A steel strip clamping force predictive control method and related device

Abstract

The embodiment of the application discloses a steel belt clamping force prediction control method and related device, wherein the method comprises: when it is identified that a target working condition is entered, a Kalman filter value of a current period is determined according to a torque estimation value of a previous period, a torque measurement value of the current period and a torque estimated measurement value of the current period; the torque measurement value of the current period is an actual torque value measured in the current period, and the torque estimated measurement value of the current period is determined according to the torque estimation value of the previous period; a torque estimation value of a next period is determined according to the Kalman filter value of the current period; and a steel belt clamping force of the current period is controlled according to the torque estimation value of the next period. The method can improve the clamping force control precision, reduce energy consumption and improve system efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, in particular to a steel belt clamping force prediction control method and related device. BACKGROUND

[0002] In actual application, a continuously variable transmission (CVT) transmits torque through friction, and a clamping force generally adopts a safety factor of 1.3, that is, 1.3 times of the clamping force corresponding to the current torque is used to transmit the engine torque of the next period. Figure 1 A schematic diagram of CVT steel belt clamping is shown in FIG. 1, where F ax is the clamping force, F t is the tangential friction force of the steel belt movement, F n is the normal pressure.

[0003] However, under the Tip in dynamic condition (that is, the condition from no throttle to throttle), the engine torque has the phenomenon of rapid increase, and if the safety factor of 1.3 times of the current torque is still used to transmit the torque of the next period, there will be a certain risk, so in order to prevent the steel belt system from slipping, the clamping force is usually increased according to the engine intake temperature, throttle and other information, and this operation mode will lead to more complicated calibration work, and the safety margin is too large, which will also reduce the transmission efficiency of the whole transmission system. SUMMARY

[0004] The embodiments of the present application provide a steel belt clamping force prediction control method and related device, which can improve the clamping force control precision, reduce energy consumption and improve system efficiency.

[0005] Therefore, the first aspect of the present application provides a steel belt clamping force prediction control method, which comprises:

[0006] When it is identified that the target condition is entered, the Kalman filter value of the current period is determined according to the torque estimation value of the last period, the torque measurement value of the current period and the torque estimation measurement value of the current period; the torque measurement value of the current period is the torque value actually measured in the current period, and the torque estimation measurement value of the current period is determined according to the torque estimation value of the last period;

[0007] The torque estimation value of the next period is determined according to the Kalman filter value of the current period;

[0008] The steel belt clamping force of the current period is controlled according to the torque estimation value of the next period.

[0009] Optionally, the torque estimation measurement value of the current period is determined by the following way:

[0010] determining a torque estimation measurement value of the current period according to the vehicle speed of the current period and the torque estimation value of the previous period through an observation equation; the observation equation is an observation equation taking torque and vehicle speed as state variables.

[0011] Optionally, the determining the Kalman filtering value of the current period according to the torque estimation value of the previous period, the torque measurement value of the current period and the torque estimation measurement value of the current period comprises:

[0012] calculating a difference between the torque measurement value of the current period and the torque estimation measurement value of the current period as a reference difference value;

[0013] multiplying the reference difference value by a Kalman gain to obtain a reference product;

[0014] calculating a sum value of the reference product and the torque estimation value of the previous period to obtain the Kalman filtering value of the current period.

[0015] Optionally, the determining the torque estimation value of the next period according to the Kalman filtering value of the current period comprises:

[0016] determining the torque estimation value of the next period according to the Kalman filtering value of the current period through a system equation; the system equation is obtained by linearizing a whole vehicle longitudinal driving dynamics equation taking torque and vehicle speed as state variables.

[0017] Optionally, the controlling the steel belt clamping force of the current period according to the torque estimation value of the next period comprises:

[0018] determining the steel belt clamping force of the current period according to the torque estimation value of the next period, a pulley cone angle, a pulley friction coefficient, a pulley radius and a pulley cylinder acting area.

[0019] Optionally, the determining the steel belt clamping force of the current period according to the torque estimation value of the next period, a pulley cone angle, a pulley friction coefficient, a pulley radius and a pulley cylinder acting area comprises:

[0020] selecting a maximum value between the torque estimation value of the next period and the torque measurement value of the current period as a target torque estimation value;

[0021] determining the steel belt clamping force of the current period according to the target torque estimation value, a pulley cone angle, a pulley friction coefficient, a pulley radius and a pulley cylinder acting area.

[0022] Optionally, the method further comprises:

[0023] When it is detected that a deviation between the torque estimation value of the next cycle and the torque measurement value of the next cycle is less than a preset threshold, exiting execution of a torque estimation algorithm used to determine the torque estimation value.

[0024] Optionally, the method further comprises:

[0025] When it is detected that the accelerator is depressed or no accelerator is depressed, exiting execution of the torque estimation algorithm used to determine the torque estimation value.

[0026] The second aspect of the present application provides a steel strip clamping force prediction control device, the device comprising:

[0027] A first determination module is configured to determine a Kalman filter value of a current cycle according to a torque estimation value of a previous cycle, a torque measurement value of the current cycle, and a torque estimation measurement value of the current cycle when it is identified that a target working condition is entered; the torque measurement value of the current cycle is an actually measured torque value in the current cycle, and the torque estimation measurement value of the current cycle is determined according to the torque estimation value of the previous cycle;

[0028] A second determination module is configured to determine a torque estimation value of a next cycle according to the Kalman filter value of the current cycle.

[0029] A control module is configured to control a steel strip clamping force of the current cycle according to the torque estimation value of the next cycle.

[0030] Optionally, the device further comprises:

[0031] A third determination module is configured to determine the torque estimation measurement value of the current cycle according to the vehicle speed of the current cycle and the torque estimation value of the previous cycle through an observation equation; the observation equation is an observation equation taking torque and vehicle speed as state variables.

[0032] Optionally, the first determination module is specifically configured to:

[0033] Calculate a difference between the torque measurement value of the current cycle and the torque estimation measurement value of the current cycle as a reference difference value;

[0034] Multiply the reference difference value by a Kalman gain to obtain a reference product;

[0035] Calculate a sum value of the reference product and the torque estimation value of the previous cycle to obtain the Kalman filter value of the current cycle.

[0036] Optionally, the second determination module is specifically configured to:

[0037] determine the torque estimation value of the next period according to the Kalman filtering value of the current period through a system equation; the system equation is obtained by linearizing a vehicle longitudinal driving dynamics equation with torque and vehicle speed as state variables.

[0038] Optionally, the control module is specifically configured to:

[0039] determine the steel belt clamping force of the current period according to the torque estimation value of the next period, the belt wheel cone angle, the belt wheel friction coefficient, the belt wheel radius, and the belt wheel cylinder action area.

[0040] Optionally, the control module is specifically configured to:

[0041] select the maximum value between the torque estimation value of the next period and the torque measurement value of the current period as a target torque estimation value;

[0042] determine the steel belt clamping force of the current period according to the target torque estimation value, the belt wheel cone angle, the belt wheel friction coefficient, the belt wheel radius, and the belt wheel cylinder action area.

[0043] Optionally, the control module is further configured to:

[0044] when it is detected that the deviation between the torque estimation value of the next period and the torque measurement value of the next period is less than a preset threshold, exit the execution of the torque estimation algorithm for determining the torque estimation value.

[0045] Optionally, the control module is further configured to:

[0046] when it is detected that the accelerator is reduced or there is no accelerator, exit the execution of the torque estimation algorithm for determining the torque estimation value.

[0047] As can be seen from the above technical solutions, the embodiments of the present application have the following advantages:

[0048] The embodiments of the present application provide a steel belt clamping force prediction control method, which comprises: when it is identified that a target working condition is entered, determining a Kalman filtering value of a current period according to a torque estimation value of a previous period, a torque measurement value of the current period, and a torque estimation measurement value of the current period; the torque measurement value of the current period is an actually measured torque value in the current period, and the torque estimation measurement value of the current period is determined according to the torque estimation value of the previous period; then, determining a torque estimation value of a next period according to the Kalman filtering value of the current period; and further, controlling a steel belt clamping force of the current period according to the torque estimation value of the next period.

[0049] In the conventional Kalman filtering algorithm, the "estimation" result is an intermediate variable, and the "correction" result is a final control variable. The embodiment of the application innovatively takes the "correction" result as an intermediate variable and takes the "estimation" result as a final control variable, that is, taking the Kalman filtering value of the current period as an intermediate variable and taking the torque estimation value of the next period as a final control variable. In this way, the torque generated by the engine in the next period is optimally estimated in real time, and it is taken as the basis for calculating the clamping force in the current period, rather than estimating and correcting the torque transmitted by the steel belt system in the current period. This method uses the estimation function algorithm implicitly included in the Kalman filtering to optimally estimate the engine torque, which can meet the real-time requirement and reduce the working pressure of the steel belt system and improve the system efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 Fig. 1 is a schematic diagram of CVT steel belt clamping;

[0051] Figure 2 Fig. 2 is a flowchart of the steel belt clamping force prediction control method provided by the embodiment of the application;

[0052] Figure 3 Fig. 3 is a schematic diagram of the principle of the torque estimation algorithm provided by the embodiment of the application;

[0053] Figure 4 Fig. 4 is a flowchart of the steel belt clamping force prediction control method provided by the embodiment of the application;

[0054] Figure 5 Fig. 5 is a schematic diagram of the test results provided by the embodiment of the application;

[0055] Figure 6 Fig. 6 is a schematic diagram of the structure of the steel belt clamping force prediction control device provided by the embodiment of the application. DETAILED DESCRIPTION

[0056] In order to enable personnel in the technical field to better understand the scheme of the application, the technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the application.

[0057] The terms "first", "second", "third", "fourth" and the like in the description and in the claims of the present application, and above-described drawings, if any, are used to distinguish between similar objects and are not necessarily used to describe a particular sequential or chronological order. It is to be understood that the use of the terms so

[0058] The steel strip clamping force prediction control method provided by the present application is introduced below through a method embodiment.

[0059] Referring to Figure 2 , Figure 2 A flowchart of the steel strip clamping force prediction control method provided by the embodiments of the present application is shown in FIG. 1. As shown in FIG. 1, the method comprises the following steps: Figure 2

[0060] Step 201: When it is identified that the target working condition is entered, the Kalman filter value of the current period is determined according to the torque estimation value of the last period, the torque measurement value of the current period, and the torque estimation measurement value of the current period; the torque measurement value of the current period is the torque value actually measured in the current period, and the torque estimation measurement value of the current period is determined according to the torque estimation value of the last period.

[0061] In the embodiments of the present application, a Tip in working condition identification algorithm can be established to identify the working condition from no throttle to throttle; specifically, the throttle change rate inflection point can be used as a judgment activation condition to achieve accurate identification of the working condition to be optimized (i.e. the target working condition).

[0062] When it is identified that the vehicle enters the target working condition (i.e. the Tip in working condition), the modeling of the Extended Kalman Filter (EKF) can be performed. That is, the system equation and the observation equation with torque and vehicle speed as state variables are established, the forward Euler method is used for discretization processing, the Jacobian matrix is solved, and the extended Kalman filter equation is established. Specifically, the vehicle longitudinal driving dynamics equation with torque and vehicle speed as state variables can be established and linearized to obtain the system equation; and the torque signal transmitted by the engine through the bus signal is used as the observation quantity to establish the observation equation. The established system equation and observation method are shown in the following formula (1) and formula (2):

[0063] ​

[0064]

[0065] wherein V K and T E(K) are the vehicle speed and torque of the Kth cycle, V K-1 and T E(K-1) are the vehicle speed and torque of the K-1th cycle. At is the sampling time of each cycle, i cvt and io are the speed ratio of the steel belt and the main reduction, gf and gi are the rolling resistance and the slope resistance, respectively, m is the mass of the vehicle, r is the tire radius, A a is the windward area, C D is the wind resistance coefficient, and p is the air density, η steel belt system efficiency.

[0066] The Jacobian matrix is shown in the following formula (3):

[0067]

[0068] Further, the estimation function in the "estimation-correction" of the Kalman filtering algorithm can be used to optimally estimate the torque of the next cycle, and the estimated torque can be used as the control basis for the clamping force of the steel belt system in the current cycle.

[0069] Referring to Figure 3 , Figure 3 is a schematic diagram of the principle of the torque estimation algorithm provided by the embodiments of the present application. In the following, the K-2th cycle is taken as the last cycle, the K-1th cycle is taken as the current cycle, and the Kth cycle is taken as the next cycle, and the implementation process of estimating the torque of the next cycle is introduced.

[0070] Before determining the torque estimation value of the next cycle, the Kalman filtering value (i.e., the gray circle in the K-1th cycle in FIG. 6) of the current cycle needs to be determined according to the torque estimation value of the last cycle (i.e., the triangle in the K-2th cycle in FIG. 6), the torque measurement value of the current cycle (i.e., the black circle in the K-1th cycle in FIG. 6, which is the actual measured torque value transmitted by the engine through the bus signal), and the torque estimation measurement value of the current cycle (i.e., the white circle in the K-1th cycle in FIG. 6, which is the torque estimation value obtained through the observation equation). Figure 3 Figure 3 Figure 3 Figure 3

[0071] ​​​​Specifically, the torque prediction measurement value for the current cycle can be determined using the observation equation (i.e., equation (2) above) based on the vehicle speed of the current cycle and the torque prediction value of the previous cycle. That is, the torque prediction value of the K-2 cycle is substituted into the observation equation to obtain the torque prediction measurement value of the K-1 cycle.

[0072] When calculating the Kalman filter value for the current period, the difference between the measured torque value and the estimated torque value for the current period can be calculated as a reference difference. The Kalman gain is then multiplied by this reference difference to obtain a reference product. The sum of this reference product and the estimated torque value from the previous period is then calculated to obtain the Kalman filter value for the current period. This calculation process can be represented by the following equation (4):

[0073]

[0074] in, This is the Kalman filter value for the (K-1)th period. Here is the torque estimate for the (K-2)th cycle, G is the Kalman gain, and z... K-1 This is the torque measurement value for the (K-1)th cycle. The torque prediction measurement for the (K-1)th cycle is the torque prediction value for the (K-2)th cycle. The estimated measured value obtained by substituting it into the observation equation.

[0075] Step 202: Determine the torque estimate for the next cycle based on the Kalman filter value of the current cycle.

[0076] After calculating the Kalman filter value for the current period, the torque estimate for the next period can be determined based on this Kalman filter value.

[0077] Still with Figure 3 Taking the schematic diagram of the torque prediction method shown as an example, in step 202, it is necessary to calculate the Kalman filter value of the (K-1)th cycle (i.e. Figure 3 The gray circle in the (K-1)th period is used to determine the torque estimate for the Kth period (i.e., Figure 4 (The triangle in the (K-1)th cycle). It should be noted that in the conventional Kalman filter algorithm, the torque estimate is used as an intermediate variable to predict the final control variable for the next cycle. However, in the Kalman filter algorithm of this application embodiment, the torque estimate is used as the final control variable for the current cycle to control the steel strip clamping force for the current cycle.

[0078] In practice, the torque estimate for the next cycle can be determined using a system equation based on the Kalman filter value of the current cycle. This system equation is equation (1) mentioned above. The torque estimate for the next cycle... The Kalman filter value of the (K-1)th cycle is then substituted into the system equation as the torque estimate for the Kth cycle.

[0079] Step 203: Control the steel strip clamping force of the current cycle based on the torque estimate of the next cycle.

[0080] After determining the torque estimate for the next cycle through step 202, the torque estimate can be used as the basis for controlling the steel strip clamping force in the current cycle, that is, the steel strip clamping force in the current cycle is controlled based on the torque estimate for the next cycle.

[0081] When specifically controlling the steel belt clamping force in the current cycle, the clamping force can be determined based on the estimated torque for the next cycle, the pulley cone angle, the pulley friction coefficient, the pulley radius, and the effective area of ​​the pulley cylinder. The specific calculation formula for the steel belt clamping force is shown in equation (5) below:

[0082]

[0083] Among them, F K-1 T is the clamping force of the steel strip in the (K-1)th cycle. E(K) The torque estimate for the next cycle, as mentioned above. α is the cone angle of the belt pulley, μ, R p and A s These are the pulley friction coefficient, pulley radius, and pulley cylinder working area, respectively.

[0084] In some cases, to avoid incorrect clamping force prediction due to inappropriate initial parameters, the current torque can be used as the minimum torque for protection. That is, the maximum value between the torque estimate for the next cycle and the torque measurement for the current cycle is selected as the target torque estimate; then, based on the target torque estimate, pulley cone angle, pulley friction coefficient, pulley radius, and pulley cylinder working area, the steel belt clamping force for the current cycle is determined. In other words, the above equation (5) is modified into the following equation (6):

[0085]

[0086] Among them, T E(K-1)_Actual This represents the actual torque of the engine in the K-1 cycle.

[0087] Thus, steps 201 to 203 are repeated cyclically to predict and control the steel strip clamping force in each cycle under the target working condition. For example, for the Kth cycle, the predicted torque value for the (K-1)th cycle can be... Substituting into the observation equation, we obtain the estimated torque measurement value for the Kth period. Furthermore, based on the torque observation value of the (K-1)th cycle, using the following equation (7)... The torque measurement value z in the Kth cycle K And the estimated torque measurement value for the Kth cycle. Determine the Kalman filter value for the Kth period:

[0088]

[0089] Then, the Kalman filter value of the Kth period is substituted into the system equation to determine the torque estimate for the (K+1)th period, i.e. The clamping force of the steel strip in the Kth cycle is determined by using the torque estimate of the K+1th cycle, and this process is repeated continuously.

[0090] In practical applications, when the deviation between the estimated torque value for the next cycle and the measured torque value for the next cycle (i.e., the actual measured engine torque value for the next cycle) is less than a preset threshold, the torque estimation method is terminated, i.e., steps 201 to 203 are terminated. Alternatively, when a decrease in throttle or no throttle is detected, the torque estimation method is terminated, i.e., steps 201 to 203 are terminated.

[0091] The overall implementation process of this solution can also be found in [link / reference]. Figure 4 The flowchart is shown below. Figure 5 As shown in the embodiment of this application, the Tip-in working condition can be identified first, and then Kalman correction can be performed through the above formula (4), followed by Kalman estimation. Based on the Kalman estimation result, the steel strip clamping force of the current cycle can be calculated. After the Kalman estimation is completed, Kalman correction for the next cycle can be performed, and so on, periodically updated.

[0092] The test results obtained by performing tests based on the methods provided in the embodiments of this application are as follows: Figure 5 As shown, through Figure 6 It can be seen that the clamping force determined by this application (i.e. the optimized clamping force) is significantly reduced, and the efficiency is significantly improved, with an efficiency improvement of about 20% under the same working conditions.

[0093] The embodiment of the present application provides a steel belt clamping force prediction control method, which comprises the following steps: when it is identified that a target working condition is entered, a Kalman filter value of a current period is determined according to a torque estimation value of a previous period, a torque measurement value of the current period and a torque estimation measurement value of the current period; the torque measurement value of the current period is an actual torque value measured in the current period, and the torque estimation measurement value of the current period is determined according to the torque estimation value of the previous period; then, a torque estimation value of a next period is determined according to the Kalman filter value of the current period; and finally, a steel belt clamping force of the current period is controlled according to the torque estimation value of the next period. In a conventional Kalman filter algorithm, an "estimation" result is an intermediate variable, and a "correction" result is a final control variable. The embodiment of the present application innovatively takes the "correction" result as the intermediate variable and takes the "estimation" result as the final control variable, that is, takes the Kalman filter value of the current period as the intermediate variable and takes the torque estimation value of the next period as the final control variable, so that the torque generated by the engine in the next period is optimally estimated in real time, and the torque estimation value is taken as a calculation basis of the clamping force of the current period, instead of estimating and correcting the torque transmitted by the steel belt system in the current period. The method optimally estimates the engine torque by using the estimation function algorithm implicitly included in the Kalman filter, can meet the real-time requirement, can reduce the working pressure of the steel belt system and can improve the system efficiency.

[0094] The embodiment of the present application also provides a steel belt clamping force prediction control device. Figure 6 , Figure 6 A structure diagram of the steel belt clamping force prediction control device provided by the embodiment of the present application is shown in the figure. ​ The device comprises:

[0095] A first determination module 601 is configured to determine a Kalman filter value of a current period according to a torque estimation value of a previous period, a torque measurement value of the current period and a torque estimation measurement value of the current period when it is identified that a target working condition is entered; the torque measurement value of the current period is an actual torque value measured in the current period, and the torque estimation measurement value of the current period is determined according to the torque estimation value of the previous period.

[0096] A second determination module 602 is configured to determine a torque estimation value of a next period according to the Kalman filter value of the current period.

[0097] A control module 603 is configured to control a steel belt clamping force of the current period according to the torque estimation value of the next period.

[0098] Optionally, the device further comprises:

[0099] a third determining module configured to determine a torque estimation measurement value of the current period according to the vehicle speed of the current period and the torque estimation value of the previous period by an observation equation, the observation equation being an observation equation taking torque and vehicle speed as state variables.

[0100] Optionally, the first determining module is specifically configured to:

[0101] calculate a difference between the torque measurement value of the current period and the torque estimation measurement value of the current period as a reference difference value;

[0102] multiply the reference difference value by a Kalman gain to obtain a reference product;

[0103] calculate a sum value of the reference product and the torque estimation value of the previous period to obtain the Kalman filtering value of the current period.

[0104] Optionally, the second determining module is specifically configured to:

[0105] determine the torque estimation value of the next period according to the Kalman filtering value of the current period by a system equation, the system equation being obtained by linearizing a whole vehicle longitudinal driving dynamics equation taking torque and vehicle speed as state variables.

[0106] Optionally, the control module is specifically configured to:

[0107] determine the steel belt clamping force of the current period according to the torque estimation value of the next period, the belt wheel cone angle, the belt wheel friction coefficient, the belt wheel radius and the belt wheel cylinder acting area.

[0108] Optionally, the control module is specifically configured to:

[0109] select a maximum value between the torque estimation value of the next period and the torque measurement value of the current period as a target torque estimation value;

[0110] determine the steel belt clamping force of the current period according to the target torque estimation value, the belt wheel cone angle, the belt wheel friction coefficient, the belt wheel radius and the belt wheel cylinder acting area.

[0111] Optionally, the control module is further configured to:

[0112] when detecting that a deviation between the torque estimation value of the next period and the torque measurement value of the next period is less than a preset threshold value, exit the torque estimation algorithm for determining the torque estimation value.

[0113] Optionally, the control module is further configured to:

[0114] When a throttle decrease is detected, or no throttle is detected, exit the torque estimation algorithm for determining the torque estimate

[0115] The application embodiment innovatively takes the "correction" result in the Kalman filtering algorithm as an intermediate variable and takes the "estimation" result as a final control variable, that is, takes the Kalman filtering value of the current period as an intermediate variable and takes the torque estimate of the next period as a final control variable, thus optimally estimating the torque generated by the engine in the next period in real time and taking it as the basis for calculating the clamping force in the current period, rather than estimating and correcting the torque transmitted by the steel belt system in the current period. The device optimally estimates the engine torque by using the estimation function algorithm implicitly in the Kalman filtering, which can meet the real-time requirement and reduce the working pressure of the steel belt system and improve the system efficiency.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0117] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0118] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0119] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0120] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (English full name: Read-Only Memory, English abbreviation: ROM), a random access memory (English full name: Random Access Memory, English abbreviation: RAM), a magnetic disk or an optical disk, and various computer program storage media.

[0121] It should be understood that in the present application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, "A and / or B" can represent: only A, only B, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents that the associated objects before and after are in an "or" relationship. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0122] The above-described and above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting and controlling the clamping force of a steel strip, characterized in that, The method includes: When the target operating condition is detected, the Kalman filter value for the current cycle is determined based on the torque estimate of the previous cycle, the torque measurement value of the current cycle, and the torque estimate measurement value of the current cycle. The torque measurement value of the current cycle is the actual torque value measured in the current cycle, and the torque estimate measurement value of the current cycle is determined based on the torque estimate of the previous cycle. Based on the Kalman filter value of the current cycle, determine the torque estimate for the next cycle; Based on the estimated torque for the next cycle, the steel strip clamping force for the current cycle is controlled.

2. The method according to claim 1, characterized in that, The estimated torque measurement for the current cycle is determined in the following way: The torque prediction measurement value for the current cycle is determined by the observation equation based on the vehicle speed of the current cycle and the torque prediction value of the previous cycle; the observation equation is an observation equation with torque and vehicle speed as state variables.

3. The method according to claim 1, characterized in that, The step of determining the Kalman filter value for the current period based on the torque estimate of the previous period, the torque measurement value of the current period, and the torque estimate measurement value of the current period includes: Calculate the difference between the measured torque value of the current cycle and the estimated torque value of the current cycle, and use it as a reference difference value; The reference product is obtained by multiplying the Kalman gain by the reference difference. The sum of the reference product and the torque estimate of the previous cycle is calculated to obtain the Kalman filter value for the current cycle.

4. The method according to claim 1, characterized in that, The step of determining the torque estimate for the next cycle based on the Kalman filter value of the current cycle includes: The torque estimate for the next cycle is determined by using the system equations and the Kalman filter value of the current cycle. The system equations are obtained by linearizing the longitudinal driving dynamics equations of the vehicle with torque and vehicle speed as state variables.

5. The method according to claim 1, characterized in that, The step of controlling the steel strip clamping force in the current cycle based on the torque estimate for the next cycle includes: The steel belt clamping force for the current cycle is determined based on the estimated torque for the next cycle, the pulley cone angle, the pulley friction coefficient, the pulley radius, and the effective area of ​​the pulley cylinder.

6. The method according to claim 5, characterized in that, The step of determining the steel belt clamping force for the current cycle based on the estimated torque for the next cycle, the pulley cone angle, the pulley friction coefficient, the pulley radius, and the effective area of ​​the pulley cylinder includes: The maximum value between the estimated torque value for the next cycle and the measured torque value for the current cycle is selected as the target estimated torque value. The steel belt clamping force for the current cycle is determined based on the target torque estimate, pulley cone angle, pulley friction coefficient, pulley radius, and pulley cylinder operating area.

7. The method according to claim 1, characterized in that, The method further includes: When the deviation between the estimated torque value of the next cycle and the measured torque value of the next cycle is less than a preset threshold, the execution of the torque estimation method used to determine the estimated torque value is terminated.

8. The method according to claim 1, characterized in that, The method further includes: When a decrease in throttle or no throttle is detected, the execution of the torque estimation method used to determine the torque estimate is terminated.

9. A steel strip clamping force prediction and control device, characterized in that, The device includes: The first determining module is used to determine the Kalman filter value for the current cycle when the target working condition is detected, based on the torque estimate of the previous cycle, the torque measurement value of the current cycle, and the torque estimate measurement value of the current cycle; the torque measurement value of the current cycle is the actual torque value measured in the current cycle, and the torque estimate measurement value of the current cycle is determined based on the torque estimate of the previous cycle. The second determining module is used to determine the torque estimate for the next cycle based on the Kalman filter value of the current cycle. The control module is used to control the steel strip clamping force in the current cycle based on the torque estimate for the next cycle.

10. The apparatus according to claim 9, characterized in that, The device further includes: The third determining module is used to determine the estimated torque measurement value for the current cycle based on the vehicle speed of the current cycle and the estimated torque value of the previous cycle through the observation equation; the observation equation is an observation equation with torque and vehicle speed as state variables.

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