CN112305938B - Control model open-loop simulation verification method, device, equipment and medium - Google Patents
Control model open-loop simulation verification method, device, equipment and medium Download PDFInfo
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Abstract
本发明公开了一种控制模型开环仿真验证方法、装置、设备和介质，方法包括：根据待搭建控制模型的设计需求，确定构成待搭建控制模型的多个待搭建子模型；从多个待搭建子模型中选择一个待搭建子模型作为第一目标搭建子模型；向第一目标搭建子模型引入车辆测试输入数据，得到由第一目标搭建子模型根据车辆测试输入数据输出的待验证输出数据；判断待验证输出数据与车辆测试输出数据之间的差异是否小于第一预设阈值；当差异小于第一预设阈值时，从多个待搭建子模型中选择一个待搭建子模型作为第二目标搭建子模型。本申请采用边搭建边验证的方式保证控制模型中各个模块的逻辑合理性，以减少闭环联合仿真的调试压力和难度，可以缩短验证周期。
The invention discloses a method, device, equipment and medium for open-loop simulation verification of a control model. The method includes: determining a plurality of sub-models to be built that constitute the control model to be built according to the design requirements of the control model to be built; Selecting a sub-model to be built as the first target building sub-model in building sub-models; introducing vehicle test input data into the first target building sub-model, and obtaining to-be-verified output data output by the first target building sub-model according to the vehicle test input data ; determine whether the difference between the output data to be verified and the vehicle test output data is less than the first preset threshold; when the difference is less than the first preset threshold, select a sub-model to be built from a plurality of sub-models to be built as the second The target builds the submodel. The present application adopts the method of building and verifying to ensure the logical rationality of each module in the control model, so as to reduce the debugging pressure and difficulty of closed-loop co-simulation, and can shorten the verification period.
Description
Technical Field
The invention relates to the technical field of automobile simulation, in particular to a method, a device, equipment and a medium for controlling model open-loop simulation verification.
Background
With the increasing severity of energy crisis and air pollution problems, the advantages that electric vehicles can reduce automobile exhaust pollution and petroleum resources are more and more prominent. The core components of the electric automobile comprise a motor, a battery and an electric controller. The electric control component is used as a whole vehicle control component of the electric vehicle and needs to participate in all actions of the electric vehicle in the running process, so that the performance of the electric control component is particularly important in the electric vehicle.
In the related art, a control model building engineer generally completes building of a control model of an electric control component based on a Simulink platform. Simulink is a modular diagram environment for multi-domain simulation and model-based design. It supports system design, simulation, automatic code generation, and continuous testing and verification of embedded systems. Simulink provides a graphical editor, a customizable library of modules, and a solver, enabling dynamic system modeling and simulation.
However, in the related art, a control model building engineer can only use a constant module, a curve module, and the like in Simulink as basic inputs of the control model to simply verify the control model. However, the verification method provided in the related art can only verify whether each module in the control model can output data when data is input, and whether the output data obtained according to the input data is correct or not cannot be verified in the control model of the related art. That is, the related art cannot verify whether the logic of the control algorithm in the built control model is wrong, that is, cannot verify the rationality of the control algorithm of each module in the control model. In the related art, as the rationality of a control algorithm in a control model cannot be verified in a simulation stage of the control model, the correctness of the control model and the debugging pressure and difficulty of the rationality of the control algorithm in closed-loop joint simulation can be increased when the closed-loop joint simulation is performed through a complete vehicle parameterized model in the later stage.
Disclosure of Invention
The embodiment of the application provides a control model open-loop simulation verification method, device, equipment and medium, solves the technical problem that the rationality of the control algorithm of each module in the control model cannot be verified in the prior art, and achieves the technical effect of verifying the rationality of the control algorithm of each module in the control model.
In a first aspect, the present application provides a method for controlling model open-loop simulation verification, where the method includes:
determining a plurality of submodels to be built for forming the control model to be built according to the design requirements of the control model to be built;
selecting one submodel to be built from a plurality of submodels to be built as a first target building submodel, and completing building operation on the first target building submodel;
introducing vehicle test input data into the first target building sub-model to obtain output data to be verified, which is output by the first target building sub-model according to the vehicle test input data;
judging whether the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold value or not; wherein the vehicle test output data corresponds to the vehicle test input data;
when the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold value, selecting one submodel to be built from a plurality of submodels to be built as a second target building submodel, and completing building operation on the second target building submodel; wherein the first target construction submodel is different from the second target construction submodel.
Further, the method further comprises:
when the difference between the output data to be verified and the vehicle test output data is not smaller than a first preset threshold value, executing the building operation on the first target building sub-model again to obtain a first updated target building sub-model;
introducing vehicle test input data into the first updated target building sub-model to obtain updated output data to be verified, which is output by the first updated target building sub-model according to the vehicle test input data;
judging whether the difference between the updated output data to be verified and the vehicle test output data is smaller than a first preset threshold value or not;
when the difference between the updated output data to be verified and the vehicle test output data is smaller than a first preset threshold value, selecting one submodel to be built from a plurality of submodels to be built as a third target building submodel, and completing building operation on the third target building submodel; wherein the first target construction submodel is different from the third target construction submodel.
Further, the vehicle test input data and the vehicle test output data are obtained by:
receiving actual driving data of a qualified vehicle running on a target working condition road section;
selecting actual driving data to be processed related to the first target building sub-model from the actual driving data;
extracting effective sections of actual driving data to be processed to obtain effective test data;
and filtering the effective test data to obtain vehicle test input data and vehicle test output data.
Further, the filtering process is performed on the valid test data, and specifically includes:
sampling the effective test data to obtain a plurality of sampling data;
sequencing according to the time sequence of the plurality of sampling data in the effective test data to form an ordered data set;
selecting one sampling data from the ordered data set as a target sampling data;
judging whether the change speed of the sampling data before the target sampling data in the ordered data set exceeds a second preset threshold value or not;
when the change speed exceeds a second preset threshold, the weight of the target sampling data is increased, and a filtering output value of the target sampling data is obtained;
when the change speed does not exceed a second preset threshold value, taking the filtering output value of the last sampling data of the target sampling data in the ordered data set as the filtering output value of the target sampling data;
when the change speed is 0, the target sample data is directly used as a filter output value of the target sample data.
Further, introducing vehicle test input data into the first target building sub-model specifically comprises:
converting vehicle test input data into a preset file with a preset format;
and introducing a preset file into the first target building sub-model.
In a second aspect, the present application provides a control model open-loop simulation verification apparatus, comprising:
the first determining module is used for determining a plurality of submodels to be built for forming the control model to be built according to the design requirements of the control model to be built;
the first selection module is used for selecting one submodel to be built from a plurality of submodels to be built as a first target building submodel and completing building operation on the first target building submodel;
the first input module is used for introducing vehicle test input data into the first target building sub-model to obtain output data to be verified, which is output by the first target building sub-model according to the vehicle test input data;
the first judgment module is used for judging whether the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold value or not; wherein the vehicle test output data corresponds to the vehicle test input data;
the second selection module is used for selecting one sub-model to be built from the plurality of sub-models to be built as a second target building sub-model when the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold value, and completing building operation on the second target building sub-model; wherein the first target construction submodel is different from the second target construction submodel.
Further, the apparatus further comprises:
the first building module is used for re-executing building operation on the first target building sub-model when the difference between the output data to be verified and the vehicle test output data is not smaller than a first preset threshold value so as to obtain a first updated target building sub-model;
the second input module is used for introducing vehicle test input data into the first update target building sub-model to obtain to-be-verified update output data output by the first update target building sub-model according to the vehicle test input data;
the second judgment module is used for judging whether the difference between the updated output data to be verified and the vehicle test output data is smaller than a first preset threshold value or not;
the third selection module is used for selecting one sub-model to be built from the plurality of sub-models to be built as a third target building sub-model when the difference between the updated output data to be verified and the vehicle test output data is smaller than a first preset threshold value, and completing building operation on the third target building sub-model; wherein the first target construction submodel is different from the third target construction submodel.
Further, the first input module includes:
the receiving submodule is used for receiving actual driving data of the qualified vehicle running on the target working condition road section;
the first selection submodule is used for selecting actual driving data to be processed related to the first target building submodel from the actual driving data;
the effective segment extraction submodule is used for extracting an effective segment of actual driving data to be processed to obtain effective test data;
and the filtering processing submodule is used for filtering the effective test data to obtain vehicle test input data and vehicle test output data.
In a third aspect, the present application provides an electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute to implement a control model open loop simulation verification method.
In a fourth aspect, the present application provides a non-transitory computer readable storage medium having instructions that, when executed by a processor of an electronic device, enable the electronic device to perform a control model open loop simulation verification method.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
1. this application is at the in-process of setting up the control model, adopts the mode of setting up the limit and verifying to guarantee the logic rationality of each module in the control model to reduce the closed loop joint simulation's in later stage control model's the exactness and the debugging pressure and the degree of difficulty of control algorithm rationality, can shorten verification period.
2. The control model that this application was built and is verified based on real vehicle test data, can guarantee the degree of accuracy of logic rationality, consequently, can be repeatedly applied to the whole car control strategy development of all motorcycle types, need not to build the control model again according to the design demand of new car again, can shorten verification cycle and research and development cycle.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a flow chart of a control model open-loop simulation verification method provided herein;
FIG. 2 is a schematic structural diagram of a control model to be built according to the present application;
fig. 3 is a schematic structural diagram of a control model open-loop simulation verification apparatus provided in the present application;
fig. 4 is a schematic structural diagram of an electronic device provided in the present application.
Detailed Description
The embodiment of the application provides a control model open-loop simulation verification method, and solves the technical problem that the rationality of a control algorithm of each module in a control model cannot be verified in the prior art.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
a method for controlling an open loop simulation verification of a model comprises the following steps: determining a plurality of submodels to be built for forming the control model to be built according to the design requirements of the control model to be built; selecting one submodel to be built from a plurality of submodels to be built as a first target building submodel, and completing building operation on the first target building submodel; introducing vehicle test input data into the first target building sub-model to obtain output data to be verified, which is output by the first target building sub-model according to the vehicle test input data; judging whether the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold value or not; wherein the vehicle test output data corresponds to the vehicle test input data; when the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold value, selecting one submodel to be built from a plurality of submodels to be built as a second target building submodel, and completing building operation on the second target building submodel; wherein the first target construction submodel is different from the second target construction submodel.
This application is at the in-process of setting up the control model, adopts the mode of setting up the limit and verifying to guarantee the logic rationality of each module in the control model to reduce the closed loop joint simulation's in later stage control model's the exactness and the debugging pressure and the degree of difficulty of control algorithm rationality, can shorten verification period.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
First, it is stated that the term "and/or" appearing herein is merely one type of associative relationship that describes an associated object, meaning that three types of relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The application provides a control model open-loop simulation verification method as shown in fig. 1, which comprises the following steps:
step S11, determining a plurality of submodels to be built for forming the control model to be built according to the design requirements of the control model to be built.
The control model to be built related to the step S11 mainly refers to a control model to be obtained by simulating according to the design requirements of the vehicle control unit of the electric vehicle. The application platform of this application is mainly based on Simulink, and when the application platform was Simulink, the control model that waits to be built promptly is the Simulink control model that waits to be built.
The design requirement of the control model to be built is determined based on the control logic of the whole electric vehicle, and the control logic of the whole electric vehicle can comprise power on and off of the whole vehicle, analysis of driving intention, torque vector control, drive anti-skidding and brake energy recovery, and the coordination work of components or control modules such as an ABS (anti-lock brake system), an ESP (electronic stability system), an EHB (electronic hydraulic brake system), an EPB (electronic parking brake system) and the like.
According to the control logic of the whole electric vehicle, the control model to be built can be decomposed into a plurality of submodels to be built, the submodels to be built can be the minimum functional units forming the control model to be built, and the minimum functional units refer to virtual modules corresponding to the tail-end execution units capable of executing a certain control signal of the electric vehicle. For example, the pedal generates a brake signal, the brake signal is used for changing the rotating speed of a hub motor of the electric vehicle, then the last execution unit of the brake signal is the hub motor, a module is required to be built for the hub motor according to various parameters of the hub motor, and the module is the minimum function unit of the control model to be built.
The submodel to be built can also be a small-scale functional unit integrated by a plurality of minimum functional units, and can also be a medium-scale functional unit integrated by a plurality of small-scale functional units.
Therefore, when the control model to be built is decomposed into a plurality of submodels to be built, the scale of the submodels to be built can be determined according to specific conditions. When the requirement on the accuracy of the control model to be built is high, the control model to be built can be decomposed into a plurality of submodels to be built with small scales; when the accuracy requirement of the control model to be built is low, the control model to be built can be decomposed into a plurality of large-scale submodels to be built.
For a better illustration of the present application, an example is provided in connection with fig. 2:
as shown in fig. 2, the structure of the control model to be built is obtained according to the design requirement of the electric vehicle. The "driving intent recognition" receives the accelerator pedal, brake pedal and gear information, and then sends the desired braking torque to the "RBS braking energy recovery strategy", while also sending the desired driving torque to the "RBS driving force vector allocation strategy". The RBS braking energy recovery strategy outputs actual braking torque requirements of 4 wheels according to expected braking torque and sends the actual braking torque requirements to the CA control distributor, and the CA control distributor performs preliminary torque distribution according to the braking torque requirements of the 4 wheels and differential torque compensation torque of the 4 wheels. The RBS driving force vector distribution strategy takes the analysis requirement of the driving intention of the driver and the steady state area of the vehicle as input, and then the difference torque compensation moment in the running process of the vehicle can be calculated.
The 'vehicle speed estimator' carries out real-time vehicle speed estimation required in the control algorithm; the friction coefficient estimator estimates the real-time friction coefficient of the road surface; the online tire model and the friction coefficient estimator jointly complete accurate estimation of the road surface friction coefficient; the vehicle stability region identification can identify the vehicle running steady-state region through a vehicle speed estimator, a friction coefficient estimator and an online tire model. The 'coordination control strategy' considers external products RBS, ABS, DYC, ESC and the like, and then carries out corresponding coordination control mechanisms.
The individual parameters in fig. 2 have the following meanings:
αdrv: accelerator pedal opening; alpha is alphabrk: brake pedal opening; i isshift: a gear position; deltaSW: steering wheel turning; t is* brk_tot: a desired braking torque; t is* drv_tot: a desired drive torque; Δ MZ: differential torque compensation moment;
T* brk(1)，T* brk(2)，T* brk(3)，T* brk(4): desired four-wheel output brake torque;
T* mot(1)，T* mot(2)，T* mot(3)，T* mot(4): desired four-wheel output drive torque;
Treq mot(1)，Treq mot(2)，Treq mot(3)，Treq mot(4): actual four wheel drive torque demand;
K*: a steady state factor; s: sliding mode coefficients of two parameters of the inherent frequency and the centroid slip angle; omega* n: the natural original frequency; zeta*: expected periodic point periodic state data; ζ: a damping coefficient; omega* r: a desired natural frequency; omegar: an actual natural frequency; beta: actual centroid slip angle; beta is a*: the desired centroid slip angle; λ: centroid slip angle weight;
the centroid slip angle first derivative;
the X-direction speed of the whole vehicle;
the Y-direction speed of the whole vehicle;
coefficient of friction of the road surface;
the ith wheel center X-direction tire force;
the ith wheel center Y tire force.
The driving intention recognition, the RBS braking energy recovery strategy, the CA control distributor, the vehicle speed estimator, the friction coefficient estimator, the vehicle stability domain recognition, the online tire model, the coordinated control strategy and the like in fig. 2 can be used as the sub-models to be built of the control model to be built. Further, when the requirement on the accuracy of the control model to be built is high, each submodel to be built in fig. 2 may be further subdivided, for example, "driving intention recognition" may be divided into smaller submodels to be built, such as "driving", "braking", and the like.
And step S12, selecting one sub-model to be built from the plurality of sub-models to be built as a first target building sub-model, and completing building operation on the first target building sub-model.
After the control model to be built is decomposed into a plurality of submodels to be built, the levels of the submodels to be built can be classified according to the relation among the submodels to be built. For example, there are five submodels to be built, which are a module a, a module B, a module C, a module D, and a module E, respectively, where an output signal of the module a is an input signal of the module B; the output signal of the module B is the input signal of the module C; the input signal and the output signal of the module D and the module E are irrelevant to the module A, the module B and the module C.
Therefore, the module a, the module B and the module C can be classified, the module a is positioned at a first level (the first level is a module requiring the most advanced model building), the module B is positioned at a second level (the second level is a module requiring the building of the module related thereto in the first level), and the module C is positioned at a third level (the third level is a module requiring the building of the module related thereto in the second level). Since module D and module E are relatively independent, any level can be located.
Returning to the step S12, selecting one to-be-built sub-model from the multiple to-be-built sub-models as a first target building sub-model, where the first target building sub-model may be selected after grading the to-be-built sub-models according to the above-mentioned manner; or may be selected at will. After determining the first target construction submodel, the construction of the first target construction submodel is completed in Simulink.
For a better illustration of the present application, an example is provided in connection with fig. 2:
according to the flow direction of the signals in fig. 2, the relationship among the driving intention recognition, the RBS braking energy recovery strategy and the CA control distributor can be determined, the levels among the driving intention recognition, the RBS braking energy recovery strategy and the CA control distributor can be further determined, the driving intention recognition can be used as a first target construction sub-model, and the construction operation can be completed. After the model building of the first target building sub-model is completed and the control logic of the first target building sub-model is correct, the "RBS braking energy recovery strategy" may be used as a second target building sub-model.
Step S13, vehicle test input data are introduced into the first target building sub-model, and output data to be verified, which are output by the first target building sub-model according to the vehicle test input data, are obtained.
After the first target building sub-model is completed, the logic reasonableness of the first target building sub-model is judged.
According to the method and the device, the vehicle test input data are introduced into the first target building sub-model, so that the first target building sub-model outputs the corresponding output data to be verified according to the vehicle test input data.
Introducing vehicle test input data into the first target building sub-model, specifically comprising:
converting vehicle test input data into a preset file with a preset format; the preset format is a format suitable for a platform for building the first target building sub-model. For example, when the platform building the first target building sub-model is Simulink, the preset file of the preset format refers to an.m file.
And introducing a preset file into the first target building sub-model. After the vehicle test input data are converted into the preset files, when the logic rationality of the first target building sub-model needs to be verified, the preset files are imported into the first target building sub-model.
For example, vehicle test input data is written into an m file of MATLAB, the vehicle test input data is read into a working space (works space) of a first target building sub-model in different variable forms, and then variables are called into the first target building sub-model, so that data input of the first target building sub-model is realized.
More specifically, the following steps may be included:
and quantizing the vehicle test input data, and importing the vehicle test input data into a working space of Matlab through the m file.
and setting the path and the opening mode of the m file of the vehicle test input data.
defining different variables according to vehicle test input data;
and 4, variable assignment:
assigning corresponding specific numerical values in the vehicle test input data to different variables;
step 5, variable import:
importing the vehicle test input data into a working space of Matlab through the m file;
step 6, control model quote:
and introducing variables in the working space into the first target building model through a workspace from module to serve as input data of the first target building model.
For a better illustration of the present application, an example is provided in connection with fig. 2:
with the "driving intention recognition" in fig. 2 as a first target construction sub-model, vehicle test input data (e.g., one or more of accelerator pedal opening, brake pedal opening, and gear position) is introduced into the "driving intention recognition", resulting in output data to be verified (e.g., one or more of desired brake torque to be verified and desired drive torque to be verified) output by the "driving intention recognition".
Step S14, judging whether the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold value; wherein the vehicle test output data corresponds to the vehicle test input data.
The vehicle test input data and the vehicle test output data are obtained after passing a qualified vehicle test, that is, the vehicle test input data are introduced into the first target building sub-model, if the data output by the first target building sub-model is greatly different from the vehicle test output data, it is indicated that the first target building sub-model is problematic, otherwise, it is indicated that the first target building sub-model is not problematic.
For a better illustration of the present application, an example is provided in connection with fig. 2:
the method comprises the steps of judging the relation between the expected braking torque to be verified and the expected driving torque to be verified and the corresponding expected braking torque preset threshold value and the corresponding expected driving torque preset threshold value, determining whether the expected braking torque to be verified is smaller than the corresponding expected braking torque preset threshold value, and determining whether the expected driving torque to be verified is smaller than the corresponding expected driving torque preset threshold value.
The vehicle test input data and the vehicle test output data referred to in step S14 are obtained by the following method:
and step S141, receiving actual driving data of the qualified vehicle running on the target working condition road section.
When the qualified vehicle runs on the target working condition road section, test data which can be processed by the first target building sub-model can be obtained. That is, the target condition road section is a road section where test data relating to the first target construction sub-model can be obtained.
And all the actual driving data of the vehicle running on the target working condition road section are collected. On one hand, test data required by the first target building sub-model can be obtained, on the other hand, test data required by other sub-models to be built can be obtained simultaneously, and times and time for testing through qualified vehicles can be effectively reduced.
The target condition may be an angular pulse, a double shift line, a snake, a steady rotation, a U _ turn, a J _ turn, an amplification trapezoid, etc.
And step S142, selecting the actual driving data to be processed related to the first target building sub-model from the actual driving data.
The actual driving data obtained in step S141 is all the original data of the vehicle when the vehicle runs on the target operating condition road section, and includes the actual driving data to be processed related to the first target construction sub-model. Upon verifying the logical reasonableness of the first target construction submodel, step S142 acquires only data related to the first target construction submodel.
And S143, extracting effective sections of the actual driving data to be processed to obtain effective test data.
When the vehicle actually runs on the target working condition road section, the to-be-processed actual driving data acquired in step S142 includes data of several seconds before the vehicle enters the target working condition and data of several seconds after the vehicle leaves the target working condition, because data integrity of the vehicle running on the target working condition road section needs to be ensured. And when the logic rationality of the first target building sub-model is verified, data of a vehicle in a few seconds before entering the target working condition and data of a vehicle in a few seconds after leaving the target working condition are required to be removed, and only the data in the target working condition are effective test data.
And step S144, filtering the effective test data to obtain vehicle test input data and vehicle test output data.
The effective test data comprises vehicle test input data and vehicle test output data, and the vehicle test input data and the vehicle test output data correspond to each other.
Effective test data are data directly collected from the vehicle, and due to the influence of surrounding environments such as collection equipment, burrs often exist in the effective test data, and the existence of the burrs can reduce the accuracy of verifying the logic rationality of the first target building sub-model, so that the effective test data need to be filtered.
The dynamic filtering is adopted for the effective test data in the application, and the filtering processing is carried out on the effective test data, and specifically includes the following steps, and for better explaining the filtering principle adopted in the application, the following steps are explained in combination with the formula (1).
y(n)＝a*x(n)+(1-a)*y(n-1) (1)
Wherein a is a filter coefficient; x (n) is target sampling data; y (n-1) is a filtering output value obtained according to sampling data before the target sampling data; and y (n) is a filtering output value corresponding to the target sampling data.
Step S1441, sampling the valid test data to obtain a plurality of sampled data.
The sampling data can be randomly selected or determined according to the same step size. The number of the sampling data can be set according to specific situations. When the quality requirement on effective test data is high, the number of the collected data can be relatively large; when the quality requirement on the effective test data is not high, the number of the sampling data can be relatively less.
Step S1442, sequencing the sampling data according to the time sequence of the sampling data in the effective test data to form an ordered data set.
And sequencing the plurality of sampling data according to the time sequence in the effective test data so as to facilitate the subsequent processing of the effective test data.
In step S1443, one sample data is selected from the ordered data set as a target sample data.
And according to the sequence of the sampling data in the ordered data set, sequentially taking each sampling data in the ordered data set as target sampling data respectively, and executing the step S1444 to the step S1447 respectively to finish the filtering operation of the effective test data.
In step S1444, it is determined whether the change speed of the sample data before the target sample data in the ordered data set exceeds a second preset threshold.
With respect to the target sampling data x (n) determined in step S1443, it is determined whether the change speed of the sampling data before the target sampling data x (n) exceeds a second preset threshold.
And step S1445, when the change speed exceeds a second preset threshold, increasing the weight of the target sampling data to obtain a filtering output value of the target sampling data.
When the change speed exceeds the second preset threshold, the stability of the sampling data before the target sampling data is not high, the influence of the external is large, and the sensitivity of filtering needs to be improved, so that the weight of the target sampling data needs to be improved, namely, the filter coefficient a needs to be improved, when a is higher, the (1-a) is smaller, and the weight of the target sampling data is improved and the weight of the filter output value corresponding to the sampling data before the target sampling data is reduced by combining the formula (1).
In step S1446, when the variation speed does not exceed the second preset threshold, the filter output value of the last sample data of the target sample data in the ordered data set is used as the filter output value of the target sample data.
When the change speed does not exceed the second preset threshold, the sampling data before the target sampling data tends to be stable and is less influenced by the outside, the weight of the target sampling data can be reduced to 0, that is, the filter coefficient a is set to 0, and then the target sampling data can be ignored, and the filter output value determined by the sampling data before the target sampling data is directly used as the filter output value of the target sampling data.
In step S1447, when the change speed is 0, the target sample data is directly used as the filter output value of the target sample data.
When the change speed is 0, it means that the sample data before the target sample data is already stable, and it can be shown that the sample data after the target sample data is also stable, so that the target sample data itself is hardly affected by the external environment, and the target sample data itself can be directly used as the filter output value of the target sample data.
According to the method and the device, effective test data collected from the vehicle can be effectively optimized through dynamic filtering, and the accuracy and the verification precision can be effectively improved when the logic rationality of the first target building sub-model is verified.
Returning to the step S14, when the difference between the output data to be verified and the vehicle test output data is less than the first preset threshold, executing a step S15; when the difference between the output data to be verified and the vehicle test output data is not less than the first preset threshold, step S16 is performed.
Step S15, when the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold value, selecting one sub-model to be built from a plurality of sub-models to be built as a second target building sub-model, and completing building operation on the second target building sub-model; wherein the first target construction submodel is different from the second target construction submodel.
When the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold value, the first target building submodel which is built can correctly process the vehicle test input data to obtain the output data to be verified, which is not much different from the vehicle test output data, namely the logic of the first target building submodel which is built is correct and has logic rationality.
After the first target building sub-model is verified to have logic rationality, one sub-model to be built is selected from a plurality of sub-models to be built except the first target building sub-model to serve as a second target building sub-model.
For a better illustration of the present application, an example is provided in connection with fig. 2: and when the expected braking torque to be verified is smaller than the corresponding expected braking torque preset threshold value and the expected driving torque to be verified is determined to be smaller than the corresponding expected driving torque preset threshold value, the driving intention identification logic is considered to be correct and logic rationality is achieved. Otherwise, if the logic of the driving intention recognition is wrong and the logic is not reasonable, the driving intention recognition needs to be reconstructed, or the driving intention recognition needs to be adjusted until the difference between the output data to be verified input to the driving intention recognition and the output vehicle test output data is smaller than a first preset threshold value.
Aiming at the built second target building sub-model, the following steps are executed:
step S151, introducing second vehicle test input data into a second target building sub-model to obtain second to-be-verified output data output by the second target building sub-model according to the second vehicle test input data;
step S151 is similar to step S13 and will not be described here. Wherein, different target building submodels correspond to different vehicle test input data.
Step S152, judging whether the difference between the second output data to be verified and the second vehicle test output data is smaller than a third preset threshold value; wherein the second vehicle test output data corresponds to the second vehicle test input data;
step S152 is similar to step S14 and will not be described herein.
Step S153, when the difference between the second to-be-verified output data and the second vehicle test output data is smaller than a third preset threshold value, selecting one to-be-built sub-model from a plurality of to-be-built sub-models as a fourth target building sub-model, and completing building operation on the fourth target building sub-model; wherein the second target construction submodel is different from the fourth target construction submodel.
Step S153 is similar to step S15 and will not be described herein.
And step S16, when the difference between the output data to be verified and the vehicle test output data is not less than a first preset threshold value, re-executing the building operation on the first target building sub-model to obtain a first updated target building sub-model.
When the difference between the output data to be verified and the vehicle test output data is not smaller than a first preset threshold value, the situation that the built first target building sub-model cannot correctly process vehicle test input data is shown, namely the logic of the first target building sub-model has a problem, and the sub-model needs to be built again to obtain a new first target building sub-model, namely a first updated target building sub-model.
Step S17, vehicle test input data are introduced into the first update target building sub-model, and updated output data to be verified, which are output by the first update target building sub-model according to the vehicle test input data, are obtained.
Step S17 is similar to step S13 and will not be described here.
Step S18, judging whether the difference between the updated output data to be verified and the vehicle test output data is smaller than a first preset threshold value;
step S18 is similar to step S14 and will not be described here.
Step S19, when the difference between the updated output data to be verified and the vehicle test output data is smaller than a first preset threshold value, selecting one sub-model to be built from a plurality of sub-models to be built as a third target building sub-model, and completing building operation on the third target building sub-model; wherein the first target construction submodel is different from the third target construction submodel.
Step S19 is similar to step S15 and will not be described here.
To sum up, this application is treating the in-process that builds of building the control model, every pair one is waited to build the submodel and is accomplished to build, just carries out logic rationality to it and verifies to judge whether there is the mistake in the submodel of waiting to build of accomplishing the building. When the built submodel to be built has errors, the submodel to be built is executed again to ensure the logic rationality of each submodel to be built after the building is completed, and further ensure the logic rationality of the control model.
That is to say, this application is at the in-process of setting up the control model, and the logical rationality of each module in the control model is guaranteed to the mode of adopting the limit to set up the limit and verifying to reduce the closed loop joint simulation's in later stage control model's the exactness and the debugging pressure and the degree of difficulty of control algorithm rationality, can shorten verification period. In addition, the control model built by the method is built and verified based on real vehicle test data, and the accuracy of logic rationality can be guaranteed, so that the method can be repeatedly applied to the development of the whole vehicle control strategy of all vehicle types, the control model is not required to be re-built according to the design requirement of a new vehicle, and the verification period and the research and development period can be shortened.
Based on the same inventive concept, the present application provides a control model open-loop simulation verification apparatus as shown in fig. 3, the apparatus comprising:
the first determining module 31 is used for determining a plurality of submodels to be built for forming the control model to be built according to the design requirements of the control model to be built;
the first selection module 32 is used for selecting one sub-model to be built from a plurality of sub-models to be built as a first target building sub-model and completing building operation on the first target building sub-model;
the first input module 33 is used for introducing vehicle test input data into the first target building sub-model to obtain output data to be verified, which is output by the first target building sub-model according to the vehicle test input data;
the first judging module 34 is configured to judge whether a difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold; wherein the vehicle test output data corresponds to the vehicle test input data;
the second selection module 35 is configured to, when the difference between the output data to be verified and the vehicle test output data is smaller than a first preset threshold, select one to-be-built sub-model from the multiple to-be-built sub-models as a second target building sub-model, and complete building operation on the second target building sub-model; wherein the first target construction submodel is different from the second target construction submodel.
Further, the apparatus further comprises:
the first building module is used for re-executing building operation on the first target building sub-model when the difference between the output data to be verified and the vehicle test output data is not smaller than a first preset threshold value so as to obtain a first updated target building sub-model;
the second input module is used for introducing vehicle test input data into the first update target building sub-model to obtain to-be-verified update output data output by the first update target building sub-model according to the vehicle test input data;
the second judgment module is used for judging whether the difference between the updated output data to be verified and the vehicle test output data is smaller than a first preset threshold value or not;
the third selection module is used for selecting one sub-model to be built from the plurality of sub-models to be built as a third target building sub-model when the difference between the updated output data to be verified and the vehicle test output data is smaller than a first preset threshold value, and completing building operation on the third target building sub-model; wherein the first target construction submodel is different from the third target construction submodel.
Further, the first input module 33 includes:
the receiving submodule is used for receiving actual driving data of the qualified vehicle running on the target working condition road section;
the first selection submodule is used for selecting actual driving data to be processed related to the first target building submodel from the actual driving data;
the effective segment extraction submodule is used for extracting an effective segment of actual driving data to be processed to obtain effective test data;
and the filtering processing submodule is used for filtering the effective test data to obtain vehicle test input data and vehicle test output data.
Further, the filtering processing sub-module includes:
the sampling submodule is used for sampling the effective test data to obtain a plurality of sampling data;
the sequencing submodule is used for sequencing the sampling data according to the time sequence of the sampling data in the effective test data to form an ordered data set;
a second selection submodule for selecting one sample data from the ordered data set as a target sample data;
the judgment submodule is used for judging whether the change speed of the sampling data before the target sampling data in the ordered data set exceeds a second preset threshold value or not;
the first output submodule is used for increasing the weight of the target sampling data when the change speed exceeds a second preset threshold value to obtain a filtering output value of the target sampling data;
the second output submodule is used for taking the filtering output value of the last sampling data of the target sampling data in the ordered data set as the filtering output value of the target sampling data when the change speed does not exceed a second preset threshold;
and the third output submodule is used for directly taking the target sampling data as a filtering output value of the target sampling data when the change speed is 0.
Further, the first input module 33 further includes:
the conversion submodule is used for converting the vehicle test input data into a preset file with a preset format;
and the import submodule is used for importing a preset file into the first target building submodel.
Based on the same inventive concept, the present application provides an electronic device as shown in fig. 4, including:
a processor 41;
a memory 42 for storing instructions executable by the processor 41;
wherein the processor 41 is configured to execute to implement a control model open loop simulation verification method.
Based on the same inventive concept, the present application provides a non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor 41 of an electronic device, enable the electronic device to perform a method for implementing an open-loop simulation verification of a control model.
Since the electronic device described in this embodiment is an electronic device used for implementing the method for processing information in this embodiment, a person skilled in the art can understand the specific implementation manner of the electronic device of this embodiment and various variations thereof based on the method for processing information described in this embodiment, and therefore, how to implement the method in this embodiment by the electronic device is not described in detail here. Electronic devices used by those skilled in the art to implement the method for processing information in the embodiments of the present application are all within the scope of the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
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