CN115648944A - Early warning method, device, equipment and storage medium for power battery - Google Patents
Early warning method, device, equipment and storage medium for power battery Download PDFInfo
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Abstract
The invention relates to the field of data processing, and discloses an early warning method, an early warning device, early warning equipment and a storage medium for a power battery. The method comprises the following steps: acquiring respective voltage monitoring data of a plurality of battery monomers under a preset working condition; processing the voltage monitoring data to delete abnormal data or correct the abnormal data to obtain processed voltage monitoring data; determining the absolute value of the voltage difference between the voltage of each battery monomer and the average voltage at each sampling moment based on the processed voltage monitoring data; determining a mean and a variance of the absolute values; respectively determining a mean extremum offset and a variance extremum offset, a mean extremum offset rate and a variance extremum offset rate based on the mean and the variance; and determining whether an early warning condition is met according to one or more of the mean extremum offset, the variance extremum offset, the mean extremum offset rate and the variance extremum offset rate. The embodiment improves the early warning accuracy.
Description
Technical Field
The invention relates to the field of data processing, in particular to an early warning method, an early warning device, early warning equipment and a storage medium for a power battery.
Background
The power battery is a key component of the electric vehicle, and if the power battery fails, the normal use of the electric vehicle is directly influenced. And with the rapid development of electric vehicles, the importance of safety early warning of the power battery becomes more prominent.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
In order to solve the technical problems, the invention provides an early warning method, an early warning device, early warning equipment and a storage medium for a power battery, and the early warning accuracy is improved.
The embodiment of the invention provides an early warning method of a power battery, which comprises the following steps: acquiring respective voltage monitoring data of a plurality of battery monomers under a preset working condition;
processing the voltage monitoring data to delete abnormal data or correct the abnormal data to obtain processed voltage monitoring data;
determining an absolute value of a voltage difference between each single battery voltage and an average voltage at each sampling time based on the processed voltage monitoring data, wherein the average voltage is an average value of the plurality of single battery voltages at the corresponding sampling time;
determining a mean and a variance of the absolute values;
determining a mean extremum offset and a variance extremum offset based on the mean and the variance, respectively;
if the preset condition is met, respectively determining a mean extreme value migration rate and a variance extreme value migration rate based on the mean value and the variance;
determining whether an early warning condition is met according to one or more of the mean extremum offset, the variance extremum offset, the mean extremum offset rate and the variance extremum offset rate;
and if the early warning condition is met, early warning is carried out.
The embodiment of the invention provides an early warning device of a power battery, which comprises:
the acquisition module is used for acquiring voltage monitoring data of each of the plurality of battery monomers under a preset working condition;
the processing module is used for processing the voltage monitoring data to delete abnormal data or correct the abnormal data to obtain processed voltage monitoring data;
a first determining module, configured to determine, based on the processed voltage monitoring data, an absolute value of a voltage difference between each battery cell voltage and an average voltage at each sampling time, where the average voltage is an average value of the plurality of battery cell voltages at the corresponding sampling time;
a second determining module for determining a mean and a variance of the absolute values;
a third determining module for determining a mean extremum offset and a variance extremum offset based on the mean and the variance, respectively;
the fourth determining module is used for respectively determining a mean extreme value migration rate and a variance extreme value migration rate based on the mean value and the variance if the preset conditions are met;
a fifth determining module, configured to determine whether an early warning condition is met according to one or more of the mean extremum offset, the variance extremum offset, the mean extremum offset rate, and the variance extremum offset rate;
and the early warning module is used for carrying out early warning if the early warning condition is met.
An embodiment of the present invention provides an electronic device, including:
a processor and a memory;
the processor is used for executing the steps of the early warning method of the power battery in any embodiment by calling the program or the instruction stored in the memory.
The embodiment of the invention provides a computer-readable storage medium, which stores a program or instructions, wherein the program or instructions enable a computer to execute the steps of the early warning method of the power battery in any embodiment.
The embodiment of the invention has the following technical effects:
the method comprises the steps of monitoring data by acquiring respective voltages of a plurality of battery monomers under a preset working condition; processing the voltage monitoring data to delete abnormal data or correct the abnormal data to obtain processed voltage monitoring data; determining an absolute value of a voltage difference between each single battery voltage and an average voltage at each sampling time based on the processed voltage monitoring data, wherein the average voltage is an average value of the plurality of single battery voltages at the corresponding sampling time; determining a mean and a variance of the absolute values; determining a mean extremum offset and a variance extremum offset based on the mean and the variance, respectively; if the preset conditions are met, respectively determining a mean extreme value migration rate and a variance extreme value migration rate based on the mean value and the variance; determining whether an early warning condition is met according to one or more of the mean extremum offset, the variance extremum offset, the mean extremum offset rate and the variance extremum offset rate; if the early warning condition is met, early warning is carried out, and the early warning accuracy is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an early warning method for a power battery according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an early warning method for a power battery according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the disclosed embodiments are merely exemplary of the invention, and are not intended to be exhaustive or exhaustive. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The early warning method of the power battery provided by the embodiment of the invention can be executed by electronic equipment. Fig. 1 is a flowchart of an early warning method for a power battery according to an embodiment of the present invention. Referring to fig. 1, the early warning method of the power battery specifically includes the following steps:
and S110, acquiring respective voltage monitoring data of the plurality of battery monomers under the preset working condition.
The preset working condition can be a charging working condition or a discharging working condition.
The method comprises the steps of obtaining respective voltage monitoring data of a plurality of battery monomers under a preset working condition, namely obtaining the voltage monitoring data of each battery monomer respectively.
Optionally, the respective voltage monitoring data of the multiple battery cells under the preset working condition may be periodically obtained according to a set frequency.
And S120, processing the voltage monitoring data to delete abnormal data or correct the abnormal data to obtain the processed voltage monitoring data.
Illustratively, the processing the voltage monitoring data to delete abnormal data or correct abnormal data to obtain processed voltage monitoring data includes the following substeps:
121. and determining abnormal voltage data in the voltage monitoring data based on a preset mode.
The preset mode may be a mode based on a preset numerical range or a mode based on data integrity. Specifically, a reasonable data range of the voltage of the battery cell is preset, and if the voltage monitoring data is not in the data range, the voltage monitoring data can be determined to be abnormal voltage data. And/or if the voltage monitoring data is a null value, determining that the voltage monitoring data is abnormal voltage data.
122. If the proportion of the number of the battery monomers with abnormal voltage data to the total number of the battery monomers at the target acquisition time exceeds a first threshold value, determining all the voltage monitoring data at the target acquisition time as abnormal data, and otherwise determining all the voltage monitoring data at the target acquisition time as non-abnormal data.
123. And if all the voltage monitoring data at the target acquisition moment are abnormal data, deleting all the voltage monitoring data at the target acquisition moment to obtain processed voltage monitoring data.
124. And if all the voltage monitoring data at the target acquisition moment are non-abnormal data, replacing the abnormal voltage data with the average value of the normal voltage data at the target acquisition moment to obtain the processed voltage monitoring data.
S130, determining an absolute value of a voltage difference between each single battery voltage and an average voltage at each sampling time based on the processed voltage monitoring data, wherein the average voltage is an average value of the multiple single battery voltages at the corresponding sampling time.
And setting a total of N time sampling points in a certain charging working condition, and setting the number of the single batteries as M. Marking the voltage of the jth battery cell at the ith time sampling point in the charging working condition as the voltage of the jth battery cellU ij I =1,2,3.. N, j =1,2,3.. M. Calculating the average voltage of all the battery cells at the ith time sampling point in the working condition
. Using the voltage of all the battery cells at the ith time sampling point in the working conditionU ij Subtract the average voltage of all the cells at the ith time sampling point
Obtaining the absolute value delta of the voltage difference between the single cell voltage and the average voltageU ij I.e. by
。
And S140, determining the mean and the variance of the absolute values.
Determining a mean and a variance of the absolute values based on:
wherein, the first and the second end of the pipe are connected with each other,U mean_j denotes the absolute value ΔU ij The average value of (a) of (b),ithe reference numerals indicating the time sampling points are,jthe reference numerals indicating the battery cells are given to,Mrepresents the total number of the battery cells,Nrepresents the total number of time sample points,U std_j denotes the absolute value ΔU ij The variance of (c).
And S150, respectively determining a mean extreme value offset and a variance extreme value offset based on the mean and the variance.
Illustratively, the determining the mean extremum offset and the variance extremum offset based on the mean and the variance, respectively, includes:
determining an upper-mean quartile, a lower-mean quartile, a maximum mean value and a minimum mean value based on the mean values of the battery cells;
determining an upper variance quartile, a lower variance quartile, a maximum variance and a minimum variance based on the variance of each battery cell;
determining the mean extremum offset and the variance extremum offset based on:
flag mean =max（flag mean_max ，flag mean_min ）
flag std = max（flag std_max ，flag std_min ）
flag mean_max =(U mean_max - Q mean_1 )/( Q mean_1 - Q mean_2 )
flag mean_min =(Q mean_2 - U mean_min )/( Q mean_1 - Q mean_2 )
flag std_max =(U std_max - Q std_1 )/( Q std_1 - Q std_2 )
flag std_min =(Q std_2 - U std_min )/( Q std_1 - Q std_2 )
wherein the content of the first and second substances,flag mean the amount of mean extremum shift is represented,flag std representing the variance extremum offset, max () is a function that takes the maximum value,flag mean_max the mean-maximum offset is represented as,flag mean_min the mean-min offset is represented as,flag std_max the offset of the maximum value of the variance is represented,flag std_min the variance minimum offset is represented as the offset of,U mean_max the maximum value of the mean value is represented,U mean_min the minimum value of the mean value is represented,U std_max the maximum value of the variance is represented,U std_min the minimum value of the variance is represented,Q mean_1 the number of quartiles on the mean is expressed,Q mean_2 the number of quartiles below the mean is expressed,Q std_1 representing the number of quartiles over the variance,Q std_2 representing the quartile under variance.
And S160, if the preset condition is met, respectively determining a mean extreme value migration rate and a variance extreme value migration rate based on the mean value and the variance.
The preset condition may be that the execution times of the accumulated preset working condition exceed the set times Num.
The determining a mean extremum shift rate and a variance extremum shift rate based on the mean and the variance, respectively, comprises:
respectively determining the mean extreme value offset and the variance extreme value offset under the preset working condition based on the mean value and the variance;
recording the mean extreme value offset and the variance extreme value offset which are closest to the current time and are set for times under the preset working condition;
taking the natural number sequence 1 to the set times as an abscissa, and respectively taking the mean extreme value offset and the variance extreme value offset under the preset working condition corresponding to the times as an ordinate, and obtaining a mean extreme value offset curve and a variance extreme value offset curve through linear fitting;
and determining the slope of the mean extremum offset curve as a mean extremum offset rate, and determining the slope of the variance extremum offset curve as a variance extremum offset rate.
Taking a preset working condition as a charging working condition as an example, if the execution times of the accumulated charging working condition exceeds the set times Num, recording the mean extreme value offset and the variance extreme value offset of the nearest Num charging working condition, taking a natural number sequence 1 to the set times as an abscissa, respectively taking the mean extreme value offset and the variance extreme value offset under the preset working condition of the corresponding times as an ordinate, and obtaining a mean extreme value offset curve and a variance extreme value offset curve through linear fitting; and determining the slope of the mean extremum offset curve as a mean extremum offset rate, and determining the slope of the variance extremum offset curve as a variance extremum offset rate.
S170, determining whether an early warning condition is met according to one or more of the mean extremum offset, the variance extremum offset, the mean extremum offset rate and the variance extremum offset rate.
Illustratively, if the preset condition is not met, comparing the mean extremum offset with a first mean threshold value, and comparing the variance extremum offset with a first variance threshold value; if the mean extreme value offset is larger than a first mean threshold value or the variance extreme value offset is larger than a first variance threshold value, determining that an early warning condition is met;
if the preset condition is met, comparing the mean extreme value offset with a first mean threshold, comparing the variance extreme value offset with a first variance threshold, comparing the mean extreme value offset rate with a second mean threshold, and comparing the variance extreme value offset rate with a second variance threshold; and if the mean extreme value offset is greater than a first mean threshold, or the variance extreme value offset is greater than a first variance threshold, or the mean extreme value offset rate is greater than a second mean threshold, or the variance extreme value offset rate is greater than a second variance threshold, determining that the early warning condition is met.
Optionally, taking the preset working condition as a charging working condition as an example, if the execution times of the current charging working condition does not exceed Num times, comparing the mean extreme value offset and the variance extreme value offset under the current charging working condition with corresponding thresholds respectively, and if a certain index (the mean extreme value offset or the variance extreme value offset) is greater than the corresponding threshold, performing an early warning that the single offset is too large.
If the execution times of the current charging working condition exceeds Num times, respectively comparing the mean extreme value offset rate, the variance extreme value offset rate, the mean extreme value offset and the variance extreme value offset under the current charging working condition with corresponding thresholds, and if a certain index (the mean extreme value offset rate, the variance extreme value offset rate, the mean extreme value offset or the variance extreme value offset) is larger than the corresponding threshold, performing the over-large single offset early warning.
And S180, if the early warning condition is met, early warning is carried out.
Similarly, if the preset working condition is a discharging working condition, the early warning discrimination logic is similar to the early warning discrimination logic of the charging working condition, and the difference is that the first variance threshold, the first mean threshold, the second mean threshold, and the second variance threshold under the charging working condition are different from the first variance threshold, the first mean threshold, the second mean threshold, and the second variance threshold under the discharging working condition.
Optionally, referring to a schematic flow chart of an early warning method for a power battery shown in fig. 2, the method specifically includes:
and step 210, acquiring monitoring data of the vehicle charging state and the battery cell voltage, and identifying whether the vehicle is in a charging working condition or not based on the mark number of the vehicle charging state field in the monitoring data.
For example, the flag number of 1 indicates that the vehicle is currently in a parking charging state, and the flag number of 3 indicates that the vehicle is currently in an uncharged state.
And step 220, if the vehicle is in a charging working condition, cleaning the voltage monitoring data to delete abnormal data or correct the abnormal data to obtain the processed voltage monitoring data.
And step 230, calculating the absolute value of the voltage difference between the single voltage and the average voltage at each sampling moment.
And 240, when the current charging working condition is finished, calculating the mean value and the variance of the pressure difference of each battery monomer in the charging working condition.
And step 250, calculating the extreme value offset of the single battery.
And step 260, if the accumulated charging working condition number is larger than the time threshold, executing step 270a, otherwise executing step 270b.
And 270a, calculating the extreme value deviation rate of the single battery under the charging working conditions of the threshold value of the latest times.
And 271a, judging whether the extreme value deviation rate is in the threshold range, if so, executing a step 270b, and otherwise, early warning.
And 270b, judging whether the extreme value offset is in the threshold range, if so, executing a step 271b, and otherwise, performing early warning.
271b, recording the extreme value offset under the current charging working condition, and entering the next charging working condition.
For example, the determining whether the warning condition is satisfied according to one or more of the mean extremum shift amount, the variance extremum shift amount, the mean extremum shift rate, and the variance extremum shift rate may further include:
if the number of the current charging working conditions does not exceed Num times, the mean extreme value offset and the variance extreme value offset under the current working conditions are respectively compared with the corresponding preset thresholds, and if a certain index is larger than the corresponding threshold, the single offset is recorded to be abnormal. And if the monomer offset is abnormal in three continuous working conditions, carrying out the over-large early warning on the monomer offset.
And if the number of the current charging working conditions exceeds Num times, comparing the mean extreme value offset rate, the variance extreme value offset rate, the mean extreme value offset and the variance extreme value offset under the current working conditions with respective corresponding preset thresholds respectively. And if the offset rate and the offset are both larger than respective threshold values, immediately carrying out early warning of overlarge monomer offset.
And if the number of the current charging working conditions exceeds Num times, comparing the mean extreme value offset rate, the variance extreme value offset rate, the mean extreme value offset and the variance extreme value offset under the current working conditions with respective corresponding preset thresholds. And if only the offset is larger than the threshold value, recording that the monomer offset is abnormal, and if the monomer offset in three continuous working conditions is abnormal, performing an over-large monomer offset early warning.
And if the number of the current charging working conditions exceeds Num times, comparing the mean extreme value offset rate, the variance extreme value offset rate, the mean extreme value offset and the variance extreme value offset under the current working conditions with respective corresponding preset thresholds. And if the deviation rates are larger than the threshold values, recording the abnormal deviation rate of the monomer, and if the deviation rates of the monomer in the six continuous working conditions are abnormal, performing early warning on the excessive deviation rate of the monomer.
The technical scheme of the embodiment of the invention is as follows:
the method for recognizing and processing the abnormal data of the single voltage of the vehicle is provided, a judgment method for time frame abnormality is determined according to the abnormal condition of the single voltage, a processing method is provided according to slightly abnormal single voltage time frame data, and the influence of abnormal data on vehicle safety early warning is reduced.
And the power battery early warning evaluation index of the extreme value offset is provided. And calculating the extreme value deviation degree of the mean voltage difference quantity aiming at the single battery voltage. Compared with the traditional method, the method can more intuitively express the offset degree of the extreme single body in the single body voltage data. The size of the deviation degree determines the usability of the single battery, so that the safety early warning accuracy of the single battery can be improved. Meanwhile, the method is suitable for charging/discharging working conditions, and the safety early warning models in various working conditions can ensure the safety of the vehicle to a greater extent.
And the power battery early warning evaluation index of the extreme value deviation rate is provided.
Aiming at the extreme value offset, the index for measuring the offset degree of the extreme value single body provides the concept of extreme value offset rate. The extreme value deviation rate represents the deviation change rate condition of the extreme value single battery along with the charge and discharge use of the vehicle battery. Different from the traditional safety early warning method in a single working condition, the extreme value deviation rate early warning method based on a plurality of charging and discharging working conditions can greatly reduce the false alarm phenomenon caused by extreme conditions in the single working condition and improve the early warning accuracy. Meanwhile, the extreme value deviation rate is provided, so that the prediction of the residual service life of the power battery is greatly promoted.
An effective processing and cleaning method is established for abnormal data of the battery monomer, and data filling treatment is carried out on slight abnormal data, so that the influence of the abnormal data on safety early warning accuracy can be reduced. Extreme value offset is selected as an early warning index, wherein the upper quartile and the lower quartile are utilized, so that the false alarm phenomenon of extreme fluctuation of data under a single working condition is reduced, and compared with the traditional method, the method has the advantages of more effective and precise calculation and lower model false alarm rate. The extreme value offset rate is selected as an early warning index, index change conditions in a plurality of working conditions are used as early warning conditions by the extreme value offset rate early warning method based on a plurality of charging and discharging working conditions, the phenomenon of false alarm can be reduced, potential aggravated potential safety hazards of vehicles can be identified, and early warning accuracy is improved. The extreme value offset and the extreme value offset rate selected at this time are used for balancing the abnormal condition of the offset between the monomers, but not for focusing on the numerical fluctuation condition of a certain monomer, so that the method is suitable for various complex working conditions such as charging and discharging.
It should be noted that the indicators (mean extremum shift rate, variance extremum shift rate) in the present invention can also be used as the determination indicators for predicting the remaining life of the battery. The non-linear curve relation between the battery full life cycle characteristic and the extreme value deviation rate can be established through laboratory conditions, and therefore the battery life can be predicted.
The embodiment of the invention also provides an early warning device for the power battery, which comprises: the acquisition module is used for acquiring voltage monitoring data of each of the plurality of battery monomers under a preset working condition; the processing module is used for processing the voltage monitoring data to delete abnormal data or correct the abnormal data to obtain processed voltage monitoring data; a first determining module, configured to determine, based on the processed voltage monitoring data, an absolute value of a voltage difference between each battery cell voltage and an average voltage at each sampling time, where the average voltage is an average value of the plurality of battery cell voltages at the corresponding sampling time; a second determining module for determining a mean and a variance of the absolute values; a third determining module for determining a mean extremum offset and a variance extremum offset based on the mean and the variance, respectively; the fourth determining module is used for respectively determining a mean extreme value migration rate and a variance extreme value migration rate based on the mean value and the variance if the preset conditions are met; a fifth determining module, configured to determine whether an early warning condition is met according to one or more of the mean extremum offset, the variance extremum offset, the mean extremum offset rate, and the variance extremum offset rate; and the early warning module is used for carrying out early warning if the early warning condition is met.
The early warning process of the embodiment of the invention can be used for a hardware warning device, and the hardware warning device is directly connected with a vehicle, collects vehicle data in real time and is used as an independent warning device to ensure the safety of users. The early warning process can also be used for being built on a safety early warning cloud platform, receiving real-time data acquired by vehicles and feeding early warning model results back to the vehicles and users.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 3, the electronic device 400 includes one or more processors 401 and memory 402.
The processor 401 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.
In one example, the electronic device 400 may further include: an input device 403 and an output device 404, which are interconnected by a bus system and/or other form of connection mechanism (not shown). The input device 403 may include, for example, a keyboard, a mouse, and the like. The output device 404 can output various information to the outside, including warning prompt information, braking force, and the like. The output devices 404 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 400 relevant to the present invention are shown in fig. 3, omitting components such as buses, input/output interfaces, and the like. In addition, electronic device 400 may include any other suitable components depending on the particular application.
In addition to the above-described methods and apparatus, embodiments of the present invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of the power cell warning method provided by any of the embodiments of the present invention.
The computer program product may write program code for carrying out operations for embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the power battery warning method provided by any of the embodiments of the present invention.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present application. As used in this specification, the terms "a", "an" and/or "the" are not intended to be inclusive of the singular, but rather are intended to be inclusive of the plural, unless the context clearly dictates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of additional like elements in a process, method, or apparatus that comprises the element.
It is further noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," "coupled," and the like are to be construed broadly and encompass, for example, both fixed and removable coupling or integral coupling; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.
Claims (9)
1. The early warning method of the power battery is characterized by comprising the following steps:
acquiring respective voltage monitoring data of a plurality of battery monomers under a preset working condition;
processing the voltage monitoring data to delete abnormal data or correct the abnormal data to obtain processed voltage monitoring data;
determining an absolute value of a voltage difference between each single battery voltage and an average voltage at each sampling time based on the processed voltage monitoring data, wherein the average voltage is an average value of the plurality of single battery voltages at the corresponding sampling time;
determining a mean and a variance of the absolute values;
determining a mean extremum offset and a variance extremum offset based on the mean and the variance, respectively;
if the preset conditions are met, respectively determining a mean extreme value migration rate and a variance extreme value migration rate based on the mean value and the variance;
determining whether an early warning condition is met according to one or more of the mean extremum offset, the variance extremum offset, the mean extremum offset rate and the variance extremum offset rate;
and if the early warning condition is met, early warning is carried out.
2. The processing method according to claim 1, wherein the processing the voltage monitoring data to delete abnormal data or correct abnormal data to obtain processed voltage monitoring data comprises:
determining abnormal voltage data in the voltage monitoring data based on a preset mode;
if the proportion of the number of the battery monomers with abnormal voltage data to the total number of the battery monomers at the target acquisition time exceeds a first threshold value, determining all the voltage monitoring data at the target acquisition time as abnormal data, and otherwise determining all the voltage monitoring data at the target acquisition time as non-abnormal data;
if all the voltage monitoring data at the target acquisition moment are abnormal data, deleting all the voltage monitoring data at the target acquisition moment to obtain processed voltage monitoring data;
and if all the voltage monitoring data at the target acquisition moment are non-abnormal data, replacing the abnormal voltage data with the average value of the normal voltage data at the target acquisition moment to obtain the processed voltage monitoring data.
3. The processing method of claim 1, wherein determining a mean extremum offset and a variance extremum offset based on the mean and the variance, respectively, comprises:
determining an upper-mean quartile, a lower-mean quartile, a maximum mean value and a minimum mean value based on the mean values of the battery cells;
determining an upper variance quartile, a lower variance quartile, a maximum variance and a minimum variance based on the variance of each battery cell;
determining the mean extremum offset and the variance extremum offset based on:
flag mean =max（flag mean_max ，flag mean_min ）
flag std = max（flag std_max ，flag std_min ）
flag mean_max =(U mean_max - Q mean_1 )/( Q mean_1 - Q mean_2 )
flag mean_min =(Q mean_2 - U mean_min )/( Q mean_1 - Q mean_2 )
flag std_max =(U std_max - Q std_1 )/( Q std_1 - Q std_2 )
flag std_min =(Q std_2 - U std_min )/( Q std_1 - Q std_2 )
wherein the content of the first and second substances,flag mean the amount of mean extremum shift is represented,flag std representing the variance extremum offset, max () is a function that takes the maximum value,flag mean_max the mean-maximum offset is represented as,flag mean_min the mean-min offset is represented as,flag std_max the offset of the maximum value of the variance is represented,flag std_min the variance minimum offset is represented as the offset of,U mean_max the maximum value of the mean value is represented,U mean_min the minimum value of the mean value is represented,U std_max the maximum value of the variance is represented,U std_min the minimum value of the variance is represented,Q mean_1 the number of quartiles on the mean is expressed,Q mean_2 the number of quartiles below the mean is expressed,Q std_1 representing the number of quartiles over the variance,Q std_2 representing the quartile under variance.
4. The method of claim 1, wherein determining a mean extremum shift rate and a variance extremum shift rate based on the mean and the variance, respectively, comprises:
respectively determining the mean extreme value offset and the variance extreme value offset under the preset working condition based on the mean value and the variance;
recording the mean extremum offset and the variance extremum offset of the preset working condition for the set number of times nearest to the current time;
taking the natural number sequence 1 to the set times as an abscissa, and respectively taking the mean extreme value offset and the variance extreme value offset under the preset working condition corresponding to the times as an ordinate, and obtaining a mean extreme value offset curve and a variance extreme value offset curve through linear fitting;
and determining the slope of the mean extremum offset curve as a mean extremum offset rate, and determining the slope of the variance extremum offset curve as a variance extremum offset rate.
5. The method of claim 1, wherein determining whether an early warning condition is satisfied based on one or more of the mean extremum shift amount, the variance extremum shift amount, the mean extremum shift rate, and the variance extremum shift rate comprises:
if the preset condition is not met, comparing the mean extreme value offset with a first mean threshold value, and comparing the variance extreme value offset with a first variance threshold value; if the mean extreme value offset is larger than a first mean threshold value or the variance extreme value offset is larger than a first variance threshold value, determining that an early warning condition is met;
if the preset condition is met, comparing the mean extreme value offset with a first mean threshold, comparing the variance extreme value offset with a first variance threshold, comparing the mean extreme value offset rate with a second mean threshold, and comparing the variance extreme value offset rate with a second variance threshold; and if the mean extreme value offset is greater than a first mean threshold, or the variance extreme value offset is greater than a first variance threshold, or the mean extreme value offset rate is greater than a second mean threshold, or the variance extreme value offset rate is greater than a second variance threshold, determining that the early warning condition is met.
6. The treatment method of claim 1, wherein the preset condition comprises a charging condition or a discharging condition.
7. The utility model provides a power battery's early warning device which characterized in that includes:
the acquisition module is used for acquiring voltage monitoring data of each of the plurality of battery monomers under a preset working condition;
the processing module is used for processing the voltage monitoring data to delete abnormal data or correct the abnormal data to obtain processed voltage monitoring data;
a first determining module, configured to determine, based on the processed voltage monitoring data, an absolute value of a voltage difference between each battery cell voltage and an average voltage at each sampling time, where the average voltage is an average value of the battery cell voltages at the corresponding sampling time;
a second determining module for determining a mean and a variance of the absolute values;
a third determining module, configured to determine a mean extremum offset and a variance extremum offset based on the mean and the variance, respectively;
the fourth determining module is used for respectively determining a mean extreme value migration rate and a variance extreme value migration rate based on the mean value and the variance if the preset conditions are met;
a fifth determining module, configured to determine whether an early warning condition is met according to one or more of the mean extremum offset, the variance extremum offset, the mean extremum offset rate, and the variance extremum offset rate;
and the early warning module is used for carrying out early warning if the early warning condition is met.
8. An electronic device, characterized in that the electronic device comprises:
a processor and a memory;
the processor is used for executing the steps of the early warning method of the power battery according to any one of claims 1 to 7 by calling the program or the instructions stored in the memory.
9. A computer-readable storage medium, characterized in that it stores a program or instructions that causes a computer to execute the steps of the early warning method of a power battery according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116008820A (en) * | 2023-03-24 | 2023-04-25 | 中国汽车技术研究中心有限公司 | Method, device and medium for detecting inconsistency of vehicle battery cells |
| CN116821618A (en) * | 2023-06-21 | 2023-09-29 | 宁波麦思捷科技有限公司武汉分公司 | Sea surface monitoring radar clutter suppression method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116008820A (en) * | 2023-03-24 | 2023-04-25 | 中国汽车技术研究中心有限公司 | Method, device and medium for detecting inconsistency of vehicle battery cells |
| CN116008820B (en) * | 2023-03-24 | 2023-10-10 | 中国汽车技术研究中心有限公司 | Method, device and medium for detecting inconsistency of vehicle battery cells |
| CN116821618A (en) * | 2023-06-21 | 2023-09-29 | 宁波麦思捷科技有限公司武汉分公司 | Sea surface monitoring radar clutter suppression method and system |
| CN116821618B (en) * | 2023-06-21 | 2024-03-15 | 宁波麦思捷科技有限公司武汉分公司 | Sea surface monitoring radar clutter suppression method and system |
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