CN115308629A - Battery performance testing method and device, storage medium and electronic equipment - Google Patents

Battery performance testing method and device, storage medium and electronic equipment Download PDF

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CN115308629A
CN115308629A CN202211015070.5A CN202211015070A CN115308629A CN 115308629 A CN115308629 A CN 115308629A CN 202211015070 A CN202211015070 A CN 202211015070A CN 115308629 A CN115308629 A CN 115308629A
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battery
feedback
target
test
driving
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CN115308629B (en
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潘垂宇
蒋延艳
李雪
张志�
李学达
任毅
董谦
蔡洪亮
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FAW Group Corp
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FAW Group Corp
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery

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Abstract

The invention discloses a method and a device for testing battery performance, a storage medium and electronic equipment. Acquiring vehicle running data of a target vehicle provided with a running battery of a target model, wherein the vehicle running data is used for indicating the running condition of the target vehicle under a target running condition; converting vehicle driving data into excitation parameters; exciting the test battery of the target model through the excitation parameters to obtain test feedback parameters of the test battery, wherein the test feedback parameters are used for indicating the battery working condition of the test battery; and determining the battery performance of the battery of the target model according to the test feedback parameter and the driving feedback parameter, wherein the driving feedback parameter is used for indicating the battery working condition of the driving battery. By adopting the technical scheme, the technical problem of low battery performance testing efficiency in the related technology is solved, and the technical effect of improving the battery performance testing efficiency is realized.

Description

Battery performance testing method and device, storage medium and electronic equipment
Technical Field
The invention relates to the technical field of hybrid electric vehicles, in particular to a method and a device for testing battery performance, a storage medium and electronic equipment.
Background
The battery plays a very important role in the driving of the vehicle as an important component of a new energy automobile, so before the mass production of the battery, the battery needs to be tested and functional components need to be developed, and in order to test the performance of the battery in actual use, the battery needs to be installed on the vehicle for a vehicle road test. This requires that the test scenarios for the same batch of test cells remain consistent.
Currently, a plurality of groups of test vehicles are often arranged, that is, the test batteries of the same batch are respectively installed on different vehicles of the same model, and a plurality of testers drive the test vehicles to run on the same test road section according to a more consistent running state. Although the mode can realize the test of multiple groups of batteries, the defects are more, firstly, the mode increases the test expense, secondly, the variable in the test process is controlled, but the control effect is poor, namely different people have different test data due to different driving habits, and therefore, the data which are printed by the test can be influenced to a certain extent, and further the final performance test result is influenced.
Disclosure of Invention
In view of the above, an object of the present disclosure is to provide a method and an apparatus for testing battery performance, a storage medium, and an electronic device, so as to at least solve the technical problem of low efficiency in testing battery performance in the prior art.
In order to achieve the above object, in a first aspect, the present disclosure provides a method for testing battery performance, which collects vehicle driving data of a target vehicle mounted with a target model of driving battery, wherein the vehicle driving data is used for indicating a driving condition of the target vehicle under a target driving condition; converting the vehicle running data into an excitation parameter, wherein the excitation parameter is used for indicating the energy delivery condition when the running battery drives the target vehicle to run according to the target running condition; exciting the test battery of the target model through the excitation parameters to obtain test feedback parameters of the test battery, wherein the test feedback parameters are used for indicating the battery working condition of the test battery; and determining the battery performance of the battery of the target model according to the test feedback parameters and the driving feedback parameters, wherein the driving feedback parameters are used for indicating the battery working condition of the driving battery.
Optionally, the converting the vehicle driving data into the excitation parameters includes: determining an operation data stream matched with a reference current data stream included in the vehicle running data, wherein the reference current data stream is used for indicating the charging and discharging conditions of the running battery during the running of the target vehicle according to the target running condition, the operation data stream is used for exciting the operation condition of a model under the charging and discharging conditions indicated by the reference current data stream, and the excitation model is used for exciting the battery to operate; extracting the excitation parameters from the operational data stream, wherein the excitation parameters comprise a time-varying current value profile, and an excitation model with the excitation parameters is used for exciting the test battery.
Optionally, the determining an operation data stream matched with a reference current data stream included in the vehicle driving data includes: converting the reference current data stream into a first current data stream that matches a data stream format used by the excitation model; replacing first current values which do not fall into a target value interval in the first current data stream to obtain second current data streams of which the current values all fall into the target value interval; determining the second current data stream as the operational data stream.
Optionally, the replacing the first current value that does not fall within the target value interval in the first current data stream includes one of: acquiring a second current value from the first current data stream in an adjacent time period of the first current value, wherein the second current value falls in the target value interval; replacing the first current value with the second current value; obtaining a plurality of third current values from the first current data stream in adjacent time periods of the first current value, wherein the plurality of third current values fall within the target value interval; replacing the first current value with an average of a plurality of the third current values; obtaining a plurality of fourth current values from the first current data stream in adjacent time periods of the first current value, wherein the plurality of fourth current values fall within the target value interval; carrying out cubic spline interpolation calculation on the fourth current values to obtain fifth current values; replacing the first current value with the fifth current value; obtaining a plurality of sixth current values from the first current data stream in adjacent time periods of the first current value; and performing linear interpolation calculation on the plurality of sixth current values to obtain a seventh current value, and replacing the first current value with the seventh current value.
Optionally, the determining the battery performance of the battery of the target model according to the test feedback parameter and the driving feedback parameter includes: determining a plurality of feedback characteristics of the battery of the target model according to the test feedback parameters and the driving feedback parameters, wherein the plurality of feedback characteristics are used for dividing the battery performance of the battery of the target model from a plurality of dimensions; and determining the battery performance of the battery with the target model according to the plurality of feedback characteristics.
Optionally, the determining the battery performance of the battery of the target model according to the multiple feedback characteristics includes: extracting a target abnormal feedback feature from the plurality of feedback features, wherein the target abnormal feedback feature is a feedback feature with abnormal feature values; under the condition that the target abnormal feedback feature is not extracted, determining that the battery performance is in a normal state; and under the condition that the target abnormal feedback feature is extracted, determining that the battery performance is in an abnormal state, and determining a target abnormal state type corresponding to a feature value of the target abnormal feedback feature from an abnormal feature value and an abnormal state type which have a corresponding relation.
Optionally, the determining a plurality of feedback characteristics of the battery of the target model according to the test feedback parameter and the driving feedback parameter includes at least one of: calculating feedback parameter deviation between feedback parameters included in a feedback parameter set, wherein the feedback parameter set includes the test feedback parameters and the driving feedback parameters, the test feedback parameters include test temperature and/or test voltage, and the driving feedback parameters include driving temperature and/or driving voltage; determining the feedback parameter deviation as the feedback characteristic; calculating feedback parameter outliers among feedback parameters included in a feedback parameter set, wherein the feedback parameter set includes the test feedback parameters and the driving feedback parameters, the test feedback parameters include test temperature and/or test voltage, and the driving feedback parameters include driving temperature and/or driving voltage; determining the feedback parameter outliers as the feedback features; calculating feedback parameter deviation outliers among feedback parameters included in a feedback parameter set, wherein the feedback parameter set comprises the test feedback parameters and the driving feedback parameters, the test feedback parameters comprise test temperatures and/or test voltages, and the driving feedback parameters comprise driving temperatures and/or driving voltages; determining the feedback parameter deviation outliers as the feedback features; acquiring a target battery from the test battery and the running battery, wherein the target battery is a battery of which the current change value is greater than or equal to a target threshold value in a target time period, the test feedback parameters comprise a voltage value and a current value of the test battery, and the running feedback parameters comprise a voltage value and a current value of the running battery; calculating an internal resistance deviation value and/or a battery energy deviation value of the target battery according to the voltage value and the current value of the target battery in the target time period; determining the internal resistance deviation value and/or battery energy deviation value as the feedback characteristic.
In a second aspect, the present disclosure further provides a device for testing battery performance, including an acquisition module, configured to acquire vehicle driving data of a target vehicle with a driving battery of a target model installed, where the vehicle driving data is used to indicate a driving condition of the target vehicle under a target driving condition; the conversion module is used for converting the vehicle running data into excitation parameters, wherein the excitation parameters are used for indicating the energy transmission condition when the running battery drives the target vehicle to run according to the target running condition; the excitation module is used for exciting the test battery of the target model through the excitation parameters to obtain test feedback parameters of the test battery, wherein the test feedback parameters are used for indicating the battery working condition of the test battery; and the determining module is used for determining the battery performance of the battery of the target model according to the test feedback parameters and the driving feedback parameters, wherein the driving feedback parameters are used for indicating the battery working condition of the driving battery.
The method comprises the steps of acquiring vehicle running data of a target vehicle provided with a running battery of a target model, wherein the vehicle running data is used for indicating the running condition of the target vehicle under a target running condition; converting vehicle driving data into excitation parameters, wherein the excitation parameters are used for indicating energy transmission conditions when a driving battery drives a target vehicle to drive the target vehicle to drive according to target driving conditions; exciting the test battery of the target model through the excitation parameters to obtain test feedback parameters of the test battery, wherein the test feedback parameters are used for indicating the battery working condition of the test battery; and determining the battery performance of the battery of the target model according to the test feedback parameters and the driving feedback parameters, wherein the driving feedback parameters are used for indicating the battery working condition of the driving battery. The method comprises the steps that the running condition of a target vehicle under a target running condition can be reflected by vehicle running data of the target vehicle provided with a running battery of a target model is collected, the vehicle running data is converted into an energy transmission condition used for indicating the running battery to drive the target vehicle according to the target running condition, and then the test battery of the target model is excited by the excitation parameter, so that the test battery can obtain a test environment consistent with the running battery, the battery working condition of the test battery when the target vehicle is driven to run according to the target running condition can be reflected by the obtained test feedback parameter of the test battery, the battery performance of the target model battery can be determined according to the test feedback parameter and the running feedback parameter, the test environment of the test battery of the same model can be simulated by the running data of the target vehicle provided with the running battery of the target model in the whole test process, the test environment of the test battery is consistent with the test environment of the running battery, and the consistency and accuracy of the test data can be guaranteed on the basis that the expense of test equipment is not increased. By adopting the technical scheme, the technical problem of low battery performance testing efficiency in the related technology is solved, and the technical effect of improving the battery performance testing efficiency is realized.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention and do not constitute a limitation of the invention. In the drawings:
FIG. 1 is a flow chart of a method of testing battery performance provided in accordance with the present disclosure;
FIG. 2 is a flow diagram of data processing provided by the present disclosure;
FIG. 3 is a schematic illustration of the stepped interpolation provided by the present disclosure;
FIG. 4 is a schematic diagram of linear interpolation provided by the present disclosure;
FIG. 5 is a flow chart of the anomaly characteristic feedback provided by the present disclosure;
FIG. 6 is a battery test flow diagram provided by the present disclosure;
fig. 7 is a block diagram of a battery performance testing apparatus provided in the present disclosure.
Detailed Description
Specific embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings, but the present disclosure is not limited thereto.
It will be understood that various modifications may be made to the embodiments disclosed herein. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Other modifications will occur to those skilled in the art within the scope and spirit of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present disclosure will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present disclosure has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of the disclosure, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure that may be embodied in various forms. Well-known and/or repeated functions and structures have not been described in detail so as not to obscure the present disclosure with unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely as a basis and representative basis for the application to teach one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
It should be noted that the terms "first", "second", and the like in the description and the drawings of the present disclosure are used for distinguishing similar objects, and are not necessarily used for describing a particular order or sequence. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation 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.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
The present disclosure is further described with reference to the following figures and specific examples.
Fig. 1 is a flowchart of a method for testing battery performance according to the present disclosure, and as shown in fig. 1, details of steps of the method for testing battery performance according to the present disclosure are described as follows:
step S102, vehicle running data of a target vehicle provided with a running battery of a target model is collected, wherein the vehicle running data is used for indicating the running condition of the target vehicle under a target running condition;
step S104, converting the vehicle running data into excitation parameters, wherein the excitation parameters are used for indicating the energy transmission condition when the running battery drives the target vehicle to run according to the target running condition;
step S106, exciting the test battery of the target model through the excitation parameters to obtain test feedback parameters of the test battery, wherein the test feedback parameters are used for indicating the battery working condition of the test battery;
and S108, determining the battery performance of the battery with the target model according to the test feedback parameter and the driving feedback parameter, wherein the driving feedback parameter is used for indicating the battery working condition of the driving battery.
Through the steps, the running condition of the target vehicle under the target running condition can be reflected by the vehicle running receiving, the vehicle running data of the target vehicle provided with the running battery of the target model is collected, the vehicle running data is converted into the energy transmission condition used for indicating the running battery to drive the target vehicle according to the target running condition, and then the test battery of the target model is excited through the excitation parameter, so that the test battery can obtain the test environment consistent with the running battery, the obtained test feedback parameter of the test battery can embody the battery working condition of the test battery when the target vehicle is driven to run according to the target running condition, the battery performance of the battery of the target model can be determined according to the test feedback parameter and the running feedback parameter, the running data of the target vehicle provided with the running battery of the target model can simulate the test environment of the test battery of the same model, the test environment of the test battery is consistent with the test environment of the running battery, and the consistency and the accuracy of the test data are guaranteed on the basis of not increasing the expense of the test equipment. By adopting the technical scheme, the technical problem of low efficiency in testing the battery performance in the related technology is solved, and the technical effect of improving the efficiency in testing the battery performance is realized.
In the technical solution provided in step S102, the target driving condition is used to indicate a test environment for testing the driving battery, and the target driving condition may include, but is not limited to, a road condition, a real-time vehicle speed of the vehicle, a driving habit of a driver, and the like.
Optionally, in this technical solution, the vehicle driving data may include, but is not limited to, a driving speed of the vehicle, current information of a circuit during driving, vehicle power output information, and the like, which is not limited by this solution.
Optionally, in the present technical solution, the vehicle driving data may be acquired in real time from a target vehicle in a driving project, for example, the driving data of the vehicle is acquired in real time and accessed to a database by a streaming data method such as kafka, and the database may be a relational database or a non-relational database. For example, data is stored through a non-relational database, and a hadoop architecture is adopted, so that data lake construction with large enough data scale can be realized. And after the data enters the database, the data is called by the data analysis module. Meanwhile, the database can be accessed to an Internet of vehicles platform, and the Internet of vehicles platform is the historical data of national standard GB32960 generated by vehicles of the enterprise in actual operation. And reading historical data of optional vehicles in the Internet of vehicles platform, and generating corresponding historical test environment data.
In the technical solution provided in step S104, the conversion of the vehicle driving data into the excitation parameter may be obtained by converting a related data conversion model, for example, when the vehicle driving data is a real-time vehicle speed of the vehicle, the vehicle speed and the excitation parameter conversion model output the excitation parameter matched with the vehicle speed, or the vehicle driving data may be current information of a circuit in the driving process, and the current information is numerically filtered, and the filtered result is used as the excitation parameter.
In the technical solution provided in step S106, the test battery of the target model may be excited by a target excitation model, and the excitation model having the excitation parameters is used to excite the test battery to perform a discharging point in a manner matching with the vehicle driving data, so as to simulate a test environment consistent with the driving battery for the test battery.
In the technical solution provided in step S108, the feedback parameter may include, but is not limited to, a voltage value of the battery, a temperature value of the battery, a current value of the battery, and the like.
Alternatively, in the present solution, the battery performance may include, but is not limited to, electromotive force, rated capacity, open circuit voltage, internal resistance, lifetime, self-discharge rate, and the like.
As an alternative embodiment, the converting the vehicle driving data into the excitation parameters includes:
determining an operation data stream matched with a reference current data stream included in the vehicle running data, wherein the reference current data stream is used for indicating the charging and discharging conditions of the running battery during the running of the target vehicle according to the target running condition, the operation data stream is used for exciting the operation condition of a model under the charging and discharging conditions indicated by the reference current data stream, and the excitation model is used for exciting the battery to operate;
extracting the excitation parameters from the operational data stream, wherein the excitation parameters comprise a time-varying current value profile, and an excitation model with the excitation parameters is used for exciting the test battery.
Optionally, in this embodiment, the operation data stream may be, but is not limited to, obtained by performing data processing on the reference current data stream, and the data processing may be, but is not limited to, four parts, such as data offset, outlier filtering, intelligent interpolation, and limit protection, where, for example, the data offset is used to offset and clean data of GB32960 according to an adjustable proposed policy, and table 1 is an optional data offset format table according to this embodiment, as shown in table 1:
Figure BDA0003812254490000081
Figure BDA0003812254490000091
fig. 2 is a flow chart of data processing provided by the present disclosure, as shown in fig. 2, including the following steps:
step S201, performing data migration on the data, so that the data format after the data migration matches with the excitation model.
And step S202, managing the null value, the abnormal value and the overrun invalid value marked in the reference current data stream. The treatment method can be directly deleted.
In step S203, the current value from which the abnormal value is deleted is replaced with a normal current value, and the method may include, but is not limited to, linear interpolation, median filling, two-point adjacent interpolation, cubic spline interpolation, and the like.
Step S204, setting protection values, including two sets of protection values, one set being a device side protection value and the other being a data side protection value. When the current is excited according to the current of a data stream and exceeds the limit value of the protection value, the charging and discharging equipment and the tested battery are protected by adopting a method of reducing power or soft/hard cutting of the equipment, and similarly, when the voltage, the temperature and the like fed back by the tested battery are sampled by the testing equipment and exceed the limit value of the protection, the charging and discharging equipment and the tested battery are protected by adopting a method of soft/hard cutting of the equipment; and the data protection end can reduce the current by adopting a proportion reduction method when the current in the data stream exceeds a protection limit value, and cut off the equipment to avoid the risk of the equipment and the tested battery in the same way when the voltage, the temperature and the like in the data stream exceed the protection limit value.
Fig. 3 is a schematic diagram of the step interpolation provided by the present disclosure, and as shown in fig. 3, the step interpolation is to keep the original current value before the current value of the next frame, that is, to replace the abnormal current value with the normal current value of the previous frame.
Fig. 4 is a schematic diagram of linear interpolation provided by the present disclosure, as shown in fig. 4, that is, the abnormal current value is replaced by a linear data value according to the linear distribution number of the current values of two frames.
As an alternative embodiment, the determining an operation data stream matching a reference current data stream included in the vehicle travel data includes:
converting the reference current data stream into a first current data stream that matches a data stream format used by the excitation model;
replacing first current values which do not fall into a target value interval in the first current data stream to obtain second current data streams of which the current values all fall into the target value interval;
determining the second current data stream as the operational data stream.
Optionally, in this embodiment, the conversion of the data stream format may include, but is not limited to, data cleansing and data offset, for example, the data cleansing is to delete control and characters in the data, and the data offset is to offset a value of a data value.
As an alternative embodiment, the replacing the first current value in the first current data stream, which does not fall within the target value interval, includes one of the following:
acquiring a second current value from the first current data stream in an adjacent time period of the first current value, wherein the second current value falls within the target value interval; replacing the first current value with the second current value;
obtaining a plurality of third current values from the first current data stream in adjacent time periods of the first current value, wherein the plurality of third current values fall within the target value interval; replacing the first current value with an average of a plurality of the third current values;
obtaining a plurality of fourth current values from the first current data stream in adjacent time periods of the first current value, wherein the plurality of fourth current values fall within the target value interval; carrying out cubic spline interpolation calculation on the fourth current values to obtain fifth current values; replacing the first current value with the fifth current value;
obtaining a plurality of sixth current values from the first current data stream over adjacent time periods of the first current value; and performing linear interpolation calculation on the plurality of sixth current values to obtain a seventh current value, and replacing the first current value with the seventh current value.
As an alternative embodiment, the determining the battery performance of the battery of the target model according to the test feedback parameter and the driving feedback parameter includes:
determining a plurality of feedback characteristics of the battery of the target model according to the test feedback parameters and the driving feedback parameters, wherein the plurality of feedback characteristics are used for dividing the battery performance of the battery of the target model from a plurality of dimensions;
and determining the battery performance of the battery of the target model according to the plurality of feedback characteristics.
Optionally, in the present embodiment, the feedback parameter may include, but is not limited to, an operating voltage of the battery, an operating current of the battery, an operating temperature of the battery, and the like.
Optionally, in this embodiment, the test feedback parameters may be acquired in real time during the operation of the target vehicle.
Alternatively, in the present embodiment, the feedback characteristics may include, but are not limited to, a voltage deviation, a voltage outlier, a voltage deviation outlier, a temperature deviation outlier, an internal resistance deviation value, an energy deviation value, and the like.
As an alternative embodiment, the determining the battery performance of the battery of the target model according to the plurality of feedback characteristics includes:
extracting a target abnormal feedback feature from the plurality of feedback features, wherein the target abnormal feedback feature is a feedback feature with abnormal feature values;
under the condition that the target abnormal feedback feature is not extracted, determining that the battery performance is in a normal state;
and under the condition that the target abnormal feedback feature is extracted, determining that the battery performance is in an abnormal state, and determining a target abnormal state type corresponding to a feature value of the target abnormal feedback feature from an abnormal feature value and an abnormal state type which have a corresponding relation.
Optionally, in this embodiment, the abnormal state type may have a corresponding relationship with one or more target abnormal feedback features, that is, the abnormal state type corresponding to one target abnormal feedback feature may be determined according to the one target abnormal feedback feature, or the abnormal state type corresponding to a combination of multiple target abnormal feedback features may be determined according to the combination.
Optionally, in this embodiment, the abnormal feedback features include an abnormal voltage deviation and an abnormal voltage deviationGroup value, abnormal voltage deviation group value, abnormal temperature outlier, abnormal temperature deviation group value, abnormal internal resistance deviation value, abnormal energy deviation value, and the like. For example, voffset1 represents the feedback voltage of the driving battery, voffsetn represents the feedback voltage of the test battery, and the actual voltage deviation of the feedback voltages of the driving battery and the test battery may be the vdrive-V test, and similarly, toffset1 represents the feedback temperature of the driving battery, toffsetn represents the feedback temperature of the test battery, and the actual temperature deviation of the feedback voltages of the driving battery and the test battery may be the tdrive-T test. Abnormal voltage deviation threshold, i.e. Voffset>A threshold M1 is set, which indicates that the voltage of the battery deviates beyond the threshold value under the same current excitation. Abnormal voltage outlier, i.e. battery voltage outlier generated by current excitation, can be 3sigma outlier representation, quartile value outlier, etc. The abnormal voltage deviation outlier threshold, that is, the obtained Voffset has an outlier, the outlier may be a 3sigma outlier representation, a quartile value outlier, or the median or average value Voffset _ mean of Voffset1 to Voffset exceeds the threshold N. Abnormal temperature deviation threshold, toffset>A threshold M2 is set, which indicates that the voltage of the battery deviates beyond the threshold value under the same current excitation. Abnormal temperature outlier, i.e. the temperature of the battery is outlier through current excitation, which may be 3sigma outlier representation, quartile value outlier, etc. The abnormal temperature deviation outlier threshold, that is, the acquired Toffset has an outlier, the outlier may be a 3sigma outlier, a quartile value outlier, or may be a median or an average value of Toffset1 to Toffsetn, toffset _ mean exceeding the threshold N, etc. Internal resistance deviation, according to battery direct current internal resistance calculation law, when having the heavy current sudden change (use T1 for moment before the sudden change, T2 is the first frame moment after the sudden change), can estimate the direct current internal resistance of battery, compare high in the clouds direct current internal resistance and feedback direct current internal resistance deviation this moment, can analyze whether transfinite the threshold value, the internal resistance can be calculated through following formula:
Figure BDA0003812254490000121
wherein V1 is the feedback voltage value at the time of T1, and I1 is the current value at the time of T1V2 is the feedback voltage value at time T2, and I2 is the current value at time T2. The energy deviation is based on ampere-hour voltage integration method of battery energy calculation standard method, the time is T1-T2, and the capacity at a period of time can be expressed as
Figure BDA0003812254490000122
And when the deviation of the internal resistance and the energy reaches a certain threshold value, the abnormal value is obtained.
Optionally, in this embodiment, after the target abnormal characteristic is determined, a notification message for only the abnormal characteristic information of the battery of the corresponding terminal device may be generated according to the determined target abnormal characteristic and sent to the corresponding terminal device. Fig. 5 is a flow chart of the anomaly feature feedback provided by the present disclosure, as shown in fig. 5, which may include, but is not limited to, the following steps:
in step S501, a plurality of feedback characteristics of the target battery are obtained by comparing and calculating the test feedback parameter and the driving feedback parameter, where the plurality of feedback characteristics may include, but are not limited to, a voltage deviation, a voltage outlier, a voltage deviation outlier, a temperature deviation, a temperature outlier, a temperature deviation outlier, an internal resistance deviation value, an energy deviation value, and the like.
Step S502, determining abnormal feedback features in the plurality of feedback features, wherein the abnormal feedback features do not fall into the features of the prefabricated interval corresponding to the feedback features.
Step S503, recording the test information of the running battery and the test information of the test battery, and simultaneously providing an experiment report of result information, time information, operator information, information of the tested object and the like of the abnormal feedback characteristic, so that the whole test process can be known in detail according to the experiment report.
Step S504, risk definition is carried out according to the threshold value of each abnormal feedback characteristic, and risk early warning information is pushed to appointed personnel such as an experiment manager and a design developer, wherein the pushing method comprises but is not limited to short messages, mails, telephones and the like.
As an alternative embodiment, the determining a plurality of feedback characteristics of the target model of battery according to the test feedback parameter and the driving feedback parameter includes at least one of:
calculating feedback parameter deviation between feedback parameters included in a feedback parameter set, wherein the feedback parameter set includes the test feedback parameters and the driving feedback parameters, the test feedback parameters include test temperature and/or test voltage, and the driving feedback parameters include driving temperature and/or driving voltage; determining the feedback parameter deviation as the feedback characteristic;
calculating feedback parameter outliers among feedback parameters included in a feedback parameter set, wherein the feedback parameter set includes the test feedback parameters and the driving feedback parameters, the test feedback parameters include test temperature and/or test voltage, and the driving feedback parameters include driving temperature and/or driving voltage; determining the feedback parameter outliers as the feedback features;
calculating feedback parameter deviation outliers among feedback parameters included in a feedback parameter set, wherein the feedback parameter set includes the test feedback parameters and the driving feedback parameters, the test feedback parameters include test temperature and/or test voltage, and the driving feedback parameters include driving temperature and/or driving voltage; determining the feedback parameter deviation outliers as the feedback features;
acquiring a target battery from the test battery and the running battery, wherein the target battery is a battery of which the current change value is greater than or equal to a target threshold value in a target time period, the test feedback parameters comprise a voltage value and a current value of the test battery, and the running feedback parameters comprise a voltage value and a current value of the running battery; calculating an internal resistance deviation value and/or a battery energy deviation value of the target battery according to the voltage value and the current value of the target battery in the target time period; determining the internal resistance deviation value and/or battery energy deviation value as the feedback characteristic.
The method may be applied to a test system for testing battery performance, and may be but is not limited to a battery test performed on a battery in real time with a target vehicle mounted with a test driving battery, because a test period of the battery or the vehicle may be long, and a test period for testing battery performance is shortened by transmitting vehicle driving data of the target vehicle in real time, where the test period for testing battery synchronization is obtained, and fig. 6 is a battery test flowchart provided in the present disclosure, as shown in fig. 6, and may be but is not limited to include the following steps:
s601, vehicle driving data (taking reference current data flow of a driving battery installed on a target vehicle as an example) required by national standard GB32960 electric vehicle remote service and management system technical specification sent by the vehicle is received and is accessed into a database through a flow data method such as kafka, and the database can be divided into two paths, wherein one path is a relational database, and the other path is a non-relational database. Relational databases are characterized by fast query speeds, but relatively small sizes, and are suitable for the real-time digital twin described herein. The non-relational database is used for storing data, and a hadoop framework is adopted, so that the construction of a data lake with large enough data scale can be realized. And after the data enters the database, the data is called by the data analysis module. Meanwhile, the database can be accessed to an Internet of vehicles platform, and the Internet of vehicles platform is the historical data of national standard GB32960 generated by vehicles of the enterprise in actual operation. The database can read the historical data of optional vehicles in the Internet of vehicles platform, and then generates corresponding historical working condition mirror images.
And S602, performing data processing on the reference current data stream to obtain an excitation parameter for exciting the test battery, wherein the excitation parameter comprises four sub-modules of data migration, abnormal value filtering, intelligent interpolation and limit protection. In the data analysis module, there is an adjustable time delay T _ delay, and this parameter can be used to adjust the time delay from the real-time stream data access to the subsequent policy enforcement module, and the time delay is in the range of 1s-9999s. The meaning of the time delay parameter is that the data is washed to the serial execution of the four sub-modules protected by the limit value within the time delay range, and the executed data enters the strategy making module. Further, describing the function of the data migration sub-module, the data migration sub-module migrates and cleans the data of GB32960 according to the adjustable proposed strategy. Further, outlier filtering, outliers including null, data default, invalid values out of the battery data range, is flagged. In this module, the definitions of the abnormal value and the overrun invalid value need to be set in advance. Furthermore, intelligent interpolation is carried out, and the module governs null values, abnormal values and over-limit invalid values marked by the module. The governing method can be directly deleted, and can also adopt a method of interpolating according to previous and next data, and the method is not limited to linear interpolation, median filling, two-point interpolation near, cubic spline interpolation and the like. The data after treatment is a standard data stream of 10 seconds and 1 frame, but the existing data stream is a discrete value relative to the time precision of the test equipment, and the data stream is filled by adopting two interpolation modes. Further, a protection value module is set, and the module comprises two groups of protection values, one group is an equipment end protection value, and the other group is a data end protection value. When the current is excited according to the current of a data stream and exceeds the limit value of the protection value, the charging and discharging equipment and the battery to be tested are protected by adopting a method of reducing power or soft/hard sectioning the equipment, and similarly, when the voltage, the temperature and the like fed back by the battery to be tested are sampled by the test equipment and exceed the limit value of the protection value, the charging and discharging equipment and the battery to be tested are protected by adopting a method of soft/hard sectioning the equipment; and when the current in the data stream exceeds the protection limit value, the data protection end can reduce the current by adopting a proportion reduction method, and similarly, when the voltage, the temperature and the like in the data stream exceed the protection limit value, the equipment must be cut off in the delay T _ delay so as to avoid the risk of the equipment and the battery to be tested.
Step S603, the test module excites the test battery according to the excitation parameters to obtain test feedback parameters of the test battery, where the test feedback parameters may include, but are not limited to, a voltage value, a current value, and a temperature value of the test battery.
In step S604, feedback characteristics of the battery of the target model are calculated according to the test feedback parameters of the test battery and the driving feedback parameters of the driving battery, wherein the feedback characteristics may include, but are not limited to, voltage deviation, voltage outlier, voltage deviation outlier, temperature deviation, temperature outlier, temperature deviation outlier, internal resistance deviation value, energy deviation value, and the like.
And step S605, determining corresponding target abnormal feedback characteristics according to the calculated values of the feedback characteristics and the corresponding threshold values.
Step S606, recording the test information of the battery and the test information of the battery, and simultaneously issuing an experiment report of result information, time information, operator information, information of the object to be tested, and the like of the abnormal feedback feature, so that the whole test process can be known in detail according to the experiment report.
Step S607, performing risk definition according to the threshold of each target abnormal feedback feature, and pushing risk early warning information to designated personnel such as an experiment manager and a design developer, where the pushing method includes but is not limited to short message, email, telephone, and the like.
The method can be applied to simulating a synchronous test scene of vehicles on road test, for example, two battery packs are required to synchronously simulate the real-time operation condition of one off-road test run, and the battery deviation condition of the vehicle on road test run is analyzed. Firstly, the states of the test battery and the running battery on the foreign test vehicle are set to be consistent. And setting abnormal value filtering, intelligent interpolation and limit protection submodules. An example setting method includes: the time delay T _ delay =5 minutes, linear interpolation is used for null values and abnormal values, and linear interpolation is used for data streams. The single voltage setting of the protection value of the equipment end and the protection value of the data end is 2V-4.2V, the temperature protection limit value is-20 ℃ to 45 ℃, and the system is disconnected when the temperature exceeds the limit value. The protection value of the current is set to 200A. And starting to test the battery pack according to preset conditions, and simultaneously recording various feedback parameters. And calculating feedback characteristics and judging whether the feedback characteristics deviate from the threshold value. And (5) providing an experimental report.
The method can also be used for simulating a testing environment of fee synchronization testing, and a testing battery needs to be tested according to typical urban working conditions in A city to evaluate the energy consumption of the battery pack. Selecting a typical vehicle under the driving working condition of a city A city from a database, introducing the system, and setting the battery pack of the system to be consistent with the state of the vehicle to be simulated; and setting abnormal value filtering, intelligent interpolation and limit protection submodules of the system. An example of the setting method is as follows: and (4) time delay T _ delay =5 minutes, linear interpolation is adopted for null values and abnormal values, linear interpolation is adopted for data streams, and in step 4, the monomer voltage settings of the equipment end protection value and the data end protection value are both 2V-4.2V, the temperature protection limit value is-20 ℃ to 45 ℃, and the system is disconnected when the temperature protection limit value exceeds the limit value. The protection value of the current is set to 200A. And starting to test the test battery according to preset conditions, and simultaneously recording various feedback values. And calculating feedback characteristics and judging whether the feedback characteristics deviate from the threshold value. And (5) providing an experimental report.
The present application also discloses a device for testing battery performance, fig. 7 is a block diagram of the device for testing battery performance provided by the present disclosure, and as shown in fig. 7, the device includes: the acquisition module 72 is used for acquiring vehicle running data of a target vehicle provided with a running battery of a target model, wherein the vehicle running data is used for indicating the running condition of the target vehicle under a target running condition;
a conversion module 74, configured to convert the vehicle driving data into an excitation parameter, where the excitation parameter is used to indicate an energy delivery condition when the driving battery drives the target vehicle to drive according to the target driving condition;
the excitation module 76 is configured to excite the test battery of the target model according to the excitation parameters to obtain test feedback parameters of the test battery, where the test feedback parameters are used to indicate a battery working condition of the test battery;
a determining module 78, configured to determine battery performance of the battery of the target model according to the test feedback parameter and a driving feedback parameter, where the driving feedback parameter is used to indicate a battery operating condition of the driving battery.
Optionally, the conversion module includes: a first determination unit configured to determine an operation data stream that matches a reference current data stream included in the vehicle travel data, wherein the reference current data stream is used to indicate charging and discharging conditions of the travel battery during travel of the target vehicle according to the target travel condition, the operation data stream is used to excite an operation condition of a model in the charging and discharging conditions indicated by the reference current data stream, and the excitation model is used to excite battery operation; an extraction unit, configured to extract the excitation parameters from the operational data stream, where the excitation parameters include a current value curve that changes over time, and an excitation model with the excitation parameters is used to excite the test battery.
Optionally, the first determining unit is configured to: converting the reference current data stream into a first current data stream that matches a data stream format used by the excitation model; replacing first current values which do not fall into a target value interval in the first current data stream to obtain second current data streams of which the current values all fall into the target value interval; determining the second current data stream as the operational data stream.
Optionally, the first determining unit is configured to perform any of the following operations: acquiring a second current value from the first current data stream in an adjacent time period of the first current value, wherein the second current value falls within the target value interval; replacing the first current value with the second current value; obtaining a plurality of third current values from the first current data stream in adjacent time periods of the first current value, wherein the plurality of third current values fall within the target value interval; replacing the first current value with an average of a plurality of the third current values; obtaining a plurality of fourth current values from the first current data stream in adjacent time periods of the first current value, wherein the plurality of fourth current values fall within the target value interval; carrying out cubic spline interpolation calculation on the fourth current values to obtain fifth current values; replacing the first current value with the fifth current value; obtaining a plurality of sixth current values from the first current data stream in adjacent time periods of the first current value; and performing linear interpolation calculation on the plurality of sixth current values to obtain a seventh current value, and replacing the first current value with the seventh current value.
Optionally, the determining module includes: a second determination unit, configured to determine a plurality of feedback characteristics of the battery of the target model according to the test feedback parameter and the driving feedback parameter, where the plurality of feedback characteristics are used to divide battery performance of the battery of the target model from a plurality of dimensions; and the third determining unit is used for determining the battery performance of the battery of the target model according to a plurality of feedback characteristics.
Optionally, the third determining module is configured to: extracting a target abnormal feedback feature from the plurality of feedback features, wherein the target abnormal feedback feature is a feedback feature with abnormal feature values; under the condition that the target abnormal feedback feature is not extracted, determining that the battery performance is in a normal state; and under the condition that the target abnormal feedback feature is extracted, determining that the battery performance is in an abnormal state, and determining a target abnormal state type corresponding to a feature value of the target abnormal feedback feature from an abnormal feature value and an abnormal state type which have a corresponding relation.
Optionally, the second determining module is configured to perform at least one of the following operations: calculating feedback parameter deviation among feedback parameters included in a feedback parameter set, wherein the feedback parameter set comprises the test feedback parameters and the driving feedback parameters, the test feedback parameters comprise test temperature and/or test voltage, and the driving feedback parameters comprise driving temperature and/or driving voltage; determining the feedback parameter deviation as the feedback characteristic; calculating feedback parameter outliers among feedback parameters included in a feedback parameter set, wherein the feedback parameter set comprises the test feedback parameters and the driving feedback parameters, the test feedback parameters comprise test temperatures and/or test voltages, and the driving feedback parameters comprise driving temperatures and/or driving voltages; determining the feedback parameter outliers as the feedback features; calculating feedback parameter deviation outliers among feedback parameters included in a feedback parameter set, wherein the feedback parameter set includes the test feedback parameters and the driving feedback parameters, the test feedback parameters include test temperature and/or test voltage, and the driving feedback parameters include driving temperature and/or driving voltage; determining the feedback parameter deviation outliers as the feedback features; acquiring a target battery from the test battery and the running battery, wherein the target battery is a battery of which the current change value is greater than or equal to a target threshold value in a target time period, the test feedback parameters comprise a voltage value and a current value of the test battery, and the running feedback parameters comprise a voltage value and a current value of the running battery; calculating an internal resistance deviation value and/or a battery energy deviation value of the target battery according to the voltage value and the current value of the target battery in the target time period; determining the internal resistance deviation value and/or battery energy deviation value as the feedback characteristic.
The storage medium may be included in the electronic device; or may exist separately without being assembled into the electronic device.
The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquiring vehicle running data of a target vehicle provided with a running battery of a target model, wherein the vehicle running data is used for indicating the running condition of the target vehicle under a target running condition; converting the vehicle running data into an excitation parameter, wherein the excitation parameter is used for indicating the energy transmission condition when the running battery drives the target vehicle to run according to the target running condition; exciting the test battery of the target model through the excitation parameters to obtain test feedback parameters of the test battery, wherein the test feedback parameters are used for indicating the battery working condition of the test battery; and determining the battery performance of the battery of the target model according to the test feedback parameter and the driving feedback parameter, wherein the driving feedback parameter is used for indicating the battery working condition of the driving battery.
Alternatively, the storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: converting the vehicle running data into an excitation parameter, wherein the excitation parameter is used for indicating the energy delivery condition when the running battery drives the target vehicle to run according to the target running condition; exciting the test battery of the target model through the excitation parameters to obtain test feedback parameters of the test battery, wherein the test feedback parameters are used for indicating the battery working condition of the test battery; and determining the battery performance of the battery of the target model according to the test feedback parameter and the driving feedback parameter, wherein the driving feedback parameter is used for indicating the battery working condition of the driving battery.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the passenger computer, partly on the passenger computer, as a stand-alone software package, partly on the passenger 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 passenger 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 should be noted that the storage media described above in this disclosure can be computer readable signal media or computer readable storage media or any combination of the two. 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 of the computer readable storage medium may include, but are not limited to: 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 present disclosure, 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. In contrast, in the present disclosure, 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 any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any storage medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable 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. 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other combinations of features described above or equivalents thereof without departing from the spirit of the disclosure. For example, the above features and the technical features disclosed in the present disclosure (but not limited to) having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, the specific features and acts described above are merely exemplary forms of implementations.
While the present disclosure has been described in detail with reference to the embodiments, the present disclosure is not limited to the specific embodiments, and those skilled in the art can make various modifications and alterations based on the concept of the present disclosure, and the modifications and alterations should fall within the scope of the present disclosure as claimed.

Claims (10)

1. A method for testing battery performance, comprising:
acquiring vehicle running data of a target vehicle provided with a running battery of a target model, wherein the vehicle running data is used for indicating the running condition of the target vehicle under a target running condition;
converting the vehicle running data into an excitation parameter, wherein the excitation parameter is used for indicating the energy transmission condition when the running battery drives the target vehicle to run according to the target running condition;
exciting the test battery of the target model through the excitation parameters to obtain test feedback parameters of the test battery, wherein the test feedback parameters are used for indicating the battery working condition of the test battery;
and determining the battery performance of the battery of the target model according to the test feedback parameter and the driving feedback parameter, wherein the driving feedback parameter is used for indicating the battery working condition of the driving battery.
2. The battery performance testing method of claim 1, wherein the converting the vehicle driving data into excitation parameters comprises:
determining an operation data stream matched with a reference current data stream included in the vehicle running data, wherein the reference current data stream is used for indicating the charging and discharging conditions of the running battery during the running of the target vehicle according to the target running condition, the operation data stream is used for exciting the operation condition of a model under the charging and discharging conditions indicated by the reference current data stream, and the excitation model is used for exciting the battery to operate;
extracting the excitation parameters from the operational data stream, wherein the excitation parameters comprise a time-varying current value profile, and an excitation model with the excitation parameters is used for exciting the test battery.
3. The method for testing battery performance according to claim 2, wherein the determining an operation data stream matching a reference current data stream included in the vehicle driving data comprises:
converting the reference current data stream into a first current data stream that matches a data stream format used by the excitation model;
replacing first current values which do not fall into a target value interval in the first current data stream to obtain second current data streams of which the current values all fall into the target value interval;
determining the second current data stream as the operational data stream.
4. The method for testing battery performance according to claim 3, wherein the replacing the first current value in the first current data stream that does not fall within the target value interval comprises one of:
acquiring a second current value from the first current data stream in an adjacent time period of the first current value, wherein the second current value falls in the target value interval; replacing the first current value with the second current value;
obtaining a plurality of third current values from the first current data stream in adjacent time periods of the first current value, wherein the plurality of third current values fall within the target value interval; replacing the first current value with an average of a plurality of the third current values;
obtaining a plurality of fourth current values from the first current data stream in adjacent time periods of the first current value, wherein the plurality of fourth current values fall within the target value interval; carrying out cubic spline interpolation calculation on the fourth current values to obtain fifth current values; replacing the first current value with the fifth current value;
obtaining a plurality of sixth current values from the first current data stream in adjacent time periods of the first current value; and performing linear interpolation calculation on the plurality of sixth current values to obtain seventh current values, and replacing the first current values with the seventh current values.
5. The method for testing the battery performance according to claim 1, wherein the determining the battery performance of the battery of the target model according to the test feedback parameter and the driving feedback parameter comprises:
determining a plurality of feedback characteristics of the battery of the target model according to the test feedback parameters and the driving feedback parameters, wherein the plurality of feedback characteristics are used for dividing the battery performance of the battery of the target model from a plurality of dimensions;
and determining the battery performance of the battery with the target model according to the plurality of feedback characteristics.
6. The method for testing battery performance according to claim 5, wherein the determining the battery performance of the battery of the target model according to the plurality of feedback characteristics comprises:
extracting a target abnormal feedback feature from the plurality of feedback features, wherein the target abnormal feedback feature is a feedback feature with abnormal feature values;
under the condition that the target abnormal feedback feature is not extracted, determining that the battery performance is in a normal state;
and under the condition that the target abnormal feedback feature is extracted, determining that the battery performance is in an abnormal state, and determining a target abnormal state type corresponding to a feature value of the target abnormal feedback feature from an abnormal feature value and an abnormal state type which have a corresponding relation.
7. The battery performance testing method of claim 5, wherein the determining a plurality of feedback characteristics of the target model of battery based on the test feedback parameters and the driving feedback parameters comprises at least one of:
calculating feedback parameter deviation between feedback parameters included in a feedback parameter set, wherein the feedback parameter set includes the test feedback parameters and the driving feedback parameters, the test feedback parameters include test temperature and/or test voltage, and the driving feedback parameters include driving temperature and/or driving voltage; determining the feedback parameter deviation as the feedback characteristic;
calculating feedback parameter outliers among feedback parameters included in a feedback parameter set, wherein the feedback parameter set includes the test feedback parameters and the driving feedback parameters, the test feedback parameters include test temperature and/or test voltage, and the driving feedback parameters include driving temperature and/or driving voltage; determining the feedback parameter outliers as the feedback features;
calculating feedback parameter deviation outliers among feedback parameters included in a feedback parameter set, wherein the feedback parameter set includes the test feedback parameters and the driving feedback parameters, the test feedback parameters include test temperature and/or test voltage, and the driving feedback parameters include driving temperature and/or driving voltage; determining the feedback parameter deviation outliers as the feedback features;
acquiring a target battery from the test battery and the running battery, wherein the target battery is a battery of which the current change value is greater than or equal to a target threshold value in a target time period, the test feedback parameters comprise a voltage value and a current value of the test battery, and the running feedback parameters comprise a voltage value and a current value of the running battery; calculating an internal resistance deviation value and/or a battery energy deviation value of the target battery according to the voltage value and the current value of the target battery in the target time period; determining the internal resistance deviation value and/or battery energy deviation value as the feedback characteristic.
8. A device for testing battery performance, comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring vehicle running data of a target vehicle provided with a running battery of a target model, and the vehicle running data is used for indicating the running condition of the target vehicle under a target running condition;
the conversion module is used for converting the vehicle running data into excitation parameters, wherein the excitation parameters are used for indicating the energy transmission condition when the running battery drives the target vehicle to run according to the target running condition;
the excitation module is used for exciting the test battery of the target model through the excitation parameters to obtain test feedback parameters of the test battery, wherein the test feedback parameters are used for indicating the battery working condition of the test battery;
and the determining module is used for determining the battery performance of the battery of the target model according to the test feedback parameters and the driving feedback parameters, wherein the driving feedback parameters are used for indicating the battery working condition of the driving battery.
9. A computer-readable storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method for testing battery performance according to any one of claims 1 to 7.
10. An electronic device, characterized in that the electronic device comprises one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method for running a program, wherein the program is arranged to perform the method for testing battery performance of any of claims 1 to 7 when run.
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