CN115946573A - Battery electric connection abnormity identification method and device, storage medium and new energy automobile - Google Patents
Battery electric connection abnormity identification method and device, storage medium and new energy automobile Download PDFInfo
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Abstract
The disclosure relates to the technical field of battery management, in particular to a battery electric connection abnormity identification method and device, a storage medium and a new energy automobile. The battery electrical connection abnormality recognition method includes: acquiring multiple groups of voltage data of a battery system, wherein each group of voltage data comprises an acquisition moment and a voltage value of each single battery acquired at the acquisition moment; calculating the median of each group of voltage data to obtain a plurality of groups of voltage median data; and determining abnormal single batteries in the battery system and abnormal moments of the abnormal single batteries according to the multiple groups of voltage data and the multiple groups of voltage median data. According to the battery electric connection abnormity identification method, the battery electric connection abnormity identification device, the storage medium and the new energy automobile, based on the multiple groups of voltage data and the multiple groups of voltage median data, the single battery with abnormity in electric connection in the battery system can be identified, and the acquisition time of the single battery with abnormity can be acquired.
Description
Technical Field
The disclosure relates to the technical field of battery management, in particular to a battery electric connection abnormity identification method and device, a storage medium and a new energy automobile.
Background
The power battery is used as a power source of a new energy automobile and is the most important part in the whole automobile. The safety accidents caused by abnormal heat generation of the battery system due to abnormal electrical connection of the single batteries in the battery system are increased suddenly. Therefore, how to identify whether the electrical connection of the single batteries in the battery system is abnormal is an urgent technical problem to be solved.
Disclosure of Invention
The present disclosure provides a battery electrical connection abnormality recognition method, device, storage medium, and new energy vehicle, which have solved a problem that it is impossible to recognize whether a cell electrical connection in a battery system is abnormal.
In order to achieve the above object, the present disclosure provides a battery electrical connection abnormality identification method, the method including:
acquiring multiple groups of voltage data of a battery system, wherein the battery system comprises a plurality of single batteries, and each group of voltage data comprises an acquisition time and a voltage value of each single battery acquired at the acquisition time;
calculating the median of each group of voltage data to obtain a plurality of groups of voltage median data, wherein each group of voltage median data comprises an acquisition time and the median of voltage values of all single batteries of the battery system acquired at the acquisition time;
and determining abnormal single batteries in the battery system and abnormal moments of the abnormal single batteries according to the multiple groups of voltage data and the multiple groups of voltage median data, wherein the abnormal single batteries are the single batteries which are electrically connected in the battery system and are abnormal, and the abnormal moments are the acquisition moments of the abnormal single batteries.
Optionally, determining an abnormal single battery in the battery system according to the plurality of sets of voltage data and the plurality of sets of voltage median data, and the abnormal time of the abnormal single battery includes:
for the multiple groups of voltage data, differentiating the voltage value of each single battery at each acquisition time with the voltage value at the previous acquisition time, and accumulating the square of the difference obtained by differentiation by using a first preset step length to obtain multiple groups of differential accumulated data, wherein each group of differential accumulated data comprises a first acquisition time period and a differential accumulated value of each single battery in the first acquisition time period, and the first acquisition time period is determined according to the first preset step length and the acquisition time;
for the multiple groups of voltage median data, differentiating the voltage median of each acquisition moment with the voltage median of the previous acquisition moment, and accumulating the squares of the differences obtained by the first preset step length to obtain multiple groups of median differential accumulated data, wherein each group of median differential accumulated data comprises the first acquisition time period and the median differential accumulated value in the first acquisition time period;
acquiring the ratio of the difference accumulated value of each single battery to the median difference accumulated value in each first acquisition time period to obtain multiple groups of ratio data, wherein each group of ratio data comprises the first acquisition time period and the ratio of each single battery in the first acquisition time period;
and determining abnormal single batteries in the battery system and abnormal time of the abnormal single batteries according to the multiple groups of ratio data, wherein the abnormal time is acquisition time included in a first acquisition time period when the abnormal single batteries have abnormal ratios.
Optionally, determining an abnormal single battery in the battery system according to the multiple sets of ratio data, and the abnormal time of the abnormal single battery includes:
for the multiple groups of ratio data, differentiating the average value of the ratio of each single battery in each second preset step length with the average value of the previous second step length to obtain multiple groups of mean value differential data, wherein each group of mean value differential data comprises a second acquisition time period and a mean value differential value of each single battery in the second acquisition time period, and the second acquisition time period is determined according to the second preset step length and the first acquisition time period;
acquiring an upper limit threshold of the mean difference value of each single battery according to the multiple groups of mean difference data;
and comparing each mean value difference value of each single battery with the upper limit threshold value of the single battery, and determining abnormal single batteries in the battery system and abnormal time of the abnormal single batteries, wherein the abnormal time is acquisition time included in a second acquisition time period when the abnormal single batteries have the abnormal mean value difference value.
Optionally, obtaining an upper threshold of the mean difference value of each single battery according to the plurality of sets of mean difference data includes: sequencing the plurality of mean difference values of each single battery according to the plurality of groups of mean difference data to obtain 75 quantiles and 25 quantiles of the mean difference values of each single battery; determining an upper limit threshold value of each single battery according to the 75 quantile and the 25 quantile of each single battery;
comparing each mean difference value of each single battery with the upper limit threshold of the single battery, and determining abnormal single batteries in the battery system, wherein the abnormal time of the abnormal single batteries comprises: and comparing each mean value difference value of each single battery with an upper limit threshold value of each single battery, and determining that each single battery is an abnormal single battery when the mean value difference values of a continuous preset number are greater than the upper limit threshold value, and the abnormal time of each single battery is a collection time included in a second collection time period corresponding to one mean value difference value of the mean value difference values of the continuous preset number.
Optionally, acquiring multiple sets of voltage data of the battery system comprises:
and acquiring voltage data acquired at a plurality of acquisition moments when the battery system is in a discharge state and the output current value is within a first preset range to obtain a plurality of groups of voltage data.
Optionally, when the battery system is applied to a new energy vehicle, acquiring multiple sets of voltage data of the battery system includes:
and acquiring voltage data of the battery system acquired by the new energy automobile at a plurality of acquisition moments when the driving speed is higher than the preset speed to obtain a plurality of groups of voltage data.
Optionally, before acquiring the plurality of sets of voltage data of the battery system, the method further comprises:
acquiring original voltage data of the battery system;
deleting invalid voltage data in the original voltage data to obtain valid voltage data, wherein the invalid voltage data comprises data of which the voltage value is abnormal characters, the voltage value is null and the voltage value exceeds a second preset range in the original voltage data;
acquiring multiple sets of voltage data of the battery system comprises: and acquiring multiple groups of voltage data from the effective voltage data.
The disclosed embodiment also provides a battery electrical connection abnormality recognition apparatus, the apparatus including:
the battery system comprises a plurality of single batteries, wherein each group of voltage data comprises an acquisition moment and a voltage value of each single battery acquired at the acquisition moment;
the median acquisition module is used for calculating the median of each group of voltage data to obtain a plurality of groups of voltage median data, wherein each group of voltage median data comprises an acquisition time and the median of the voltage values of all the single batteries of the battery system acquired at the acquisition time;
and the identification module is used for determining abnormal single batteries in the battery system and abnormal moments of the abnormal single batteries according to the multiple groups of voltage data and the multiple groups of voltage median data, wherein the abnormal single batteries are the single batteries which are electrically connected in the battery system and are abnormal, and the abnormal moments are the abnormal acquisition moments of the abnormal single batteries.
Embodiments of the present disclosure also provide a non-transitory computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps of the above-described method.
The embodiment of the present disclosure further provides a new energy automobile, including:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the above method.
According to the technical scheme provided by the embodiment of the disclosure, based on the multiple groups of voltage data and the multiple groups of voltage median data, the single batteries with abnormal electrical connection in the battery system can be identified (namely, the abnormal single batteries are identified), and the abnormal time of the abnormal single batteries can be identified.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure, but do not constitute a limitation of the disclosure. In the drawings:
fig. 1 is a flowchart of a method for identifying an abnormal electrical connection of a battery according to an embodiment of the present disclosure.
FIG. 2 is a flowchart illustrating sub-steps of step S30 of FIG. 1 according to an embodiment.
FIG. 3 is a flow diagram illustrating sub-steps of step S34 of FIG. 2 according to an embodiment.
Fig. 4 is a flowchart illustrating sub-steps of step S30 in fig. 2 according to another embodiment.
Fig. 5 is a block diagram of a device for identifying an electrical connection abnormality of a battery according to an embodiment of the present disclosure.
Fig. 6 is a block diagram of an electronic device provided by an embodiment of the present disclosure.
Detailed Description
The following detailed description of the embodiments of the disclosure refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
It should be noted that all actions of acquiring signals, information or data in the present disclosure are performed under the premise of complying with the corresponding data protection regulation policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
The embodiment of the disclosure provides a battery electrical connection abnormality identification method. Fig. 1 shows a flowchart of the battery electrical connection abnormality identification method. As shown in fig. 1, the method includes:
and step S10, acquiring multiple groups of voltage data of the battery system.
The battery system comprises a plurality of single batteries, and each group of voltage data comprises a collection moment and a voltage value of each single battery collected at the collection moment. For ease of understanding of the scheme, the voltage data for the multiple sets may be represented by the following matrix V. It is assumed that the battery system has n single batteries, and each column element in the matrix V represents a voltage value of one battery at each acquisition time, and each row element represents a voltage value of each single battery acquired at one acquisition time. Then
Can respectively represent the collection time of No. 1 battery monomer at No. 0 and 1Voltage values at the number acquisition time … and the number 2m acquisition time are obtained; />
The voltage values of No. 2 battery cells at No. 0 acquisition time, no. 1 acquisition time, no. … and No. 2m acquisition time can be respectively represented; />
The voltage values of the n-numbered single batteries at the No. 0 acquisition time, the No. 1 acquisition time, the No. … and the No. 2m acquisition time can be respectively represented.
Matrix V:
matrix V Z ：/>
And step S20, calculating the median of each group of voltage data to obtain multiple groups of voltage median data.
Each group of voltage median data comprises an acquisition time and median of voltage values of all single batteries of the battery system acquired at the acquisition time. Similarly, for ease of understanding the scheme, the matrix V above may be used for the sets of voltage median data Z To indicate. And let the matrix V Z Only one column, the number of rows is the same as the matrix V. Matrix V Z Each element of (a) represents the median of the voltage values of all the cells at the respective acquisition instant. Then
Can represent the median of the voltage values of the single batteries from No. 1 to No. n at the collection time No. 0, namely ^ greater than or equal to ^ greater than>
Is equal to->
A median of (d); then->
Can represent the median of the voltage values of the single batteries from No. 1 to No. n at the No. 1 acquisition time, namely ^ greater than or equal to ^ greater than>
Is equal to->
A median of (d); …; then->
Can represent the median of the voltage values of the single batteries from No. 1 to No. n at the collection time No. 2m, namely ^ greater than or equal to ^ greater than>
Is equal to
The median of (3).
And step S30, determining abnormal single batteries in the battery system and abnormal moments of the abnormal single batteries according to the multiple groups of voltage data and the multiple groups of voltage median data.
The abnormal single battery is a single battery which is electrically connected with the battery system and is abnormal, and the abnormal moment is the abnormal acquisition moment of the abnormal single battery.
The applicant finds out through research that: there are various causes of the abnormal electrical connection of the unit cells in the battery system, and the reaction includes the abnormal voltage of the unit cells at the data level. Due to the fact that the working conditions are complex in the running process of the vehicle, only the voltage change of each single battery is researched, and contingency can exist. Therefore, when determining whether each of the unit cells is an abnormal unit cell, it is necessary to study not only the voltage value change of each of the unit cells but also the voltage median change of all the unit cells of the battery system. Therefore, according to the technical scheme provided by the embodiment of the disclosure, based on the multiple groups of voltage data and the multiple groups of voltage median data, the identification of the abnormal single battery (namely, the identification of the abnormal single battery) electrically connected in the battery system and the abnormal time of the abnormal single battery can be realized.
Optionally, before step S10, the method further comprises: raw voltage data of the battery system is acquired. And deleting invalid voltage data in the original voltage data to obtain valid voltage data, wherein the invalid voltage data comprises data of which the voltage value is abnormal characters, the voltage value is null and the voltage value exceeds a second preset range in the original voltage data.
Acquiring multiple sets of voltage data of a battery system includes: and acquiring multiple groups of voltage data from the effective voltage data.
The voltage value is an abnormal character, that is, the voltage value is not a number, such as NAN. The second preset range is determined according to a voltage use (operation) range of the battery system, and is not limited herein. For example, the second preset range may be 1V to 6V.
According to the technical scheme, the original voltage data of the battery system are cleaned, the data that the voltage value is abnormal characters, the voltage value is null and the voltage value exceeds a second preset range in the original voltage data are deleted, effective voltage data are obtained, and the multiple groups of voltage data are obtained from the effective voltage data.
Optionally, in an embodiment, step S10 includes: and acquiring voltage data acquired at a plurality of acquisition moments when the battery system is in a discharge state and the output current value is within a first preset range to obtain a plurality of groups of voltage data.
In specific implementation, the charging and discharging state of the battery system can be obtained according to the charging and discharging state code output by the battery system. For example, the charge/discharge state code output from a certain battery system is represented by charge _ status, where charge _ status =1 represents the charge state and charge _ status =3 represents the discharge state. When acquiring a plurality of sets of voltage data of the battery system in a discharge state, acquiring a plurality of sets of voltage data of charge _ status = 3. The first preset range is determined according to the change range of the current when the battery system is used as a main load (when the battery system is a power source of the new energy automobile, the main load is a motor, and is not equipment such as a vehicle-mounted air conditioner and a sound box) for discharging. For example, the first preset range is a current value greater than 20A and a current value less than-10A. Then, obtaining multiple sets of voltage data collected at multiple collection times when the battery system is in a discharge state and the output current value is within a first preset range may be: and acquiring voltage data acquired at a plurality of acquisition moments with the charge _ status =3 and the current value being more than 20A or less than-10A to obtain the plurality of groups of voltage data (matrix V).
Alternatively, in another embodiment, when the battery system is applied to a new energy vehicle, step S10 includes: and acquiring voltage data of the battery system acquired by the new energy automobile at a plurality of acquisition moments when the running speed of the new energy automobile is higher than a preset speed to obtain a plurality of groups of voltage data.
When the running speed of the new energy automobile is larger than the preset speed, namely the new energy automobile runs, the battery system is in a discharging state and supplies power to a main load (a motor). The preset speed may be 0km/h, 5km/h, and the like, which is not limited herein. Through the technical scheme, the voltage data of the new energy automobile in the driving state can be obtained and used as the multiple groups of voltage data (matrix V).
Alternatively, as shown in fig. 2, step S30 includes:
step S31, for the multiple sets of voltage data, differentiating the voltage value of each single battery at each acquisition time from the voltage value at the previous acquisition time, and accumulating the squares of the differences obtained by the differentiation by a first preset step length to obtain multiple sets of differential accumulated data, where each set of differential accumulated data includes a first acquisition time period and a differential accumulated value of each single battery in the first acquisition time period.
And the first acquisition time period is determined according to the first preset step length and the acquisition time. The first preset step length is set according to the user requirement, for example, a general battery system collects the signals once every 10s (the time interval of the collection time is 10 s), that is, the time interval between the collection time No. 0 and the collection time No. 1 is 10s, and the first preset step length may be 800. In order to facilitate the understanding of the scheme,again, for the sets of voltage data, represented by matrix V, the first preset step size is represented by m and the sets of differential accumulation data are represented by matrix C below. Then
The square sum of the first m voltage value differences of the No. 1 battery cell is represented, and the difference accumulated value of the No. 1 battery cell in the No. 0 first collection time period is represented. />
The square sum of the second m voltage value differences of the No. 1 battery cell is represented, and the difference accumulated value of the No. 1 battery cell in the No. 1 first collection time period is represented. />
The sum of squares of the difference values of the first m voltage values of the No. 2 battery cell is represented, and the difference accumulated value of the No. 2 battery cell in the No. 0 first collection time period is represented. />
The square sum of the second m voltage value differences of the No. 2 battery cell is represented, and the difference accumulated value of the No. 2 battery cell in the No. 1 first collection time period is represented.
The square sum of the first m voltage value differences of the battery cell number n is shown, and the difference accumulated value of the battery cell number n in the first collection time period number 0 is shown.
The square sum of the second m voltage value differences of the battery cell number n is shown, and the difference accumulated value of the battery cell number n in the first collection time period number 1 is shown. In a similar manner, it is possible to derive->
And step S32, for the multiple groups of voltage median data, differentiating the voltage median at each acquisition time with the voltage median at the previous acquisition time, and accumulating the squares of the differential values obtained by the first preset step length to obtain multiple groups of median differential accumulated data, wherein each group of median differential accumulated data comprises the first acquisition time interval and the median differential accumulated value in the first acquisition time interval.
For the convenience of scheme understanding, the matrix V is used for multiple groups of voltage median data Z To show that the multi-group median difference accumulation data uses the above matrix C Z To indicate. Then the
Representing a sum of squares of median differences for the first m voltage values, representing a median differential accumulated value for the first acquisition period number 0;
to representThe sum of the squares of the median difference values for the second m voltage values, representing the median difference cumulative value for the first acquisition period number 1. In the same way, it can be deduced
Etc., and are not to be inferred herein for economy of space. />
Matrix C:
matrix C Z ：/>
Step S33, obtaining a ratio of the difference cumulative value of each single battery to the median difference cumulative value in each first collection period, to obtain multiple sets of ratio data, where each set of ratio data includes the first collection period and the ratio of each single battery in the first collection period.
Similarly, to facilitate understanding of the scheme, the sets of ratio data may be represented by a matrix D. Then
Represents the ratio of # 1 cell in the first collection period # 0, #>
And the ratio of No. 1 battery cell in No. 1 first collection time interval is represented by …. Then->
Represents the ratio of # 2 cell in the first collection period # 0, #>
The ratio of No. 2 battery cells in No. 1 first acquisition time period is represented, …. Then->
Battery with n numberRatio of the monomer in the first acquisition period # 0, <' > based on>
And the ratio of the number n of the single batteries in the number 1 first collection time interval is represented by …. In the same way, it can be deduced
And step S34, determining abnormal single batteries in the battery system and abnormal time of the abnormal single batteries according to the multiple groups of ratio data.
The abnormal moment is a collection moment included in a first collection time period when the abnormal specific value of the abnormal single battery occurs. That is, whether an abnormal ratio occurs in each unit cell is determined by performing research analysis on a plurality of sets of ratio data (matrix D). The single battery with the abnormal ratio is the abnormal single battery, and an acquisition time included in a first acquisition time period (such as a first acquisition time period No. 0, a first acquisition time period No. 1, a first acquisition time period No. k, and the like) corresponding to the abnormal ratio is determined as the abnormal time. For example, when the ratio of No. 1 single battery
If the abnormal ratio is the abnormal ratio, determining that the abnormal single cell is the No. 1 single cell, and the abnormal time is a collection time corresponding to the No. 0 first collection time period, wherein the collection time can be one of the No. 0 collection time, the No. 1 collection time, … and the No. m collection time, specifically which collection time isOne may be specified by the user, for example, the collection time is defined as the first collection time corresponding to the first collection time period, i.e., the collection time No. 0, and the collection time is defined as the last collection time corresponding to the first collection time period, i.e., the collection time No. m. Also for example, when the ratio of the # 1 cell is->
If the abnormal ratio is an abnormal ratio, determining that the abnormal single battery is the single battery No. 1, and the abnormal time is a collection time corresponding to the first collection time period No. 1, where the collection time may be one of the collection time No. m, the collection time No. m +1, the collection time No. …, and the collection time No. 2m, and specifically which one of the collection times can be specified by a user, if the collection time is defined as the first collection time corresponding to the first collection time period, that is, the collection time No. m, if the collection time is defined as the last collection time corresponding to the first collection time period, that is, the collection time No. 2 m. So on, it is not described herein.
Alternatively, as shown in fig. 3, step S34 includes:
step S341, for the multiple sets of ratio data, differentiating the average value of the ratio of each single battery in each second preset step from the average value of the previous second step to obtain multiple sets of mean difference data, where each set of mean difference data includes a second acquisition time period and a mean difference value of each single battery in the second acquisition time period.
And the second acquisition time period is determined according to the second preset step length and the first acquisition time period. The second preset step length is set according to the user requirement. For the sake of easy understanding of the scheme, the average difference data are represented by a matrix S, and the second preset step size is k. Then
And the mean difference value of the No. 1 single battery in the No. 0 second acquisition period is represented. Then->
And the mean difference value of the No. 1 single battery in the No. 1 second acquisition time period is represented. Then->
And the mean difference value of the No. 2 single battery in the No. 0 second acquisition time period is represented. Then->
And the mean difference value of the No. 2 single battery in the No. 1 second acquisition time period is shown. Then->
And the mean difference value of the number n single batteries in the number 0 second acquisition time period is represented. Then
And the mean difference value of the No. n single batteries in the No. 1 second acquisition time period is represented.
Matrix S:
step S342, obtaining an upper threshold of the mean difference value of each single battery according to the plurality of sets of mean difference data.
The upper threshold of the mean difference value of each single battery is determined according to each mean difference value of each single battery, and there are various determination methods, which are not limited herein. For example, the mean difference value according to No. 1 battery cell
… (the first column of data in matrix S) may determine the upper threshold Y of the mean difference value for cell number 1 1 (ii) a Based on the mean value difference value of the number 2 single battery>
… (the second column of data in matrix S) may determine the upper threshold Y of the mean difference value for cell number 2 2 (ii) a Based on the mean value difference value of the n number of single batteries>
… (the nth column data of matrix S) can determine the upper threshold Y of the mean difference value of the n number of single batteries n 。
Step S343, comparing each mean difference value of each single battery with the upper threshold of the single battery, and determining an abnormal single battery in the battery system and an abnormal time of the abnormal single battery.
And the abnormal moment is a collection moment included in a second collection time period when the abnormal single battery has the abnormal mean value difference value. Similarly, the abnormal time may be specified by a user, specifically which collection time included in the second collection time period.
Optionally, in the foregoing scheme, as shown in fig. 4, step S342 includes: sequencing the plurality of mean difference values of each single battery according to the plurality of groups of mean difference data to obtain 75 quantiles and 25 quantiles of the mean difference values of each single battery; and determining the upper limit threshold value of each single battery according to the 75 quantiles and the 25 quantiles of each single battery.
In particular implementations, a 75 quantile and a 25 quantile means equal to or approximately equal to the 75 quantile and the 25 quantile. For convenience of understanding, the above steps are described by taking a No. 1 single battery as an example: mean value difference value of No. 1 single battery
… (first column data of the matrix S) is sequenced to obtain 75 quantiles and 25 quantiles of the first column data of the matrix S, and an upper limit threshold Y of the No. 1 single battery is determined according to the 75 quantiles and the 25 quantiles 1 . Wherein, the 75 quantiles of the first column data of the matrix S can be calculated by adopting a strict 75 quantile calculation method, and can also adopt a method of calculating the 75 quantilesThe following formula is taken for approximate calculation: />
Where i = L × 75%, L is the number of rows in the matrix S, and if i is not an integer, i is rounded up. Wherein->
And representing the 75 quantile of the first column data of the matrix S, namely the 75 quantile of the mean difference value of the No. 1 single battery. />
And the mean difference value of the No. 1 single battery in the No. i second acquisition time period is represented. />
And the mean difference value of the No. 1 single battery in the No. i +1 second acquisition time period is represented. Similarly, the 25 quantile of the first column data of the matrix S may be calculated by a strict 25 quantile calculation method, or may be approximated by the following formula: />
Where p = L × 75%, L is the number of rows in the matrix S, and if p is not an integer, p is rounded up. Wherein it is present>
And representing 25 quantiles of the first column data of the matrix S, namely 25 quantiles of the mean difference value of the No. 1 single battery. />
And the mean difference value of the No. 1 single battery in the No. p second acquisition period is shown. />
And the mean difference value of the No. 1 single battery in the second acquisition period p +1 is represented. Determining an upper threshold of each of the single batteries according to the 75 quantiles and the 25 quantiles of each of the single batteriesThe values may, but are not limited to, take the following formulas: />
Wherein, c 1 The threshold coefficient of the No. 1 single battery can be determined according to specific experiments or experiences, such as c for a certain vehicle type or a certain battery system 1 Taking different values to calculate the upper threshold Y 1 And using respective upper threshold values Y 1 C, identifying abnormal single batteries and ensuring good identification effect 1 The value is determined as c of No. 1 single battery of the type of vehicle or the type of battery system 1 The value is obtained. Similarly, the number of the 75 quantiles of the mean difference value of the No. 2 single battery can be calculated>
25 quantile>
Coefficient of threshold c 2 Then the upper threshold Y is obtained 2 (ii) a …; the number of the greater than or equal to 75 quantiles of the mean value difference value of the number n single batteries can also be calculated>
25 quantile->
Coefficient of threshold c n Then the upper threshold Y is obtained n And will not be described herein. According to the formula, the upper limit threshold (Y) of each battery cell can be known 1 、Y 2 、Y n ) The voltage of the single battery cell can be changed in real time, and is not a fixed value.
Step S343 includes: and comparing each mean value difference value of each single battery with an upper limit threshold value of each single battery, and determining that each single battery is an abnormal single battery when the mean value difference values of a continuous preset number are greater than the upper limit threshold value, and the abnormal time of each single battery is a collection time included in a second collection time period corresponding to one mean value difference value of the mean value difference values of the continuous preset number.
The preset number may be set by a user according to the recognition effect, and is not limited herein, for example, the preset number may be equal to 4. For convenience of understanding, the above steps are described by taking a No. 1 single battery as an example: each data of the first column of the matrix S (e.g.
…) and an upper threshold Y 1 Comparing, if a preset number (such as 4) of continuous data greater than Y 1 Time (assume presence +>
Are all greater than Y 1 ) Determining the single battery 1 as an abnormal single battery, wherein the abnormal time is a collection time included in a second collection time period corresponding to one of the average difference values in the preset number of the average difference values (if the abnormal time is the average difference value ≥)>
A corresponding one of the second acquisition periods comprises an acquisition instant). In the above, the abnormal time may be specifically defined by which of the acquisition times included in the second acquisition time period corresponding to which of the mean difference values corresponds may be specified by the user. For example, the abnormal time is defined as a first acquisition time included in a second acquisition time period corresponding to a first mean difference value in a continuous preset number. Then in the above example the first mean difference value is ≥ l>
According to>
The calculation formula shows that the acquisition time related to the second acquisition time interval No. 0 comprises a first acquisition time interval No. 0, a first acquisition time interval No. 1, … and a first acquisition time interval No. 2 k-1; and the acquisition time corresponding to the first acquisition time interval No. 0 comprises: acquisition time 0, acquisition time 1, acquisition time … and acquisition time m. Therefore, in the above example, the abnormal time is the first mean difference value ≧ greater>
And the No. 0 acquisition time corresponding to the No. 0 second acquisition time period.
Through the technical scheme, the multiple groups of voltage data of the single batteries of the battery system are researched and tested, and the statistics mode is combined, so that the situation that whether the electric connection of each single battery is abnormal or not is identified, and the time when the electric connection of each single battery is abnormal can be output.
Based on the inventive concept, the embodiment of the disclosure further provides a battery electrical connection abnormality recognition device. Fig. 5 is a block diagram illustrating a battery electrical connection abnormality recognition apparatus according to an embodiment of the present disclosure. As shown in fig. 5, the battery electrical connection abnormality recognition apparatus includes:
the voltage acquisition module 10 is configured to acquire multiple sets of voltage data of a battery system, where the battery system includes multiple single batteries, and each set of voltage data includes a collection time and a voltage value of each single battery collected at the collection time.
And the median acquisition module 20 is configured to calculate the median of each group of voltage data to obtain multiple groups of voltage median data, where each group of voltage median data includes a collection time and the median of the voltage values of all the single batteries of the battery system collected at the collection time.
The identification module 30 is configured to determine, according to the multiple sets of voltage data and the multiple sets of voltage median data, an abnormal single cell in the battery system and an abnormal time of the abnormal single cell, where the abnormal single cell is a single cell in the battery system that is electrically connected abnormally, and the abnormal time is a collection time when the abnormal single cell is abnormal.
Through the technical scheme, based on the multiple groups of voltage data and the multiple groups of voltage median data, the abnormal single batteries can be identified (namely, the abnormal single batteries can be identified) in the battery system, and the abnormal time of the abnormal single batteries can be identified.
Optionally, the voltage obtaining module is specifically configured to obtain voltage data, which are collected at a plurality of collection moments when the battery system is in a discharge state and an output current value is within a first preset range, so as to obtain a plurality of groups of voltage data.
Optionally, when the battery system is applied to a new energy automobile, the voltage acquisition module is specifically configured to acquire voltage data of the battery system acquired at a plurality of acquisition moments when the driving speed of the new energy automobile is greater than a preset speed, so as to obtain a plurality of sets of voltage data.
Optionally, the apparatus further comprises: the device comprises an original voltage acquisition module and a deletion module.
And the original voltage acquisition module is used for acquiring original voltage data of the battery system.
And the deleting module is used for deleting invalid voltage data in the original voltage data to obtain valid voltage data, wherein the invalid voltage data comprises data of which the voltage value is abnormal characters, the voltage value is null and the voltage value exceeds a second preset range in the original voltage data.
Then, the voltage obtaining module is configured to obtain multiple sets of voltage data from the effective voltage data.
Optionally, the identification module comprises:
and the difference submodule is used for carrying out difference on the voltage value of each single battery at each acquisition moment and the voltage value at the previous acquisition moment according to the multiple groups of voltage data, accumulating the square of the difference obtained by difference by using a first preset step length to obtain multiple groups of difference accumulated data, wherein each group of difference accumulated data comprises a first acquisition time interval and the difference accumulated value of each single battery in the first acquisition time interval, and the first acquisition time interval is determined according to the first preset step length and the acquisition moment.
And the median difference submodule is used for differentiating the median of the voltage value at each acquisition moment with the median of the voltage value at the previous acquisition moment according to the multiple groups of voltage median data, and accumulating the squares of the differences obtained by the differentiation by a first preset step length to obtain multiple groups of median difference accumulated data, wherein each group of median difference accumulated data comprises the first acquisition time period and the median difference accumulated value in the first acquisition time period.
And the ratio submodule is used for acquiring the ratio of the difference accumulated value of each single battery in each first acquisition time interval to the median difference accumulated value to obtain a plurality of groups of ratio data, and each group of ratio data comprises the first acquisition time interval and the ratio of each single battery in the first acquisition time interval.
And the ratio identification submodule is used for determining abnormal single batteries in the battery system and abnormal moments of the abnormal single batteries according to the multiple groups of ratio data, wherein the abnormal moments are acquisition moments included in a first acquisition time period when the abnormal single batteries have abnormal ratios.
Optionally, the ratio identifier sub-module comprises:
and the first submodule is used for differentiating the average value of the ratio of each single battery in each second preset step with the average value of the previous second step according to the multiple groups of ratio data to obtain multiple groups of mean value differential data, each group of mean value differential data comprises a second acquisition time interval and a mean value differential value of each single battery in the second acquisition time interval, and the second acquisition time interval is determined according to the second preset step and the first acquisition time interval.
And the second submodule is used for acquiring the upper limit threshold of the mean difference value of each single battery according to the multiple groups of mean difference data.
And the third submodule is used for comparing each mean value difference value of each single battery with the upper limit threshold of the single battery, and determining abnormal single batteries in the battery system and abnormal time of the abnormal single batteries, wherein the abnormal time is acquisition time included in a second acquisition time period when the abnormal single batteries have abnormal mean value difference values.
Optionally, the second sub-module is specifically configured to sort the multiple mean difference values of each single battery according to the multiple sets of mean difference data, and obtain a 75-quantile and a 25-quantile of the mean difference value of each single battery; and determining the upper limit threshold value of each single battery according to the 75 quantile and the 25 quantile of each single battery.
The third submodule is specifically configured to compare, for each of the single batteries, each mean difference value of the single battery with an upper threshold of the single battery, and when a preset number of consecutive mean difference values are greater than the upper threshold, determine that the single battery is an abnormal single battery, and an abnormal time of the single battery is a collection time included in a second collection time period corresponding to one mean difference value of the preset number of consecutive mean difference values.
Through the technical scheme, the multiple groups of voltage data of the single batteries of the battery system are researched and tested, and the statistics mode is combined, so that the situation that whether the electric connection of each single battery is abnormal or not is identified, and the time when the electric connection of each single battery is abnormal can be output.
With regard to the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be described in detail here.
Fig. 6 is a block diagram illustrating an electronic device 700 according to an example embodiment. As shown in fig. 6, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps in the above-mentioned method for identifying abnormal battery electrical connection. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically Erasable Programmable Read-Only Memory (EEPROM), erasable Programmable Read-Only Memory (EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination thereof, which is not limited herein. The corresponding communication component 705 may thus include: 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 (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described battery electrical connection abnormality recognition method.
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 electrical connection abnormality identification method. For example, the computer readable storage medium may be the memory 702 including the program instructions, which are executable by the processor 701 of the electronic device 700 to perform the battery electrical connection abnormality recognition method described above.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure as long as it does not depart from the gist of the present disclosure.
Claims (10)
1. A battery electrical connection abnormality recognition method, characterized by comprising:
acquiring multiple groups of voltage data of a battery system, wherein the battery system comprises a plurality of single batteries, and each group of voltage data comprises an acquisition time and a voltage value of each single battery acquired at the acquisition time;
calculating the median of each group of voltage data to obtain a plurality of groups of voltage median data, wherein each group of voltage median data comprises an acquisition time and the median of voltage values of all single batteries of the battery system acquired at the acquisition time;
and determining abnormal single batteries in the battery system and abnormal moments of the abnormal single batteries according to the multiple groups of voltage data and the multiple groups of voltage median data, wherein the abnormal single batteries are the single batteries which are electrically connected in the battery system and are abnormal, and the abnormal moments are the acquisition moments of the abnormal single batteries.
2. The method of claim 1, wherein determining abnormal cells in the battery system according to the plurality of sets of voltage data and the plurality of sets of voltage median data, and the abnormal time of the abnormal cells comprises:
for the multiple groups of voltage data, differentiating the voltage value of each single battery at each acquisition time with the voltage value at the previous acquisition time, and accumulating the square of the difference obtained by differentiation by using a first preset step length to obtain multiple groups of differential accumulated data, wherein each group of differential accumulated data comprises a first acquisition time period and a differential accumulated value of each single battery in the first acquisition time period, and the first acquisition time period is determined according to the first preset step length and the acquisition time;
for the multiple groups of voltage median data, differentiating the voltage median at each acquisition moment with the voltage median at the previous acquisition moment, and accumulating the squares of the differential values obtained by the differentiation by a first preset step length to obtain multiple groups of median differential accumulated data, wherein each group of median differential accumulated data comprises the first acquisition time period and the median differential accumulated value in the first acquisition time period;
acquiring the ratio of the difference accumulated value of each single battery to the median difference accumulated value in each first acquisition time period to obtain multiple groups of ratio data, wherein each group of ratio data comprises the first acquisition time period and the ratio of each single battery in the first acquisition time period;
and determining abnormal single batteries in the battery system and abnormal time of the abnormal single batteries according to the multiple groups of ratio data, wherein the abnormal time is acquisition time included in a first acquisition time period when the abnormal single batteries have abnormal ratios.
3. The method of claim 2, wherein determining abnormal cells in the battery system according to the plurality of sets of ratio data, and the abnormal time of the abnormal cells comprises:
for the multiple groups of ratio data, differentiating the average value of the ratio of each single battery in each second preset step length with the average value of the previous second step length to obtain multiple groups of mean value differential data, wherein each group of mean value differential data comprises a second acquisition time period and a mean value differential value of each single battery in the second acquisition time period, and the second acquisition time period is determined according to the second preset step length and the first acquisition time period;
acquiring an upper limit threshold of the mean difference value of each single battery according to the multiple groups of mean difference data;
and comparing each mean value difference value of each single battery with the upper limit threshold value of the single battery, and determining abnormal single batteries in the battery system and abnormal time of the abnormal single batteries, wherein the abnormal time is acquisition time included in a second acquisition time period when the abnormal single batteries have the abnormal mean value difference value.
4. The method according to claim 3, wherein obtaining an upper threshold of the mean difference value of each of the single batteries according to the plurality of sets of mean difference data comprises: sequencing the plurality of mean difference values of each single battery according to the plurality of groups of mean difference data to obtain 75 quantiles and 25 quantiles of the mean difference values of each single battery; determining an upper limit threshold value of each single battery according to the 75 quantile and the 25 quantile of each single battery;
comparing each mean difference value of each single battery with the upper limit threshold of the single battery, and determining abnormal single batteries in the battery system, wherein the abnormal time of the abnormal single batteries comprises: and comparing each mean value difference value of each single battery with an upper limit threshold value of each single battery, and determining that each single battery is an abnormal single battery when the mean value difference values of a continuous preset number are greater than the upper limit threshold value, and the abnormal time of each single battery is a collection time included in a second collection time period corresponding to one mean value difference value of the mean value difference values of the continuous preset number.
5. The method of claim 1, wherein the step of obtaining a plurality of sets of voltage data for the battery system comprises:
and acquiring voltage data acquired at a plurality of acquisition moments when the battery system is in a discharge state and the output current value is within a first preset range to obtain a plurality of groups of voltage data.
6. The method of claim 1, wherein the step of obtaining a plurality of sets of voltage data of the battery system when the battery system is applied to a new energy vehicle comprises:
and acquiring voltage data of the battery system acquired by the new energy automobile at a plurality of acquisition moments when the running speed of the new energy automobile is higher than a preset speed to obtain a plurality of groups of voltage data.
7. The method of any of claims 1-6, wherein prior to acquiring the plurality of sets of voltage data for the battery system, the method further comprises:
acquiring original voltage data of the battery system;
deleting invalid voltage data in the original voltage data to obtain valid voltage data, wherein the invalid voltage data comprises data of which the voltage value is abnormal characters, the voltage value is null and the voltage value exceeds a second preset range in the original voltage data;
acquiring multiple sets of voltage data of the battery system comprises: and acquiring multiple groups of voltage data from the effective voltage data.
8. An apparatus for recognizing abnormality in electrical connection of a battery, said apparatus comprising:
the battery system comprises a plurality of single batteries, wherein each group of voltage data comprises an acquisition time and a voltage value of each single battery acquired at the acquisition time;
the median acquisition module is used for calculating the median of each group of voltage data to obtain a plurality of groups of voltage median data, wherein each group of voltage median data comprises an acquisition time and the median of the voltage values of all the single batteries of the battery system acquired at the acquisition time;
and the identification module is used for determining abnormal single batteries in the battery system and abnormal moments of the abnormal single batteries according to the multiple groups of voltage data and the multiple groups of voltage median data, wherein the abnormal single batteries are the single batteries which are electrically connected in the battery system and are abnormal, and the abnormal moments are the abnormal acquisition moments of the abnormal single batteries.
9. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. The utility model provides a new energy automobile which characterized in that includes:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
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