JP2008045651A - Shift simulation device, shift simulation program, and automobile - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an input command value for automatic transmission control tolerant to fluctuation of characteristic values of components of a device controlling the input command value or speed change operation. <P>SOLUTION: The input command value is initialized (S1), and the input command value is changed to carry out speed change simulation by using a model (S2). On the basis of output of the simulation, a target waveform is corrected such that fluctuation by the output becomes small (S4). An input command value providing the corrected target waveform is provided by learning and calculation (S5). By returning to S2 and repeating processing and placing fluctuation within a predetermined range, an appropriate target waveform is provided, and an input command value achieving the target waveform can be provided. The target waveform can be determined by fuzzy inference or sensitivity analysis. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a shift simulation for simulating a shift operation of an automatic transmission system for an automobile using a model.

  Conventionally, an automatic transmission is mounted on many vehicles, and the speed change operation in the automatic transmission is controlled so that the output shaft torque and the turbine rotational speed change as intended. In order to obtain this control command value (input command value), it is necessary to perform an actual vehicle running test under various conditions.

  However, since such a running test requires a large amount of time and work, a simulation using a model of an automatic transmission is also performed as an alternative.

For example, Patent Document 1, the torque ratio of the clutch-to-clutch shift, the input torque T _in, the torque capacity T ₁ of the engagement side frictional element, the torque capacity of the disengagement side frictional element T _2, T _in the T ₁ In accordance with the equation of motion T _in = AT ₁ + BT ₂ + C set by the correction term C, A representing the torque ratio between A and T _in representing T, and T ₂ balanced in the gear stage before the shift. It has been shown to determine the hydraulic command for either one of the elements.

  In addition, there exist proposals of patent document 2, patent document 3, etc. as what uses a fuzzy reasoning about shift control. Patent Document 4 describes air-fuel ratio control of an automobile engine using fuzzy reasoning. In quality engineering, there is a technique for finding a level for attenuating an error factor (noise) that varies in quality and determining stable design that is resistant to noise. Patent Documents 5 and 6 describe robot control using the idea of quality engineering.

JP 2004-340287 A JP-A-9-79364 JP-A-7-167274 JP-A-5-133263 JP 2002-175101 A JP 2003-186530 A "Introduction Taguchi Method" Kazuo Tatebayashi Published by Nikka Giren Publishers 2004

  In Patent Document 1, it is necessary to obtain in advance a hydraulic command for either the disengagement side or the engagement side friction element at the time of shifting. Further, the response to the performance variation is performed by on-board feedback control and learning control (these are included in the correction term), and no attempt is made to cope with the performance variation by feedforward control.

  Further, in Patent Documents 2 to 4, although fuzzy inference is used, the adaptation work for the parameters of the target device is necessary as before, and it does not lead to drastic reduction of man-hours.

  Furthermore, Patent Documents 5 and 6 are not applicable to automatic transmission control, not feedforward control, and Non-Patent Document 1 relates to determination of design specifications such as the mechanism, shape, and material of the target product. It is not intended for dynamic systems.

  The present invention relates to a shift simulation device that simulates a shift operation of an automatic transmission system for an automobile using a model, and is an apparatus that controls an input command value or a shift operation when performing a shift operation in the system. The characteristic value of the component is simulated with a variation, and the output value of the system as a result of the simulation is evaluated based on the magnitude of the influence on the variation. From the evaluation result, the output value of the variation is evaluated. The target output value that satisfies the required specification is determined by learning calculation to determine the target output value that satisfies the required specification and obtains the input command value to obtain the output that matches the determined target output value. It is characterized by.

Further, the present invention is a shift simulation device that simulates a shift operation of an automatic transmission system for an automobile using a model,
(A) A simulation is performed by giving a variation to an input command value or a characteristic value of a component of a device that controls the shift operation when performing a shift operation in the system;
(B) evaluating the output value of the system, which is the result of this simulation, based on the magnitude of the effect on the variation;
(C) Based on the evaluation result, the target output value is corrected by fuzzy inference,
(D) A simulation is performed using the corrected target output value, a new input command value is calculated by the learning calculation,
(E) Update the input command value with the calculated input command value, return to (a), and repeat the processes (a) to (d) until the evaluation in (b) satisfies the required specification. An input command value satisfying the above is obtained.

Further, the present invention is a shift simulation device that simulates a shift operation of an automatic transmission system for an automobile using a model,
(A) A plurality of levels are set for a plurality of control factors at the target output value,
(B) setting a plurality of target output values determined by a combination of a plurality of levels for a plurality of set control factors;
(C) Using a plurality of set target output values, simulating each of the input command value or the characteristic value of the component of the device that controls the shift operation by performing a shift operation in the system, respectively,
(D) evaluating a plurality of output values of the system as a result of the simulation based on the magnitude of the influence on the fluctuation, and determining a target output value that the obtained evaluation result satisfies the required specifications;
(E) Using the determined target output value, an input command value satisfying the required specifications is obtained by learning calculation.

  In (b), it is preferable that combinations of a plurality of levels of a plurality of control factors are determined using an orthogonal table, and (c) is performed for all the determined combinations.

  Furthermore, the present invention relates to a shift program for realizing the shift simulation apparatus as described above using a computer.

  According to the present invention, the input command value or the characteristic value of the component of the device that controls the shift operation is varied to simulate the shift operation of the automatic transmission system and detect how the corresponding output value varies. . Then, a target output value that is robust against fluctuations in the input command value or the characteristic value of the component of the device that controls the shift operation is calculated, and the input command value is obtained based on the obtained target output value. Therefore, an input command value that is resistant to fluctuations can be obtained, and an appropriate control value for the automatic transmission can be obtained.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  In the present embodiment, an input command value of an automatic transmission in which a gear ratio is set according to the engagement / release states of a plurality of friction engagement devices is adapted by simulation. In particular, by appropriately setting the target output value, an input command value that is resistant to variations in the input command value or the characteristic values of the components of the control device that controls the shift operation is obtained.

  The automatic transmission to be adapted is configured by a plurality of planetary gears and a hydraulic friction engagement device that is frictionally engaged by a plurality of hydraulic actuators, and the hydraulic actuators are electronically controlled by an in-vehicle computer. . The simulation is performed using a computer, and the input command value obtained as a result is stored in an in-vehicle memory or the like. Then, the in-vehicle computer controls the operation at the time of shifting using the input command value stored in the in-vehicle memory.

"Simulation by model"
FIG. 8 shows an outline of the adaptation work on a CAE (computer aided engineering) model. The CAE model includes an engine / torque converter model 10, a gear train model 12 composed of planetary gears of an automatic transmission, a release-side brake model 14, and an engagement-side brake model 16, and these are input to these models. The correction amount of the input command value is calculated by the correction amount calculation unit 18. The correction amount calculation unit 18 calculates the correction amount by learning calculation based on the comparison between the actual output value and the target output value.

  An initial value for the engine retard amount is supplied to the adder 20. Here, a correction amount for the engine retardation amount is supplied from the correction amount calculation unit 18, and the engine retardation amount is corrected by adding both. The corrected engine retard amount is supplied to the engine / torque converter model 10. Accordingly, the engine output is controlled according to the supplied engine retard amount, and the output turbine torque from the torque converter is determined.

The initial value of the release side hydraulic pressure command is supplied to the adder 22, and the correction amount of the release side hydraulic pressure command from the correction amount calculation unit 18 is supplied here. Accordingly, the release side hydraulic pressure command corrected by the adder 22 is obtained and supplied to the release side brake model 14, and the corresponding release side torque capacity T ₁ is output from the release side brake model 14.

Further, the initial value of the engagement side hydraulic pressure command is supplied to the adder 24, and here, the correction amount of the engagement side hydraulic pressure command from the correction amount calculation unit 18 is supplied. Therefore, the engagement hydraulic pressure command that is modified is obtained at the adder 24, which is supplied to the engaging-side brake model 16, the engagement side torque capacity T ₂ corresponding the engaging-side brake model 16 is output.

The turbine torque output from the engine torque converter model 10, the release side torque capacity T ₁ output from the release side brake model 14, and the corresponding engagement side torque capacity T ₂ from the engagement side brake model 16 are the gear train model. 12 is supplied. Thereby, the output shaft torque T _o and the turbine angular acceleration corresponding to an input from the gear train model 12 (d / dt) ω _t is output.

Then, the output shaft torque T _o is supplied as a negative value to the adder 26 where it is subtracted from the target value, resulting errors are supplied to the correction amount calculation unit 18. Further, the turbine angular acceleration (d / dt) ω _t is supplied to the adder 28 as a negative value, wherein the target value T _od, (d / dt) is subtracted from each of omega _td, resulting error is corrected amount It is supplied to the calculation unit 18.

  Based on the supplied error, the correction amount calculation unit 18 calculates and calculates correction amounts for the engine retardation amount, the release side hydraulic pressure command, and the engagement side hydraulic pressure command, and these are added to the adders 20, 22, and 24, respectively. Supply.

"Embodiment using fuzzy reasoning"
In the present embodiment, a feedforward command value for realizing an ideal shift operation is derived through a numerical calculation simulation for an automatic transmission mounted on an automobile. The shift stages of the automatic transmission are becoming multi-stage, and the feed-forward that realizes the target waveform by optimizing the target output shaft torque waveform and the target turbine angular acceleration waveform at the time of shifting between the stages. The command value (release side and engagement side brake hydraulic pressure and engine retard amount) is derived by learning calculation.

  Here, in an actual automatic transmission, the mechanical characteristics of the automatic transmission vary due to variations in individual parts, changes in traveling conditions, and the like. Without considering this characteristic variation, assuming that the automatic transmission follows the design constants (this case is called the nominal state) and deriving the input command value, the output result varies, and the fluctuation range is There is also a possibility that the tolerance is greatly deviated.

  Therefore, in this embodiment, the target output value (target waveform) used when the input command value is derived by learning calculation is made resistant to fluctuations in the input command value or the characteristic value of the component of the device that controls the shift operation. . That is, a target output value that falls within an allowable range in which a change in the output result is required even when there is a change in the input command value or the characteristic value of the component of the device that controls the shift operation is determined.

  Therefore, in this embodiment, a target waveform (target output shaft torque waveform in this embodiment) is set by numerical simulation on a computer, and an input command value for realizing this target waveform is set. To derive. In particular, fuzzy reasoning is used to determine the target waveform. These processes are automatically performed using a computer.

  FIG. 1 shows a flow of adapting work for obtaining the input command value of the automatic transmission in this embodiment. First, in step S1, an initial value of an input command value input to the automatic transmission is set. The initial command value only needs to be able to perform a predetermined shift stage switching operation. Therefore, a very simple input waveform such as a ramp input as shown in FIG. FIG. 2 (i) shows an example of initial values for the input command values (release side hydraulic pressure command and engagement side hydraulic pressure command) for brake grip replacement. According to FIG. 8 described above, there are three input command values: an engagement side hydraulic pressure command, a release side hydraulic pressure command, and a retard amount.

  Next, in step S2, a shift simulation is executed with an input command value or a characteristic value of a component of a device that controls the shift operation as an input. In the example of FIG. 2 (ii), the hydraulic pressure command value on the engagement side is assumed to be three inputs including a plus variation and a minus variation, and each is simulated. Further, FIG. 3 shows three results (output shaft torque) when a shift simulation is performed in a state where ± variation is added to the engagement-side hydraulic waveform as shown in FIG. 2 (ii). . As shown in FIG. 3, the change in the input command value causes a large difference in the output result.

  In step S3, a plurality of predetermined output results of the shift simulation are determined even when the input command value or the characteristic value of the component of the device that controls the shift operation is changed from the result of step S2. It is determined whether the evaluation index satisfies a standard value (required specification). As shown in (ii) and (iii) of FIG. 2, the evaluation index includes increase / decrease in output shaft torque during the shifting operation, maximum / minimum values of the output shaft torque, and shifting is completed from the start of shifting operation. And the amount of heat absorbed when the friction material stops relative rotation between the rotating bodies during the shifting operation. In addition, with the intention of avoiding an instantaneous shock, the gradient of the output shaft torque, the heat absorption rate of the friction material, and the like are employed as evaluation indexes.

  These evaluation indices are input to the membership function of the antecedent part of the fuzzy logic, and it is determined to which fuzzy set each index belongs to which degree (grade). An example of the membership function of the antecedent part is shown in FIG. In the case of torque increase / decrease in FIG. 4A, the torque increase / decrease is divided into three sets of small (S), middle (M), and large (B). 4B is divided into three sets of short (S), middle (M), and long (L).

  Then, the determination in step S3 is performed depending on whether each evaluation index belongs to a desirable fuzzy set.

  If the determination in step S3 is No, the correction is made so that the determination is Yes. For this purpose, in step S4, the target waveform is corrected by fuzzy inference.

  That is, when each evaluation index does not belong to the desired fuzzy set, as shown in (iii) of FIG. 2, the target output axis is set according to a plurality of rules described in the IF-THEN format as shown in Table 1. Determine the amount of torque waveform correction.

  Regarding the consequent part, membership functions as shown in FIG. 5 are prepared, and the degree of the fuzzy set of the consequent part is obtained by fuzzy inference from the degree of the antecedent part of each rule. Then, the inference results of each rule are collected to obtain a final inference result. This inference result is not a fuzzy variable such as “good” or “bad”, but is defuzzified, that is, a numerical value, and this is a correction amount of the target output shaft torque waveform. In the example of FIG. 8, there is a turbine angular velocity waveform as the target waveform, and the correction amount is obtained in the same manner.

  In this way, when the correction amount of the target waveform is obtained, the input command value corrected by the learning calculation is performed in step S5 by using the target waveform obtained and performing the adaptation work using the model of FIG. Get. The fitting operation in step S5 will be described later.

  In step S5, when the input command value obtained using the corrected target waveform is obtained, the processing from step S2 is repeated using the new input command value. Then, while repeatedly executing the simulation, the correction amount of the corrected target waveform that becomes Yes in step S3 is obtained. An example of the state of correction for the target waveform of the output shaft torque, which is an output waveform, is shown in FIG. As described above, the initial waveform is gradually corrected first time and second time.

  By repeating such a process, as shown in FIG. 7, even when a characteristic variation occurs in which the determination result in step S3 is Yes, a target waveform that falls within the allowable range can be created. An input command value corresponding to the target waveform can be obtained. Therefore, by controlling the automatic transmission using the input command value obtained in this way, even if there is a change in the input command value or the characteristic value of the component of the device that controls the shift operation, the output is performed. A speed change operation that suppresses the influence on is achieved.

  If the number of fuzzy sets and rule groups is increased, the problem becomes complicated, but on the other hand, the target waveform can be adjusted more finely.

  By the above-described method, the target waveform that is robust against fluctuations in the input command value or the characteristic value of the component of the device that controls the shift operation, and the feedforward input command value for realizing the target waveform are actually simulated. The derived results are shown below.

  The CAE model used for the simulation is shown in FIG. 8, and the 2nd-3rd speed upshift of this transmission was targeted.

  FIG. 9 shows the result of the shift simulation performed with the initial input command value varied (result of step S2 in FIG. 1).

  FIG. 9C shows the initial state of the hydraulic waveform supplied to the friction engagement elements on the engagement side / release side. Thus, both brake hydraulic pressures have a relatively simple ramp waveform. FIG. 9D shows the retard amount of the ignition timing of the engine. Thus, in the initial state, the retardation amount is always zero.

  FIGS. 9A and 9B respectively show the engine speed and the output shaft torque of the transmission that are output as a result of the shift simulation. The solid lines in FIGS. 9 (a) and 9 (b) show the cases where the input command value waveforms shown in FIGS. 9 (c) and 9 (d) are directly input to the vehicle when the simulation is executed (this is the nominal state). The simulation results).

  On the other hand, the result of executing simulation by intentionally adding an offset (variation) of ± 10 [kPa] to the engagement-side hydraulic waveform is shown by broken lines in FIGS. 9 (a) and 9 (b). From the difference between the solid line and the broken line, it can be seen that the shift shock increases or the shift time is prolonged due to the fluctuation of the input command value. In FIG. 9A and FIG. 9B, description of the allowable range is omitted, but in this case, the determination in step S3 of FIG. 1 is No.

  Therefore, the simulation result shown in FIG. 9 is input to the fuzzy inference device to correct the target output shaft torque waveform (step S4 in FIG. 1). The result is shown in FIG. Note that this correction is based on the processing of the consequent part of fuzzy inference as described above.

  It can be seen that the gradient and height of the output shaft torque are changed at each time in order to suppress the influence of fluctuations in the input command value. FIG. 10 shows only the target output shaft torque waveform. However, when the output shaft torque waveform is changed, the waveform of the target turbine speed is simultaneously changed so that no contradiction occurs as a physical phenomenon. Correction is also performed, and in the speed change operation, the output shaft torque is also adjusted so that the speed change is completed at the moment when the rotational speeds of the rotating bodies in the gear train are synchronized.

  When the target output shaft torque waveform is set in this way, the process proceeds to S5 in FIG. 1, and an input command value for obtaining an output result close to the target waveform is calculated repeatedly based on learning calculation. Calculate using. This learning calculation method is characterized in that the deviation from the target value is gradually reduced by sequentially obtaining an appropriate correction value of the command value using the equation of motion of the system. FIG. 11 shows the flow of this learning calculation.

  Here, a specific description of the learning calculation method will be given, taking as an example the gripping and changing operation of the brakes B1 and B2 in FIG. The equation of motion relating to the shift of the automatic transmission is described as shown in the following equation.

Here, T _O is the output shaft torque, ω _t · (= dω _t / dt) is the turbine angular acceleration, T _B1 is the torque capacity of the disengagement side brake B1, T _B2 is the torque capacity of the engagement side brake B2, and T _t Is the turbine torque, and _Tw is the running load torque. Each coefficient A ₁₁ , A ₁₂ ,..., A ₂₄ in the above expression represents a constant determined by the gear ratio of the gear train system constituted by the planetary gears and the moment of inertia of each part.

  Using the matrix representation, the above equation is rewritten as shown in the following equation.

  Then, similarly to S1 in FIG. 1, the initial values of the hydraulic pressure waveform and the retard amount are determined in S11 in FIG. 11, and the shift simulation is executed with the brake torque capacity and the turbine torque as inputs (step S12 in FIG. 11). .

In step S13 in FIG. 11, whether the output value waveform is sufficiently close to the target waveform or the like, ie, the errors ΔT _O and Δω _t between the output shaft torque and the target value of the turbine angular acceleration are predetermined. If it is No, the process proceeds to step S14 in FIG. 11 and the pseudo inverse matrix A ⁺ of A and the gain matrix K for adjusting the convergence to the target value are used to determine the brake torque capacity / turbine torque. The correction amount is obtained using the following equation.

  With respect to the brake torque capacity, a new brake torque capacity is obtained by adding this correction amount to the previous brake torque capacity.

  In step S15 of FIG. 11, the feedforward command value is corrected based on the result of step S14. The engaging brake is converted to a brake hydraulic pressure that generates torque capacity through the torque phase and inertia phase according to the following relational expression.

Here, P _B2 is the brake hydraulic pressure, μ _B2 is the friction coefficient, N _B2 is the number of friction materials, R _B2 is the friction material effective radius, S _B2 is the piston pressure receiving area, and F _B2 is the return spring load.

In the torque phase, to the correction of the torque capacity T _B2 of the engaging brake, the relationship between the torque capacity and the turbine torque of the engagement side brake, is blown phenomenon of tie-up and the engine speed occurs The hydraulic waveform of the release side brake is corrected so that it does not.

  Further, during the inertia phase, the retard control of the engine is performed in order to suppress the peak value of the output shaft torque. From the turbine torque correction amount, the engine retardation amount correction amount is obtained according to the following equation.

  Here, α represents a retard amount, Δα represents a retard amount correction amount, t represents a torque ratio between the engine torque and the turbine torque, and k represents a constant.

  After the correction of each feedforward command value, the process returns to step S12 in FIG. 11, and the shift simulation using the corrected command value is performed again.

  This command value correction operation is repeatedly executed until the learning calculation end condition is satisfied in step S13 of FIG. Examples of the learning end condition include a case where the number of repetitions of the learning calculation exceeds a predetermined number or a case where an error from the target value becomes a sufficiently small value.

  FIG. 12 shows the result of obtaining the input command value for realizing the target waveform using such learning calculation. In (a) to (e) of FIG. 12, a broken line indicates a waveform in an initial state, and a solid line indicates a waveform after learning.

  FIG. 12F shows a history of output shaft torque errors. By repeating the learning calculation and repeatedly correcting the input command value, an appropriate feedforward input command value is obtained.

  FIG. 13 shows a shift simulation result with the input command value finally obtained as a result of alternately repeating the correction of the target waveform and the derivation of the input command value corresponding to the target waveform according to the above-described method.

  FIG. 13C shows a hydraulic pressure waveform supplied to the engagement side / release side friction engagement element, and FIG. 13D shows a retard amount of the ignition timing of the engine. FIGS. 13A and 13B respectively show the engine speed and the output shaft torque of the transmission that are output as a result of the shift simulation. The solid lines in FIGS. 13A and 13B represent the simulation results when the input waveforms shown in FIGS. 13C and 13D are directly input to the vehicle when the simulation is executed.

  On the other hand, the result of executing simulation by intentionally adding an offset of ± 10 [kPa] to the engagement-side hydraulic waveform is shown by broken lines in FIGS. Compared with the result when the same fluctuation is applied to the initial input command value shown in FIG. 9, the peak of the output shaft torque waveform during the shift is suppressed as a whole, and the shift shock is reduced. You can see that

  Here, in the above description, the variation of the input command value is taken up as the variation that affects the output. However, the fluctuation factors are not only the input command value, but also the fluctuation of the friction coefficient of the engagement friction material, the dimensional error of the path / throttle diameter (orifice) in the hydraulic circuit, the deviation of the engine torque drop timing due to the engine retarding control, etc. Any characteristic variation in the device for controlling the shifting operation can be considered, and these variations can be targeted.

  As described above, by using the method of the present embodiment, the target waveform that is robust against fluctuations in the input command value or the characteristic value of the component of the device that controls the shift operation as described above, and each input command corresponding thereto. The value can be obtained by computer simulation in a very short time compared with the man-hours required for the actual machine test on the bench.

"Embodiment using sensitivity analysis"
In the above embodiment, a fuzzy inference is used to obtain a target waveform that is resistant to fluctuations in the input command value or the characteristic value of the component of the device that controls the shift operation, and the input command value is determined using the target waveform.

  In the present embodiment, sensitivity analysis, which is a quality engineering technique, is applied to design time-series feedforward command values.

  FIG. 14 shows the flow of adaptation work in this embodiment. In the following description, while a vehicle equipped with the automatic transmission shown in FIG. 8 is traveling at a low speed, the throttle opening is once returned to zero, jumped over the medium speed and upshifted to a high speed, and then again. Power-off / power-on multiple shifts that run at high speeds with the throttle opening increased are the targets.

  In step S21, first, an initial value of a command value input to the automatic transmission is set. Since the initial command value only needs to be able to perform a predetermined gear shift operation, an input command value having a simple shape as shown on the left side of FIG. 15 may be given.

  Next, in step S22 of FIG. 14, as shown on the right side of FIG. 15, with reference to the output result in step S21, three levels are set for the feature quantity of the output shaft torque waveform. Here, three characteristic values are adopted: the output shaft torque value To1 at the moment when the throttle opening rises, the output shaft torque To2 immediately before the output shaft torque falls at the end of the shift, and the shift time tsc. The control factor at the target value.

  In the following, this combination of control factors will be discussed, but there are innumerable combinations of control factors as shown in FIG. For this reason, when the number of control factors increases, the number of simulations that must be executed for design becomes enormous. Therefore, in this embodiment, these control factors are allocated to the orthogonal table shown in FIG. Hereinafter, it is assumed that To1 is assigned to control factor A in the orthogonal table of FIG. 17, To2 is assigned to B, and tsc is assigned to C.

  In step S23 of FIG. 14, a feedforward input command value for realizing the target output shaft torque waveform set according to the orthogonal table of FIG. 17 is obtained by learning calculation. The calculation of the input command value by this learning calculation is performed in the same manner as in the above-described embodiment.

  In step S24 of FIG. 14, an offset of ± β is given to the feed pressure input command value of the brake hydraulic pressure derived by the learning calculation method, and this is assigned outside the orthogonal table of FIG. 17 as an error (variation) factor. The error (variation) factor β includes not only the brake hydraulic pressure offset (variation) but also the variation of the friction coefficient of the engagement friction material, the path / throttle diameter (orifice) dimensional error in the hydraulic circuit, and the engine delay angle. It is possible to give any characteristic variation in the device that controls the shift operation, such as a shift in engine torque drop timing due to control. As the variation, not only the input command value but also the variation of the characteristic value of the component of the device that controls the shift operation can be given.

  In step S25 of FIG. 14, a shift simulation is executed with the input command value to which this disturbance has been added. Then, the S / N ratio / average value of each evaluation index determined to determine whether the speed change operation has been completed smoothly is obtained from the obtained result.

  FIG. 18 shows an example of the evaluation index. In this example, as shown in the figure, a shift shock, shift time, engine speed at the time of shift, and the like calculated from increase / decrease in output shaft torque are employed as evaluation indexes. In addition, since the processing of the heat generated to synchronize the rotation of the gears inside the transmission becomes a problem at the time of shifting, the heat absorption amount and the heat absorption rate that the friction engagement element must absorb are also used as evaluation indexes. In addition, the acceleration, jerk, and torsional vibration of the drive shaft that transmits the power to the tire can be employed as an evaluation index.

  Moreover, the SN ratio and the average value regarding the combination of the nth control factor in the orthogonal table of FIG. 17 are obtained by the following equations.

Here, y _{n, 1} , y _{n, 2} , y _{n, 3} represent the values of evaluation indices obtained from the shift simulation results when each error (variation) factor is added, and η is the SN ratio , Μ represents an average value.

  The above-described operations from step S23 to step S25 in FIG. 14 are executed for all factor combinations defined in the orthogonal table in FIG. That is, in step S26, it is determined whether or not the end of all combinations has been completed. If not completed, the process returns to step S23 to repeat the process.

  In step S27 of FIG. 14, a factorial effect diagram is created based on data obtained from all shift simulation results, and the average value can be moved to a level that satisfies the design requirements while suppressing variations in each evaluation index. Consider combinations of control factors.

  FIG. 19 to FIG. 23 show factor effect diagrams relating to shift shock, shift time, engine speed of rotation, friction material heat absorption, and heat absorption rate, respectively. In these effect diagrams, it is desirable that the SN ratio has a larger value and the average value has a smaller value.

  It is examined how to set the value of each control factor from these factor effect diagrams to attenuate the influence of the error (fluctuation) factor. As can be seen by comparing FIG. 19 to FIG. 23, FIG. In the S / N ratio, the C3 level is desirable, but the C1 level is desirable in other cases.

  Therefore, a nonlinear optimization technique is used to obtain an optimal solution while avoiding such contradictions. First, the characteristics in the factor-effect diagram are approximated by a quadratic function to create an evaluation function as shown below.

  Here, the variable x represents what value the control factor has between level 1 and level 3, and ai, bi, ci (i = 1, 2,..., 10) are coefficients of a quadratic function. Represents. In the above formula, the larger the S / N ratio, the better the characteristic. However, by reversing the sign, the function vector F (x) is changed to obtain the minimum value when optimizing. To do.

When minimizing a nonlinear multi-objective function vector such as F (x) above, it is possible to use techniques such as fuzzy inference, genetic algorithm, and neurocomputing. A so-called multi-objective goal attainment method is used. The multi-objective goal attainment method is a method of minimizing the variable γ under the constraint condition represented by the weight function w _i and the design goal f ^* _{i as} shown by the following equation.

  As a result of optimization by applying this method, the levels shown in Table 2 were obtained as optimum values. For the actual calculation, The MathWorks, Inc. The software manufactured by MATLAB (registered trademark) was used. It should be noted that other optimization methods may be employed as a method for obtaining the optimum value for each level.

  In FIG. 14 step S28, each feedforward input command value which implement | achieves the target output shaft torque waveform designed from the said optimized control factor is derived | led-out by the above-mentioned learning calculation method.

  FIG. 24 shows a shift simulation result based on the initial input command value, and FIG. 25 shows a simulation result based on the finally obtained input command value. In each figure, (a) is the output shaft torque, (b) is the engine speed, (c) is the engine torque, (d) is the brake hydraulic pressure supplied to the engagement / release side frictional engagement elements, (e ) Represents the throttle opening, and (f) represents the retard amount of the ignition timing of the engine.

  In FIG. 24, since the brake hydraulic pressure is changed abruptly, a large shift shock is generated at the position indicated by the arrow in FIG. 24A, but FIG. 25 using an optimized command value is used. In this case, the brake hydraulic pressure is gradually changed and at the same time the torque output from the engine is reduced by controlling the ignition timing of the engine, thereby reducing the shift shock appearing in the output shaft torque waveform.

  FIG. 26 shows a simulation result when an offset of −10 [kPa] is added as a disturbance to the optimized input command value to the engagement-side brake. Since the target output shaft torque waveform and the feedforward input command value are designed in advance in consideration of reducing the influence of such disturbances, the difference between the output results in FIG. 25 and FIG. 26 is minimized. Yes.

  As described above, when the method according to the present embodiment is used, the target output waveform that is robust against the characteristic fluctuation (input command value or characteristic value of the component of the device that controls the shift operation) inside the system as described above, And each feedforward input command value corresponding to it can be calculated | required by computer simulation in a very short time compared with the man-hour required for the real machine test on a bench. In addition, by quantitatively determining the influence of disturbance and noise at the time of designing, it is possible to easily carry out an optimum design even if it is not an expert who is familiar with the characteristics of the system.

"Comparison of using fuzzy reasoning and sensitivity analysis"
Common points and differences between the embodiment using fuzzy inference and the embodiment using sensitivity analysis are shown below.

[Common point]
・ The output value from the automatic transmission for automobiles is intended to always satisfy a certain required specification even when characteristic fluctuations occur in the system.
A method for automatically obtaining a control command value for realizing the above object.
The above-mentioned required specifications are determined by the values of a plurality of evaluation indexes (sensory index representing shift shock, heat absorption amount of friction material, etc.).
In order to satisfy the required specifications described above, a target output value that is robust against characteristic fluctuation is derived with reference to the value of the evaluation index when the output value is adversely affected by characteristic fluctuation.
Subsequently, a control input for obtaining an output result that actually matches the target output value is obtained by a learning calculation method.

[Difference]
"When fuzzy reasoning is used"
・ Establishing membership functions / correction rules requires man-hours for the experience of skilled adaptors and trial and error.
If the above function / rule group is set appropriately, the correct correction amount can be obtained as an inference result.

"When using sensitivity analysis"
・ Quantitatively evaluate the fluctuation range of the output value due to the fluctuation value of the input.
-By using a mixed system orthogonal table, sensitivity analysis can be performed efficiently with a minimum number of trials.
・ A separate method for obtaining the optimal solution based on the quantitative analysis results is required.

It is a flowchart of command value generation concerning an embodiment. It is a figure which shows correction of a target value waveform. It is a figure which shows the influence of input variation. It is a figure which shows the membership function of an antecedent part. It is a figure which shows the membership function of a consequent part. It is a figure which shows the correction log | history of a target waveform. It is a figure which shows the correction result of a target waveform. It is a figure which shows the outline | summary of the adaptation work on a CAE model. It is a figure which shows the shift simulation result in an initial input value. It is a figure which shows correction of a target output shaft torque waveform. It is a flowchart of learning calculation. It is a figure which shows the learning calculation result for an input command value production | generation. It is a figure which shows the shift simulation result by the input command value finally obtained. It is a flowchart of command value generation concerning other embodiments. It is a figure which shows the level setting of an initial command value and a control factor. It is a figure which shows the pattern of a target axial torque. It is a figure which shows an orthogonal table. It is a figure which shows an evaluation parameter | index. It is a factor effect figure of SN ratio and average value of a shift shock. It is a factor effect figure of S / N ratio and average value of speed change time. It is a factor effect figure of SN ratio and average value of engine speed. It is a factor effect figure of SN ratio and average value of friction material heat absorption. It is a factor effect figure of SN ratio and average value of a friction material heat absorption rate. It is a figure which shows the shift simulation result by the initial input command value. It is a figure which shows the shift simulation result by the input command value after optimization. It is a figure which shows the shift simulation result (when disturbance is added) by the input command value after optimization.

Explanation of symbols

  10 Torque converter model, 12 Gear train model, 14 Release side brake model, 16 Engagement side brake model, 18 Correction amount calculation unit, 20, 22, 24, 26, 28 Adder.

Claims (8)

A shift simulation device that simulates a shift operation of an automatic transmission system for an automobile using a model,
For the input command value when performing a shift operation in the system or the characteristic value of the component of the device that controls the shift operation, a simulation is performed by giving a variation,
The output value of the system as a result of this simulation is evaluated based on the magnitude of the influence on the fluctuation,
From this evaluation result, a target output value that satisfies the required specifications is determined for the influence of the variation on the output value,
A shift simulation device characterized by obtaining an input command value that satisfies a required specification by obtaining an input command value for obtaining an output that matches the determined target output value by learning calculation. A shift simulation device that simulates a shift operation of an automatic transmission system for an automobile using a model,
(A) A simulation is performed by giving a variation to an input command value or a characteristic value of a component of a device that controls the shift operation when performing a shift operation in the system;
(B) evaluating the output value of the system, which is the result of this simulation, based on the magnitude of the effect on the variation;
(C) Based on the evaluation result, the target output value is corrected by fuzzy inference,
(D) A simulation is performed using the corrected target output value, a new input command value is calculated by the learning calculation,
(E) Update the input command value with the calculated input command value, return to (a), repeat the processes (a) to (d) until the evaluation in (b) satisfies the required specifications,
A speed change simulation apparatus characterized by obtaining an input command value satisfying a required specification. A shift simulation device that simulates a shift operation of an automatic transmission system for an automobile using a model,
(A) A plurality of levels are set for a plurality of control factors at the target output value,
(B) setting a plurality of target output values determined by a combination of a plurality of levels for a plurality of set control factors;
(C) Using a plurality of set target output values, simulating each of the input command value or the characteristic value of the component of the device that controls the shift operation by performing a shift operation in the system, respectively,
(D) evaluating a plurality of output values of the system as a result of the simulation based on the magnitude of the influence on the fluctuation, and determining a target output value that the obtained evaluation result satisfies the required specifications;
(E) Using the determined target output value, find an input command value that satisfies the required specifications by learning calculation;
A shift simulation apparatus characterized by the above. In the shift simulation device according to claim 3,
In (b), a combination of a plurality of levels of a plurality of control factors is determined using an orthogonal table, and (c) is performed for all the determined combinations. A shift simulation program for simulating a shift operation of an automatic transmission system for an automobile using a model,
On the computer,
For the input command value or the characteristic value of the component of the device that controls the shift operation when performing the shift operation in the system, the simulation is performed by giving a variation,
The output value of the system as a result of this simulation is evaluated based on the magnitude of the influence on the fluctuation,
From this evaluation result, the target output value that satisfies the required specifications is determined by the influence of the variation on the output value,
A shift simulation program characterized in that an input command value for obtaining an output that matches a determined target output value is obtained by learning calculation to obtain an input command value that satisfies a required specification. A shift simulation program for simulating a shift operation of an automatic transmission system for an automobile using a model,
On the computer,
(A) The input command value when performing a shift operation in the system or the characteristic value of the component of the device that controls the shift operation is simulated by giving a variation,
(B) let the system output value, which is the result of this simulation, be evaluated based on the magnitude of the effect on the variation;
(C) Based on the evaluation result, the target output value is corrected by fuzzy inference,
(D) A simulation is performed using the corrected target output value, and a new input command value is calculated by the learning calculation.
(E) Update the input command value with the calculated input command value, return to (a), repeat the processes (a) to (d) until the evaluation in (b) satisfies the required specifications,
A shift simulation program characterized by obtaining an input command value that satisfies a required specification. A shift simulation program for simulating a shift operation of an automatic transmission system for an automobile using a model,
On the computer,
(A) A plurality of levels are set for a plurality of control factors in the target output value,
(B) setting a plurality of target output values determined by a combination of a plurality of levels for a plurality of set control factors;
(C) Using a plurality of set target output values, the input command value when performing a shift operation in the system or a characteristic value of a component of a device that controls the shift operation is given a variation, and each is simulated.
(D) Evaluating a plurality of output values of the system as a result of the simulation based on the magnitude of the influence on the variation, and determining a target output value that the obtained evaluation result satisfies the required specifications;
(E) Using the determined target output value, an input command value that satisfies the required specifications is determined by learning calculation.
A shift simulation program characterized by the above. A memory for storing a shift control command value based on an input command value obtained by using any one shift simulation device according to any one of claims 1 to 4 or any one shift simulation program according to any one of claims 5 to 7 is mounted. An automobile for controlling an automatic transmission mounted based on a shift control command value stored in the memory.

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