running process of the vehicle, calculating the corresponding vehicle speeds under a plurality of constant-speed working conditions through a formula (1), and calculating the corresponding whole vehicle resistance F under each vehicle speed through a linear interpolation methodfiCalculating corresponding vehicle speeds under 9 constant-speed working conditions through a formula (1), and calculating corresponding vehicle resistance F under 9 vehicle speedsfiThe method comprises the following steps:
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Vehicle speed 9 |
| Ff1 | Ff2 | Ff3 | Ff4 | Ff5 | Ff6 | Ff7 | Ff8 | Ff9 |
Wherein the vehicle speed is 1, 2, 3, 4, 5, 6, 7, 8 and 9;
a denser range of vehicle speeds may be selected in the common vehicle speed range, as exemplified below:
| vehicle speed (km/h) | 20 | 30 | …… | 70 | 75 | 80 | 85 | …… | 100 |
| F | Ff1 | Ff2 | Ff3 | Ff4 | Ff5 | Ff6 | Ff7 |
The whole vehicle resistance under other vehicle speeds can be obtained by a linear interpolation method;
step four, doing so would bring about the following problems: referring to fig. 4, since the real resistance is linear with the square of the vehicle speed, the interpolated resistance is larger than the real resistance at the vehicle speed;
in the vehicle acceleration process, the whole vehicle mass calculation mode is as follows:
wherein: fD-vehicle driving force, m+Vehicle mass at acceleration, Ff-vehicle resistance, a-acceleration;
in the vehicle deceleration process, the whole vehicle mass calculation mode is as follows:
wherein: m is-Vehicle mass at deceleration, Ff-vehicle resistance, a-acceleration;
the whole vehicle mass m in the whole vehicle acceleration process can be obtained by the formula+Smaller and the calculated mass m of the whole vehicle in the process of deceleration-On the large side, the overall vehicle mass can theoretically be obtained from the mean of these two masses:
in the actual process, the whole vehicle has a large number of acceleration processes and a small number of deceleration processes under straight road conditions during running, and has a small number of acceleration processes and a large number of deceleration processes in the processes of ascending and descending, so that the acceleration quality m under different road conditions+And a decelerating mass m-The reliability of the vehicle is different, so that the estimated value of the mass of the whole vehicle under the comprehensive road condition is as follows:
0＜k＜1 (4)，
wherein: m-estimated vehicle mass, m+Vehicle mass at acceleration, m--the vehicle mass at deceleration, k-the correction ratio, the choice of k being calibrated in real vehicle tests.
Referring to fig. 5, the vehicle quality estimation system adapted to various road conditions of the present invention includes the following components:
the queue synchronization module is used for acquiring vehicle driving force and vehicle speed signals from a running whole vehicle by the ECU before calculating the vehicle acceleration in real time, inputting the vehicle driving force and the vehicle speed signals into a first-in first-out queue, then resynchronizing the interior of the ECU in a queue mode, and determining the time delay of signal synchronization in the process of testing and calibrating the real vehicle;
an acceleration calculation module: and in the running process of the vehicle, calculating the acceleration of the vehicle in real time:
wherein, a-acceleration, V-terminal velocity, V--initial velocity, t-acceleration time;
a linear interpolation module: in the running process of the vehicle, the corresponding vehicle speeds under a plurality of constant-speed working conditions are calculated through a formula (1), and the corresponding vehicle resistance F under each vehicle speed is calculated through a linear interpolation methodfiCalculating corresponding vehicle speeds under 9 constant-speed working conditions through a formula (1), and calculating corresponding vehicle resistance F under 9 vehicle speedsfiThe method comprises the following steps:
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Vehicle | Vehicle speed | 6 | |
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Vehicle speed 9 |
| Ff1 | Ff2 | Ff3 | Ff4 | Ff5 | Ff6 | Ff7 | Ff8 | Ff9 |
Wherein the vehicle speed is 1, 2, 3, 4, 5, 6, 7, 8 and 9;
the whole vehicle mass estimation module: in the vehicle acceleration process, the whole vehicle mass calculation mode is as follows:
wherein: fD-vehicle driving force, m+Vehicle mass at acceleration, Ff-vehicle resistance, a-acceleration;
in the vehicle deceleration process, the whole vehicle mass calculation mode is as follows:
wherein: m is-Vehicle mass at deceleration, Ff-vehicle resistance, a-acceleration;
the estimated value of the mass of the whole vehicle under the comprehensive road condition is as follows:
0＜k＜1 (4)，
wherein: m-estimated vehicle mass, m+Vehicle mass at acceleration, m--the vehicle mass at deceleration, k-the correction ratio, the choice of k being calibrated in real vehicle tests.
At the present that the electrification of commercial vehicles and various trucks is accelerated gradually, the requirements of vehicle safety and vehicle networking are formally promoted by research and development schedules of various large companies, and various functions of the layout of a host factory, such as predictive driving, predictive maintenance and other projects, need to take the quality of the whole vehicle as basic data. According to the invention, the quality estimation of the whole vehicle is independently completed based on the mass production requirement of a host factory without additional cooperation of a manufacturing department on the premise of not increasing the cost of the sensor, the invention CAN be modeled by MATLAB/Simulink (the Simulink is a visual simulation tool in MATLAB) and generates embedded codes, the embedded codes are compiled and linked into the existing whole vehicle controller for operation, the quality data CAN be sent out through a CAN bus, and other controllers on the bus CAN synchronously use technical achievements.
The real-time acceleration of the vehicle can be calculated through the differentiation of vehicle speed information sent by a vehicle instrument, when the average value of the acceleration in time t is in the range of 0 +/-k, the average value of the vehicle speed in time t and the driving force of the whole vehicle are recorded, and at the moment, the vehicle resistance under the vehicle speed (the road surface condition and the environmental factors are unchanged) is known to be equal to the driving force of the whole vehicle through Newton's law. The driving force (resistance) of the whole vehicle under different vehicle speeds can be obtained by polynomial interpolation in theory. Because embedded development consumes a large amount of storage space, and many variables in the actual vehicle running process are found in the development and test process, the polynomial interpolation method cannot ensure the accuracy of the polynomial interpolation method. Therefore, the invention is mostly obtained by linear interpolation, which brings a great defect to the algorithm, is also a factor which is not considered by other patents and utility models based on the algorithm at present, and introduces the credibility k obtained by actual vehicle test calibration in the text to perfectly solve the aeipathia.
The real-time driving force of the vehicle can be calculated through the output torque of the engine, the speed ratio of the gearbox, the speed ratio of the rear axle, the transmission loss of the gearbox and the transmission loss of the rear axle. A first-in first-out queue is established, the queue input quantity is the real-time driving force of a vehicle, the length of the queue is related to the filtering strength of acceleration and the calibration of a test process, the purpose of doing so is that on the premise that any sensor (an acceleration sensor) is not added, the acceleration of the vehicle is obtained by the differentiation of the vehicle speed, the delay between a vehicle speed signal provided by an instrument and the current real vehicle speed needs to be calibrated in real vehicle verification, the filtering processing of the obtained original vehicle speed is inevitable, although the real-time performance of Kalman filtering is excellent, the mean filtering in an embedded code is also one of the most efficient modes, and all parameters of the whole vehicle are directly obtained from the external environment by an ECU, and also have parameters for secondary calculation in the ECU. The purpose of establishing queues is to synchronize the real-time performance of each uncertain parameter, since the quality estimation is very sensitive to each parameter.
Estimating the mass estimation value of the whole vehicle under acceleration through the resistance torque difference value obtained by looking up a table under the condition that the engine outputs the torque in real time and at different vehicle speeds;
and estimating the mass estimation value under the deceleration of the whole vehicle by using the resistance moment obtained by looking up the table at different vehicle speeds.
Because the calculation mode of the resistance can not only include the transmission efficiency, but also calculate the resistance coefficient of the unconventional road and the additional equivalent resistance of different ramps, the adaptability of the method to different types of roads is greatly improved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Those not described in detail in this specification are within the skill of the art.
Claims (10)
1. A whole vehicle mass estimation method adaptive to various road conditions is characterized by comprising the following steps: the method comprises the following steps:
step one, calculating the acceleration of the vehicle in real time in the running process of the vehicle:
wherein, a is acceleration, V is terminal speed, V is initial speed, t is acceleration time;
step two, in the running process of the vehicle, calculating the corresponding vehicle speeds under a plurality of constant speed working conditions through a formula (1), and calculating the corresponding whole vehicle resistance F under each vehicle speed through a linear interpolation methodfi；
Step three, in the vehicle acceleration process, the whole vehicle mass calculation mode:
wherein: fD-vehicle driving force, m+Vehicle mass at acceleration, Ff-vehicle resistance, a-acceleration;
in the vehicle deceleration process, the whole vehicle mass calculation mode is as follows:
wherein: m is-Vehicle mass at deceleration, Ff-vehicle resistance, a-acceleration;
the estimated value of the mass of the whole vehicle under the comprehensive road condition is as follows:
0＜k＜1 (4)，
wherein: m-estimated vehicle mass, m+Vehicle mass at acceleration, m--vehicle mass at deceleration, k-correction ratio.
2. The vehicle mass estimation method adaptive to multiple road conditions according to claim 1, wherein: in the third step, the selection of k is obtained by calibration in the real vehicle test.
3. The vehicle mass estimation method adaptive to multiple road conditions according to claim 2, wherein: in the first step, before the acceleration of the vehicle is calculated in real time, the ECU acquires the driving force and the speed signal of the vehicle from the running whole vehicle, inputs the driving force and the speed signal into a first-in first-out queue, and then resynchronizes the ECU by adopting a queue mode.
4. The vehicle mass estimation method adaptive to multiple road conditions according to claim 3, wherein: in the first step, when the ECU is internally resynchronized in a queue mode, the time delay of signal synchronization is determined in the process of real vehicle test calibration.
5. The vehicle mass estimation method adaptive to multiple road conditions according to claim 4, wherein: in the second step, the corresponding vehicle speed under 9 constant-speed working conditions is calculated through the formula (1), and the corresponding vehicle resistance F under 9 vehicle speeds is calculatedfiThe method comprises the following steps:
Wherein the vehicle speed is 1, 2, 3, 4, 5, 6, 7, 8 and 9.
6. A whole vehicle mass estimation system adaptive to various road conditions is characterized in that: the method comprises the following steps:
an acceleration calculation module: and in the running process of the vehicle, calculating the acceleration of the vehicle in real time:
wherein, a-acceleration, V-terminal velocity, V--initial velocity, t-acceleration time;
a linear interpolation module: in the running process of the vehicle, the corresponding vehicle speeds under a plurality of constant-speed working conditions are calculated through a formula (1), and the corresponding vehicle resistance F under each vehicle speed is calculated through a linear interpolation methodfi；
The whole vehicle mass estimation module: in the vehicle acceleration process, the whole vehicle mass calculation mode is as follows:
wherein: fD-vehicle driving force, m+Vehicle mass at acceleration, Ff-vehicle resistance, a-acceleration;
in the vehicle deceleration process, the whole vehicle mass calculation mode is as follows:
wherein: m is-Vehicle mass at deceleration, Ff-vehicle resistance, a-acceleration;
the estimated value of the mass of the whole vehicle under the comprehensive road condition is as follows:
0＜k＜1 (4)，
wherein: m-estimated vehicle mass, m+Vehicle mass at acceleration, m--vehicle mass at deceleration, k-correction ratio.
7. The vehicle mass estimation system according to claim 6, wherein the vehicle mass estimation system is adapted to a plurality of road conditions: in the whole vehicle mass estimation module, the selection of k is obtained by calibration in a real vehicle test.
8. The vehicle mass estimation system according to claim 7, wherein the vehicle mass estimation system is adapted to a plurality of road conditions: the vehicle acceleration control system further comprises a queue synchronization module, wherein before vehicle acceleration is calculated in real time, the ECU acquires vehicle driving force and vehicle speed signals from a running whole vehicle, the vehicle driving force and the vehicle speed signals are input into a first-in first-out queue, and then the ECU is internally resynchronized in a queue mode.
9. The vehicle mass estimation system according to claim 8, wherein the vehicle mass estimation system is adapted to a plurality of road conditions: in the queue synchronization module, when the ECU is internally resynchronized in a queue mode, the time delay of signal synchronization is determined in the real vehicle test calibration process.
10. The vehicle mass estimation system according to claim 9, wherein: in the linear interpolation module, the corresponding vehicle speed under 9 constant-speed working conditions is calculated through a formula (1), and the corresponding vehicle resistance F under 9 vehicle speeds is calculatedfiThe method comprises the following steps:
Wherein the vehicle speed is 1, 2, 3, 4, 5, 6, 7, 8 and 9.
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Cited By (2)
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| CN113119727A (en) * | 2021-05-07 | 2021-07-16 | 恒大新能源汽车投资控股集团有限公司 | Vehicle overload processing method and device |
| CN113581192A (en) * | 2021-08-05 | 2021-11-02 | 东风汽车集团股份有限公司 | Vehicle quality correction method for identifying slope gradient based on vehicle quality |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010249597A (en) * | 2009-04-14 | 2010-11-04 | Sumitomo Rubber Ind Ltd | Vehicle mass estimation apparatus, method, and program, and tire pneumatic pressure drop detection apparatus, method, and program |
| US20110218764A1 (en) * | 2010-03-03 | 2011-09-08 | Hajime Fujita | Apparatus, method and program for vehicle mass estimation |
| CN104848923A (en) * | 2014-05-05 | 2015-08-19 | 北汽福田汽车股份有限公司 | Vehicle load detection method and detection device |
| EP2956343A1 (en) * | 2013-02-14 | 2015-12-23 | Scania CV AB | Simultaneous estimation of at least mass and rolling resistance |
| CN105667521A (en) * | 2016-04-11 | 2016-06-15 | 潍柴动力股份有限公司 | Method and system for calculating total mass of vehicle |
| CN106564504A (en) * | 2016-11-02 | 2017-04-19 | 陕西法士特齿轮有限责任公司 | Computing method of AMT finished automobile mass |
| CN108152050A (en) * | 2017-12-22 | 2018-06-12 | 南京越博动力系统股份有限公司 | A kind of whole-car parameters calibration method |
| CN109466561A (en) * | 2018-09-28 | 2019-03-15 | 潍柴动力股份有限公司 | Vehicular gross combined weight calculation method and system |
| CN111198032A (en) * | 2018-11-19 | 2020-05-26 | 陕西汽车集团有限责任公司 | Real-time estimation method for automobile mass |
-
2020
- 2020-06-29 CN CN202010605603.XA patent/CN111891133B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010249597A (en) * | 2009-04-14 | 2010-11-04 | Sumitomo Rubber Ind Ltd | Vehicle mass estimation apparatus, method, and program, and tire pneumatic pressure drop detection apparatus, method, and program |
| US20110218764A1 (en) * | 2010-03-03 | 2011-09-08 | Hajime Fujita | Apparatus, method and program for vehicle mass estimation |
| EP2956343A1 (en) * | 2013-02-14 | 2015-12-23 | Scania CV AB | Simultaneous estimation of at least mass and rolling resistance |
| CN104848923A (en) * | 2014-05-05 | 2015-08-19 | 北汽福田汽车股份有限公司 | Vehicle load detection method and detection device |
| CN105667521A (en) * | 2016-04-11 | 2016-06-15 | 潍柴动力股份有限公司 | Method and system for calculating total mass of vehicle |
| CN106564504A (en) * | 2016-11-02 | 2017-04-19 | 陕西法士特齿轮有限责任公司 | Computing method of AMT finished automobile mass |
| CN108152050A (en) * | 2017-12-22 | 2018-06-12 | 南京越博动力系统股份有限公司 | A kind of whole-car parameters calibration method |
| CN108152050B (en) * | 2017-12-22 | 2020-02-14 | 南京越博动力系统股份有限公司 | Finished automobile parameter calibration method |
| CN109466561A (en) * | 2018-09-28 | 2019-03-15 | 潍柴动力股份有限公司 | Vehicular gross combined weight calculation method and system |
| CN111198032A (en) * | 2018-11-19 | 2020-05-26 | 陕西汽车集团有限责任公司 | Real-time estimation method for automobile mass |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113119727A (en) * | 2021-05-07 | 2021-07-16 | 恒大新能源汽车投资控股集团有限公司 | Vehicle overload processing method and device |
| CN113581192A (en) * | 2021-08-05 | 2021-11-02 | 东风汽车集团股份有限公司 | Vehicle quality correction method for identifying slope gradient based on vehicle quality |
| CN113581192B (en) * | 2021-08-05 | 2023-10-03 | 东风汽车集团股份有限公司 | Vehicle mass correction method based on vehicle mass recognition ramp gradient |
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