CN113378403A - Simulation test modeling method, system, test method, device and storage medium - Google Patents
Simulation test modeling method, system, test method, device and storage medium Download PDFInfo
- Publication number
- CN113378403A CN113378403A CN202110721514.6A CN202110721514A CN113378403A CN 113378403 A CN113378403 A CN 113378403A CN 202110721514 A CN202110721514 A CN 202110721514A CN 113378403 A CN113378403 A CN 113378403A
- Authority
- CN
- China
- Prior art keywords
- charging
- sample data
- test
- simulation
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Secondary Cells (AREA)
Abstract
The invention discloses a simulation test modeling method, a simulation test modeling system, a simulation test method, simulation test equipment and a storage medium for a charging management system, wherein the modeling method comprises the following steps: determining charging sample data based on the influence of each parameter in a preset charging database on the charging state; extracting charging scene information based on the charging sample data; determining target model configuration parameters and target test conditions according to the charging sample data and the charging scene information; and establishing a full-working-condition simulation model according to the target model configuration parameters and the target test working conditions. According to the invention, the simulation test model of the charging management system is optimized by setting the charging sample data and the charging scene information, and the alternating current charging working scene under the full working condition is simulated, so that the real charging state is favorably restored, the simulation test compatibility is improved, and the test effect and the development quality are improved.
Description
Technical Field
The invention relates to the technical field of charging management system testing, in particular to a simulation test modeling method, a simulation test modeling system, a simulation test testing method, simulation test equipment and a storage medium for a charging management system.
Background
With the development of new energy technologies, the number of electric vehicles in the market is gradually increased, and the charging safety performance of the electric vehicle is one of important indexes for measuring the reliability of the electric vehicle, wherein the test for the charging safety performance of the electric vehicle comprises a test of a power battery charging management system.
At present, the test of a power battery charging management system is mainly completed by adopting a real vehicle test, so that the test cost is higher, and the product development progress is influenced. And part of the whole car factories adopt a software testing method to test the charging control function of the battery management system required in the alternating current charging process, so that software problems can be found early in research and development, economic loss caused by errors is reduced, and the cost for manufacturing sample cars is reduced. However, in the existing testing method for the power battery charging management system, the charging function of the battery management system is verified by configuring specific battery parameters, the control function in a fault state cannot be verified, the set parameters need to be adjusted manually for testing under different working conditions, the operation is complex, the validity and the real-time performance of test data are poor, and the reliability of a test result is poor.
Disclosure of Invention
The invention provides a simulation test modeling method for a charging management system, which solves the problem of poor reliability of the simulation test of the existing charging management system and improves the test efficiency and the development quality of the charging management system.
In a first aspect, an embodiment of the present invention provides a simulation test modeling method for a charging management system, including the following steps: determining charging sample data based on the influence of each parameter in a preset charging database on the charging state; extracting charging scene information based on the charging sample data; determining target model configuration parameters and target test conditions according to the charging sample data and the charging scene information; and establishing a full-working-condition simulation model according to the target model configuration parameters and the target test working condition.
Optionally, determining charging sample data according to the influence of each parameter in the preset charging database on the charging state, includes the following steps: classifying and screening the process parameters, the state parameters and the fault types related to battery charging in the preset charging database to obtain a plurality of data sets; extracting an initial trigger parameter that triggers the battery to enter a state of charge based on the plurality of data sets; extracting an expiration trigger parameter for battery termination or exiting a state of charge based on the plurality of data sets; extracting the change characteristics, the change time sequence and the incidence relation among the parameters of the starting triggering parameter and the ending triggering parameter; and determining the charging sample data according to the starting trigger parameter, the ending trigger parameter, the change characteristic, the change time sequence and the incidence relation.
Optionally, extracting charging scenario information based on the charging sample data includes the following steps: simulating a battery charging process based on the charging sample data; judging whether the charging gun connection is abnormal at the sampling moment, and if the charging gun connection is abnormal, extracting a working scene of the charging gun connection abnormity; judging whether a battery charging fault occurs at the sampling moment, and if so, extracting a charging stopping working scene; judging whether the battery reaches a full-charge state at the sampling moment, and if the battery reaches the full-charge state, extracting a full-charge working scene; judging whether the battery receives a charging permission instruction at the sampling moment, and if the duration of the battery not receiving the charging permission instruction exceeds the preset waiting time, extracting an overtime dormancy working scene; if the charging gun is not connected abnormally, the battery is not charged in a fault, the battery does not reach a full-charge state, and the battery receives a charging permission instruction, a normal charging working scene is extracted.
Optionally, determining a target model configuration parameter according to the charging sample data and the charging scenario information, includes the following steps: determining a target parameter group according to the change characteristics, the change time sequence and the incidence relation among the parameters in the charging sample data; determining a target charging scenario according to a correlation between the target parameter set and the charging scenario information; and determining the target model configuration parameters according to the target parameter group and the target charging scene.
Optionally, determining a target parameter group according to the change characteristic, the change timing sequence, and the association relationship between parameters in the charging sample data, includes the following steps: determining a simulation step length according to the change characteristics and the change time sequence of each parameter; extracting parameters in the charging sample data based on the simulation step length and the incidence relation between the parameters; and establishing the target parameter group according to the extracted parameters.
Optionally, determining a target test condition according to the charging sample data and the charging scenario information, including the following steps: acquiring an initial test working condition set; optimizing the initial test working condition according to the change characteristics, the change time sequence and the incidence relation among the parameters in the charging sample data to obtain a full working condition test set; extracting a group of parameters to be tested and a charging scene to be tested based on the charging sample data and the charging scene information; and checking a table of the full working condition test set according to the parameters to be tested and the charging scene to be tested, and determining the target test working condition according to a table checking result.
In a second aspect, an embodiment of the present invention further provides a simulation test modeling system for a charging management system, including: a parameter configuration module and a model building module; the parameter configuration module is used for determining charging sample data according to the influence of each parameter in a preset charging database on the charging state and extracting charging scene information based on the charging sample data; and the model building module is used for determining target model configuration parameters and target test working conditions according to the charging sample data and the charging scene information, and building a full-working-condition simulation model according to the target model configuration parameters and the target test working conditions.
In a third aspect, an embodiment of the present invention further provides a testing method, where a full-operating-condition simulation model is established based on the simulation test modeling method for a charge management system, and the testing method further includes the following steps:
testing the charging management system to be tested based on the full-working-condition simulation model to obtain an initial simulation test result;
acquiring real vehicle test data after the charging management system to be tested is loaded;
comparing the real vehicle test data with the initial simulation test result;
according to the comparison result, correcting the charging sample data and the charging scene information of the simulation test modeling method for the charging management system;
and optimizing the full-working-condition simulation model according to the corrected charging sample data and charging scene information.
In a fourth aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the simulation test modeling method for a charging management system when executing the program.
In a fifth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the simulation test modeling method for a charging management system.
The test method, the computer device and the computer storage medium provided by the embodiment of the invention execute a simulation test modeling method for a charging management system, the method determines charging sample data according to the influence of each parameter in a preset charging database on the charging state, extracts charging scene information based on the charging sample data, determines target model configuration parameters and target test working conditions according to the charging sample data and the charging scene information, and establishes a full-working-condition simulation model according to the target model configuration parameters and the target test working conditions, the full-working-condition simulation model can realize the simulation test of the charging management system under multiple scenes and multiple working conditions, the problem of poor reliability of the simulation test of the existing charging management system is solved, the simulation test model of the charging management system is optimized through the charging sample data and the charging scene information to simulate the alternating current charging working scene under the full working conditions, the method is beneficial to restoring the real charging state, improves the compatibility of simulation test working conditions, and improves the test effect and the development quality.
Drawings
Fig. 1 is a flowchart of a simulation test modeling method for a charging management system according to an embodiment of the present invention;
fig. 2 is a flowchart of another simulation test modeling method for a charging management system according to an embodiment of the present invention
Fig. 3 is a flowchart of a simulation test modeling method for a charging management system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a simulation test modeling system for a charging management system according to a second embodiment of the present invention;
FIG. 5 is a flowchart of a testing method according to a third embodiment of the present invention
Fig. 6 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a simulation test modeling method for a charging management system according to an embodiment of the present invention, where the charging management system may be used to perform ac charging management on a power battery, and this embodiment may be applied to an application scenario in which simulation testing is performed on a battery charging management system by simulating parameters in different ac charging states, and the method may be executed by a test platform configured with a software or hardware structure of a specific simulation algorithm model, and specifically includes the following steps:
step S1: and determining charging sample data based on the influence of each parameter in the preset charging database on the charging state.
The preset charging database comprises all parameters of the power battery charging process, and the charging sample data comprises parameter values causing charging state change in the preset charging database.
Typically, the preset charging database includes charging process parameters, charging state parameters and charging fault type parameters for normal charging operation, abnormal charging operation and charging state change, wherein the charging process parameters refer to parameters for representing operation time sequences of devices such as a power battery, a charger, a detection unit, a communication module and a charging gun which participate in a charging process; the charging state parameters are parameters for representing the running states of equipment such as a power battery, a charger, a detection unit, a communication module, a charging gun and the like which participate in the charging process; the charging fault type parameter refers to a parameter for representing fault types and fault positions of all devices participating in the charging process, such as the power battery, the charger, the detection unit, the communication module, the charging gun and the like.
Step S2: and extracting charging scene information based on the charging sample data.
Optionally, the charging scenario information includes scenarios of performing ac charging, starting charging, fully charging the battery, abnormally stopping charging, battery failure, disconnecting the charging gun, and charging timeout sleep.
Step S3: and determining target model configuration parameters and target test conditions according to the charging sample data and the charging scene information.
And the target model configuration parameters correspond to the target test working conditions one by one.
Step S4: and establishing a full-working-condition simulation model according to the target model configuration parameters and the target test working conditions.
Specifically, before a simulation model is established, charging sample data and different charging scene information which affect an alternating current charging process are extracted through a big data analysis technology, when the simulation model for the charging management system is established, a combined data set of a group of charging scene information and a group of charging sample data corresponds to a target test condition and a group of target model configuration parameters, the target model configuration parameters are imported into an initial charging simulation model to obtain a simulation model under the target test condition, a full-working-condition simulation model is obtained through configuring different alternating current charging scenes and changes of relevant parameters in the charging sample data, the full-working-condition simulation model can realize simulation test of the charging management system under multiple scenes and multiple working conditions, the problem of poor reliability of simulation test of the existing charging management system is solved, and the simulation test model of the charging management system is optimized through the charging and charging scene information, the alternating current charging working scene under the full working condition is simulated, the real charging state is favorably restored, the compatibility of the simulation test working condition is improved, and the test effect and the development quality are improved.
Fig. 2 is a flowchart of another simulation test modeling method for a charging management system according to an embodiment of the present invention.
Optionally, as shown in fig. 2, determining charge sample data based on the influence of each parameter in the preset charge database on the charge state includes the following steps:
step S101: and classifying and screening the process parameters, the state parameters and the fault types related to battery charging in a preset charging database to obtain a plurality of data sets.
Optionally, the plurality of data sets include, but are not limited to, cell voltage, battery temperature, cell temperature difference, battery remaining capacity, charging voltage, charging current, charging time, charging gun connection status, and other parameters, and sampling fault, sensor fault, communication fault, fault location, and other fault types that cause parameter abnormality.
Step S102: an initial trigger parameter that triggers the battery to enter a state of charge is extracted based on the plurality of data sets.
Step S103: an expiration triggering parameter for battery termination or exiting the state of charge is extracted based on the plurality of data sets.
Step S104: and extracting the change characteristics, the change time sequence and the incidence relation among the parameters of the starting trigger parameter and the ending trigger parameter.
Step S105: and determining the charging sample data according to the initial trigger parameter, the cut-off trigger parameter, the change characteristic, the change time sequence and the incidence relation.
Specifically, in the process of extracting charging sample data, the change characteristics, the change time sequence and the parameter relevance of each parameter need to be acquired, so that not only are abnormal parameters in the alternating-current charging state extracted, but also faults causing parameter abnormity are extracted as parameters, and the simulation model is optimized and favorable for simulating all abnormal parameter changes in the alternating-current charging state.
Optionally, the plurality of data sets includes a cell voltage data set, a cell temperature difference data set, a battery remaining capacity data set, a charging voltage data set, a charging current data set, and a charging time data set,
optionally, extracting charging sample data based on the battery cell voltage data set includes the following steps: judging whether the voltage of the single battery is within a preset normal threshold range; if the voltage of the battery monomer is within the range of the preset normal threshold value, not extracting the current parameters; if the voltage of the single battery is not within the preset normal threshold range, judging whether the voltage of the single battery is abnormal due to sampling faults; if the single voltage is abnormal due to sampling faults, extracting sampling fault types and fault positions; if the single voltage is not abnormal due to sampling faults, judging whether the single voltage is abnormal due to sensor faults; if the single voltage is abnormal due to the sensor fault, extracting the fault type and the fault position of the sensor; if the cell voltage is not abnormal due to the sensor fault, judging whether the cell voltage is abnormal due to the communication fault; if the single voltage is abnormal due to the communication fault, extracting the communication fault type and the fault position; if the cell voltage is not abnormal due to the communication fault, the change characteristic and the cell voltage position of the cell voltage in the abnormal state are extracted.
It should be noted that the method for extracting charging sample data based on the data group other than the cell voltage data group is the same as the cell voltage data group, and is not described in detail herein.
Fig. 3 is a flowchart of a simulation test modeling method for a charging management system according to an embodiment of the present invention.
Optionally, as shown in fig. 3, extracting charging scenario information based on the charging sample data includes the following steps:
step S201: and simulating a battery charging process based on the charging sample data.
Step S202: and judging whether the charging gun is connected abnormally at the sampling moment.
In this embodiment, it may be determined whether a charging gun connection abnormality occurs according to the charging gun connection state parameter, for example, a sensor may be provided to detect whether a charging gun connection port is connected to the charging gun, the sensor outputs the charging gun connection state parameter according to a detection result, and if the charging gun connection state parameter is at a high level, it may be determined that the charging gun connection abnormality does not occur; if the charging gun connection state parameter is low level, the charging gun connection abnormity can be judged.
If the charging gun connection is abnormal, executing step S203; otherwise, step S204 is executed.
Step S203: and extracting the abnormal working scene of the charging gun connection.
Step S204: and judging whether a battery charging fault occurs at the sampling moment.
In the present embodiment, the battery charging failure includes, but is not limited to, battery temperature abnormality, battery cell voltage difference abnormality, battery cell temperature abnormality, and battery temperature abnormality.
If a battery charging failure occurs, go to step S205; otherwise, step S206 is executed.
Step S205: and extracting a charging stopping working scene.
Step S206: and judging whether the battery reaches a full-charge state at the sampling moment.
If the battery reaches the full state, go to step S207; otherwise, step S208 is performed.
Step S207: and extracting a full-electricity working scene.
Step S208: and judging whether the battery receives a charging permission instruction at the sampling moment.
If the battery does not receive the charging permission instruction, step S209 is executed; otherwise, step S211 is executed.
Step S209: and judging whether the duration of the battery not receiving the charging permission instruction exceeds the preset waiting time.
If the duration time that the battery does not receive the charging permission instruction exceeds the preset waiting time, executing step S210; otherwise, step S211 is executed.
Step S210: and extracting a timeout dormancy working scene.
Step S211: and extracting a normal charging working scene.
Specifically, a battery charging process is simulated based on charging sample data, a charging abnormal state is identified according to detection parameters at different sampling moments, and if the charging gun connection is abnormal at the sampling moments, a charging gun connection abnormal working scene is extracted; the charging stop working scene, the full-power working scene and the overtime dormancy working scene are respectively extracted by adopting the steps, and the simulation model of the charging management system is optimized by configuring different charging working scene parameters.
If the charging gun is not connected abnormally, the battery is not charged in a charging fault, the battery does not reach a full-charge state, and a charging permission instruction is received within a preset waiting time, the current sampling moment is determined to be normally charged, and the parameter of the current sampling moment is determined to be a normal charging working scene, so that the initial simulation test model simulates a normal charging process according to the normal charging working scene parameter.
Optionally, determining the target model configuration parameters according to the charging sample data and the charging scenario information includes the following steps: determining a target parameter group according to the change characteristics, the change time sequence and the incidence relation among the parameters in the charging sample data; determining a target charging scene according to the correlation between the target parameter group and the charging scene information; and determining target model configuration parameters according to the target parameter group and the target charging scene, and importing the target model configuration parameters into the initial charging simulation model to obtain a simulation model corresponding to the current target model configuration parameters.
In this embodiment, the correlation between the parameters in each target parameter group is greater than the preset correlation threshold, that is, the parameters in each target parameter group act together to affect the change of the charging state of the battery.
Optionally, determining a target parameter group according to the change characteristic, the change timing sequence, and the association relationship between parameters in the charge sample data, includes the following steps: determining simulation step length according to the change characteristics and the change time sequence of each parameter; extracting parameters in the charging sample data based on the simulation step length and the incidence relation between the parameters; and establishing a target parameter group according to the extracted parameters.
Optionally, the trend of change includes, but is not limited to: the remaining battery capacity decreases, the battery temperature increases, and the like, and the variation timing includes, but is not limited to: when the cell voltage of the battery rises to the preset cell voltage threshold value, the remaining capacity of the battery is displayed as 100%.
The variation characteristics of different parameters have the characteristic of periodic fluctuation, and the set simulation step length needs to take into account the variation characteristics of different parameters and the variation time sequence among the parameters so that the extracted target parameter group is compatible with the real working condition.
Specifically, based on a big data analysis technology, parameter values outside a preset normal threshold range, the change trend of each parameter, the change time sequence and the correlation among the parameters in a preset charging database are recorded and stored, a group of correlation parameters in charging sample data are extracted based on a simulation step length and the correlation among the parameters and serve as target model configuration parameters for a subsequent operation of a simulation test model, and a simulation test is performed on the charging management system to be tested.
Typically, the relevance between the voltage rise of the battery cell and the increase of the remaining capacity of the battery is high, and when the charging parameters are corrected, a plurality of groups of charging parameters representing a full-charge working scene can be determined by combining the relative relation between the voltage parameters of the battery cell and the remaining capacity parameters of the battery.
For example, defining the cell voltage charge termination critical threshold to be 4.164V, the extracted charge parameters may include the following 6 full electrical operating scene parameters, based on the above embodiment:
(1) the voltage value of the battery monomer at the current sampling moment is greater than or equal to 4.164V;
(2) the voltage value of the battery monomer is less than 4.164V, the self-defined cut-off residual capacity exists, the numerical value of the self-defined cut-off residual capacity is between 50% and 100% of the critical threshold value of the charge termination of the residual capacity of the battery, and the residual capacity of the battery at the current sampling moment is greater than or equal to the self-defined cut-off residual capacity;
(3) the voltage value of the battery monomer is less than 4.164V, the self-defined cut-off residual capacity exists, the numerical value of the self-defined cut-off residual capacity is lower than 50% of the critical threshold value of the charge termination of the residual capacity of the battery, and the residual capacity of the battery at the current sampling moment reaches 100%;
(4) the voltage value of the battery monomer is less than 4.164V, the self-defined cut-off residual capacity exists, the numerical value of the self-defined cut-off residual capacity is higher than 100% of the critical threshold value of the charge termination of the residual capacity of the battery, and the residual capacity of the battery at the current sampling moment reaches 100%;
(5) the voltage value of the battery monomer is less than 4.164V, the self-defined cutoff residual capacity is not set, and the residual capacity of the battery at the current sampling moment reaches 100%;
(6) the voltage value of the single battery is less than 4.164V, the charging management system and the communication module are in abnormal communication, and the residual electric quantity of the battery at the current sampling moment reaches 100%.
Therefore, in the process of establishing the alternating current charging model, the change of the alternating current charging related parameters is considered to extract the working scene of the alternating current charging of the battery, and then the multi-dimensional multi-scene alternating current charging simulation model is established through the extracted working scene of the alternating current charging of the battery, so that the true alternating current charging state can be better restored.
Optionally, determining a target test condition according to the charge sample data and the charge scenario information, including the following steps: acquiring an initial test working condition set; optimizing the initial test condition according to the change characteristics, the change time sequence and the incidence relation among the parameters in the charging sample data to obtain a full-condition test set; extracting a group of parameters to be tested and a charging scene to be tested based on the charging sample data and the charging scene information; and checking a table of the full working condition test set according to the parameters to be tested and the charging scene to be tested, and determining the target test working condition according to the table checking result.
Specifically, in the process of establishing the test working condition, the change of an alternating current charging working scene and related parameters is mainly considered, and the real alternating current charging working condition is simulated more comprehensively by establishing a multi-dimensional multi-scene alternating current charging simulation model, so that the alternating current charging test of the whole working condition is carried out.
Optionally, the charging sample data and the charging scene information can be updated according to the real alternating current charging working scene, so that the simulation model is optimized, and the real alternating current charging state can be better restored.
Example two
Fig. 4 is a schematic structural diagram of a simulation test modeling system for a charging management system according to a second embodiment of the present invention, where the simulation test modeling system according to the second embodiment of the present invention can execute a simulation test modeling method according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.
As shown in fig. 4, the simulation test modeling system 00 includes: a parameter configuration module 01 and a model building module 02; the parameter configuration module 01 is used for determining charging sample data according to the influence of each parameter in a preset charging database on the charging state, and extracting charging scene information based on the charging sample data; and the model building module 02 is used for determining target model configuration parameters and target test conditions according to the charging sample data and the charging scene information, and building a full-condition simulation model according to the target model configuration parameters and the target test conditions.
Optionally, the parameter configuration module 01 is configured to classify and screen process parameters, state parameters, and fault types related to battery charging in a preset charging database to obtain a plurality of data sets; extracting an initial trigger parameter that triggers the battery to enter a state of charge based on the plurality of data sets; extracting a cutoff triggering parameter for battery termination or exiting a state of charge based on the plurality of data sets; extracting the change characteristics, the change time sequence and the incidence relation among the initial trigger parameters and the cut-off trigger parameters; and determining the charging sample data according to the initial trigger parameter, the cut-off trigger parameter, the change characteristic, the change time sequence and the incidence relation.
Optionally, the parameter configuration module 01 is further configured to simulate a battery charging process based on the charging sample data; judging whether the charging gun connection is abnormal at the sampling moment, and if the charging gun connection is abnormal, extracting a working scene of the charging gun connection abnormity; judging whether a battery charging fault occurs at the sampling moment, and if so, extracting a charging stopping working scene; judging whether the battery reaches a full-charge state at the sampling moment, and if the battery reaches the full-charge state, extracting a full-charge working scene; judging whether the battery receives a charging permission instruction at the sampling moment, and if the duration of the battery not receiving the charging permission instruction exceeds the preset waiting time, extracting an overtime dormancy working scene; if the charging gun is not connected abnormally, the battery is not charged in a fault, the battery does not reach a full-charge state, and the battery receives a charging permission instruction, a normal charging working scene is extracted.
Optionally, the model building module 02 is configured to determine a target parameter group according to a change characteristic, a change timing sequence, and an association relationship between parameters of each parameter in the charging sample data; determining a target charging scene according to the correlation between the target parameter group and the charging scene information; and determining target model configuration parameters according to the target parameter group and the target charging scene.
Optionally, determining a target parameter group according to the change characteristic, the change timing, and the association relationship between parameters in the charge sample data, includes: determining simulation step length according to the change characteristics and the change time sequence of each parameter; extracting parameters in the charging sample data based on the simulation step length and the incidence relation between the parameters; and establishing a target parameter group according to the extracted parameters.
Optionally, the model building module 02 is further configured to obtain an initial test condition set; optimizing the initial test condition according to the change characteristics, the change time sequence and the incidence relation among the parameters in the charging sample data to obtain a full-condition test set; extracting a group of parameters to be tested and a charging scene to be tested based on the charging sample data and the charging scene information; and checking a table of the full working condition test set according to the parameters to be tested and the charging scene to be tested, and determining the target test working condition according to the table checking result.
Optionally, the model building module 02 includes, but is not limited to, the following subunits: the single voltage simulation subunit is used for simulating a single battery voltage signal; the battery temperature simulation subunit is used for simulating a battery temperature signal; the charging voltage simulation subunit is used for simulating a charging voltage signal of the battery; the charging current simulation subunit is used for simulating a charging current signal of the battery; the battery SOC simulation subunit is used for simulating the SOC of the battery; the battery temperature fault simulation subunit is used for simulating a battery temperature sampling fault or a sensor fault, wherein the fault comprises a sampling line open circuit fault, a sampling line ground short circuit fault, a sampling line power short circuit fault, a sampling line virtual connection fault or a sensor self fault; the single voltage fault simulation subunit is used for simulating single battery voltage sampling faults or sensor faults, wherein the faults comprise sampling line open circuit faults, sampling line ground short circuit faults, sampling line power supply short circuit faults, sampling line virtual connection faults or sensor self faults; the current sensor fault simulation unit is used for simulating faults such as open circuit, short circuit and the like of the current sensor; the communication fault simulation subunit is used for simulating communication faults and comprises communication line disconnection, short circuit, a short power supply and a short ground; the charging gun fault simulation subunit is used for simulating faults such as abnormal connection of the charging gun and the like; a communication simulation subunit for simulating communication of the BMS with other controllers; the low-voltage constant-voltage source is used for simulating a low-voltage power supply system; the battery total voltage simulation subunit is used for simulating a battery total voltage signal; the current sensor simulation subunit is used for simulating a total battery current signal; the key door simulation subunit is used for simulating a key door signal; the charging gun connection simulation subunit is used for simulating different connection states of the alternating current charging gun; the high-voltage contactor state simulation subunit is used for simulating all high-voltage contactor states including a contactor contact state, a coil state and a fault state; and the fault injection subunit is used for determining a fault injection item and a fault injection time sequence of the fault parameter.
Specifically, the model building module 02 determines a target model configuration parameter and a target test condition according to the charge sample data and the charge scene information, and sets a cell voltage simulation subunit, a battery temperature simulation subunit, a charge voltage simulation subunit, a charge current simulation subunit, a battery SOC simulation subunit, a battery temperature fault simulation subunit, a cell voltage fault simulation subunit, a current sensor fault simulation subunit, a communication fault simulation subunit, a charge gun fault simulation subunit, a communication simulation subunit, a low-voltage constant-voltage source, a battery total voltage simulation subunit, a current sensor simulation subunit, a key gate simulation subunit, a charge gun connection simulation subunit, a high-voltage contactor state simulation subunit and a fault injection subunit according to the target model configuration parameter to obtain a simulation model under the target test condition, the full-working-condition simulation model is obtained by configuring different alternating current charging scenes and the change of related parameters in the charging sample data, can realize the simulation test of the charging management system under multiple scenes and multiple working conditions, solves the problem of poor reliability of the simulation test of the conventional charging management system, is optimized by the charging sample data and the charging scene information, simulates the alternating current charging working scene under the full working conditions, is favorable for restoring the real charging state, improves the compatibility of the simulation test working conditions, and improves the test effect and the development quality.
EXAMPLE III
Fig. 5 is a flowchart of a testing method provided in the third embodiment of the present invention, where the testing method establishes a full-operating-condition simulation model based on the simulation test modeling method for a charging management system, and has the beneficial effects of the simulation test modeling method for a charging management system.
As shown in fig. 5, the testing method further includes the steps of:
step S10: and testing the charging management system to be tested based on the full-working-condition simulation model to obtain an initial simulation test result.
Step S20: and acquiring real vehicle test data after the charging management system to be tested is loaded.
Step S30: and comparing the real vehicle test data with the initial simulation test result.
Step S40: and correcting the charging sample data and the charging scene information of the simulation test modeling method for the charging management system according to the comparison result.
Step S50: and optimizing the full-working-condition simulation model according to the corrected charging sample data and charging scene information.
The test method provided by the embodiment of the invention establishes the full-working-condition simulation model through the simulation test modeling method of the charging management system, the modeling method determines the charging sample data according to the influence of each parameter in the preset charging database on the charging state, extracts the charging scene information based on the charging sample data, determines the target model configuration parameter and the target test working condition according to the charging sample data and the charging scene information, and establishes the full-working-condition simulation model according to the target model configuration parameter and the target test working condition, the full-working-condition simulation model can realize the simulation test of the charging management system under multiple scenes and multiple working conditions, solves the problem of poor reliability of the simulation test of the existing charging management system, optimizes the simulation test model of the charging management system through the charging sample data and the charging scene information, simulates the alternating current charging working scene under the full working condition, the method is beneficial to restoring the real charging state, improves the compatibility of simulation test working conditions, and improves the test effect and the development quality.
Example four
Based on the above embodiments, a fourth embodiment of the present invention provides a computer device.
Fig. 6 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 6 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 6, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors 16, a memory 28, a bus 18 connecting different system components (including the memory 28 and the processor 16), and a computer program stored on the memory and executable on the processor, wherein the processor 16 executes various functional applications and data processing by executing the program stored in the memory 28, for example, implementing the simulation test modeling method of the charging management system provided by the embodiment of the present invention.
The memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method for modeling the charging management system in the simulation test is implemented, where the method includes:
acquiring charging parameters of a power battery in a historical charging process, wherein the charging parameters comprise charging operation parameters, fault parameters and charging working scene parameters;
carrying out synchronization processing on the charging operation parameters, the charging working scene parameters and the fault parameters, and determining an initial simulation test model according to a synchronization processing result;
carrying out simulation test on the charging management system according to the initial simulation test model, and determining an initial simulation test result;
acquiring a real vehicle test result of the charging management system to be tested;
performing correlation comparison on the initial simulation test result and the real vehicle test result, and correcting the initial simulation test model according to the comparison result to obtain a simulation test correction model;
and performing performance test on the charging management system to be tested according to the simulation test correction model.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having 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. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A simulation test modeling method for a charging management system is characterized by comprising the following steps:
determining charging sample data based on the influence of each parameter in a preset charging database on the charging state;
extracting charging scene information based on the charging sample data;
determining target model configuration parameters and target test conditions according to the charging sample data and the charging scene information;
and establishing a full-working-condition simulation model according to the target model configuration parameters and the target test working condition.
2. The simulation test modeling method for the charging management system according to claim 1, wherein determining the charging sample data based on the influence of each parameter in the preset charging database on the charging state comprises the following steps:
classifying and screening the process parameters, the state parameters and the fault types related to battery charging in the preset charging database to obtain a plurality of data sets;
extracting an initial trigger parameter that triggers the battery to enter a state of charge based on the plurality of data sets;
extracting an expiration trigger parameter for battery termination or exiting a state of charge based on the plurality of data sets;
extracting the change characteristics, the change time sequence and the incidence relation among the parameters of the starting triggering parameter and the ending triggering parameter;
and determining the charging sample data according to the starting trigger parameter, the ending trigger parameter, the change characteristic, the change time sequence and the incidence relation.
3. The simulation test modeling method for the charge management system according to claim 1, wherein extracting charging scenario information based on the charging sample data comprises the steps of:
simulating a battery charging process based on the charging sample data;
judging whether the charging gun connection is abnormal at the sampling moment, and if the charging gun connection is abnormal, extracting a working scene of the charging gun connection abnormity;
judging whether a battery charging fault occurs at the sampling moment, and if so, extracting a charging stopping working scene;
judging whether the battery reaches a full-charge state at the sampling moment, and if the battery reaches the full-charge state, extracting a full-charge working scene;
judging whether the battery receives a charging permission instruction at the sampling moment, and if the duration of the battery not receiving the charging permission instruction exceeds the preset waiting time, extracting an overtime dormancy working scene;
if the charging gun is not connected abnormally, the battery is not charged in a fault, the battery does not reach a full-charge state, and the battery receives a charging permission instruction, a normal charging working scene is extracted.
4. The simulation test modeling method for the charging management system according to claim 1, wherein determining target model configuration parameters according to the charging sample data and the charging scenario information comprises:
determining a target parameter group according to the change characteristics, the change time sequence and the incidence relation among the parameters in the charging sample data;
determining a target charging scenario according to a correlation between the target parameter set and the charging scenario information;
and determining the target model configuration parameters according to the target parameter group and the target charging scene.
5. The simulation test modeling method for a charging management system according to claim 4, wherein determining a target parameter set according to the variation characteristics, variation timing and correlation among parameters in the charging sample data comprises:
determining a simulation step length according to the change characteristics and the change time sequence of each parameter;
extracting parameters in the charging sample data based on the simulation step length and the incidence relation between the parameters;
and establishing the target parameter group according to the extracted parameters.
6. The simulation test modeling method for the charge management system according to claim 1, wherein determining a target test condition according to the charge sample data and the charge scenario information comprises:
acquiring an initial test working condition set;
optimizing the initial test working condition according to the change characteristics, the change time sequence and the incidence relation among the parameters in the charging sample data to obtain a full working condition test set;
extracting a group of parameters to be tested and a charging scene to be tested based on the charging sample data and the charging scene information;
and checking a table of the full working condition test set according to the parameters to be tested and the charging scene to be tested, and determining the target test working condition according to a table checking result.
7. A simulation test modeling system for a charge management system, comprising: a parameter configuration module and a model building module;
the parameter configuration module is used for determining charging sample data according to the influence of each parameter in a preset charging database on the charging state and extracting charging scene information based on the charging sample data;
and the model building module is used for determining target model configuration parameters and target test working conditions according to the charging sample data and the charging scene information, and building a full-working-condition simulation model according to the target model configuration parameters and the target test working conditions.
8. A testing method, wherein a full-condition simulation model is established based on the simulation test modeling method for a charge management system of any one of claims 1 to 6, the testing method further comprising the steps of:
testing the charging management system to be tested based on the full-working-condition simulation model to obtain an initial simulation test result;
acquiring real vehicle test data after the charging management system to be tested is loaded;
comparing the real vehicle test data with the initial simulation test result;
according to the comparison result, correcting the charging sample data and the charging scene information of the simulation test modeling method for the charging management system;
and optimizing the full-working-condition simulation model according to the corrected charging sample data and charging scene information.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the simulation test modeling method for a charge management system according to any of claims 1-6 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out a simulation test modeling method for a charge management system according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110721514.6A CN113378403B (en) | 2021-06-28 | 2021-06-28 | Simulation test modeling method, system, test method, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110721514.6A CN113378403B (en) | 2021-06-28 | 2021-06-28 | Simulation test modeling method, system, test method, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113378403A true CN113378403A (en) | 2021-09-10 |
| CN113378403B CN113378403B (en) | 2023-03-24 |
Family
ID=77579485
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110721514.6A Active CN113378403B (en) | 2021-06-28 | 2021-06-28 | Simulation test modeling method, system, test method, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113378403B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676559A (en) * | 2022-03-07 | 2022-06-28 | 海尔数字科技（上海）有限公司 | Pipeline simulation method, device, equipment and storage medium |
| CN115460278A (en) * | 2022-07-29 | 2022-12-09 | 重庆长安汽车股份有限公司 | Vehicle charging state pushing method and device |
| CN116702523A (en) * | 2023-08-08 | 2023-09-05 | 北京中电普华信息技术有限公司 | Simulation method for power resource regulation, electronic equipment and computer medium |
| CN116990699A (en) * | 2023-07-24 | 2023-11-03 | 北京三维天地科技股份有限公司 | New energy battery detection method and system |
| CN117289683A (en) * | 2023-11-21 | 2023-12-26 | 晶科储能科技有限公司 | Energy storage battery management system testing method and system, electronic equipment and storage medium |
| WO2024073890A1 (en) * | 2022-10-08 | 2024-04-11 | 宁德时代新能源科技股份有限公司 | Battery lithium plating mapping acquisition method, apparatus, device, medium, and program product |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107340441A (en) * | 2017-06-07 | 2017-11-10 | 同济大学 | A kind of fuel cell car power assembly integrated test system |
| CN110909443A (en) * | 2019-10-12 | 2020-03-24 | 北京航空航天大学 | High-precision battery pack charging remaining time estimation method and system |
| CN111055722A (en) * | 2019-12-20 | 2020-04-24 | 华为技术有限公司 | Method and device for estimating charging time and storage medium |
| CN111199104A (en) * | 2019-12-31 | 2020-05-26 | 浙江吉利新能源商用车集团有限公司 | Battery residual performance analysis method, device, equipment and storage medium |
| CN111506977A (en) * | 2019-12-11 | 2020-08-07 | 安徽贵博新能科技有限公司 | Power battery modeling method |
| CN112213643A (en) * | 2020-09-30 | 2021-01-12 | 蜂巢能源科技有限公司 | Method, system and equipment for predicting initial capacity and health state of battery |
| CN112380679A (en) * | 2020-11-02 | 2021-02-19 | 中国第一汽车股份有限公司 | Battery thermal runaway simulation method, device, equipment and storage medium |
-
2021
- 2021-06-28 CN CN202110721514.6A patent/CN113378403B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107340441A (en) * | 2017-06-07 | 2017-11-10 | 同济大学 | A kind of fuel cell car power assembly integrated test system |
| CN110909443A (en) * | 2019-10-12 | 2020-03-24 | 北京航空航天大学 | High-precision battery pack charging remaining time estimation method and system |
| CN111506977A (en) * | 2019-12-11 | 2020-08-07 | 安徽贵博新能科技有限公司 | Power battery modeling method |
| CN111055722A (en) * | 2019-12-20 | 2020-04-24 | 华为技术有限公司 | Method and device for estimating charging time and storage medium |
| CN111199104A (en) * | 2019-12-31 | 2020-05-26 | 浙江吉利新能源商用车集团有限公司 | Battery residual performance analysis method, device, equipment and storage medium |
| CN112213643A (en) * | 2020-09-30 | 2021-01-12 | 蜂巢能源科技有限公司 | Method, system and equipment for predicting initial capacity and health state of battery |
| CN112380679A (en) * | 2020-11-02 | 2021-02-19 | 中国第一汽车股份有限公司 | Battery thermal runaway simulation method, device, equipment and storage medium |
Non-Patent Citations (4)
| Title |
|---|
| A.EMAMDOUST等: "Partial oxidation of methane over SiO_2 supported Ni and NiCe catalysts", 《JOURNAL OF ENERGY CHEMISTRY》 * |
| 刘轶鑫等: "基于SOC-OCV曲线特征的SOH估计方法研究", 《汽车工程》 * |
| 朱彬等: "电动汽车直流充电机自动检测平台设计", 《电网与清洁能源》 * |
| 欧阳凌云: "高能超级电容城市客车快充管理系统设计", 《交通与港航》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676559A (en) * | 2022-03-07 | 2022-06-28 | 海尔数字科技（上海）有限公司 | Pipeline simulation method, device, equipment and storage medium |
| CN115460278A (en) * | 2022-07-29 | 2022-12-09 | 重庆长安汽车股份有限公司 | Vehicle charging state pushing method and device |
| WO2024073890A1 (en) * | 2022-10-08 | 2024-04-11 | 宁德时代新能源科技股份有限公司 | Battery lithium plating mapping acquisition method, apparatus, device, medium, and program product |
| CN116990699A (en) * | 2023-07-24 | 2023-11-03 | 北京三维天地科技股份有限公司 | New energy battery detection method and system |
| CN116990699B (en) * | 2023-07-24 | 2024-02-06 | 北京三维天地科技股份有限公司 | New energy battery detection method and system |
| CN116702523A (en) * | 2023-08-08 | 2023-09-05 | 北京中电普华信息技术有限公司 | Simulation method for power resource regulation, electronic equipment and computer medium |
| CN116702523B (en) * | 2023-08-08 | 2023-10-27 | 北京中电普华信息技术有限公司 | Simulation method for power resource regulation, electronic equipment and computer medium |
| CN117289683A (en) * | 2023-11-21 | 2023-12-26 | 晶科储能科技有限公司 | Energy storage battery management system testing method and system, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113378403B (en) | 2023-03-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113378403B (en) | Simulation test modeling method, system, test method, device and storage medium | |
| WO2022089650A1 (en) | Battery thermal runaway simulation method and apparatus, device and storage medium | |
| CN111312325B (en) | BBU fault diagnosis method and device, electronic equipment and storage medium | |
| CN110824367A (en) | Hardware-in-loop test system and method for new energy automobile battery management system | |
| CN109633458B (en) | Vehicle hardware-in-loop test system and method | |
| CN114148216A (en) | Battery self-discharge rate abnormality detection method, system, device and storage medium | |
| WO2022156403A1 (en) | Relay diagnosis test method, apparatus and system, and storage medium and upper computer | |
| CN113219340A (en) | Method, device, equipment and storage medium for testing battery equalization function | |
| CN113837596A (en) | Fault determination method and device, electronic equipment and storage medium | |
| CN115689534B (en) | Method, device, equipment and medium for managing service life of equipment based on big data | |
| CN115437336A (en) | Test method and device for test case, electronic equipment and storage medium | |
| CN111525202A (en) | Method, system, equipment and medium for monitoring DCR in lithium ion battery cycle | |
| CN113590471B (en) | Communication terminal equipment simulation system and application method thereof | |
| CN111060826A (en) | Battery system detection method and device, terminal equipment and storage medium | |
| CN111077420A (en) | System and method for automatically testing voltage sag tolerance capability of sensitive equipment | |
| CN114474149A (en) | Automatic testing method, device, server and readable storage medium | |
| CN112307647A (en) | Charging pile communication interruption testing method and device, storage medium and processor | |
| CN111552584A (en) | Test system, method and device for primary fault diagnosis isolation and recovery functions of satellite | |
| CN116185737A (en) | DC abnormal power failure detection method and device based on notebook computer and computer equipment | |
| CN115757099A (en) | Automatic test method and device for platform firmware protection recovery function | |
| CN112486717A (en) | Method, system, terminal and storage medium for verifying consistency of disk data | |
| Liu et al. | Fault Test Analysis of Abnormal Remaining Amount of Smart Meter | |
| WO2023226039A1 (en) | Fast-charging detection method and apparatus, and electronic device and storage medium | |
| CN117192251A (en) | Aging performance testing method, device and equipment for super capacitor and storage medium | |
| CN111722032A (en) | Quick pile filling simulation device and system for HIL test |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |