CN114154387B - Optimal index oil stirring resistance model identification method in gear shifting synchronization process - Google Patents
Optimal index oil stirring resistance model identification method in gear shifting synchronization process Download PDFInfo
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- CN114154387B CN114154387B CN202111001317.3A CN202111001317A CN114154387B CN 114154387 B CN114154387 B CN 114154387B CN 202111001317 A CN202111001317 A CN 202111001317A CN 114154387 B CN114154387 B CN 114154387B
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Abstract
The invention discloses an optimal index oil stirring resistance model identification method in a gear shifting synchronization process, belonging to the technical field of electric automobile gear shifting control. The method comprises the following steps: step 1: setting a relational expression of the oil stirring resistance and the torque and the rotating speed of the driving motor; and 2, step: collecting the rotating speed and the torque of a driving motor under different driving torque working conditions; and step 3: selecting a target function to establish an optimal index model; and 4, step 4: solving the optimal index in the step 3 by using a genetic algorithm; and 5: acquiring a torque value and a rotating speed value of a driving motor in a primary neutral gear, judging whether the data is valid, and if so, turning to the step 6; if not, outputting the current polynomial coefficient; and 6: and identifying the polynomial coefficient of the oil stirring resistance on line, adding the torque and the rotating speed value of the driving motor into the data queue of the synchronous process, and turning to the step 3. The invention can eliminate unreasonable data and improve the anti-electromagnetic interference capability, thereby improving the on-line oil stirring resistance identification precision.
Description
Technical Field
The invention relates to the technical field of gear shifting control of pure electric vehicles, extended range electric vehicles and parallel and series-parallel hybrid electric vehicles, in particular to an identification method of an optimal index oil stirring resistance model in a gear shifting synchronization process.
Background
An electrically driven mechanical transmission (EMT) system has the comprehensive advantages of simple structure, low cost, high system efficiency, small volume, light weight and the like, and has recently gained importance in the industry and is increasingly applied to pure electric vehicles and hybrid electric vehicles.
There are two main directions for the current control of gear shifting: (1) an electrically driven mechanical transmission system having a synchronizer; and (2) an electrically driven mechanical transmission system without a synchronizer. An electrically driven mechanical transmission with a synchronizer needs to rapidly perform an active synchronous speed process; an electrically driven mechanical transmission without a synchronizer requires a fast active synchronization of the rotational speed and the rotational angle. In the above synchronization process, in order to complete the synchronization process more quickly to improve the shift performance, it is necessary to compensate for the oil churning resistance from the inside of the transmission, that is, to identify the oil churning resistance from the inside of the transmission.
Identify the oil mixing resistance model that needs comparatively accurate to oil mixing resistance, the source of current model mainly has two kinds: (1) a theoretical model; and (2) an experimental model. The theoretical model is established under quasi-static state (namely constant rotating speed) according to fluid mechanics knowledge, only the influence of the rotating speed and temperature on the oil stirring resistance is considered, and the influence of driving torque and the dynamic response of the oil stirring resistance are not considered; the experimental model is fitted according to experimental data, and the formula is obtained by an empirical formula. On one hand, the method is limited to an empirical formula and cannot be flexibly changed, and on the other hand, the online identification is not facilitated due to the nonlinear characteristics.
In order to solve the limitation of oil stirring resistance identification and further improve the gear shifting performance of the electrically-driven mechanical transmission, the invention provides an optimal index oil stirring resistance model identification method in the gear shifting synchronization process.
Disclosure of Invention
The invention aims to provide an identification method of an optimal index oil stirring resistance model in a gear shifting synchronization process, which is characterized by comprising the following steps of:
step 1: setting a relational expression of the oil stirring resistance and the torque and the rotating speed of the driving motor, and then turning to the step 2;
and 2, step: in a neutral position, acquiring the rotating speed and the torque of the driving motor under different driving torque working conditions by utilizing the state that the joint sleeve is disengaged from the joint gear ring, and then turning to the step 3;
and step 3: establishing an optimal index model by taking the minimum sum of the sums of the variances of different coefficients obtained by least square identification under different working conditions as a target function, and then turning to step 4;
and 4, step 4: solving the optimal index in the step 3 by using a genetic algorithm, and then turning to the step 5;
and 5: acquiring a torque value and a rotating speed value of a driving motor in a primary neutral position, judging whether the data is valid, and if so, turning to the step 6; if not, outputting the current polynomial coefficient;
step 6: and (4) identifying the polynomial coefficient of the oil stirring resistance on line by using a recursive least square method, adding the torque and rotating speed values of the driving motor into the data queue of the synchronization process, expanding and solving an optimal index database, and turning to the step 3.
In the step 1, a relational expression of the oil stirring resistance and the torque and the rotating speed of the driving motor is set as follows:
the parameters in the formula are physical quantities at the time k,
to estimate the oil churning resistance, ω (k) is the rotational speed of the drive motor, T t (k) To drive the torque of the motor, x i (i =1,2,3,4) is the index to be solved, a i (k) (i =1,2,3,4) is the coefficient to be identified; wherein the exponential number of ω (k) increases or decreases depending on the actual operating conditions.
The different driving torque working conditions in the step 2 refer to the interval of 5Nm from 5Nm to the maximum driving torque of the driving motor.
The objective function in step 3 is as follows:
in the formula, 4 represents 4 coefficients in the formula (1), and N represents the total working condition number; the variable is [ x ] 1 ,x 2 ,x 3 ,x 4 ]。
In the step 5, the criterion for the invalidity of the data is that the acquired rotating speed value is suddenly changed due to electromagnetic interference, namely the calculated average acceleration exceeds the maximum acceleration which can be currently realized by the driving motor.
The invention has the beneficial effects that:
1. according to the oil stirring resistance model, influence factors of driving torque are added, and the optimal index is achieved;
2. the database for solving the optimal index on a long time scale can be expanded, and the optimal index can be updated off line; the oil stirring resistance model coefficient on a short time scale can be identified on line;
3. the method can eliminate unreasonable data and improve the anti-electromagnetic interference capability to a certain extent, thereby improving the on-line oil stirring resistance identification precision, providing technical support for resistance compensation in the synchronization process of the electrically-driven mechanical transmission (with a synchronizer or without the synchronizer), and accelerating the synchronization process to further approach the optimal control level.
Drawings
FIG. 1 is a flowchart of the method for identifying an optimal index oil stirring resistance model in the gear shifting synchronization process according to the present invention.
Detailed Description
The invention provides a method for identifying an optimal index oil stirring resistance model in a gear shifting synchronization process, which is further explained by combining an attached drawing and a specific embodiment.
FIG. 1 is a flowchart of the method for identifying an optimal index oil stirring resistance model in the gear shifting synchronization process according to the present invention. The specific implementation is as follows:
1) And setting a relational expression of the oil stirring resistance, the torque and the rotating speed of the driving motor. The invention sets the oil stirring resistance as a polynomial form of the torque and the rotating speed of the driving motor, and the rotating speed corresponding indexes are 3. Namely that
Wherein the physical quantities at the time k are all represented in the formula,
to estimate the oil churning resistance, ω (k) is the rotational speed of the drive motor, T t (k) To drive the torque of the motor, x i For the index to be solved, a i (k) Is the coefficient to be identified. Go to 2);
2) And in the neutral position, the rotating speed and the torque of the driving motor under different driving torque working conditions (from 5Nm to the maximum driving torque of the driving motor, with the interval of 5 Nm) are acquired by utilizing the state that the engaging sleeve is disengaged from the engaging gear ring. Taking the maximum torque of the drive motor as 25Nm for example, the drive motor torques are set to 5Nm,10Nm,20 Nm, and 25nm, respectively, and the drive motor is increased from zero to the maximum rotational speed, and then the drive motor torque is set to 0 to reduce the rotational speed to zero. And then taking the torque and the rotating speed of the driving motor in the speed increasing stage as the working condition data sequence used in the step 3). Then 5 different condition data sequences can be obtained. Go to 3);
3) And establishing an optimal problem model. Setting the optimal target as follows: under different working conditions, the sum of the variances of different coefficients obtained by least square identification is minimum, namely the objective function is as follows:
in the formula, 4 represents 4 coefficients in the formula (3), N represents the total number of working conditions, and the initial value of N is 5 according to the step 2); the variable is [ x ] 1 ,x 2 ,x 3 ,x 4 ]. Go to 4);
4) Solving the optimal problem in 3) by using a genetic algorithm. The specific operation steps can be referred to as follows:
(1) randomly generating 100 groups, wherein the number of genes in each individual is 20, and each 5 genes code and represent a power series x i ；
(2) Selecting a Fitness function as Fitness (x) =1/J (x), and eliminating with the survival rate of 60%;
(3) randomly selecting parents to mate (the mating probability is 60%), randomly selecting to exchange all genes behind a certain gene to obtain offspring with the number equal to the eliminated number in the step (2);
(4) obtaining the eliminated parents in the step (2) and the offspring population obtained in the step (3) through the steps, carrying out gene variation on the individuals with the probability of 3.3% to obtain a new round of population, finishing iteration if the sum of the mean square difference between the individual with the maximum fitness in the previous round and the individual with the maximum fitness in the current round is less than 0.2, and otherwise, turning to the step (2).
After the optimal index is obtained, 5) is turned to;
5) In neutral position, taking the optimal index obtained by the solution in the step 4) as the current optimal index, and utilizing a recursive least square identification algorithm to identify the formula (1) on line) Coefficient a of (1) i (k) And then the oil stirring resistance value is obtained on line and used in the subsequent gear shifting control so as to improve the gear shifting quality. For the operation of recursive least square identification in a certain synchronization process, the following can be referred to:
(1) assuming that the acceleration of the driving motor is not changed in delta T time, the oil stirring resistance T in the transmission can be obtained by analyzing the stress of the input shaft of the motor and the input shaft of the transmission f As shown in formula (5)
In the formula (5) J in Is the rotational inertia of the input end of the speed changer. If the obtained oil stirring resistance value is less than 0, rejecting the group of data points; if the oil stirring resistance value is more than or equal to 0, performing the following least square identification;
(2) if it is the initial time, setting the initial value
P (0) =0. Wherein
If not, directly turning to the step (3);
(3) according to the sampling structure
(4) Calculating a correction factor
(5) The estimated values of the parameters are calculated,
(6) the intermediate transition matrix is updated and,
(7) and (5) recursion is carried out by one step of k +1 → k, and then the step goes to 2) for the next step of parameter identification.
Go to 6);
6) Step 5) can collect the torque value and the rotating speed value of the driving motor under the working condition of the synchronous process at the same time, and the torque value and the rotating speed value are used for expanding a database for solving the optimal index, namely increasing the value of N in 3); go to 3).
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
1. An optimal index oil stirring resistance model identification method in a gear shifting synchronization process is characterized by comprising the following steps of:
step 1: setting a relational expression of the oil stirring resistance and the torque and the rotating speed of the driving motor, and then turning to the step 2;
and 2, step: in a neutral position, acquiring the rotating speed and the torque of the driving motor under different driving torque working conditions by utilizing the state that the joint sleeve is disengaged from the joint gear ring, and then turning to the step 3;
and 3, step 3: establishing an optimal index model by taking the minimum sum of the sums of the variances of different coefficients obtained by least square identification under different working conditions as a target function, and then turning to step 4;
and 4, step 4: solving the optimal index in the step 3 by using a genetic algorithm, and then turning to a step 5;
and 5: acquiring a torque value and a rotating speed value of a driving motor in a primary neutral position, judging whether the data is valid, and if so, turning to the step 6; if not, outputting the current polynomial coefficient;
step 6: and (3) identifying the polynomial coefficient of the oil stirring resistance on line by using a recursive least square method, adding the torque and rotating speed values of the driving motor into the data queue of the synchronization process, expanding and solving an optimal index database, and turning to the step 3.
2. The method for identifying the optimal index oil stirring resistance model in the gear shifting synchronization process according to claim 1, wherein the relation between the oil stirring resistance and the torque and the rotating speed of the driving motor is set in the step 1 as follows:
the parameters in the formula are physical quantities at the time k,
for the estimated value of the oil churning resistance, ω (k) is the rotational speed of the drive motor, T t (k) To drive the torque of the motor, x i (i =1,2,3,4) is the index to be solved, a i (k) (i =1,2,3,4) is the coefficient to be identified; wherein the exponential number of ω (k) increases or decreases depending on the actual operating conditions.
3. The method for identifying the optimal exponential oil stirring resistance model in the gear shifting synchronization process according to claim 1, wherein the different driving torque working conditions in the step 2 refer to the maximum driving torque starting from 5Nm to the driving motor at intervals of 5 Nm.
4. The method for identifying the optimal exponential oil stirring resistance model in the gear shifting synchronization process according to claim 1, wherein the objective function in the step 3 is as follows:
in the formula, 4 represents 4 coefficients in the formula (1), and N represents the total working condition number; the variable is [ x ] 1 ,x 2 ,x 3 ,x 4 ]。
5. The method for identifying the optimal exponential oil stirring resistance model in the gear shifting synchronization process according to claim 1, wherein the criterion that the data is invalid in step 5 is that the acquired rotation speed value is suddenly changed due to electromagnetic interference, that is, the average acceleration is required to exceed the maximum acceleration currently achievable by the driving motor.
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Citations (2)
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| 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 |
| CN107139929A (en) * | 2017-05-15 | 2017-09-08 | 北理慧动（常熟）车辆科技有限公司 | A kind of estimation of heavy fluid drive vehicle broad sense resistance coefficient and modification method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN107139929A (en) * | 2017-05-15 | 2017-09-08 | 北理慧动（常熟）车辆科技有限公司 | A kind of estimation of heavy fluid drive vehicle broad sense resistance coefficient and modification method |
Non-Patent Citations (2)
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