CN116859272A - Battery capacity variation detection method, device, computer equipment and storage medium - Google Patents
Battery capacity variation detection method, device, computer equipment and storage medium Download PDFInfo
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- 230000014759 maintenance of location Effects 0.000 claims abstract description 145
- 238000000034 method Methods 0.000 claims abstract description 27
- 230000015654 memory Effects 0.000 claims description 28
- 230000035772 mutation Effects 0.000 claims description 22
- 238000012423 maintenance Methods 0.000 claims description 12
- 238000013450 outlier detection Methods 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 description 11
- 230000006870 function Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 238000004146 energy storage Methods 0.000 description 4
- 238000007781 pre-processing Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000009956 embroidering Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
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- 238000012502 risk assessment Methods 0.000 description 1
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Classifications
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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
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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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The invention relates to the technical field of battery safety, and discloses a battery capacity variation detection method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: extracting the capacity retention rate of the full life cycle of the battery to be detected; constructing a sliding time window of capacity retention rate; and detecting the battery capacity variation based on the relation between the median of the capacity retention rate in the sliding time window and a preset threshold value. According to the invention, the detection data cover the full life cycle of the battery, and meanwhile, the battery capacity variation in the full life cycle of the battery is detected by adopting the relation between the median of the capacity retention rate in the sliding time window and the preset threshold value, so that the problem that the current vehicle-mounted power battery of the electric automobile is easily influenced by unbalanced data with larger deflection to generate deviation when being evaluated is solved.
Description
Technical Field
The invention relates to the technical field of battery safety, in particular to a method and a device for detecting battery capacity variation, computer equipment and a storage medium.
Background
The existing electric vehicle-mounted power battery maintenance and replacement service sites in the automobile market are not perfect in construction, and related information collection, transmission and sharing mechanisms are also immature, so that when an insurance company is developing related business of electric vehicle insurance, the insurance company cannot obtain records of whether the vehicle-mounted power battery is maintained or replaced or not at high efficiency and low cost, and the problem that a series of insurance business risk assessment work is difficult to develop is caused.
At present, the vehicle-mounted power battery is evaluated based on power battery voltage data and temperature data, a sliding time window is utilized to take the average value of sampling data and the standard deviation of a variation value thereof as a state index, and the risk evaluation is performed on the power battery of the vehicle. However, since the average value does not have a property of resisting disturbance of an abnormal value, the average value is easily affected by unbalanced data having a large degree of deviation, and thus a deviation is generated.
Disclosure of Invention
In view of the above, the present invention provides a method, apparatus, computer device and storage medium for detecting battery capacity variation, so as to solve the problem that the current vehicle-mounted power battery of an electric vehicle is easily affected by unbalanced data with larger deflection to generate deviation when being evaluated.
In a first aspect, the present invention provides a method for detecting a battery capacity variation, the method comprising:
extracting the capacity retention rate of the full life cycle of the battery to be detected;
constructing a sliding time window of capacity retention rate;
and detecting the battery capacity variation based on the relation between the median of the capacity retention rate in the sliding time window and a preset threshold value.
According to the battery capacity variation detection method provided by the embodiment of the invention, the capacity retention rate of the whole life cycle of the battery to be detected is extracted, the sliding time window of the capacity retention rate is constructed, the battery capacity variation detection is carried out according to the relation between the median of the capacity retention rate in the sliding time window and the preset threshold value, the detection data cover the whole life cycle of the battery, and meanwhile, the battery capacity variation of the whole life cycle of the battery is detected according to the relation between the median of the capacity retention rate in the sliding time window and the preset threshold value, so that the problem that the current vehicle-mounted power battery of the electric automobile is easily influenced by unbalanced data with larger deflection to generate deviation when being evaluated is solved.
In an alternative embodiment, before constructing the sliding time window of capacity retention, the method further comprises:
extracting time stamp data of the full life cycle of the battery to be detected;
sorting the capacity retention based on the time stamp data;
and carrying out pretreatment of outlier detection, outlier detection and repeated value detection at the same time on the capacity retention rate after the sorting treatment.
According to the battery capacity variation detection method provided by the embodiment of the invention, the capacity retention rate is sequenced based on the time stamp data of the whole life cycle of the battery, the detection pretreatment of eliminating abnormal values and repeated values is performed on the sequenced capacity retention rate, the acquisition efficiency of the time stamp data of the whole life cycle of the battery to be detected is high, the cost is saved, and the data of the capacity retention rate is more complete and accurate.
In an alternative embodiment, constructing a sliding time window of capacity retention includes:
setting a mesh length based on a total amount of data of the capacity retention rate; the total data amount of the capacity retention rate is larger than the size of the grid length;
and setting a sliding time window according to the length of the grid according to the time stamp data.
According to the battery capacity mutation detection method provided by the embodiment of the invention, the grid length is set to select the capacity retention rate measured value data in the time range conforming to the grid length, and the time stamp data is used for setting the sliding time window according to the grid length, so that a detection window is provided for the subsequent battery capacity mutation detection.
In an alternative embodiment, the detecting of the battery capacity variation based on the relationship between the median of the capacity retention rate in the sliding time window and the preset threshold includes:
judging whether the total data amount of the capacity retention rate after pretreatment is larger than the mesh length;
when the total data amount of the capacity retention rate is larger than the grid length, performing battery capacity variation detection based on the relation between the median of the capacity retention rate in the sliding time window and a preset threshold value;
and when the total data amount of the capacity retention rate is smaller than the size of the grid length, discarding the extracted full life cycle capacity retention rate of the battery to be detected.
According to the battery capacity variation detection method provided by the embodiment of the invention, the capacity retention rate of the battery to be detected which does not meet the detection condition is abandoned by comparing the total data amount of the capacity retention rate with the grid length, the battery to be detected which meets the detection condition is subjected to battery capacity variation detection by adopting the relationship between the median of the capacity retention rate in the sliding time window and the preset threshold, dynamic detection is realized, the detection efficiency is improved, and the detection cost is saved.
In an alternative embodiment, the detecting of the battery capacity variation based on the relationship between the median of the capacity retention rate in the sliding time window and the preset threshold value includes:
Setting a middle position time point of each sliding time window;
extracting an intermediate value from the middle position time point to the last time point of the sliding time window as a first median, and extracting an intermediate value from the starting point of the sliding time window to the middle position time point as a second median;
adopting a difference value between the first median and the second median as a first median difference value;
and detecting the battery capacity variation according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value.
According to the battery capacity variation detection method provided by the embodiment of the invention, the first median and the second median in the sliding time window are extracted, so that the first median difference value is calculated, and the battery capacity variation is detected through the relation between the first median difference value and the first preset threshold value and the second preset threshold value, so that the accurate detection of the battery capacity variation can be realized, and the detection efficiency is high.
In an alternative embodiment, detecting the battery capacity variation according to a relationship between the first median difference and the first preset threshold and the second preset threshold of the capacity retention rate includes:
when the first median difference value is larger than a first preset threshold value, judging that the to-be-detected battery has a maintenance or replacement trace at the time point of the central position of the sliding time window;
When the first median difference value is smaller than a first preset threshold value, judging whether the first median difference value is smaller than a second preset threshold value or not;
when the first median difference value is smaller than a second preset threshold value, judging that the battery to be detected has faults or battery capacity variation at the time point of the central position of the sliding time window;
and when the first median difference value is larger than a second preset threshold value, sliding backwards on the sliding time window for a preset time step according to the timestamp data, and continuously detecting the battery capacity variation according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value.
According to the battery capacity variation detection method provided by the embodiment of the invention, when the first median difference value is larger than the first preset threshold value, the condition that the to-be-detected battery has maintenance or replacement marks at the central position time point of the sliding time window is judged, when the first median difference value is smaller than the second preset threshold value, the condition that the to-be-detected battery has faults or has battery capacity variation at the central position time point of the sliding time window is judged, when the first median difference value is larger than the second preset threshold value, the to-be-detected battery capacity variation is continuously detected according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value, the presence of the embroidering or replacement marks of the to-be-detected battery or the presence of faults or capacity variation is detected, and the purposes of high-efficiency and low-cost identification of the maintenance or replacement condition of the to-be-detected battery are achieved.
In an alternative embodiment, the method further comprises: and detecting a time point of occurrence of the battery to be detected, in which the battery capacity is changed, based on all the first median differences determined by adopting the sliding time window.
In an alternative embodiment, detecting the occurrence time point of the battery to be detected in which the battery capacity is changed based on all the first median differences determined using the sliding time window includes:
generating a data set list consisting of a first median difference value of the sliding time window;
acquiring a first second median difference value larger than a first preset threshold value and a third median difference value smaller than the first preset threshold value after the second median difference value in the data set list;
selecting a maximum median difference value between the second median difference value and the third median difference value, and taking a time point of the maximum median difference value as a time point of occurrence of the mutation;
and recording the number of times of battery capacity mutation and the occurrence time point of mutation, and sliding backwards on the data set list of the sliding time window for a preset time step according to the timestamp data until the last value of the data set list is reached.
According to the battery capacity mutation detection method provided by the embodiment of the invention, mutation occurrence time points of the battery to be detected, which are subjected to battery capacity mutation occurrence, are detected by adopting all the first median difference values determined by the sliding time window, so that the time points of the occurrence of the battery capacity mutation can be accurately detected, and the characteristics of high efficiency, low cost, battery maintenance trace and battery capacity mutation time point identification are realized.
In a second aspect, the present invention provides a battery capacity variation detecting apparatus, comprising:
the extraction module is used for extracting the capacity retention rate of the full life cycle of the battery to be detected;
a building module for building a sliding time window of capacity retention;
and the detection module is used for detecting the battery capacity variation based on the relationship between the median of the capacity retention rate in the sliding time window and a preset threshold value.
In a third aspect, the present invention provides a computer device comprising: the battery capacity variation detection method according to the first aspect or any one of the embodiments thereof is implemented by the processor and the memory, the memory and the processor are in communication connection with each other, and the memory stores computer instructions, and the processor executes the computer instructions.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the battery capacity variation detection method of the first aspect or any one of the embodiments corresponding thereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a battery capacity variation detection method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another battery capacity variation detection method according to an embodiment of the present invention;
FIG. 3 is a flow chart of a further battery capacity variation detection method according to an embodiment of the present invention;
FIG. 4 is a flow chart of a method for detecting battery capacity variation according to an embodiment of the present invention;
FIG. 5 is a diagram showing a sliding time window median difference detection data, according to an embodiment of the present invention;
fig. 6 is a diagram showing a result of detecting a variation in capacity of a battery to be detected according to an embodiment of the present invention;
fig. 7 is a block diagram of a battery capacity variation detecting device according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As described in the background art, the related art uses the average value of the sampled data in the sliding time window as the state index, but the average value does not have the property of resisting the abnormal value interference, and is easily influenced by unbalanced data with a large degree of deviation to generate deviation.
In view of the above, an embodiment of the present invention provides a battery capacity variation detection method, in which a sliding time window is used to calculate a median as an evaluation index, where the median is a representative value of all capacity retention rates determined by positions where the median is located among all capacity retention rates, and is not affected by a maximum or minimum value in a capacity retention rate distribution array, so that the representative of the median to the capacity retention rate distribution array is improved to a certain extent, and deviation is not generated due to the influence of unbalanced data with a large degree of bias. Therefore, when a certain error exists between the sampled data and the true value and discrete fluctuation exists in the data, the influence of abnormal data on the detection result can be more effectively avoided by adopting the median.
According to an embodiment of the present invention, there is provided a battery capacity variation detection method embodiment, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that shown or described herein.
In this embodiment, a method for detecting battery capacity variation is provided, which may be used in an electric vehicle, and fig. 1 is a flowchart of a method for detecting battery capacity variation according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:
step S101, extracting the capacity retention rate of the full life cycle of the battery to be detected. Specifically, frame number information of a detected vehicle is obtained, and a full life cycle capacity retention rate value of the vehicle battery is extracted from new energy vehicle networking operation data, wherein the capacity retention rate refers to the ratio of the current full charge capacity of the battery to be detected to the time when the battery just leaves the factory. And calculating the relative full charge capacity according to the charge SOC and charge capacity data of the vehicle during each charge in the operation data of the Internet of vehicles, and dividing the calculated relative full charge capacity by the full charge capacity of the vehicle just leaving the factory to obtain the capacity retention rate of the current vehicle battery. The capacity retention rate of the vehicle at the current charging time can be obtained corresponding to each charging of the vehicle. The full life cycle refers to a period from the start of the vehicle on-line to the retirement, and the acquisition of the full life cycle data of the vehicle battery refers to all operation record data in the period from the start of the vehicle battery on-line to the retirement, i.e. the detection range can cover all moments of the vehicle battery during the retirement. The full life cycle capacity retention forms a distributed array.
Step S102, constructing a sliding time window of capacity retention rate. Specifically, the sliding time window refers to setting a detection window on the total amount of capacity retention rate in order to detect the battery capacity variation, the detection window being set according to a preset mesh length in frames or rows. The sliding time window is a dynamic time window. For example: in order to construct the sliding time window of the capacity retention rate, the mesh length size w of the sliding time window may be preset according to the total capacity retention rate of the full life cycle of the vehicle.
Step S103, detecting the battery capacity variation based on the relation between the median of the capacity retention rate in the sliding time window and a preset threshold. Specifically, the median of the capacity retention rate in the sliding time window refers to the median of the first half data and the median of the second half data of the capacity retention rate in the sliding time window. The preset threshold needs to be set in advance. The median is obtained by sorting, and is not affected by the maximum and minimum two extreme values. The variation of a portion of the data has no effect on the number of bits, and is often used to describe the central tendency of a set of data when individual data in the set varies significantly. For example, when detecting battery capacity variation using a relationship between the median and a preset threshold, if the battery capacity variation causes a large variation in the battery capacity retention rate, the median tends to show a large variation in the battery capacity retention rate.
According to the battery capacity variation detection method provided by the embodiment of the invention, the full life cycle capacity retention rate of the battery to be detected is extracted, the sliding time window of the capacity retention rate is constructed, the detection data covers the full life cycle of the battery, and meanwhile, the battery capacity variation of the full life cycle of the battery is detected by adopting the relation between the median of the capacity retention rate in the sliding time window and the preset threshold value, so that the problem that the current vehicle-mounted power battery of the electric automobile is easily influenced by unbalanced data with larger deflection to generate deviation when being evaluated is solved.
In this embodiment, a method for detecting battery capacity variation is provided, which may be used in an electric vehicle, and fig. 2 is a flowchart of a method for detecting battery capacity variation according to an embodiment of the present invention, as shown in fig. 2, where the flowchart includes the following steps:
step S201, extracting the full life cycle capacity retention rate of the battery to be detected. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S202, extracting time stamp data of the full life cycle of the battery to be detected; specifically, the time stamp data refers to a point in time corresponding to each capacity retention rate extracted. The function of the time stamp is to sort the calculated capacity retention rate values in time sequence so as to perform subsequent battery capacity change detection. The time stamp data of the full life cycle refers to the time points corresponding to all capacity retention rates in the period from the start of the vehicle battery on-line until the retirement.
Step S203, sorting the capacity retention rate based on the time stamp data; and carrying out pretreatment of outlier detection, outlier detection and repeated value detection at the same time on the capacity retention rate after the sorting treatment. Specifically, the acquired capacity retention rates are ordered according to the sequence of the timestamp data, outlier detection of the capacity retention rates is carried out after a capacity retention rate array is obtained, abnormal data far away from the abnormal data with large difference from the normal capacity retention rates are found, then abnormal value detection and repeated value detection at the same time are carried out, and abnormal values and repeated values are removed.
Step S204, constructing a sliding time window of capacity retention rate.
Specifically, the step S204 includes:
step S2041 of setting a mesh length based on the total amount of data of the capacity retention rate; the total amount of data for the capacity retention is greater than the size of the mesh length. Specifically, in order to construct the capacity mutation detection window, the size w of the mesh length of the sliding time window is set according to the data volume of the full life cycle of the vehicle. The size w of the grid length is smaller than the size of the total amount of data of the capacity retention rate, so that the sliding time window is a dynamic time window over the total amount of capacity retention rate.
Step S2042, a sliding time window is set according to the mesh length from the time stamp data. Specifically, setting the mesh length on the capacity retention rate in the time sequence of the acquired time stamp data forms a sliding time window, and the first sliding time window is to be set on the capacity retention rate of the first time stamp data.
Step S205, battery capacity variation detection is performed based on the relationship between the median of the capacity retention rate in the sliding time window and the preset threshold.
Specifically, the step S205 includes:
step S2051, judging whether the total data amount of the capacity retention rate after pretreatment is larger than the size of the grid length; specifically, the total amount of capacity retention after preprocessing becomes smaller due to the number of outliers and repetition values being removed, and therefore, it is necessary to compare again whether the total amount of data of the capacity retention after preprocessing is larger than the size of the mesh length.
Step S2052, when the total data amount of the capacity retention rate is larger than the grid length, performing battery capacity variation detection based on the relationship between the median of the capacity retention rate in the sliding time window and a preset threshold;
in some optional embodiments, step S2052 described above includes:
step a1, setting a middle position time point of each sliding time window; specifically, the sliding time windows are equal to the size w of the grid length, and the time point of the timestamp corresponding to the w/2 th numerical value in each sliding time window is taken as the middle position time node.
Step a2, extracting an intermediate value from the intermediate position time point to the last time point of the sliding time window as a first intermediate number, and extracting an intermediate value from the starting point of the sliding time window to the intermediate position time point as a second intermediate number; specifically, as shown in fig. 4, the size w of the sliding time window equal to the size w of the grid length is divided into 0-w/2 data segments and w/2-w, the first median extracted is an intermediate value between w/2-w data segments, and the second median extracted is an intermediate value between 0-w/2 data segments.
Step a3, adopting a difference value between the first median and the second median as a first median difference value; specifically, an intermediate value between w/2 to w data segments is used as a first intermediate value and an intermediate value between 0 to w/2 data segments is used as a second intermediate value, and the difference between the first intermediate value and the second intermediate value is subtracted from the first intermediate value to obtain a first intermediate value difference Ki.
And a4, detecting the battery capacity variation according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value.
In some alternative embodiments, step a4 above includes:
step a41, when the first median difference value is larger than a first preset threshold value, judging that the battery to be detected has a maintenance or replacement trace at the time point of the central position of the sliding time window; specifically, as shown in fig. 4, the first preset threshold is set as an upper limit threshold μ1 of capacity retention rate variation, and when the first median difference Ki is greater than the first preset threshold μ1, it is determined that the battery to be detected has a repair or replacement trace at the time point of the sliding time window center position. The first preset threshold value mu 1 is an upper limit threshold value of a preset capacity retention rate, when the first median difference value Ki is larger than the first preset threshold value mu 1, the current capacity retention rate of the battery is larger than the upper limit threshold value of the preset capacity retention rate, the capacity retention rate reaches a new height, and the energy storage function of the battery is the energy storage capacity of the battery just delivered, so that the battery is judged to be maintained or replaced, and the capacity retention rate of the battery exceeds the upper limit threshold value of the preset capacity retention rate.
Step a42, when the first median difference is smaller than the first preset threshold, judging whether the first median difference is smaller than the second preset threshold; specifically, as shown in fig. 4, the second preset threshold is set as the lower limit threshold μ2 of the capacity retention rate variation, and when the first median difference Ki is smaller than the first preset threshold μ1, it is determined whether the first median difference Ki is smaller than the second preset threshold μ2.
Step a43, when the first median difference value is smaller than a second preset threshold value, judging that the battery to be detected has a fault or has battery capacity variation at the time point of the central position of the sliding time window; specifically, as shown in fig. 4, if the first median difference Ki is smaller than the second preset threshold μ2, it is determined that the battery to be detected has a fault or a battery capacity variation at the time point of the sliding time window center position. The second preset threshold value μ2 represents a lower threshold value of the preset capacity retention rate variation, and when the first median difference Ki is smaller than the second preset threshold value μ2, it indicates that the current battery capacity retention rate is smaller than the lower threshold value of the preset capacity retention rate, and the capacity retention rate reaches a new low level, and at this time, it indicates that the energy storage function of the battery is a case when the battery fails or the energy storage function of the battery is abnormal, so it is determined that the failure or the battery capacity variation exists, and the battery capacity retention rate is smaller than the lower threshold value of the preset capacity retention rate.
And a step a44, when the first median difference value is greater than the second preset threshold value, sliding backwards on the sliding time window for a preset time step according to the timestamp data, and continuously detecting the battery capacity variation according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value. The display diagram of the median difference detection data of the sliding time window according to the embodiment of the invention is shown in fig. 5, wherein the abscissa in fig. 5 represents the total number of capacity retention, and the total number of 800 capacity retention data, and the ordinate represents the median difference of the sliding time window.
And step S2053, when the total data amount of the capacity retention rate is smaller than the size of the grid length, discarding the extracted full life cycle capacity retention rate of the battery to be detected. Specifically, if the total data of the capacity retention rate of the vehicle in the whole life cycle is smaller than the grid length w, the capacity retention rate after pretreatment is removed indicates that the number of times of charging and discharging of the vehicle battery is insufficient, belongs to a new factory vehicle and is not in line with the target battery for detecting the battery capacity variation, and after the battery is charged and discharged for more times, the battery capacity data can be detected and evaluated. The full life cycle capacity retention rate of the target battery which does not meet the detection is discarded.
According to the battery capacity variation detection method provided by the embodiment of the invention, the capacity retention rate is sequenced based on the time stamp data of the whole life cycle of the battery, the detection pretreatment of eliminating abnormal values and repeated values is performed on the sequenced capacity retention rate, the acquisition efficiency of the time stamp data of the whole life cycle of the battery to be detected is high, the cost is saved, and the data of the capacity retention rate is more complete and accurate. And selecting the capacity retention rate measured value data in the time range conforming to the grid length by setting the grid length, and setting a sliding time window according to the grid length by using the timestamp data to provide a detection window for the subsequent battery capacity variation detection. When the battery capacity variation is detected, when the first median difference value is larger than a first preset threshold value, it is judged that the to-be-detected battery has maintenance or replacement marks at the central position time point of the sliding time window, when the first median difference value is smaller than a second preset threshold value, it is judged that the to-be-detected battery has faults or the battery capacity variation at the central position time point of the sliding time window, when the first median difference value is larger than the second preset threshold value, the to-be-detected battery is slid backwards on the sliding time window for a preset time step according to timestamp data, and the battery capacity variation is continuously detected according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value, so that the embroidering or replacement marks or the faults or the capacity variation of the to-be-detected battery can be detected, dynamic detection is realized, and the purposes of high efficiency and low cost identification of the maintenance or the to-be-detected battery are achieved.
In this embodiment, a method for detecting battery capacity variation is provided, which may be used in an electric vehicle, and fig. 3 is a flowchart of a method for detecting battery capacity variation according to an embodiment of the present invention, as shown in fig. 3, where the flowchart includes the following steps:
step S301, extracting the full life cycle capacity retention rate of the battery to be detected. Please refer to step S201 in the embodiment shown in fig. 2 in detail, which is not described herein.
Step S302, a sliding time window of capacity retention rate is constructed. Please refer to step S204 in the embodiment shown in fig. 2 in detail, which is not described herein.
Step S303, performing battery capacity variation detection based on the relationship between the median of the capacity retention rate in the sliding time window and the preset threshold. Please refer to step S205 in the embodiment shown in fig. 2 in detail, which is not described herein.
And step S304, detecting the occurrence time point of the battery to be detected, in which the battery capacity is changed, based on all the first median differences determined by adopting the sliding time window.
Specifically, the step S304 includes:
step S3041, generating a data set list composed of first median differences of the sliding time window; specifically, as shown in fig. 4, all the first median differences Kw/2 to K (n-w/2) calculated in the sliding time window sequentially form a data set list L, where n is the total amount of data of the full life cycle capacity retention rate of the vehicle, kw/2 is the first median difference in the data set list L, and K (n-w/2) is the last first median difference in the data set list L.
Step S3042, obtaining a first second median difference value larger than a first preset threshold value and a third median difference value smaller than the first preset threshold value after the second median difference value in the data set list; specifically, as shown in fig. 4, starting from the first median difference Kw/2, a second median difference Kx1 is found in the data set list L, the first of which exceeds the first preset threshold μ1, and a third median difference Kx2, the first of which is lower than the first preset threshold μ1, is found after the second median difference Kx 1.
Step S3043, selecting the maximum median difference between the second median difference and the third median difference, and taking the time point of the maximum median difference as the occurrence time point of the mutation; specifically, as shown in fig. 5, the maximum median difference Km1 between the second median difference Kx1 and the third median difference Kx2 in the data set list L is selected, and it is determined that the jump in the capacity retention rate value of the vehicle occurs between the mth 1-th charge and the time of the next adjacent charge after that, that is, that the battery capacity change occurs between the mth 1-th charge and the time of the next adjacent charge after that.
Step S3044, recording the number of times of battery capacity mutation and mutation occurrence time points, and sliding backwards on the data set list of the sliding time window for a preset time step according to the timestamp data until the last value of the data set list is reached. Specifically, as shown in fig. 4, starting from the third median difference Kx2, step S4042 and step S4043 are repeated to find the position of occurrence of the next capacity hopping point, i.e., the position of battery capacity variation, in the data set list L until the sliding time window reaches the last first median difference K (n-w/2) in the data set list L. The display diagram of the battery capacity variation detection result to be detected according to the embodiment of the invention is shown in fig. 6, and the abscissa in fig. 6 represents the total number of capacity retention rate, and total 800 pieces of capacity retention rate data are used.
The capacity retention rate may exhibit continuous abnormal variation over a continuous long period of time after maintenance or replacement of the battery. Therefore, if the interval between the numbers m2 and m1 of the second maximum median difference Km2 and the maximum median difference Km1 is smaller than one half of the mesh length w of the sliding time window, the maximum value among Km2 and Km1 is taken as the capacity anomaly jump value, and the position of the maximum value is taken as the occurrence time node of the capacity jump, i.e., the battery capacity variation time point.
According to the battery capacity mutation detection method provided by the embodiment of the invention, the mutation occurrence time points of the battery to be detected, which are subjected to battery capacity mutation, are detected by adopting all the first median difference values determined by the sliding time window, so that the time points of the occurrence of the battery capacity mutation can be accurately detected, and the characteristics of high efficiency, low cost, battery maintenance trace and battery capacity mutation time point identification are realized.
The embodiment also provides a device for detecting battery capacity variation, which is used for realizing the above embodiment and the preferred implementation, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a battery capacity variation detecting device, as shown in fig. 7, including:
a first extraction module 701, configured to extract a full life cycle capacity retention rate of a battery to be detected;
a building module 702 for building a sliding time window of capacity retention;
a detection module 703, configured to perform battery capacity variation detection based on a relationship between a median of the capacity retention rate in the sliding time window and a preset threshold.
The battery capacity variation detection device further includes:
and the second extraction module is used for extracting the timestamp data of the full life cycle of the battery to be detected.
The preprocessing module is used for sorting the capacity retention rate based on the time stamp data; and carrying out pretreatment of outlier detection, outlier detection and repeated value detection at the same time on the capacity retention rate after the sorting treatment.
And the second detection module is used for detecting the occurrence time point of the variation of the battery to be detected, which is generated by the variation of the battery capacity, based on all the first median difference values determined by adopting the sliding time window.
In some alternative embodiments, the build module 702 includes:
a first setting unit configured to set a mesh length based on a total amount of data of the capacity retention rate; the total data amount of the capacity retention rate is larger than the size of the grid length;
And a second setting unit for setting a sliding time window according to the time stamp data and the grid length.
In some alternative embodiments, the detection module 703 includes:
a judging unit for judging whether the total data amount of the capacity retention rate after the pretreatment is larger than the size of the grid length;
the detection unit is used for detecting the battery capacity variation based on the relation between the median of the capacity retention rate in the sliding time window and a preset threshold value when the total data amount of the capacity retention rate is larger than the grid length;
and the discarding unit is used for discarding the extracted full life cycle capacity retention rate of the battery to be detected when the total data amount of the capacity retention rate is smaller than the size of the grid length.
In some alternative embodiments, the detection unit comprises:
a first setting subunit, configured to set a time point at a middle position of each sliding time window.
And the extraction subunit is used for extracting the intermediate value from the middle position time point to the last time point of the sliding time window as a first median, and extracting the intermediate value from the starting point of the sliding time window to the middle position time point as a second median.
And the calculating subunit is used for adopting the difference value between the first median and the second median as a first median difference value.
And the detection subunit is used for detecting the battery capacity variation according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value.
In some alternative embodiments, the detection subunit comprises:
and the first determiner is used for determining that the battery to be detected has a maintenance or replacement trace at the time point of the central position of the sliding time window when the first median difference value is larger than a first preset threshold value.
And the judging device is used for judging whether the first median difference value is smaller than a second preset threshold value or not when the first median difference value is smaller than the first preset threshold value.
And the second determiner is used for determining that the battery to be detected has faults or battery capacity variation at the time point of the central position of the sliding time window when the first median difference value is smaller than a second preset threshold value.
And the detector is used for sliding backwards on the sliding time window for a preset time step according to the timestamp data when the first median difference value is larger than a second preset threshold value, and continuously detecting the battery capacity variation according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The battery capacity variation detecting device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit ) circuit, a processor and a memory executing one or more software or a fixed program, and/or other devices that can provide the above functions.
The embodiment of the invention also provides computer equipment, which is provided with the battery capacity variation detection device shown in the figure 7.
Referring to fig. 8, fig. 8 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 8, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 8.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device further comprises input means 30 and output means 40. The processor 10, memory 20, input device 30, and output device 20 may be connected by a bus or other means, for example in fig. 8.
The input device 30 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the computer apparatus, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointer stick, one or more mouse buttons, a trackball, a joystick, and the like. The output means 40 may include a display device, auxiliary lighting means (e.g., LEDs), tactile feedback means (e.g., vibration motors), and the like. Such display devices include, but are not limited to, liquid crystal displays, light emitting diodes, displays and plasma displays. In some alternative implementations, the display device may be a touch screen.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (11)
1. A battery capacity variation detection method, characterized in that the method comprises:
extracting the capacity retention rate of the full life cycle of the battery to be detected;
constructing a sliding time window of the capacity retention rate;
and detecting the battery capacity variation based on the relation between the median of the capacity retention rate in the sliding time window and a preset threshold value.
2. The method of claim 1, further comprising, prior to said constructing the sliding time window of capacity retention rate:
extracting time stamp data of the full life cycle of the battery to be detected;
sorting the capacity retention rate based on the timestamp data;
and carrying out pretreatment of outlier detection, outlier detection and repeated value detection at the same time on the capacity retention rate after the sorting treatment.
3. The method of claim 2, wherein said constructing a sliding time window of said capacity retention rate comprises:
setting a mesh length based on the total amount of data of the capacity retention rate; the total data amount of the capacity retention rate is larger than the size of the grid length;
and setting a sliding time window according to the grid length according to the time stamp data.
4. The method of claim 3, wherein performing battery capacity variation detection based on a relationship between a median of capacity retention rates in the sliding time window and a preset threshold value comprises:
Judging whether the total data amount of the capacity retention rate after pretreatment is larger than the mesh length;
when the total data amount of the capacity retention rate is larger than the grid length, performing battery capacity variation detection based on the relation between the median of the capacity retention rate in the sliding time window and a preset threshold;
and when the total data amount of the capacity retention rate is smaller than the size of the grid length, discarding the extracted full life cycle capacity retention rate of the battery to be detected.
5. The method of claim 1, wherein performing battery capacity variation detection based on a relationship between a median of capacity retention rates in the sliding time window and a preset threshold value comprises:
setting a middle position time point of each sliding time window;
extracting an intermediate value from the middle position time point to the last time point of the sliding time window as a first median, and extracting an intermediate value from the starting point of the sliding time window to the middle position time point as a second median;
adopting a difference value between the first median and the second median as a first median difference value;
and detecting the battery capacity variation according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value.
6. The method of claim 5, wherein detecting battery capacity variation from a relationship of the first median difference to a first preset threshold and a second preset threshold of capacity retention comprises:
when the first median difference value is larger than a first preset threshold value, judging that the to-be-detected battery has a maintenance or replacement trace at the time point of the central position of the sliding time window;
when the first median difference value is smaller than a first preset threshold value, judging whether the first median difference value is smaller than a second preset threshold value or not;
when the first median difference value is smaller than a second preset threshold value, judging that the battery to be detected has faults or battery capacity variation at the time point of the central position of the sliding time window;
and when the first median difference value is larger than a second preset threshold value, sliding backwards on the sliding time window for a preset time step according to the timestamp data, and continuously detecting the battery capacity variation according to the relation between the first median difference value and the first preset threshold value and the second preset threshold value.
7. The method as recited in claim 6, further comprising: and detecting a time point of occurrence of the battery to be detected, in which the battery capacity is changed, based on all the first median differences determined by adopting the sliding time window.
8. The method according to claim 7, wherein detecting a time point of occurrence of a battery to be detected in which a battery capacity variation occurs based on all first median differences determined using a sliding time window comprises:
generating a data set list consisting of a first median difference value of the sliding time window;
acquiring a first second median difference value larger than a first preset threshold value in the data set list and a third median difference value smaller than the first preset threshold value after the second median difference value;
selecting a maximum median difference value between the second median difference value and the third median difference value, and taking a time point of the maximum median difference value as a time point of occurrence of the mutation;
and recording the number of times of battery capacity mutation and mutation occurrence time points, and sliding backwards on a data set list of a sliding time window for a preset time step according to the timestamp data until the last value of the data set list is reached.
9. A battery capacity variation detecting device, characterized by comprising:
the extraction module is used for extracting the capacity retention rate of the full life cycle of the battery to be detected;
a building module for building a sliding time window of the capacity retention rate;
And the detection module is used for detecting the battery capacity variation based on the relation between the median of the capacity retention rate in the sliding time window and a preset threshold value.
10. A computer device, comprising:
a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the battery capacity variation detection method of any one of claims 1 to 8.
11. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the battery capacity variation detection method according to any one of claims 1 to 8.
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