CN114969962B - Method, device, equipment and storage medium for generating severe vehicle RDE emission working conditions - Google Patents

Method, device, equipment and storage medium for generating severe vehicle RDE emission working conditions Download PDF

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CN114969962B
CN114969962B CN202210431036.XA CN202210431036A CN114969962B CN 114969962 B CN114969962 B CN 114969962B CN 202210431036 A CN202210431036 A CN 202210431036A CN 114969962 B CN114969962 B CN 114969962B
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condition
emission
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CN114969962A (en
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林嘉豪
唐俊
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Nanqi Xiance Nanjing Technology Co ltd
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Nanqi Xiance Nanjing Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
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    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention discloses a method, a device, equipment and a storage medium for generating severe vehicle RDE emission working conditions. According to the method, emission data of running conditions of each vehicle to be predicted are determined according to an emission prediction model, a pre-built emission prediction model, a whole vehicle dynamics model and a boundary condition library, and further a target severe condition with highest emission is determined according to the emission data, a set value range of relevant parameters of each vehicle in the whole vehicle dynamics model and the boundary condition library, so that the worst condition meeting constraints of the whole vehicle dynamics model and the boundary condition library is obtained. The worst working condition can be used for a subsequent real vehicle emission test, and only the emission data under the worst RDE working condition is required to be researched whether the emission data can meet the requirement of regulations, so that the technical problem that the traditional emission test is difficult to completely cover all working condition areas is solved. In addition, the worst working condition solved by the method accords with the dynamic characteristics of the vehicle, so that the worst working condition can be reproduced by the real vehicle on an actual road.

Description

Method, device, equipment and storage medium for generating severe vehicle RDE emission working conditions
Technical Field
The invention relates to the technical field of vehicle emission testing, in particular to a method, a device, equipment and a storage medium for generating severe vehicle RDE emission working conditions.
Background
The national pollutant emission standard of the motor vehicle in the sixth stage newly increases the important test content of the actual running pollutant emission, namely RDE (Real Drive Emission, actual running pollutant emission) test. The RDE test refers to emissions testing and certification performed by a driver driving a real vehicle in an actual road and under boundary conditions (e.g., altitude, ambient temperature, vehicle speed, etc.) conforming to national regulations. Regulations require loading PEMS (Portable Emissions Measurement System, portable emissions test system) at road test time for emissions testing, and result correction and RDE mileage assessment of the emissions pollutants using temperature, humidity, GPS, etc. auxiliary data.
However, due to numerous influencing factors in the test (such as driving style, ambient temperature, altitude, speed and the like of a driver), the RDE test has the difficulties of high uncertainty, weak reproducibility and the like, and the boundary conditions specified by the RDE test rule have wide coverage and strong random spot check. It is not exhaustive if the RDE test is performed fully on all conditions according to regulatory boundary conditions.
Therefore, in the process of implementing the present invention, it is found that at least the following technical problems exist in the prior art: the emission test is difficult to completely cover all working condition areas.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for generating severe vehicle RDE emission working conditions, which are used for realizing the generation of the worst working conditions meeting the constraint of a whole vehicle dynamics model and a boundary condition library in a vehicle emission test, and solving the technical problem that the traditional emission test is difficult to completely cover all working condition domains.
According to one aspect of the invention, a method for generating severe vehicle RDE emission conditions is provided, comprising the following steps:
acquiring a pre-constructed emission prediction model, a whole vehicle dynamics model and a boundary condition library, wherein the whole vehicle dynamics model comprises a set value range of relevant parameters of each vehicle;
generating each vehicle driving condition to be predicted, and determining emission data corresponding to each vehicle driving condition based on the emission prediction model;
and determining a target severe working condition in the vehicle driving working conditions based on the emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, wherein the emission quantity corresponding to the target severe working condition is highest.
Optionally, the determining the target severe working condition in the vehicle driving working conditions based on the emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library includes:
determining each to-be-processed running condition meeting each boundary condition in the set value range and the boundary condition library in each vehicle running condition based on the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library;
and determining the to-be-processed running condition with the highest emission based on the emission data corresponding to each to-be-processed running condition, and determining the to-be-processed running condition with the highest emission as a target severe condition.
Optionally, the vehicle related parameter is a vehicle speed parameter or a vehicle engine parameter, the boundary condition is a mileage attribute boundary, a parking time boundary, an altitude boundary or a high-speed duration boundary, the determining, based on the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, each to-be-processed running condition satisfying each boundary condition in the set value range and the boundary condition library in each running condition of the vehicle includes:
Determining each running condition to be screened in each running condition of the vehicle based on the set value range of the vehicle speed parameter and the set value range of the vehicle engine parameter;
and determining each to-be-processed driving working condition in each to-be-screened driving working condition based on the mileage attribute boundary, the parking time boundary, the altitude boundary and the high-speed duration boundary.
Optionally, the determining the target severe working condition in the vehicle driving working conditions based on the emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library includes:
determining emission reference indexes based on emission data corresponding to the vehicle running conditions and determining working condition constraint reference indexes based on working condition information of the vehicle running conditions and boundary conditions in the boundary condition library according to the vehicle running conditions meeting the set value range of each vehicle related parameter in the vehicle dynamics model in each vehicle running condition;
and determining a target severe condition in each vehicle driving condition based on the emission amount reference index and the condition constraint reference index of each vehicle driving condition.
Optionally, the determining emission data corresponding to each vehicle driving condition based on the emission prediction model includes:
for each vehicle driving condition, determining the emission value of each preset emission corresponding to the vehicle driving condition based on the vehicle speed information, the vehicle information, the environment information and the position information in the vehicle driving condition and the emission prediction model.
Optionally, the method further comprises:
acquiring actual road test data, wherein the actual road test data comprises actual vehicle speed information, actual vehicle information, actual environment information, actual position information and actual emission values;
and constructing the emission prediction model based on the actual road test data.
Optionally, the determining the target severe working condition in the driving working conditions of the vehicles includes:
determining a vehicle driving condition with the highest emission among the vehicle driving conditions;
and determining at least one of a speed working condition curve of the whole vehicle, an acceleration working condition curve of the whole vehicle and an engine output curve according to the running working condition of the vehicle with the highest emission.
According to another aspect of the present invention, there is provided a device for generating a severe vehicle RDE emission condition, including:
The system comprises an acquisition module, a prediction module, a calculation module and a boundary condition library, wherein the acquisition module is used for acquiring a pre-constructed emission prediction model, a whole vehicle dynamics model and a boundary condition library, and the whole vehicle dynamics model comprises a set value range of each vehicle related parameter;
the emission prediction module is used for generating each vehicle running condition to be predicted, and determining emission data corresponding to each vehicle running condition based on the emission prediction model;
and the severe working condition determining module is used for determining a target severe working condition in the vehicle driving working conditions based on the emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, wherein the emission quantity corresponding to the target severe working condition is highest.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method for generating vehicle RDE emissions severe conditions according to any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a method for generating a vehicle RDE emission harsh condition according to any embodiment of the present invention.
According to the technical scheme, after the pre-built emission prediction model, the pre-built whole vehicle dynamics model and the boundary condition library are obtained, the emission data of each vehicle running condition to be predicted is determined according to the emission prediction model after each vehicle running condition to be predicted is generated, then the target severe condition with the highest emission is determined in each vehicle running condition according to each emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, the automatic generation of the worst condition in the vehicle emission test is realized, the worst condition meeting the constraint of the whole vehicle dynamics model and the boundary condition library is obtained, the worst condition can be used for the subsequent real vehicle emission test, the subsequent real vehicle emission test only needs to study whether the emission data under the worst condition can meet the standard, the emission result under the worst condition meets the standard, the emission result under the rest other conditions can also meet the standard without retesting the emission result under the other conditions, the condition coverage test repeated in a large number is avoided, the technical problem that the traditional emission test is difficult to completely cover all the working conditions is solved, the emission efficiency is improved, and the cost of the whole vehicle is reduced in the development stage and the cost of the whole vehicle is reduced. In addition, the method can enable the solved worst working condition to be more in line with the dynamics characteristic of the vehicle by adopting the whole vehicle dynamics model, enables the worst working condition to enable the real vehicle to reproduce on an actual road, and is suitable for vehicles of any model.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for generating severe vehicle RDE exhaust conditions according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for generating bad vehicle RDE emission conditions according to a second embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for generating bad vehicle RDE emission conditions according to a third embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a generating device for generating severe vehicle RDE exhaust conditions according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for generating a severe vehicle RDE emission condition according to an embodiment of the present invention, where the present embodiment may be suitable for determining a worst vehicle emission condition in a vehicle emission test, that is, a vehicle running condition with a highest emission amount, where the method may be performed by a device for generating a severe vehicle RDE emission condition, where the device for generating a severe vehicle RDE emission condition may be implemented in hardware and/or software, and the device for generating a severe vehicle RDE emission condition may be configured in an electronic device such as a computer, a mobile phone, a tablet, or the like. As shown in fig. 1, the method includes:
s110, acquiring a pre-constructed emission prediction model, a whole vehicle dynamics model and a boundary condition library, wherein the whole vehicle dynamics model comprises a set value range of each vehicle related parameter.
In the present embodiment, the emission prediction model may be a model constructed from actual road test data collected in advance. The emission prediction model may be a neural network trained based on actual road test data, or may be an algorithm including machine learning based on actual road test data. The emission prediction model may be used to predict corresponding emission data based on the input operating conditions. For example, the predicted corresponding emission data is output based on the input information such as vehicle speed, altitude, ambient temperature, and the like.
The vehicle dynamics model can be a vehicle dynamics model which is constructed in advance according to the vehicle bench test data. The whole vehicle bench test data can comprise a transmitter performance bench test, the engine performance bench test can be carried out in a laboratory, and each engine model corresponds to one group of engine performance data. In the embodiment, the dynamics limit of the whole vehicle can be solved through a physical formula according to the whole vehicle bench test data, so that a whole vehicle dynamics model is constructed.
Therefore, the whole vehicle dynamics model in the embodiment may include power limit information of the engine, that is, a set value range of each vehicle related parameter. For example, the vehicle-related parameters may be parameters such as vehicle speed, acceleration, steering wheel angle, engine output power, engine output speed, engine output torque, and transmitter compression ratio.
It should be noted that, the purpose of constructing the whole vehicle dynamics model in this embodiment is to: by constructing the vehicle dynamics model containing the set value range of each vehicle related parameter, the vehicle related parameters can be restrained together through the vehicle dynamics model capable of representing the dynamic performance of the real vehicle in the process of searching the worst working conditions, so that the found worst working conditions can meet the set value range specified by the vehicle dynamics model, and the worst working conditions can be realized on the actual road.
In this embodiment, the boundary condition library may contain boundary conditions specified by regulations corresponding to the RDE test. The boundary condition may be a constraint condition on a vehicle-related parameter, such as a vehicle speed, an acceleration, and the like; alternatively, the boundary condition may be a constraint condition on a running environment parameter, such as an ambient temperature, an ambient humidity, an altitude, a start point gradient, an end point gradient, or the like; constraints on driving parameters such as driving duration, driving distance, stopping time, etc. are also possible.
Illustratively, the boundary conditions specified by the RDE test regulations are as follows: 1. the mileage ratio of urban area, suburban area and expressway is 34%, 33% and 33%, the forming errors of all sections are controlled within +/-10%, the urban area mileage is not lower than 29% of the total mileage, wherein the speed working condition of the actual road in RDE test is defined as: [0,60km/h ] is urban working condition, [60km/h,90km/h ] is suburban working condition, [90km/h,135km/h ] is expressway working condition; 2. the parking time accounts for 6-30%, and the time of single parking is not more than 180s; 3. the altitude is not lower than 700 meters; 4. the time of the speed of the vehicle is higher than 100km/h and reaches more than 5 minutes at the boundary of the high-speed duration; 5. the ambient temperature is higher than or equal to 0 ℃ and lower than or equal to 30 ℃; 6. the sum of the base load and the additional load must not exceed 90% of the maximum load of the vehicle; 7. the duration time is 90-120 minutes; 8. the 95 th percentile of the product of the vehicle speed and the forward acceleration does not exceed a specified value; 9. the difference in altitude between the test start point and the test end point must not exceed 100 meters, etc.
S120, generating running conditions of each vehicle to be predicted, and determining emission data corresponding to the running conditions of each vehicle based on the emission prediction model.
The driving condition of the vehicle can be a simulated driving process of the vehicle in a period of time. By way of example, vehicle travel conditions may be comprised of variables such as vehicle speed, altitude, start point, end point, ambient temperature, vehicle load, parking time, vehicle acceleration, etc. In this embodiment, by setting different values for the variables, each of the vehicle running conditions to be predicted may be generated.
Exemplary vehicle driving conditions are: the environment temperature is 15℃, the vehicle starts to run from the point A, the speed of the vehicle running in the first 3 minutes is 35km/h, the speed of the vehicle running in the five minutes after the vehicle running is 65km/h, and after the vehicle stops for 2 minutes, the vehicle runs for 2 minutes at the speed of 100km/h to reach the point B.
Further, after each vehicle running condition to be predicted is generated, emission data corresponding to each vehicle running condition can be predicted according to a pre-constructed emission prediction model. Specifically, the emissions data may include emissions corresponding to each of the preset emissions at each point in time during the vehicle driving condition. For example, the emission prediction model may output an emission amount-time curve corresponding to each preset emission under the running condition of the vehicle, that is, a curve in which the output emission amount varies with time. Wherein the predetermined emissions include, but are not limited to, carbon monoxide, solid emissions particulates, and nitrogen oxides.
Optionally, the emission prediction model may further directly output an accumulated emission amount corresponding to each preset emission under each vehicle driving condition, and use the accumulated emission amount corresponding to each preset emission as emission data. For example, the emission prediction model may output a cumulative emission of carbon monoxide a1, a cumulative emission of solid emissions particles a2, and a cumulative emission of nitrogen oxides a3 under a certain vehicle driving condition.
Of course, the emission prediction model may also output the total emission of all the preset emissions under the running conditions of each vehicle, i.e., the total emission of all the preset emissions is used as emission data. For example, the emission prediction model may output a comprehensive emission for a certain vehicle driving condition as A1. Wherein the emission prediction model may take the sum of the cumulative emissions of all the preset emissions as the integrated emissions; the integrated emission amount may be calculated based on the preset weights corresponding to the preset emissions and the predicted integrated emission amounts corresponding to the preset emissions. Along the above example, if weights corresponding to carbon monoxide, solid emission particles, and nitrogen oxides are β1, β2, and β3, respectively, the total emissions may be: x=β1×a1+β2×a2+β3×a3.
S130, determining a target severe working condition in the running working conditions of each vehicle based on each emission data, a set value range of each vehicle related parameter in the whole vehicle dynamics model and a boundary condition library, wherein the emission quantity corresponding to the target severe working condition is highest.
Specifically, after emission data of each vehicle under the running working condition is predicted, the working condition with the highest emission amount can be determined as the target severe working condition in the running working conditions of each vehicle according to the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library.
The vehicle running working condition with the highest emission can be determined as the target severe working condition under the constraint that the set value range of each vehicle related parameter in the whole vehicle dynamics model and the constraint of the boundary condition library are met.
In this embodiment, the number of target adverse conditions determined may be one or more. If the vehicle running working condition with the highest comprehensive emission of all the preset emissions is taken as the target severe working condition under the constraint of meeting the whole vehicle dynamics model and the boundary condition library, the number of the target severe working conditions can be one. If the vehicle running working condition with the highest accumulated emission of any one preset emission is taken as the target severe working condition under the constraint of meeting the whole vehicle dynamics model and the boundary condition library, the number of the target severe working conditions can be multiple.
Of course, the present embodiment may further use the vehicle running condition with the highest comprehensive emission under the constraint of the vehicle dynamics model and the boundary condition library as the target severe condition, and use the vehicle running condition with the highest cumulative emission of one or more preset emissions under the constraint of the vehicle dynamics model and the boundary condition library as the target severe condition, so as to obtain the condition with the highest emission of each preset emission and the condition with the highest comprehensive emission.
In this embodiment, the vehicle driving condition with the highest emission may be output under the condition of meeting the whole vehicle dynamics model and the boundary condition library based on the machine learning method. For example, the vehicle driving condition with the highest emission, i.e., the target severe condition, can be solved according to a reinforcement learning method, a gradient-free optimization learning method, and the like.
In an alternative embodiment, determining the target severe condition in the driving conditions of each vehicle based on each emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library comprises: aiming at the vehicle running working conditions meeting the set value range of each vehicle related parameter in the whole vehicle dynamics model in the running working conditions of each vehicle, determining emission quantity reference indexes based on emission data corresponding to the vehicle running working conditions, and determining working condition constraint reference indexes based on working condition information of the vehicle running working conditions and each boundary condition in a boundary condition library; and determining a target severe working condition in the running working conditions of each vehicle based on the emission reference index and the working condition constraint reference index of the running working conditions of each vehicle.
Specifically, the vehicle running conditions capable of meeting the set value range of the relevant parameters of each vehicle can be searched in all the vehicle running conditions according to the whole vehicle dynamics model. Further, aiming at the found vehicle running working conditions meeting the whole vehicle dynamics model, calculating an emission reference index according to emission data, calculating a working condition constraint reference index according to working condition information and all boundary conditions, and further determining a target severe working condition in all vehicle running working conditions according to the emission reference index and the working condition constraint reference index of all vehicle running working conditions.
In other words, a reinforcement learning method in machine learning may be employed, and a reward function in the reinforcement learning method, that is, a learning target may be composed of an emission amount reference index calculated from emission data and a condition constraint reference index calculated from condition information and boundary conditions. Designing the output of the intelligent body as the whole vehicle power, and determining the vehicle running working conditions meeting the whole vehicle dynamics model from all the vehicle running working conditions through the output of the intelligent body and the constraint of the whole vehicle power in the whole vehicle dynamics model; further, the calculation results of the reward function of each vehicle running condition, namely the emission reference index and the condition constraint reference index, are determined, and the vehicle running condition with the optimal calculation result of the reward function is determined as the target bad condition.
For example, the vehicle running condition in which the sum of the accumulation of the emission reference index and the condition constraint reference index is maximum is determined as the target severe condition, or the emission reference index and the condition constraint reference index are respectively set with corresponding weights in advance, the total reference index is calculated based on the preset weights, the emission reference index and the condition constraint reference index, and the target severe condition is determined according to the total reference index.
In the alternative embodiment, the emission reference index and the working condition constraint reference index of the vehicle running working condition are calculated aiming at the vehicle running working condition meeting the whole vehicle dynamics model, the target severe working condition is determined according to the emission reference index and the working condition constraint reference index, the automatic determination of the worst working condition based on the reinforcement learning method is realized, the determined worst working condition is the working condition with the highest emission meeting the whole vehicle dynamics model and the boundary condition constraint, and the technical problem that the whole working condition is difficult to cover in the traditional emission test is solved.
Optionally, the embodiment may further construct a generator, where the generator may be obtained by using a machine learning method, and the generator may generate, according to emission data predicted by the emission prediction model for each vehicle driving condition, the vehicle dynamics model, and the boundary condition library, a condition with the highest emission that satisfies constraints of the vehicle dynamics model and the boundary condition library.
In this embodiment, after the target severe working condition is determined, relevant information corresponding to the target severe working condition, such as a vehicle speed working condition curve, a vehicle acceleration working condition curve, or an engine output curve, may be output.
That is, optionally, determining the target severe condition in the driving conditions of each vehicle may be: determining the vehicle running working condition with the highest emission among the vehicle running working conditions; and determining at least one of a speed working condition curve of the whole vehicle, an acceleration working condition curve of the whole vehicle and an engine output curve according to the running working condition of the vehicle with the highest emission.
The speed working condition curve of the whole vehicle, the acceleration working condition curve of the whole vehicle and the output curve of the engine can be a curve of the change of the speed along with time, a curve of the change of the acceleration along with time and a curve of the change of the output of the transmitter along with time in the severe target working condition respectively. By the method, the specific working condition information of the worst working condition can be obtained for the subsequent actual vehicle emission test.
According to the technical scheme, after each vehicle running working condition to be predicted is generated by acquiring a pre-constructed emission prediction model, a pre-constructed whole vehicle dynamics model and a boundary condition library, emission data of each vehicle running working condition to be predicted is determined according to the emission prediction model, and then according to each emission data, a set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, a target severe working condition with highest emission is determined in each vehicle running working condition, automatic generation of the worst working condition in a vehicle emission test is realized, the worst working condition meeting the constraint of the whole vehicle dynamics model and the boundary condition library is obtained, the worst working condition can be used for a subsequent real vehicle emission test, the subsequent real vehicle emission test only needs to study whether the emission data under the worst working condition can meet the standard, the emission results under the rest other working conditions can meet the standard without retesting the emission results under the other working conditions, the technical problem that the traditional emission test is difficult to completely cover all working conditions is solved, the emission efficiency is improved, and the cost of the whole vehicle in the development stage and the development cost is reduced. In addition, the method can enable the solved worst working condition to be more in line with the dynamic characteristics of the vehicle by adopting the whole vehicle dynamic model, so that the worst working condition can enable the real vehicle to reappear on an actual road.
Example two
Fig. 2 is a flowchart of a method for generating a severe exhaust condition of a RDE of a vehicle according to a second embodiment of the present invention, where, based on the foregoing embodiments, a process of determining a target severe condition according to exhaust data, a whole vehicle dynamics model, and a boundary condition library is exemplarily described. As shown in fig. 2, the method includes:
s210, acquiring a pre-constructed emission prediction model, a whole vehicle dynamics model and a boundary condition library, wherein the whole vehicle dynamics model comprises a set value range of each vehicle related parameter.
S220, generating running conditions of each vehicle to be predicted, and determining emission data corresponding to the running conditions of each vehicle based on the emission prediction model.
S230, determining each to-be-processed running condition meeting each boundary condition in the set value range and the boundary condition library in each running condition of the vehicle based on the set value range and the boundary condition library of each vehicle related parameter in the whole vehicle dynamics model.
In this embodiment, the to-be-processed running condition satisfying the whole vehicle dynamics model and the boundary condition library may be determined from the running conditions of the vehicles according to the set value range of the relevant parameters of the vehicles in the whole vehicle dynamics model and the boundary conditions in the boundary condition library.
Specifically, according to the working condition information of each vehicle running working condition, the vehicle running working conditions which do not meet the set value range and each boundary condition are removed, and the rest working conditions are used as the running working conditions to be processed, so that the severe target working conditions are further determined from the running working conditions to be processed.
The vehicle-related parameter is a vehicle speed parameter or a vehicle engine parameter, the boundary condition is a mileage attribute boundary, a parking time boundary, an altitude boundary or a high-speed duration boundary, and each to-be-processed running condition meeting each boundary condition in the set value range and the boundary condition library is determined from each vehicle running condition based on the set value range of each vehicle-related parameter in the whole vehicle dynamics model and the boundary condition library, including: determining each running condition to be screened in each running condition of the vehicle based on the set value range of the vehicle speed parameter and the set value range of the vehicle engine parameter; and determining each to-be-processed driving condition in each to-be-screened driving condition based on the mileage attribute boundary, the parking time boundary, the altitude boundary and the high-speed duration boundary.
Wherein the vehicle speed parameter may be vehicle speed or acceleration. The mileage attribute boundary may be a boundary condition defining the driving mileage, for example, the mileage ratio of urban area, suburban area and highway is 34%,33%,33% respectively, the forming error of each segment is controlled to be + -10%, and the urban area mileage is not less than 29% of the total mileage. The parking time boundary may be a condition defining a parking time during traveling, for example, a parking time period of between 6 and 30% and a single parking time period of not more than 180 seconds. The altitude boundary may be a boundary condition defining an altitude during traveling, for example, an altitude of not more than 700 meters. The high speed duration boundary may be a condition defining a high speed travel process, such as a vehicle speed greater than 100km/h for more than five minutes.
That is, the running condition to be screened meeting the whole vehicle dynamics model can be determined according to the set value ranges of the vehicle speed parameter and the vehicle engine parameter; further, according to the mileage attribute boundary, the parking time boundary, the altitude boundary and the high-speed duration boundary, the to-be-processed running conditions meeting the boundary condition library are determined from the to-be-screened running conditions.
Of course, in this embodiment, the to-be-screened driving conditions meeting the boundary condition library may be determined from the driving conditions of each vehicle according to the mileage attribute boundary, the parking time boundary, the altitude boundary and the high-speed duration boundary, and further, the to-be-processed driving conditions meeting the whole vehicle dynamics model may be determined from the to-be-screened driving conditions according to the set value ranges of the vehicle speed parameters and the vehicle engine parameters.
S240, determining the to-be-processed running condition with the highest emission based on emission data corresponding to each to-be-processed running condition, and determining the to-be-processed running condition with the highest emission as a target severe condition.
Specifically, according to emission data predicted by the emission prediction model for each running condition to be processed, the running condition to be processed with the highest emission amount is determined as a target severe condition, so that the condition with the highest emission amount meeting the constraints of the whole vehicle dynamics model and the boundary condition library is obtained.
It can be appreciated that the worst working conditions may also be generated in the following manner in this embodiment, which specifically includes: acquiring a pre-constructed emission prediction model, a whole vehicle dynamics model and a boundary condition library, and generating running conditions of each vehicle to be predicted; determining a working condition to be processed in the running working conditions of the vehicle according to the whole vehicle dynamics model and the boundary condition library; and predicting emission data corresponding to each working condition to be processed according to the emission prediction model, and determining a target severe working condition from the working conditions to be processed based on each emission data.
According to the technical scheme of the embodiment, according to the set value range of relevant parameters of each vehicle in the whole vehicle dynamics model and the boundary condition library, each running condition to be processed which meets the whole vehicle dynamics model and the boundary condition library is screened out from running conditions of each vehicle, further, the running condition to be processed with the highest emission is determined from emission data corresponding to each running condition to be processed, the running condition to be processed is determined as a target severe condition, the worst condition which meets the whole vehicle dynamics model and the boundary condition library is obtained, automatic generation of the worst condition in a vehicle emission test is realized, the worst condition can be used for a subsequent real vehicle emission test, whether the emission data under the worst condition can meet the standard is only required to be researched in the subsequent real vehicle emission test, a large number of repeated condition coverage tests are avoided, the technical problem that the traditional emission test is difficult to completely cover all working condition domains is solved, the efficiency of the emission test is improved, and the economic cost, the time cost and the manpower cost of the whole vehicle in the emission calibration development stage are reduced. In addition, the method can enable the solved worst working condition to be more in line with the dynamic characteristics of the vehicle by adopting the whole vehicle dynamic model, so that the worst working condition can enable the real vehicle to reappear on an actual road.
Example III
Fig. 3 is a flowchart of a method for generating a bad vehicle RDE emission condition according to a third embodiment of the present invention, where a process of predicting emission data corresponding to each vehicle driving condition is exemplarily described on the basis of each of the foregoing embodiments. As shown in fig. 3, the method includes:
s310, acquiring a pre-constructed emission prediction model, a whole vehicle dynamics model and a boundary condition library, wherein the whole vehicle dynamics model comprises a set value range of each vehicle related parameter.
S320, generating each vehicle running condition to be predicted, and determining the emission value of each preset emission corresponding to the vehicle running condition based on the vehicle speed information, the vehicle information, the environment information, the position information and the emission prediction model in the vehicle running condition aiming at each vehicle running condition.
The vehicle information may include information such as a vehicle engine exhaust temperature, a coolant temperature, and the like. The environmental information may include information of an ambient temperature, an ambient humidity, and the like. The location information may include altitude, longitude and latitude, etc.
Specifically, vehicle speed information, vehicle information, environment information, and position information in a vehicle running condition may be input to an emission prediction model, which may output emission values of each preset emission under the vehicle running condition.
Optionally, the emission prediction model is constructed according to actual road test data. The method, for example, further comprises: acquiring actual road test data, wherein the actual road test data comprises actual vehicle speed information, actual vehicle information, actual environment information, actual position information and actual emission values; and constructing an emission prediction model based on the actual road test data.
The method comprises the steps that actual vehicle speed information can be collected through a vehicle speed sensor or a global positioning system; acquiring actual position information (such as longitude, latitude, altitude and the like) through a global positioning system; acquiring actual vehicle information (such as engine exhaust temperature, coolant temperature and the like) through a temperature sensor; the method is characterized in that the method is in charge of collecting actual vehicle information (exhaust mass flow) through an exhaust mass flow meter on the whole vehicle; acquiring actual vehicle information (engine exhaust temperature, coolant temperature) by a temperature sensor; the actual emission value is obtained by a portable emission testing system (Portable Emissions Measurement System, PEMS) carried on the real vehicle.
The emission prediction model may be a neural network model such as a convolutional neural network, a deep neural network, a recurrent neural network, or the like. If the emission prediction model is a neural network model, a training sample set can be constructed according to actual road test data, wherein actual vehicle speed information, actual vehicle information, actual environment information and actual position information are sample data, and an actual emission value is a sample label.
Further, training a pre-constructed prediction network based on the training sample set, calculating a loss function based on a prediction result output by the prediction network for the number of samples and a sample label, reversely adjusting parameters in the prediction network according to the calculation result of the loss function until the calculation result of the loss function converges, and taking the trained prediction network as an emission prediction model.
S330, determining a target severe working condition in the vehicle driving working conditions based on the emission data, the set value ranges of the relevant parameters of each vehicle in the whole vehicle dynamics model and the boundary condition library, wherein the emission quantity corresponding to the target severe working condition is highest.
According to the technical scheme of the embodiment, for each vehicle running condition, according to the vehicle speed information, the vehicle information, the environment information, the position information and the emission prediction model in the vehicle running condition, the emission value of each preset emission corresponding to the vehicle running condition is determined, so that the accurate prediction of the emission data of each vehicle running condition is realized, and the worst condition is found.
Example IV
Fig. 4 is a schematic structural diagram of a generating device for generating a severe vehicle RDE emission condition according to a fourth embodiment of the present invention. As shown in FIG. 4, the apparatus includes an acquisition module 410, an emissions prediction module 420, and a rough condition determination module 430.
An obtaining module 410, configured to obtain a pre-constructed emission prediction model, a whole vehicle dynamics model, and a boundary condition library, where the whole vehicle dynamics model includes a set value range of each vehicle related parameter;
the emission prediction module 420 is configured to generate each vehicle driving condition to be predicted, and determine emission data corresponding to each vehicle driving condition based on the emission prediction model;
the severe working condition determining module 430 is configured to determine a target severe working condition in the driving working conditions of each vehicle based on each emission data, a set value range of each vehicle related parameter in the whole vehicle dynamics model, and the boundary condition library, where an emission amount corresponding to the target severe working condition is highest.
On the basis of the above embodiment, optionally, the severe condition determining module 430 includes a screening unit and a determining unit, where the screening unit is configured to determine, based on a set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, each to-be-processed driving condition satisfying each boundary condition in the set value range and the boundary condition library, among the driving conditions of each vehicle; the determining unit is used for determining the to-be-processed running condition with the highest emission based on the emission data corresponding to each to-be-processed running condition, and determining the to-be-processed running condition with the highest emission as a target bad condition.
On the basis of the foregoing embodiment, optionally, the vehicle related parameter is a vehicle speed parameter or a vehicle engine parameter, the boundary condition is a mileage attribute boundary, a parking time boundary, an altitude boundary, or a high-speed duration boundary, and the screening unit is specifically configured to: determining each running condition to be screened in each running condition of the vehicle based on the set value range of the vehicle speed parameter and the set value range of the vehicle engine parameter; and determining each to-be-processed driving working condition in each to-be-screened driving working condition based on the mileage attribute boundary, the parking time boundary, the altitude boundary and the high-speed duration boundary.
Based on the above embodiment, optionally, the severe operating condition determining module 430 is specifically configured to:
determining emission reference indexes based on emission data corresponding to the vehicle running conditions and determining working condition constraint reference indexes based on working condition information of the vehicle running conditions and boundary conditions in the boundary condition library according to the vehicle running conditions meeting the set value range of each vehicle related parameter in the vehicle dynamics model in each vehicle running condition; and determining a target severe condition in each vehicle driving condition based on the emission amount reference index and the condition constraint reference index of each vehicle driving condition.
On the basis of the above embodiments, the emission prediction module 420 is optionally specifically configured to:
for each vehicle driving condition, determining the emission value of each preset emission corresponding to the vehicle driving condition based on the vehicle speed information, the vehicle information, the environment information and the position information in the vehicle driving condition and the emission prediction model.
On the basis of the above embodiment, optionally, the apparatus further includes an emission model construction module, where the emission model construction module is configured to obtain actual road test data, where the actual road test data includes actual vehicle speed information, actual vehicle information, actual environment information, actual position information, and an actual emission value; and constructing the emission prediction model based on the actual road test data.
On the basis of the above embodiment, optionally, the severe condition determining module 430 includes a curve determining unit, configured to determine a vehicle driving condition with the highest emission among the vehicle driving conditions; and determining at least one of a speed working condition curve of the whole vehicle, an acceleration working condition curve of the whole vehicle and an engine output curve according to the running working condition of the vehicle with the highest emission.
According to the technical scheme of the embodiment, through obtaining the pre-constructed emission prediction model, the pre-constructed whole vehicle dynamics model and the boundary condition library, after each vehicle running working condition to be predicted is generated, emission data of each vehicle running working condition to be predicted is determined according to the emission prediction model, and further, according to each emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, the target severe working condition with the highest emission is determined in each vehicle running working condition, automatic generation of the worst working condition in the vehicle emission test is realized, the worst working condition can be used for a subsequent real vehicle emission test, the follow-up real vehicle emission test only needs to study whether the emission data under the worst working condition can meet the standard, the emission results under the rest other working conditions can also meet the standard as long as the emission results under the worst working condition meet the standard, the emission results under the rest other working conditions are not required to be retested, the technical problem that the traditional emission test is difficult to completely cover all working condition fields is solved, the emission test efficiency is improved, and the economic cost and the time cost of the whole vehicle in the emission calibration stage are reduced. In addition, the method can enable the solved worst working condition to be more in line with the dynamics characteristic of the vehicle by adopting the whole vehicle dynamics model, so that the worst working condition can enable the real vehicle to reappear on an actual road, and the method is suitable for vehicles of any model.
The generation device for the severe vehicle RDE emission working condition provided by the embodiment of the invention can execute the generation method for the severe vehicle RDE emission working condition provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the vehicle RDE emission harshness generation method.
In some embodiments, the method of generating vehicle RDE emissions-harsh operating conditions may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle RDE emission harshness generation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle RDE emission harshness generation method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program for implementing the vehicle RDE emissions harsh operating regime generating method of the invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
Example six
The sixth embodiment of the present invention also provides a computer readable storage medium, where the computer readable storage medium stores computer instructions for causing a processor to execute a method for generating a severe vehicle RDE emission condition, the method including:
acquiring a pre-constructed emission prediction model, a whole vehicle dynamics model and a boundary condition library, wherein the whole vehicle dynamics model comprises a set value range of relevant parameters of each vehicle;
generating each vehicle driving condition to be predicted, and determining emission data corresponding to each vehicle driving condition based on the emission prediction model;
and determining a target severe working condition in the vehicle driving working conditions based on the emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, wherein the emission quantity corresponding to the target severe working condition is highest.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (9)

1. The method for generating the severe vehicle RDE emission working condition is characterized by comprising the following steps of:
acquiring a pre-constructed emission prediction model, a whole vehicle dynamics model and a boundary condition library, wherein the whole vehicle dynamics model comprises a set value range of relevant parameters of each vehicle;
generating each vehicle driving condition to be predicted, and determining emission data corresponding to each vehicle driving condition based on the emission prediction model;
determining a target severe working condition in the vehicle driving working conditions based on the emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, wherein the emission quantity corresponding to the target severe working condition is highest;
the determining, based on the emission data, the set value ranges of the relevant parameters of each vehicle in the whole vehicle dynamics model, and the boundary condition library, the severe target working condition among the running working conditions of each vehicle includes:
determining emission reference indexes based on emission data corresponding to the vehicle running conditions and determining working condition constraint reference indexes based on working condition information of the vehicle running conditions and boundary conditions in the boundary condition library according to the vehicle running conditions meeting the set value range of each vehicle related parameter in the whole vehicle dynamics model in each vehicle running condition;
Determining the target severe condition in each of the vehicle driving conditions based on the emission reference index and the condition constraint reference index of each of the vehicle driving conditions;
wherein the vehicle related parameters are vehicle speed, acceleration, steering wheel rotation angle, engine output power, engine output rotation speed, engine output torque and transmitter compression ratio;
the boundary condition is a mileage attribute boundary, a parking time boundary, an altitude boundary, or a high-speed duration boundary.
2. The method of claim 1, wherein determining a target harsh condition among the vehicle driving conditions based on the emission data, the set range of values of the vehicle-related parameters in the vehicle dynamics model, and the boundary condition library comprises:
determining each to-be-processed running condition meeting each boundary condition in the set value range and the boundary condition library in each vehicle running condition based on the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library;
and determining the to-be-processed running condition with the highest emission based on the emission data corresponding to each to-be-processed running condition, and determining the to-be-processed running condition with the highest emission as a target severe condition.
3. The method according to claim 2, wherein the vehicle-related parameter is a vehicle speed parameter or a vehicle engine parameter, and the determining, based on the set value range of each vehicle-related parameter in the whole vehicle dynamics model and the boundary condition library, each of the vehicle driving conditions that satisfies each boundary condition in the set value range and the boundary condition library includes:
determining each running condition to be screened in each running condition of the vehicle based on the set value range of the vehicle speed parameter and the set value range of the vehicle engine parameter;
and determining each to-be-processed driving working condition in each to-be-screened driving working condition based on the mileage attribute boundary, the parking time boundary, the altitude boundary and the high-speed duration boundary.
4. The method of claim 1, wherein the determining emissions data for each of the vehicle driving conditions based on the emissions prediction model comprises:
for each vehicle driving condition, determining the emission value of each preset emission corresponding to the vehicle driving condition based on the vehicle speed information, the vehicle information, the environment information and the position information in the vehicle driving condition and the emission prediction model.
5. The method according to claim 4, wherein the method further comprises:
acquiring actual road test data, wherein the actual road test data comprises actual vehicle speed information, actual vehicle information, actual environment information, actual position information and actual emission values;
and constructing the emission prediction model based on the actual road test data.
6. The method of claim 1, wherein said determining a target harsh condition in each of said vehicle driving conditions comprises:
determining a vehicle driving condition with the highest emission among the vehicle driving conditions;
and determining at least one of a speed working condition curve of the whole vehicle, an acceleration working condition curve of the whole vehicle and an engine output curve according to the running working condition of the vehicle with the highest emission.
7. The utility model provides a generating device of severe operating mode is discharged to vehicle RDE which characterized in that includes:
the system comprises an acquisition module, a prediction module, a calculation module and a boundary condition library, wherein the acquisition module is used for acquiring a pre-constructed emission prediction model, a whole vehicle dynamics model and a boundary condition library, and the whole vehicle dynamics model comprises a set value range of each vehicle related parameter;
the emission prediction module is used for generating each vehicle running condition to be predicted, and determining emission data corresponding to each vehicle running condition based on the emission prediction model;
The severe working condition determining module is used for determining a target severe working condition in the vehicle driving working conditions based on the emission data, the set value range of each vehicle related parameter in the whole vehicle dynamics model and the boundary condition library, wherein the emission quantity corresponding to the target severe working condition is highest;
the severe working condition determining module is specifically configured to determine, for each of the vehicle driving conditions that satisfies a set value range of each vehicle-related parameter in the vehicle dynamics model, an emission amount reference index based on emission data corresponding to the vehicle driving condition, and a working condition constraint reference index based on working condition information of the vehicle driving condition and each boundary condition in the boundary condition library; determining the target severe condition in each of the vehicle driving conditions based on the emission reference index and the condition constraint reference index of each of the vehicle driving conditions;
wherein the vehicle related parameters are vehicle speed, acceleration, steering wheel rotation angle, engine output power, engine output rotation speed, engine output torque and transmitter compression ratio;
the boundary condition is a mileage attribute boundary, a parking time boundary, an altitude boundary, or a high-speed duration boundary.
8. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of generating vehicle RDE emissions harsh conditions of any one of claims 1-6.
9. A computer readable storage medium storing computer instructions for causing a processor to execute the method for generating the vehicle RDE emission harshness condition of any one of claims 1-6.
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