CN107139929B - A kind of estimation of heavy type fluid drive vehicle broad sense resistance coefficient and modification method - Google Patents
A kind of estimation of heavy type fluid drive vehicle broad sense resistance coefficient and modification method Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
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- B60W2510/00—Input parameters relating to a particular sub-units
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- B60W2510/104—Output speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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Abstract
The present invention relates to a kind of estimation of heavy fluid drive vehicle broad sense resistance coefficient and modification methods, comprising: establishes the Longitudinal Dynamic Model of heavy fluid drive vehicle；Using improved belt forgetting factor Recursive Least-square in Longitudinal Dynamic Model complete vehicle quality and broad sense resistance coefficient recognize；Judge abnormal data, the Recursive Least-square of the complete vehicle quality of identification, broad sense resistance coefficient and used improved belt forgetting factor is modified；Emulation and real train test are carried out to correction result.The Recursive Least-square of improved belt forgetting factor proposed by the present invention can preferably estimate broad sense resistance coefficient；The modification method of proposition can reduce interference of the abnormal data to estimated result.
Description
Technical Field
The invention relates to the technical field of automatic control, in particular to a method for estimating and correcting a generalized resistance coefficient of a heavy automatic transmission vehicle.
Background
The automatic mechanical transmission not only can reduce the labor intensity of a driver and improve the dynamic property and the economical efficiency of an automobile, but also has the advantages of high transmission efficiency, low cost, easy maintenance and the like, and is small in modification of the existing manual transmission, so that the automatic mechanical transmission becomes an important direction for the research of the field of automatic speed change of heavy vehicles. If the AMT gear shifting decision is only according to the automobile running state and the intention of a driver, the change of external resistance is not considered, the problems of frequent upslope gear shifting, aggravated mechanism abrasion, reduced comfort and the like can be caused. Nowadays, many research institutes are conducting research on gear shift decision optimization according to external resistance changes.
For the measurement of external resistance change, real-time longitudinal vehicle speed and transverse vehicle speed can be obtained by a GPS (global positioning system) additionally arranged on a vehicle, and then the road gradient is calculated by utilizing a kinematic equation and a low-pass filter.
By collecting information such as vehicle speed, braking force and driving force and combining a longitudinal dynamics equation, a Kalman filtering algorithm is further utilized, the quality and the gradient can be jointly estimated, however, a Kalman filter cannot track state mutation in real time, and therefore the estimation method utilizing the Kalman filter needs to be further researched.
By collecting information such as vehicle speed, engine torque, engine rotating speed and the like, and utilizing a recursion least square method with forgetting factors, multi-parameter online estimation can be carried out on the mass and the gradient. However, the recursive least square method itself is not robust enough and is easily affected by abnormal data, and the recursive least square method itself with a forgetting factor estimates the least square error of all the observed quantities before a certain time, and the estimated deviation becomes larger gradually.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide a method for estimating and correcting a generalized resistance coefficient of a heavy automatic transmission vehicle, so as to solve the problems that the estimation error of the generalized resistance coefficient is large, the abrupt change state cannot be tracked in real time, and the method is easily influenced by abnormal data in the prior art.
The purpose of the invention is mainly realized by the following technical scheme:
a method for estimating and correcting a generalized resistance coefficient of a heavy automatic transmission vehicle comprises the following steps:
step S1: establishing a longitudinal dynamic model of the heavy automatic transmission vehicle;
step S2: identifying the whole vehicle mass and the generalized resistance coefficient in the longitudinal dynamic model by using an improved recursive least square identification algorithm with forgetting factors;
step S3: judging abnormal data and correcting;
step S4: and carrying out simulation and real vehicle test on the correction result.
Further, the longitudinal dynamics model equation in step S1 is:
y＝ΦTθ
phi is a measurable variable, theta is a parameter to be estimated,
and is
ig-transmission ratio of the variator
i0-gear ratio of the main reducer
ηTTransmission efficiency
rwTire rolling radius
Delta-rotating mass conversion factor
m-vehicle mass
β -coefficient of generalized resistance
TeDifference between total engine torque and internal engine torque
JeEngine moment of inertia
ωeEngine speed
ω -the transmission output shaft speed.
Further, the identification equation of the total vehicle mass and the generalized resistance coefficient is as follows:
wherein,
λ1、λ2is a forgetting factor.
Further, the modification of step S3 includes modifying the identified vehicle mass, the generalized resistance coefficient, and the adopted improved recursive least square identification algorithm with forgetting factor.
Before correcting the identified whole vehicle mass, the generalized resistance coefficient and the adopted improved recursive least square identification algorithm with forgetting factors, judging abnormal data, wherein the abnormal data judgment conditions are as follows:
analyzing the identification results during gear shifting and gear non-shifting respectively to obtain | max (delta β) | and | max (delta M) | in the abnormal data judgment condition during gear shifting and gear non-shifting, and taking | delta β |, and | delta M | values with the probability of being greater than 99% according to the accumulated probability analysis of experimental data during gear shifting and gear non-shifting respectively.
And further, correcting the identified mass of the whole vehicle and the generalized resistance coefficient, wherein the correction comprises skipping the real-time estimated value of the current moment and outputting the normal estimated value of the previous moment to the gear shifting module.
Further, the improved recursive least square identification algorithm with the forgetting factor is corrected by reducing the forgetting factor of abnormal data.
And adding abnormal data every few seconds in the simulation process, and comparing results before and after correction.
In a real vehicle test, the estimation precision of the improved recursive least square identification algorithm with forgetting factors is obtained by comparing the estimated value and the true value of the generalized resistance coefficient when the gear is not shifted.
The improved recursion least square identification algorithm with forgetting factors is tested through a real vehicle test to correct abnormal data in the gear shifting process.
The invention has the following beneficial effects:
the embodiment of the invention provides an estimation and correction method for a generalized resistance coefficient of a heavy automatic transmission vehicle, and the generalized resistance coefficient can be better estimated by the improved recursive least square identification algorithm with forgetting factors; the proposed correction method can reduce the interference of abnormal data to the estimation result.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a flow chart of a method for estimating and correcting a generalized drag coefficient for a heavy automatic transmission vehicle;
FIG. 2 is a flow chart of an output signal modification algorithm;
FIG. 3 is a simulation result without exception data;
FIG. 4 is a simulation result of the exception data;
FIG. 5 is a real vehicle test result of a no-shift condition;
FIG. 6 shows the results of a shift test performed in a real vehicle.
Detailed Description
The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and which together with the embodiments of the invention serve to explain the principles of the invention.
The embodiment of the invention provides a method for estimating and correcting a generalized resistance coefficient of a heavy automatic transmission vehicle, and a flow chart of the method is shown in figure 1, and the method comprises the following steps:
step S1: establishing a longitudinal dynamic model of the heavy automatic transmission vehicle;
when the braking is not considered, the longitudinal dynamics equation of the vehicle is:
Ft＝Fi+Fw+Ff+Facc (1)
in the formula: ftTraction to which a vehicle is subjected
FiRamp resistance FwAir resistance
FfRolling resistance FacAcceleration resistance
Because Facc＝δma，Then
In the formula: t isinVariator input shaft torque
ig-transmission ratio of the variator
i0-gear ratio of the main reducer
ηT-transmission efficiency rwTire rolling radius
Delta-rotating mass conversion factor
a-vehicle acceleration m-vehicle mass
To change the ramp resistance FiAir resistance FwRolling resistance FfThe sum of these external resistances is equivalent to a generalized resistance mg β, i.e.:
Fi+Fw+Ff＝mgβ (3)
β -generalized resistance coefficient
Equation (2) can be simplified as:
the transmission input shaft torque can be estimated by establishing a clutch model, or can be obtained by adding a torque sensor, or can be approximated by the actual output torque of the engine. The engine adopted by the target vehicle model of the embodiment of the invention CAN provide total torque of the engine, internal torque of the engine and rotating speed of the engine through the CAN bus, and the rotational inertia of the engine is known, so that:
in the formula: t iseDifference between total engine torque and internal engine torque
JeEngine moment of inertia ωeEngine speed
The acceleration of the automobile can be obtained through an acceleration sensor and a GPS, the invention obtains the rotating speed of the output shaft of the gearbox through a rotating speed sensor of the output shaft of the gearbox, and then the acceleration of the automobile is obtained through calculation, as shown in the formula:
in the formula: ω -the transmission output shaft speed.
The current longitudinal dynamics model has two differential quantitiesAlthough already for the engine speed ωeThe rotating speed omega of the output shaft of the gearbox is filtered, but the signal is very sensitive to high-frequency interference after being differentiated, so that the condition that the high-frequency interference is causedThe signal deviates from the true value. For this problem, the longitudinal dynamical models are integrated simultaneously. Comprehensively considering the state change condition during driving, the non-time-varying variable comprises the transmission ratio i of the main speed reducer0η transmission efficiencyTThe mass m of the whole vehicle and the rolling radius r of the tirewThen the longitudinal dynamics model can be expressed as:
in order to apply the recognition algorithm proposed by the present invention, equation (7) can be expressed as
y＝ΦTθ (8)
Wherein
Phi is a measurable variable, and theta is a parameter to be estimated.
Step S2: identifying the whole vehicle mass and the generalized resistance coefficient in the longitudinal dynamic model by using an improved recursive least square identification algorithm with forgetting factors;
specifically, assume that there is a mathematical model as follows:
y(t)＝x1(t)θ1+x2(t)θ2+…+xn(t)θn (9)
can be written as:
Y(t)＝Φ(t)θ(t) (10)
wherein,
when the observed quantity is comparedIn many cases, it is difficult to find a reasonable estimate due to possible interference of the signalIn this case a residual epsilon (t) is introduced,
and let the least square criterion be
And at least, only:
thus, the method can obtain the product,
however, as time goes on, the observed quantity is larger and larger, the calculation quantity is larger and larger, the requirement of identifying the generalized resistance coefficient in real time cannot be met, and therefore a recursive least square identification algorithm is introduced.
Let P (t) equal to [ phi ]T(t)Φ(t)]-1Then, through derivation, it can be known that:
then
Namely, it is
And in each operation period of the vehicle-mounted single chip microcomputer, the identification value of the real-time generalized resistance coefficient can be obtained only by continuous recursion. However, the recursive least square identification algorithm is completely equivalent to performing a least square method on the whole observed quantity, and the former observed quantity interferes with the identification accuracy, so that a forgetting factor is introduced, namely, the observed quantity at each moment is weighted
Let λ denote the forgetting factor, λ ═ ρ2，
Then the formula (14) becomes
Formula (15) is changed to
As can be seen from the analysis of the longitudinal dynamics model formula (7), the update rates of the parameters of the whole vehicle mass and the generalized resistance coefficient are different, so that double forgetting factors are adopted, and the least square criterion is changed into
WhereinAnd at this timey、Can be carried by the formula (7) or the formula (8), then
By analyzing the observed quantity, it can be known that the recursive least square identification algorithm with the forgetting factor reduces the influence of the early observed quantity on the real-time parameter estimation, but the forgetting factor cannot be selected to be too small, because the algorithm which is essentially a weighted least square method becomes more unstable as the forgetting factor becomes smaller, in order to further reduce the influence of the early observed quantity on the real-time parameter estimation under the condition that the forgetting factor is not changed, and in combination with the actual condition, the data with the maximum real-time parameter association is selected from the observed quantity to be estimated, then the method can be used for estimating the real-time parameter according to the result of the recursive least square
ThenThe derivation of (c) can be simplified to:
θ2(t) cannot be directly observed, so is approximately equal toThen
Y in the recursion11(t)，Y12(t)，Y13(t) are each independently
The same can be obtained
The recursion relationship is
The formulas (25) and (26) are obtained by arranging:
it is then possible to obtain,
step S3: judging abnormal data and correcting;
because the engine speed and the gearbox output shaft speed are acquired by the sensors, and the engine torque is also related to the engine speed, the gearbox output shaft speed and the engine torque all fluctuate, even generate abnormal data, and the data can have great influence on the estimation of real-time parameters.
To reduce this effect, it is first necessary to determine which are abnormal data. If the input parameters are directly judged, on one hand, the parameters are too many, and the signal fluctuation is large, on the other hand, small disturbance interaction existing in each input signal may cause large influence on estimation, so that the abnormal data occurrence time is difficult to correctly judge from the input signals. The invention estimates two parameters in real time, wherein the mass of the whole vehicle does not change under the general driving working condition, the generalized resistance coefficient generally changes along with the gradient when the vehicle is not braked, and the change of the gradient generally does not change suddenly. Therefore, the abnormal data determination condition may be defined as:
when the gear is not shifted, the values of | max (Δ β) |, and | max (Δ M) | in the determination conditions are 0.005 and 100, respectively, where the cumulative probability of the experimental data is greater than 99%.
After the abnormal data is judged, correction is needed, on one hand, a signal output to the next application module (namely, a gear shifting module) is corrected, and on the other hand, a modified recursive least square identification algorithm with a forgetting factor is corrected.
According to statistics, the absolute value of the real-time difference of the whole vehicle mass is rarely larger than 100, while the absolute value of the difference of the generalized resistance coefficient larger than 0.005 is random, short in duration and long in interval time, and is recovered to a normal estimated value quickly after occurrence, so that the real-time estimated value of the moment can be skipped to correct the situation, and the normal estimated value of the previous moment is output to the gear shifting module. During gear shifting, the absolute value of the difference of the generalized resistance coefficients is frequently larger than 0.015, and analysis shows that the larger the difference of the generalized resistance coefficients is, the longer the estimated value deviates from the real value, and meanwhile, the gear shifting time is averagely 1 second, and the generalized resistance coefficients generally have small change in the time, so that the output signal 1 second after the sudden change is started can be corrected to be the normal estimated value at the moment before the sudden change occurs. In order to reduce unnecessary correction within 1 second, comparison of the estimated value with a normal estimated value is maintained after the correction is started, and when the estimated value satisfies a constraint condition, the output signal is updated to a real-time estimated value.
The flow chart of the correction algorithm is shown in fig. 2, and comprises the following steps:
step 1, judging a timer TcWhether the timing unit is 1 operation period and 5ms is less than or equal to 0 or not is judged, the step 2 is executed, and the step 3 is executed or not;
step 2, judging whether the difference of the generalized resistance coefficients is less than or equal to 0.005 and the difference of the whole vehicle mass is less than or equal to 100, jumping to step 8 if yes, and jumping to step 4 if not;
step 3, judging whether the difference of the generalized resistance coefficient is less than or equal to 0.005 and the difference of the whole vehicle mass is less than or equal to 100, jumping to the step 5, and jumping to the step 6;
step 4, timer TcSetting the generalized resistance coefficient difference to be 2, judging whether the generalized resistance coefficient difference is less than or equal to 0.015 and the difference of the whole vehicle mass is less than or equal to 100, jumping to the step 7, and judging whether a timer T is in the stepcSetting to 200, and jumping to step 7;
step 5, timer TcSetting to be 1, and jumping to the step 6;
step 6, judging TcWhether the value is less than 2, if yes, jumping to the step 8, and if not, jumping to the step 7;
step 7, keeping the generalized resistance coefficient and the whole vehicle quality unchanged, and jumping to step 9;
step 8, updating the generalized resistance coefficient and the whole vehicle mass, and jumping to step 9;
step 9, timer TcMinus 1.
The correction method for the output signal is based on the premise that the estimation value can be quickly restored to a normal estimation value after mutation, and the improved recursive least square identification algorithm with forgetting factors needs to be corrected when the estimation value is required to be quickly restored to the normal estimation value, so that the interference of abnormal data causing mutation is reduced. The invention realizes the correction by reducing the weighting factor of the abnormal data, namely the forgetting factor
Wherein
The recurrence relation is corrected to
The other relational expressions (25) to (28)) were not changed.
Step S4: carrying out simulation and real vehicle test on the correction result;
in order to verify the effectiveness of the improved recursive least square identification algorithm with forgetting factors, the present embodiment takes a target heavy off-road vehicle as a reference, and builds a simulation model, wherein the model parameters are shown in table 1, wherein ig3Representing the transmission ratio, delta, of the transmission in gear 33Representing the rotating mass conversion factor at gear 3. In the simulation, a constant engine load and a constant gear working condition are adopted, the engine load is 50%, the gear is 3, and the vehicle acceleration can be accurately obtained in the simulation, so that the vehicle acceleration can be directly obtained by the simulation model as an input signal. Input signal of module under simulation working condition, improved recursive least square identification algorithm with forgetting factorThe generalized resistance coefficient estimation result is compared with the real one, and compared with the estimation result of the recursive least square identification algorithm with the improved forgetting factor is shown in fig. 3.
TABLE 1
As can be seen from FIG. 3, the generalized resistance coefficient estimated after 60s by the improved recursive least square identification algorithm with forgetting factors is closer to the true value, and the influence of the previous data on identification is reduced. In order to test the anti-interference performance of the improved recursive least square identification algorithm with forgetting factors on abnormal data, the abnormal data is added every ten seconds in the simulation process, and the results before and after correction are compared, which is shown in fig. 4.
According to the observation of the simulation result, the estimation value deviation of the improved recursive least square identification algorithm with the forgetting factor before the correction is larger at the abnormal data point, particularly the estimation of the generalized resistance coefficient, but after the interference, the estimation value is quickly recovered to be normal, which shows that the influence of the abnormal data is quickly reduced by the improved recursive least square identification algorithm with the forgetting factor after the correction, thereby verifying that the influence of the abnormal data can be quickly reduced by the improved recursive least square identification algorithm with the forgetting factor after the abnormal data appears. On the other hand, we can see that the correction algorithm ensures the accuracy of the algorithm estimation value.
In order to further check the effectiveness of the improved recursive least square identification algorithm with forgetting factors, a real vehicle test is carried out and compared with the verified identification result.
First, the estimated value and the true value of the generalized resistance coefficient when no gear shift is performed are compared, and as a result, as shown in fig. 5, it can be seen from fig. 5 that the estimation accuracy of the identification algorithm is improved.
In order to test the correction effect of the improved recursive least square identification algorithm with the forgetting factor on abnormal data, particularly abnormal data in the gear shifting process, a real vehicle test is carried out, and the result is shown in fig. 6, as can be seen from fig. 6, the sudden change of an engine torque signal in the gear shifting process is large, meanwhile, the power interruption and the failure of a longitudinal dynamic model are known to exist in the gear shifting process, so that the abnormal data in the gear shifting process are more, and the influence of the improved recursive least square identification algorithm with the forgetting factor on the abnormal data is effectively corrected, so that the estimation precision is improved compared with that before improvement.
In summary, the embodiment of the present invention provides a method for estimating and correcting a generalized resistance coefficient of an automatic heavy-duty vehicle, which establishes a vehicle longitudinal dynamics model that can directly use existing signals and can be used for estimating the generalized resistance coefficient on the basis of signal analysis of an AMT heavy-duty vehicle; in order to solve the influence of early observed quantity on an estimation result, an improved recursive least square identification algorithm with a forgetting factor is provided, and meanwhile, in order to further reduce the interference of abnormal data on the identification result, a correction method based on the improved recursive least square identification algorithm with the forgetting factor is provided. The simulation analysis and real vehicle test results of the improved recursive least square identification algorithm with forgetting factors and the correction method show that: the improved recursive least square identification algorithm with forgetting factors can better estimate the generalized resistance coefficient; the proposed correction method can reduce the interference of abnormal data to the estimation result.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (9)
1. A method for estimating and correcting a generalized resistance coefficient of a heavy automatic transmission vehicle is characterized by comprising the following steps:
step S1: establishing a longitudinal dynamic model of the heavy automatic transmission vehicle;
step S2: identifying the whole vehicle mass and the generalized resistance coefficient in the longitudinal dynamic model by using an improved recursive least square identification algorithm with forgetting factors;
step S3: judging abnormal data and correcting;
step S4: carrying out simulation and real vehicle test on the correction result;
the longitudinal dynamics model equation in the step S1 is as follows:
y＝ΦTθ
phi is a measurable variable, theta is a parameter to be estimated,
and is
ig-transmission ratio of the variator
i0-gear ratio of the main reducer
ηTTransmission efficiency
rwTire rolling radius
Delta-rotating mass conversion factor
m-vehicle mass
β -coefficient of generalized resistance
TeDifference between total engine torque and internal engine torque
JeEngine moment of inertia
ωeEngine speed
ω -the transmission output shaft speed.
2. The method of claim 1, wherein the identification equation for the total vehicle mass and generalized resistance coefficient is:
wherein,
λ1、λ2is a forgetting factor.
3. The method of claim 2, wherein the modifying of step S3 includes modifying the identified vehicle mass, the generalized drag coefficient, and the modified recursive least squares identification algorithm with forgetting factor.
4. The method according to any one of claims 1 to 3, characterized in that before the identified vehicle mass, generalized resistance coefficient and the adopted improved recursive least square identification algorithm with forgetting factor are corrected, abnormal data are judged, and the abnormal data judgment conditions are as follows:
analyzing the identification results during gear shifting and gear non-shifting respectively to obtain | max (delta β) | and | max (delta M) | in the abnormal data judgment condition during gear shifting and gear non-shifting, and taking | delta β |, and | delta M | values with the probability of being greater than 99% according to the accumulated probability analysis of experimental data during gear shifting and gear non-shifting respectively.
5. The method of claim 4 wherein modifying the identified gross vehicle mass, generalized resistance coefficient includes skipping a real-time estimate of the current time and outputting a normal estimate of the previous time to the shift module.
6. The method of claim 5, wherein the correction of the improved recursive least squares with forgetting factor algorithm is achieved by reducing the forgetting factor of the anomaly data.
7. The method of claim 5 or 6, wherein exception data is added every few seconds during the simulation and the results before and after correction are compared.
8. The method according to claim 2, characterized in that in a real vehicle test, the estimation accuracy of the improved recursive least square identification algorithm with forgetting factor is obtained by comparing the estimated value and the real value of the generalized resistance coefficient when no gear is shifted.
9. The method according to claim 5, 6 or 8, characterized in that the effect of correcting abnormal data during shifting is verified by a real vehicle test by a modified recursive least squares identification algorithm with forgetting factor.
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| CN112896164B (en) * | 2021-02-05 | 2022-05-10 | 北京理工大学 | Vehicle braking method, device and medium based on vehicle weight and gradient self-adaption |
| CN113002549B (en) * | 2021-05-24 | 2021-08-13 | 天津所托瑞安汽车科技有限公司 | Vehicle state estimation method, device, equipment and storage medium |
| CN113505331B (en) * | 2021-07-28 | 2024-01-19 | 中车青岛四方机车车辆股份有限公司 | Vehicle running resistance parameter identification method and system |
| CN114154387B (en) * | 2021-08-30 | 2022-11-18 | 清华大学 | Optimal index oil stirring resistance model identification method in gear shifting synchronization process |
| JP2023062515A (en) * | 2021-10-21 | 2023-05-08 | トヨタ自動車株式会社 | steering system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103402847A (en) * | 2011-03-04 | 2013-11-20 | 奥迪股份公司 | Method for determining the driving resistance of a vehicle |
| WO2014126523A1 (en) * | 2013-02-14 | 2014-08-21 | Scania Cv Ab | Simultaneous estimation of at least mass and rolling resistance |
| CN104010913A (en) * | 2011-12-28 | 2014-08-27 | 丰田自动车株式会社 | deceleration factor estimation device |
| WO2015060771A2 (en) * | 2013-10-23 | 2015-04-30 | Scania Cv Ab | Estimating a parameter for computing at least one force acting on a vehicle |
| CN104973069A (en) * | 2015-07-10 | 2015-10-14 | 吉林大学 | Online synchronous identification method for heavy truck air resistance composite coefficient and mass |
| DE102016012465A1 (en) * | 2015-10-29 | 2017-05-04 | Scania Cv Ab | Method for determining a change in the air resistance acting on a motor vehicle |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5920529B2 (en) * | 2013-04-02 | 2016-05-18 | トヨタ自動車株式会社 | Running resistance calculation device |
-
2017
- 2017-05-15 CN CN201710339910.6A patent/CN107139929B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103402847A (en) * | 2011-03-04 | 2013-11-20 | 奥迪股份公司 | Method for determining the driving resistance of a vehicle |
| CN104010913A (en) * | 2011-12-28 | 2014-08-27 | 丰田自动车株式会社 | deceleration factor estimation device |
| WO2014126523A1 (en) * | 2013-02-14 | 2014-08-21 | Scania Cv Ab | Simultaneous estimation of at least mass and rolling resistance |
| WO2015060771A2 (en) * | 2013-10-23 | 2015-04-30 | Scania Cv Ab | Estimating a parameter for computing at least one force acting on a vehicle |
| CN104973069A (en) * | 2015-07-10 | 2015-10-14 | 吉林大学 | Online synchronous identification method for heavy truck air resistance composite coefficient and mass |
| DE102016012465A1 (en) * | 2015-10-29 | 2017-05-04 | Scania Cv Ab | Method for determining a change in the air resistance acting on a motor vehicle |
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|---|---|
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