CN115877238B - Method and device for detecting battery capacity, readable storage medium and electronic equipment - Google Patents
Method and device for detecting battery capacity, readable storage medium and electronic equipment Download PDFInfo
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- CN115877238B CN115877238B CN202211559408.3A CN202211559408A CN115877238B CN 115877238 B CN115877238 B CN 115877238B CN 202211559408 A CN202211559408 A CN 202211559408A CN 115877238 B CN115877238 B CN 115877238B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
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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]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
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Abstract
The disclosure relates to a method and a device for detecting battery capacity, a readable storage medium and electronic equipment, and relates to the field of power batteries, wherein the method comprises the following steps: firstly, acquiring battery charging process characteristic values corresponding to a plurality of charging records meeting battery capacity detection requirements, secondly, determining battery system capacities corresponding to the plurality of charging records through the battery charging process characteristic values, determining an abnormal capacity threshold value of a battery system according to the battery system capacities corresponding to the plurality of charging records, and finally, identifying the battery system capacity state of a target vehicle according to the abnormal capacity threshold value. According to the implementation mode, the battery system capacity of the power battery can be obtained through a plurality of charging records in the big data, and the abnormal capacity threshold value of the battery system is determined according to the obtained battery system capacity by combining a statistical method, so that the abnormal state of the battery system capacity of the target vehicle can be identified, and the accuracy of the battery system capacity detection can be improved.
Description
Technical Field
The disclosure relates to the technical field of power batteries, and in particular relates to a battery capacity detection method and device, a readable storage medium and electronic equipment.
Background
The battery capacity of the power battery system of the electric automobile is inevitably aged in the use process, and the endurance mileage of the electric automobile is directly affected. The optimal maintenance and replacement time of the power battery can be determined according to the abnormal state of the power battery, so that the service life of the power battery is effectively prolonged, and the endurance mileage of the electric automobile is increased, and therefore, the detection of the state of the capacity of the power battery system is necessary in the use process.
In the prior art, a detection method of replacing attenuation of rated capacity with charge capacity of partial discharge depth is generally adopted, the capacity error of the obtained battery system is large, and further whether the capacity of the battery system is abnormal cannot be accurately detected.
Disclosure of Invention
An object of the present disclosure is to provide a method and apparatus for detecting battery capacity, a readable storage medium, and an electronic device, so as to solve the above-mentioned related technical problems.
In order to achieve the above object, a first aspect of embodiments of the present disclosure provides a method for detecting a battery capacity, including:
acquiring battery charging process characteristic values corresponding to a plurality of charging records meeting battery capacity detection requirements;
determining the battery system capacity corresponding to the plurality of charging records through the battery charging process characteristic value;
Determining an abnormal capacity threshold of the battery system according to the battery system capacities corresponding to the plurality of charging records;
and identifying the battery system capacity state of the target vehicle according to the abnormal capacity threshold.
Optionally, the obtaining battery charging process feature values corresponding to the plurality of charging records that meet the battery capacity detection requirement includes:
acquiring charging records of battery systems of a plurality of vehicles in a charging process;
forming a first data matrix based on charge records of battery systems of the plurality of vehicles in a charging process;
screening the first data matrix to form a second data matrix, wherein the second data matrix comprises a plurality of screened charging records;
and acquiring the characteristic value of the battery charging process through the second data matrix.
Optionally, the acquiring the battery charging process feature value through the second data matrix includes:
acquiring the charging quantity of the battery in the charging process corresponding to each charging record in the second data matrix;
acquiring a charging terminal voltage curve in each charging record in the second data matrix, and normalizing the charging terminal voltage curve to form a relation curve of voltage and the suppliable capacity;
Obtaining the minimum suppliable capacity and the maximum suppliable capacity corresponding to the maximum voltage and the minimum voltage respectively when the battery charging corresponding to each charging record in the second data matrix is finished according to the relation curve;
and acquiring a corrected charge state corresponding to the minimum voltage at the beginning of charging of the battery corresponding to each charging record in the second data matrix.
Optionally, the determining the battery system capacity corresponding to the plurality of charging records according to the battery charging process characteristic value includes:
for each charging record of the plurality of charging records, determining a system discharge energy corresponding to the minimum voltage condition from the charge amount, the minimum replenishable capacity, and the corrected state of charge;
and determining the battery system capacity corresponding to the charging record according to the system discharging energy, the charging capacity and the maximum replenishable capacity.
Optionally, the determining the abnormal capacity threshold of the battery system according to the battery system capacities corresponding to the charging records includes:
acquiring a scatter diagram of mileage and battery system capacity according to the battery system capacity corresponding to the plurality of charging records;
And processing the battery system capacities of the plurality of vehicles corresponding to the plurality of charging records in a window sliding mode according to a preset distance step length based on the scatter diagram to obtain an abnormal capacity threshold corresponding to window sliding.
Optionally, based on the scatter diagram, processing the battery system capacities of the plurality of vehicles corresponding to the plurality of charging records in a window sliding manner according to a preset distance step length to obtain an abnormal capacity threshold corresponding to window sliding, including:
based on the scatter diagram, window sliding is carried out on the battery system capacity of the plurality of vehicles corresponding to the plurality of charging records by taking a first distance as a window sliding step length so as to obtain the median of the battery system capacity corresponding to each window sliding;
filtering abnormal data in the battery system capacity of each vehicle in the plurality of vehicles based on the median of the battery system capacities to obtain the battery system capacity of each vehicle after filtering;
summarizing the battery system capacity of each vehicle after filtering to obtain the battery system capacity of the vehicles after filtering;
and window sliding is carried out on the filtered battery system capacities of the vehicles by taking the second distance as a window sliding step length, and a corresponding battery system capacity upper limit threshold value and a corresponding battery system capacity lower limit threshold value are determined on the battery system capacity corresponding to each window sliding by using a four-bit distance method. Optionally, the identifying the battery system capacity state of the target vehicle according to the abnormal capacity threshold includes:
Acquiring a battery charging process characteristic value of the target vehicle;
determining the battery system capacity of the target vehicle according to the battery charging process characteristic value of the target vehicle;
and identifying a battery system capacity state of the target vehicle according to the battery system capacity of the target vehicle and the abnormal capacity threshold, wherein the battery system capacity state comprises abnormal battery system capacity or normal battery system capacity.
Optionally, the forming a first data matrix based on charging records of battery systems of the plurality of vehicles in a charging process includes:
cleaning the battery data;
acquiring charging records of battery systems of the vehicles in a charging process;
and screening charging records meeting preset conditions from the charging records of the battery systems of the vehicles in the charging process to form the first data matrix.
According to a second aspect of the embodiments of the present disclosure, there is provided a battery capacity detection apparatus including:
the acquisition module is used for acquiring battery charging process characteristic values corresponding to a plurality of charging records meeting the battery capacity detection requirement;
the determining module is used for determining the battery system capacity corresponding to the plurality of charging records through the battery charging process characteristic value;
The determining module is further configured to determine an abnormal capacity threshold of the battery system according to the battery system capacities corresponding to the plurality of charging records;
and the identification module is used for identifying the battery system capacity state of the target vehicle according to the abnormal capacity threshold value.
According to a third aspect of embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects described above.
According to a fourth aspect of embodiments of the present disclosure, there is provided an electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of any of the above first aspects.
In the above technical solution, firstly, battery charging process feature values corresponding to a plurality of charging records meeting the battery capacity detection requirement are obtained, secondly, battery system capacities corresponding to the plurality of charging records are determined according to the battery charging process feature values, an abnormal capacity threshold of the battery system is determined according to the battery system capacities corresponding to the plurality of charging records, and finally, the battery system capacity state of the target vehicle is identified according to the abnormal capacity threshold. According to the implementation mode, the battery system capacity of the power battery can be obtained through a plurality of charging records in the big data, and the abnormal capacity threshold value of the battery system is determined according to the obtained battery system capacity by combining a statistical method, so that the abnormal state of the battery system capacity of the target vehicle can be identified, and the accuracy of the battery system capacity detection can be improved.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
fig. 1 is a flowchart illustrating a method of detecting battery capacity according to an exemplary embodiment;
fig. 2 is a flowchart illustrating a method of detecting battery capacity according to an exemplary embodiment;
fig. 3 is a flowchart illustrating a method of detecting battery capacity according to an exemplary embodiment;
fig. 4 is a flowchart illustrating a method of detecting battery capacity according to an exemplary embodiment;
fig. 5 is a flowchart illustrating a method of detecting battery capacity according to an exemplary embodiment;
fig. 6 is a flowchart illustrating a method of detecting battery capacity according to an exemplary embodiment;
fig. 7 is a flowchart illustrating a method of detecting battery capacity according to an exemplary embodiment;
FIG. 8 is a scatter plot illustrating an exemplary embodiment;
fig. 9 is a flowchart illustrating a method of detecting battery capacity according to an exemplary embodiment;
Fig. 10 is a block diagram of a battery capacity detection apparatus according to an exemplary embodiment;
FIG. 11 is a block diagram of an electronic device, shown in accordance with an exemplary embodiment;
fig. 12 is a block diagram of an electronic device, according to an example embodiment.
Detailed Description
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
Fig. 1 is a flowchart illustrating a method of detecting battery capacity according to an exemplary embodiment, including the following steps, as shown in fig. 1.
In step S11, battery charging process characteristic values corresponding to a plurality of charging records satisfying the battery capacity detection requirement are acquired.
It can be understood that the battery capacity of the power battery used in the new energy automobile is continuously attenuated in the use process, so that the detection of the battery capacity is necessary. The plurality of charging records satisfying the battery capacity detection requirement may include a plurality of charging records obtained by counting a plurality of charging processes of the vehicle, for example, a data record including each charging process of each vehicle, and the charging process characteristic value may include data such as a pre-charging rest time, voltages at a charging start and a charging end, SOC (State of Charge) at a charging start and a charging end, and the like.
In step S12, the battery system capacity corresponding to the plurality of charging records is determined by the battery charging process characteristic value.
Wherein each of the plurality of charging records has a corresponding battery system capacity.
In step S13, an abnormal capacity threshold of the battery system is determined from the battery system capacities corresponding to the plurality of charge records.
Wherein the abnormal capacity threshold of the battery system includes an upper threshold and a lower threshold.
In step S14, the battery system capacity state of the target vehicle is identified based on the abnormal capacity threshold.
It is understood that when the battery system capacity of the target vehicle exceeds the upper limit threshold, there may be a problem in the detection process or a mismatch between the detected vehicle and the battery, etc.; when the battery system capacity of the target vehicle is below the lower threshold, it may be that the battery is severely degraded, requiring repair or replacement of the power battery. Therefore, in the case where the battery system capacity is higher than the upper limit threshold value, or lower than the lower limit threshold value, it is possible to determine that the battery system capacity state of the target vehicle is the battery system capacity abnormality.
Fig. 2 is a flowchart of a method for detecting a battery capacity according to an exemplary embodiment, and as shown in fig. 2, the step S11 of obtaining battery charging process characteristic values corresponding to a plurality of charging records that meet the battery capacity detection requirement may include the following steps:
Step S111, a charging record of battery systems of a plurality of vehicles in a charging process is acquired.
It will be appreciated that different vehicles may have multiple charging processes during use, and thus a record of the charging of the battery systems of multiple vehicles during charging may be obtained from the vehicle information database.
Step S112, forming a first data matrix based on charge records of battery systems of the plurality of vehicles in a charging process.
For example, the charging record of each charging process of each vehicle may include data such as time, voltage, current, temperature, SOC, mileage, and charging status, etc., so the charging record of each charging process of all vehicles may be extracted to form the first data matrix A1, so that the first data matrix A1 may include data such as time, voltage, current, temperature, SOC, mileage, and charging status, etc. of a plurality of charging processes of a plurality of vehicles.
Further, fig. 3 is a flowchart of a method for detecting battery capacity according to an exemplary embodiment, and as shown in fig. 3, the forming of the first data matrix based on the charge records of the battery systems of the plurality of vehicles during the charging process in the step S112 may include the following steps:
Step S1121, the battery data is cleaned.
For example, in the original charging record of each charging process of the power battery of each vehicle, there may be null values or abnormal data exceeding a threshold value, and when the charging record is acquired, the abnormal data needs to be cleaned first, and the data needing to be cleaned may include:
deleting null lines in the data for time, current, temperature and mileage;
for voltage and SOC, deleting the rows exceeding the threshold value in the data, wherein the threshold value of the voltage can be set to 0.5-4.5V, and the threshold value of the SOC can be set to 0-100;
the charge state and the deletion state of the abnormal row of the state zone bit can be exemplified by numbers 1, 2, 3 and 4, wherein 1 represents the charging state, 4 represents the charging completion state, 2 represents the driving state, 3 represents other abnormal states, and when the charge state in the charge record is displayed as 2 or 3, the state zone bit is abnormal, and the row needs to be deleted. The abnormal condition of the status flag bit can be set according to the actual condition, and the disclosure is not limited.
For example, the charge record after data cleansing may be as shown in table 1.
TABLE 1
When the SOC of the two recording points decreases and the time interval 600s, it can be considered that the last charge is ended and the next charge is started. For example, as shown in table 1, where the time interval of the two records is (7200-3600) > 600, the soc is reduced from 90 to 40, it can be considered that the two records are the record of the end of the 1 st charge and the start of the 2 nd charge, respectively.
In step S1122, a charge record of the battery systems of the plurality of vehicles during the charging process is acquired.
It will be appreciated that table 1 above is a record of multiple charging processes for each vehicle, and the records of each vehicle are summarized to obtain the charging records of the battery systems of multiple vehicles during the charging process.
Step S1123, selecting a charging record meeting a preset condition from the charging records of the battery systems of the plurality of vehicles in the charging process, to form the first data matrix.
From these records, the mileage of each vehicle is selected, the rest time before charging, and the charging start V min 、V max 、T min 、T max And SOC, charge end V min 、V max 、T min 、T max And SOC, and charge capacity data, which form a record for each vehicle. Wherein, when the absolute value of the current is smaller than C/20 (C represents the charge-discharge capacity ratio) or 3A, it can be regarded as a stationary state. The charge capacity can be obtained by ampere-hour integration. The record for each of the plurality of vehicles forms a first data matrix A1. The first data matrix A1 may contain characteristic data as shown in table 2.
TABLE 2
Step S113, performing screening processing on the first data matrix to form a second data matrix, where the second data matrix includes a plurality of screened charging records.
Further, after the first data matrix A1 is obtained, the data in A1 is screened, where the screening conditions may include one or more of the following:
the standing time before charging is longer than 30 minutes;
charging initiation V min A plateau voltage less than the SOC-OCV curve, a charge initiation SOC less than 20;
charging end V min And the charging end SOC is larger than 90.
Screening the A1 according to the conditions, and deleting records which do not meet the conditions to obtain a second data matrix A2.
Step S114, obtaining the characteristic value of the battery charging process through the second data matrix.
For example, determining the battery charging process characteristic value required for the capacity of the battery system may include: charge capacity cha Minimum voltage V at the end of charging min And maximum voltage V max Corresponding minimum capacity of filling min And maximum capacity of filling max Charging initiation V min System discharge energy capacity in state res These eigenvalues may be obtained by data in the second data matrix A2.
Further, fig. 4 is a flowchart of a method for detecting a battery capacity according to an exemplary embodiment, as shown in fig. 4, the step S114 of obtaining the characteristic value of the battery charging process through the second data matrix may include the following steps:
in step S1141, the charging amount of the battery charging process corresponding to each charging record in the second data matrix is obtained.
It will be appreciated that the second data matrix A2 is selected from the first data matrix A1, and that the charge capacity of the battery charging process has been obtained by ampere-hour integration in A1 cha Therefore, the battery charge corresponding to each charge record can be directly obtained from the second data matrix A2Charge capacity capability of an electrical process cha 。
Step S1142, a charging terminal voltage curve in each charging record in the second data matrix is obtained, and a relation curve of voltage and the suppliable capacity is formed by normalizing the charging terminal voltage curve.
Illustratively, after the second data matrix A2 is obtained, the pre-charge T in A2 is first screened min And (3) extracting a charging terminal voltage curve in original operation data corresponding to a charging record with the charging end SOC of 100 at the temperature of more than 20 ℃ to form a relation curve of voltage V and the rechargeable electric quantity, wherein the charging terminal is data with the SOC of more than 90. And dividing the mileage into different gears, taking every 1000km as a gear, for example, 0-1000 km, 1000-2000 km, 2000-3000 km and the like, storing the charging terminal voltage curve into the different gears according to the mileage, and forming a charging terminal curve database corresponding to the mileage. And normalizing the charging end curve under each mileage unit, and outputting a relation curve of voltage V and the electric quantity which can be supplemented into each gear.
Step S1143, obtaining the minimum and maximum suppliable capacities corresponding to the maximum and minimum voltages at the end of the battery charging according to the relation curve.
Further, the minimum voltage V at the end of charging in the second data matrix A2 min And maximum voltage V max Substituting into the charge end normalization curve to obtain the minimum voltage V at the end of charging min And maximum voltage V max Corresponding minimum capacity of filling min And maximum capacity of filling max 。
In step S1144, the corrected state of charge corresponding to the minimum voltage at the start of charging the battery corresponding to each charging record in the second data matrix is obtained.
Illustratively, the minimum voltage V at the start of charging in the second data matrix A2 min Substituting into SOC-OCV curve to obtain minimum voltage V at initial charge min The corresponding corrected SOC.
Fig. 5 is a flowchart of a method for detecting a battery capacity according to an exemplary embodiment, as shown in fig. 5, the determining, in step S12, the battery system capacity corresponding to the plurality of charging records according to the battery charging process characteristic value may include the following steps:
step S121, for each charging record of the plurality of charging records, determining a system discharge energy corresponding to the minimum voltage condition by the charging amount, the minimum suppliable capacity and the corrected state of charge.
Illustratively, the charge initiates V min System discharge energy capacity in state res The method can be obtained by the following formula:
in step S122, the battery system capacity corresponding to the charging record is determined according to the system discharging energy, the charging capacity and the maximum suppliable capacity.
Illustratively, battery system capacity = capacity cha +capacity max +capacity res 。
In the above technical solution, the battery system capacity corresponding to the plurality of charging records is determined by the battery charging process characteristic value.
Fig. 6 is a flowchart of a method for detecting a battery capacity according to an exemplary embodiment, and as shown in fig. 6, determining an abnormal capacity threshold of a battery system according to a battery system capacity corresponding to the plurality of charging records in step S13 may include the following steps:
step S131, obtaining a scatter diagram of mileage and battery system capacity according to the battery system capacity corresponding to the plurality of charging records.
By way of example, after the battery system capacity is obtained by the above method, the battery system capacities corresponding to the plurality of charging records are summarized, and a scatter diagram of mileage and the battery system capacity is obtained.
Step S132, based on the scatter diagram, processing the battery system capacities of the plurality of vehicles corresponding to the plurality of charging records in a window sliding mode according to a preset distance step length to obtain an abnormal capacity threshold corresponding to window sliding.
The method includes the steps of firstly processing the capacity of the battery system in a window sliding mode based on the acquired scatter diagram of mileage and the capacity of the battery system, cleaning abnormal fluctuation values in the detection process, and obtaining an abnormal capacity threshold of the battery system by combining a statistical method.
Further, fig. 7 is a flowchart of a method for detecting battery capacity according to an exemplary embodiment, and as shown in fig. 7, based on the scatter diagram, in step S132, the method may further include the steps of:
step S1321, based on the scatter diagram, performing window sliding on the battery system capacities of the plurality of vehicles corresponding to the plurality of charging records with the first distance as a window sliding step length, so as to obtain a median of the battery system capacities corresponding to each window sliding.
It can be understood that the scatter diagram is a summary of battery system capacities corresponding to a plurality of charging records, wherein the scatter diagram includes battery system capacities of each vehicle, and for each vehicle's mileage and battery system capacity scatter diagram, window sliding is performed first with a first distance as a window sliding step length to obtain a median of the battery system capacities corresponding to each window sliding.
For example, when window sliding is performed to obtain the median for the scatter diagram of mileage and battery system capacity of each vehicle, the window length may be 1000km, and the window sliding step may be 500 km.
Step S1322, filtering the abnormal data in the battery system capacity of each of the plurality of vehicles based on the median of the battery system capacities, to obtain the battery system capacity of each of the vehicles after filtering.
For example, after the window slides, a median of the battery capacities corresponding to different mileage of each vehicle is obtained, and based on the median, abnormal data in the battery system capacities of each vehicle is filtered, so as to obtain the battery system capacities corresponding to different mileage of each vehicle after filtering.
Step S1323, summing up the battery system capacities of the respective vehicles after the filtering to obtain the battery system capacities of the plurality of vehicles after the filtering.
It can be understood that the battery system capacities corresponding to the different mileage of each vehicle are summarized, so that the battery system capacities corresponding to the different mileage of the plurality of vehicles after the filtration can be obtained.
And step S1324, window sliding is carried out on the filtered battery system capacities of the plurality of vehicles by taking the second distance as a window sliding step length, and a corresponding battery system capacity upper limit threshold value and a corresponding battery system capacity lower limit threshold value are determined on the battery system capacity corresponding to each window sliding by using a four-bit distance method.
Further, in one embodiment, in the process of calculating the battery system capacity upper limit threshold and the battery system capacity lower limit threshold by performing the above-described window sliding, the battery state of health may be calculated by SOH (StateofHealth,to characterize the battery system capacity, fig. 8 is a scatter diagram illustrating an exemplary embodiment, as shown in fig. 8, in which the horizontal axis represents mileage (in km), the vertical axis represents SOH, and window sliding is performed for battery system capacities corresponding to different filtered mileage of a plurality of vehicles with a second distance as a window sliding step, and the window sliding step may be 5000km, for example.
During window activity, 75 minute thre75 and 25 minute thre25 of each window are first acquired, and calculating the battery system capacity upper limit threshold value and the battery system capacity lower limit threshold value of each window by using thre75 and thre25, wherein the calculation method can be as follows:
upper threshold thre_up' =thre75+c (thre 75-thre 25)
Lower threshold thre_down' =min { thre25-c (thre 75-thre 25), SOH min -0.5}
Wherein the units of the upper threshold value thre_up 'and the lower threshold value thre_down' are SOH, the SOH median can represent the battery capacity median, and the upper threshold value thre_up ', the lower threshold value thre_down', and the SOH median can be converted into the upper threshold value, the lower threshold value and the battery capacity median which are in units of the battery system capacity according to the need through the SOH calculation method, and the SOH is calculated by the method min Battery capacity retention at end of life, which is a quality assurance requirement, is typically 0.8; c is a super parameter, generally 1 to 3, and can be optimized according to the result.
After the thre_up 'and thre_down' of each window are obtained through calculation by the method, window sliding centering is carried out on summarized data of all windows again by taking the second distance as a window sliding step length to obtain a final battery system capacity upper limit threshold thre_up and a battery system capacity lower limit threshold thre_down, and further battery capacity upper limit threshold and battery capacity lower limit threshold corresponding to different mileage are obtained.
Fig. 9 is a flowchart illustrating a method for detecting a battery capacity according to an exemplary embodiment, and as shown in fig. 9, the identifying a battery system capacity state of a target vehicle according to the abnormal capacity threshold in step S14 may include the following steps:
step S141, a battery charging process characteristic value of the target vehicle is acquired.
Step S142, determining the battery system capacity of the target vehicle according to the battery charging process characteristic value of the target vehicle.
Step S143 of identifying a battery system capacity state of the target vehicle, which includes an abnormality in battery system capacity or a normal battery system capacity, based on the battery system capacity of the target vehicle and the abnormal capacity threshold.
For example, a method for determining the battery system capacity of the target vehicle and a method for identifying the battery system capacity state are the same as the method for detecting the battery capacity described above, and will not be described here again.
It is understood that, based on the battery system capacity of the target vehicle and the abnormal capacity threshold, the battery system capacity state of the target vehicle is identified, and when the battery system capacity of the target vehicle exceeds the upper limit threshold, there may be a problem in the detection process or a cause of mismatching between the detected vehicle and the battery, etc.; when the battery system capacity of the target vehicle is below the lower threshold, it may be that the battery is severely degraded, requiring repair or replacement of the power battery. Therefore, in the case where the battery system capacity is higher than the upper limit threshold value, or lower than the lower limit threshold value, it is possible to determine that the battery system capacity state of the target vehicle is the battery system capacity abnormality; if the battery system capacity is between the upper threshold and the lower threshold, it may be determined that the battery system capacity state of the target vehicle is that the battery system capacity is normal.
In the above technical solution, firstly, battery charging process feature values corresponding to a plurality of charging records meeting the battery capacity detection requirement are obtained, secondly, battery system capacities corresponding to the plurality of charging records are determined according to the battery charging process feature values, an abnormal capacity threshold of the battery system is determined according to the battery system capacities corresponding to the plurality of charging records, and finally, the battery system capacity state of the target vehicle is identified according to the abnormal capacity threshold. According to the implementation mode, the battery system capacity of the power battery can be obtained through a plurality of charging records in the big data, and the abnormal capacity threshold value of the battery system is determined according to the obtained battery system capacity by combining a statistical method, so that the abnormal state of the battery system capacity of the target vehicle can be identified, and the accuracy of the battery system capacity detection can be improved.
Fig. 10 is a block diagram illustrating a battery capacity detection apparatus 200 according to an exemplary embodiment, and referring to fig. 10, the apparatus 200 includes an acquisition module 210, a determination module 220, and an identification module 230.
The obtaining module 210 is configured to obtain battery charging process feature values corresponding to a plurality of charging records that meet a battery capacity detection requirement;
the determining module 220 is configured to determine a battery system capacity corresponding to the plurality of charging records according to the battery charging process characteristic value;
the determining module 220 is further configured to determine an abnormal capacity threshold of the battery system according to the battery system capacities corresponding to the plurality of charging records;
the identifying module 230 is configured to identify a battery system capacity state of the target vehicle according to the abnormal capacity threshold.
Optionally, the obtaining module 210 may include:
the first acquisition sub-module is used for acquiring charging records of battery systems of a plurality of vehicles in a charging process;
a generating sub-module for forming a first data matrix based on charging records of battery systems of the plurality of vehicles in a charging process;
the generating sub-module is used for screening the first data matrix to form a second data matrix, and the second data matrix comprises a plurality of screened charging records;
The first obtaining sub-module is used for obtaining the characteristic value of the battery charging process through the second data matrix.
Optionally, the first obtaining submodule is configured to:
acquiring the charging quantity of each charging record in the second data matrix in the corresponding battery charging process;
obtaining the minimum suppliable capacity and the maximum suppliable capacity corresponding to the maximum voltage and the minimum voltage respectively when the battery charging corresponding to each charging record in the second data matrix is finished;
and acquiring a corrected charge state corresponding to the minimum voltage at the beginning of charging of the battery corresponding to each charging record in the second data matrix.
Optionally, the determining module 220 is configured to:
for each charging record in the plurality of charging records, determining a system discharge energy corresponding to a minimum voltage condition by a charge amount, a minimum suppliable capacity and a corrected state of charge;
and determining the battery system capacity corresponding to the charging record through the system discharging energy, the charging capacity and the maximum suppliable capacity.
Optionally, the determining module 220 may include a second acquiring sub-module and a processing sub-module:
the second obtaining submodule is used for obtaining a scatter diagram of mileage and battery system capacity according to the battery system capacity corresponding to the plurality of charging records;
The processing sub-module is used for processing the battery system capacities of the plurality of vehicles corresponding to the plurality of charging records in a window sliding mode according to a preset distance step length based on the scatter diagram to obtain an abnormal capacity threshold corresponding to window sliding.
Optionally, the processing submodule is configured to:
based on the scatter diagram, window sliding is carried out on the battery system capacity of each vehicle in the plurality of vehicles corresponding to the plurality of charging records by taking the first distance as a window sliding step length so as to obtain the median of the battery system capacity corresponding to each window sliding;
filtering abnormal data in the battery system capacity of each vehicle in the plurality of vehicles based on the median of the battery system capacity to obtain the filtered battery system capacity of each vehicle;
summarizing the battery system capacity of each vehicle after filtering to obtain the battery system capacity of a plurality of vehicles after filtering;
and window sliding is carried out on the filtered battery system capacities of the plurality of vehicles by taking the second distance as a window sliding step length, and a corresponding battery system capacity upper limit threshold value and a corresponding battery system capacity lower limit threshold value are determined on the battery system capacity corresponding to each window sliding by using a four-bit distance method.
Optionally, the identification module 230 may include a determination sub-module and an identification sub-module:
the acquisition sub-module is used for acquiring the characteristic value of the battery charging process of the target vehicle;
the determining submodule is used for determining the battery system capacity of the target vehicle according to the battery charging process characteristic value of the target vehicle;
the identifying sub-module is used for identifying the battery system capacity state of the target vehicle according to the battery system capacity of the target vehicle and the abnormal capacity threshold, wherein the battery system capacity state comprises abnormal battery system capacity or normal battery system capacity.
Optionally, the generating submodule is configured to:
cleaning the battery data;
acquiring charging records of battery systems of the plurality of vehicles in a charging process;
and screening charging records meeting preset conditions from the charging records of the battery systems of the vehicles in the charging process to form the first data matrix.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 11 is a block diagram of an electronic device 700, according to an example embodiment. As shown in fig. 11, the electronic device 700 may include: a processor 701, a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the electronic device 700 to perform all or part of the steps in the above-described method for detecting battery capacity. The memory 702 is used to store various types of data to support operation on the electronic device 700, which may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, and so forth. The Memory 702 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 703 can include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 702 or transmitted through the communication component 705. The audio assembly further comprises at least one speaker for outputting audio signals. The input/output interface 704 provides an interface between the processor 701 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding communication component 705 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic device 700 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processor (Digital Signal Processor, abbreviated as DSP), digital signal processing device (Digital Signal Processing Device, abbreviated as DSPD), programmable logic device (Programmable Logic Device, abbreviated as PLD), field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA), controller, microcontroller, microprocessor, or other electronic component for performing the battery capacity detection method described above.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions which, when executed by a processor, implement the steps of the above-described battery capacity detection method. For example, the computer readable storage medium may be the memory 702 including the program instructions described above, which may be executed by the processor 701 of the electronic device 700 to perform the method of detecting battery capacity described above.
Fig. 12 is a block diagram illustrating an electronic device 1900 according to an example embodiment. For example, electronic device 1900 may be provided as a server. Referring to fig. 12, the electronic device 1900 includes a processor 1922, which may be one or more in number, and a memory 1932 for storing computer programs executable by the processor 1922. The computer program stored in memory 1932 may include one or more modules each corresponding to a set of instructions. Further, the processor 1922 may be configured to execute the computer program to perform the battery capacity detection method described above.
In addition, the electronic device 1900 may further include a power component 1926 and a communication component 1950, the power component 1926 may be configured to perform power management of the electronic device 1900, and the communication component 1950 may be configured to enable communication of the electronic device 1900, e.g., wired or wireless communication. In addition, the electronic device 1900 may also include an input/output interface 1958. Electronic device 1900 may operate based on an operating system stored in memory 1932.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions which, when executed by a processor, implement the steps of the above-described battery capacity detection method. For example, the non-transitory computer readable storage medium may be the memory 1932 including program instructions described above that are executable by the processor 1922 of the electronic device 1900 to perform the battery capacity detection method described above.
In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned method of detecting battery capacity when executed by the programmable apparatus.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.
Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.
Claims (8)
1. A method for detecting battery capacity, the method comprising:
acquiring battery charging process characteristic values corresponding to a plurality of charging records meeting battery capacity detection requirements;
determining the battery system capacity corresponding to the plurality of charging records through the battery charging process characteristic value;
acquiring a scatter diagram of mileage and battery system capacity according to the battery system capacity corresponding to the plurality of charging records;
Based on the scatter diagram, processing the battery system capacities of the plurality of vehicles corresponding to the plurality of charging records in a window sliding mode according to a preset distance step length to obtain an abnormal capacity threshold corresponding to window sliding; based on the scatter diagram, processing the battery system capacities of the plurality of vehicles corresponding to the plurality of charging records in a window sliding manner according to a preset distance step length to obtain an abnormal capacity threshold corresponding to window sliding, including: based on the scatter diagram, window sliding is carried out on the battery system capacity of the plurality of vehicles corresponding to the plurality of charging records by taking a first distance as a window sliding step length so as to obtain the median of the battery system capacity corresponding to each window sliding; filtering abnormal data in the battery system capacity of each vehicle in the plurality of vehicles based on the median of the battery system capacities to obtain the battery system capacity of each vehicle after filtering; summarizing the battery system capacity of each vehicle after filtering to obtain the battery system capacity of the vehicles after filtering; window sliding is carried out on the filtered battery system capacities of the vehicles by taking the second distance as a window sliding step length, and a corresponding battery system capacity upper limit threshold value and a corresponding battery system capacity lower limit threshold value are determined on the battery system capacity corresponding to each window sliding by using a four-bit distance method;
And identifying the battery system capacity state of the target vehicle according to the abnormal capacity threshold.
2. The method of claim 1, wherein the obtaining battery charging process characteristic values corresponding to a plurality of charging records that satisfy a battery capacity detection requirement comprises:
acquiring charging records of battery systems of a plurality of vehicles in a charging process;
forming a first data matrix based on charge records of battery systems of the plurality of vehicles in a charging process;
screening the first data matrix to form a second data matrix, wherein the second data matrix comprises a plurality of screened charging records;
and acquiring the characteristic value of the battery charging process through the second data matrix.
3. The method of claim 2, wherein the obtaining the battery charging process characteristic value through the second data matrix comprises:
acquiring the charging quantity of the battery in the charging process corresponding to each charging record in the second data matrix;
acquiring a charging terminal voltage curve in each charging record in the second data matrix, and normalizing the charging terminal voltage curve to form a relation curve of voltage and the suppliable capacity;
Obtaining the minimum suppliable capacity and the maximum suppliable capacity corresponding to the maximum voltage and the minimum voltage respectively when the battery charging corresponding to each charging record in the second data matrix is finished according to the relation curve;
and acquiring a corrected charge state corresponding to the minimum voltage at the beginning of charging of the battery corresponding to each charging record in the second data matrix.
4. The method of claim 3, wherein determining battery system capacity corresponding to the plurality of charging records based on the battery charging process characteristic value comprises:
for each charging record of the plurality of charging records, determining a system discharge energy corresponding to the minimum voltage condition from the charge amount, the minimum replenishable capacity, and the corrected state of charge;
and determining the battery system capacity corresponding to the charging record through the system discharging energy, the charging electric quantity and the maximum replenishable capacity.
5. The method of claim 1, wherein the identifying the battery system capacity status of the target vehicle based on the abnormal capacity threshold comprises:
acquiring a battery charging process characteristic value of the target vehicle;
Determining the battery system capacity of the target vehicle according to the battery charging process characteristic value of the target vehicle;
and identifying a battery system capacity state of the target vehicle according to the battery system capacity of the target vehicle and the abnormal capacity threshold, wherein the battery system capacity state comprises abnormal battery system capacity or normal battery system capacity.
6. A battery capacity detection apparatus, the apparatus comprising:
the acquisition module is used for acquiring battery charging process characteristic values corresponding to a plurality of charging records meeting the battery capacity detection requirement;
the determining module is used for determining the battery system capacity corresponding to the plurality of charging records through the battery charging process characteristic value;
the determining module is further used for obtaining a scatter diagram of mileage and battery system capacity according to the battery system capacity corresponding to the plurality of charging records; based on the scatter diagram, processing the battery system capacities of the plurality of vehicles corresponding to the plurality of charging records in a window sliding mode according to a preset distance step length to obtain an abnormal capacity threshold corresponding to window sliding; based on the scatter diagram, processing the battery system capacities of the plurality of vehicles corresponding to the plurality of charging records in a window sliding manner according to a preset distance step length to obtain an abnormal capacity threshold corresponding to window sliding, including: based on the scatter diagram, window sliding is carried out on the battery system capacity of the plurality of vehicles corresponding to the plurality of charging records by taking a first distance as a window sliding step length so as to obtain the median of the battery system capacity corresponding to each window sliding; filtering abnormal data in the battery system capacity of each vehicle in the plurality of vehicles based on the median of the battery system capacities to obtain the battery system capacity of each vehicle after filtering; summarizing the battery system capacity of each vehicle after filtering to obtain the battery system capacity of the vehicles after filtering; window sliding is carried out on the filtered battery system capacities of the vehicles by taking the second distance as a window sliding step length, and a corresponding battery system capacity upper limit threshold value and a corresponding battery system capacity lower limit threshold value are determined on the battery system capacity corresponding to each window sliding by using a four-bit distance method;
And the identification module is used for identifying the battery system capacity state of the target vehicle according to the abnormal capacity threshold value.
7. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the method according to any of claims 1-5.
8. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of any one of claims 1-5.
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| CN202211559408.3A CN115877238B (en) | 2022-12-06 | 2022-12-06 | Method and device for detecting battery capacity, readable storage medium and electronic equipment |
| PCT/CN2022/137645 WO2024119449A1 (en) | 2022-12-06 | 2022-12-08 | Battery capacity inspection method and apparatus, and readable storage medium and electronic device |
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| CN202211559408.3A CN115877238B (en) | 2022-12-06 | 2022-12-06 | Method and device for detecting battery capacity, readable storage medium and electronic equipment |
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| CN116413609B (en) * | 2023-06-08 | 2023-08-29 | 江苏正力新能电池技术有限公司 | Battery diving identification method and device, electronic equipment and storage medium |
| CN116859263A (en) * | 2023-06-28 | 2023-10-10 | 中国第一汽车股份有限公司 | Method, device and vehicle for estimating health status of power battery |
| CN117647748B (en) * | 2024-01-30 | 2024-05-28 | 宁德时代新能源科技股份有限公司 | Abnormal cell detection method, device, equipment and storage medium |
| CN118151019B (en) * | 2024-05-08 | 2024-09-10 | 北汽福田汽车股份有限公司 | Power battery abnormality recognition method and device, storage medium and vehicle |
| CN119291549B (en) * | 2024-12-10 | 2025-03-14 | 北汽福田汽车股份有限公司 | Vehicle battery health status assessment method, device, medium and equipment |
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| WO2024119449A1 (en) | 2024-06-13 |
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