CN115389965B - Battery safety performance testing system and method based on big data - Google Patents
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- 239000013543 active substance Substances 0.000 claims abstract description 49
- 238000004073 vulcanization Methods 0.000 claims abstract description 36
- UCKMPCXJQFINFW-UHFFFAOYSA-N Sulphide Chemical compound [S-2] UCKMPCXJQFINFW-UHFFFAOYSA-N 0.000 claims description 37
- 238000011056 performance test Methods 0.000 claims description 24
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- 238000009825 accumulation Methods 0.000 claims description 22
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- PIJPYDMVFNTHIP-UHFFFAOYSA-L lead sulfate Chemical compound [PbH4+2].[O-]S([O-])(=O)=O PIJPYDMVFNTHIP-UHFFFAOYSA-L 0.000 description 4
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention relates to the technical field of battery performance testing, in particular to a battery safety performance testing system and a battery safety performance testing method based on big data. The method analyzes the battery safety performance in the current state of the battery by combining the actual use condition of the battery by a user in historical data, accurately predicts the corresponding residual service life of the battery in different use states by considering the relation between the placing time and the electric quantity change of the battery, the relation between the cathode vulcanization rate and the power shortage time and the relation between the falling rate of the anode active substance and the working temperature of the battery, and realizes effective supervision on the battery safety performance.
Description
Technical Field
The invention relates to the technical field of battery performance testing, in particular to a battery safety performance testing system and method based on big data.
Background
The acid lead storage battery is used as a storage battery, has the characteristics of low cost, high cost performance, charging and repeated use, and is further frequently used as a power source on an electric vehicle by people.
In the daily use process of the acid-lead storage battery, along with the difference of the temperature in the use environment, the softening and falling speeds of the active substances in the positive plate of the battery are different, and the softening and falling of the active substances on the positive plate can reduce the capacity of the battery and seriously affect the service life of the battery; meanwhile, under the condition that the acid lead storage battery is not used for a long time, the electric quantity in the battery can be slowly reduced, if the duration is too long, the condition of battery power shortage can even occur, and the battery power shortage can result in vulcanization of the negative electrode of the battery, white hard lead sulfate crystals can be generated on a battery polar plate, the lead sulfate crystals are very difficult to be converted into lead during charging, the lead sulfate has poor conductivity and large resistance, the solubility and the dissolving speed are very low, the lead sulfate crystals are difficult to recover during charging, and then the capacity of the battery can be reduced, and the service life of the battery can be shortened.
The current battery safety performance test system based on big data only tests the battery performance under the high and low temperature, short circuit, explosion-proof, overcharge and overdischarge, low pressure, temperature cycle, vibration, acceleration impact, fall, extrusion, acupuncture, heavy object impact, heat abuse, combustion and washing states through the existing equipment, and then evaluates the comprehensive safety performance of the battery, but the prior art has a great defect, and the battery safety performance under the current state of the battery is analyzed without combining the specific actual use condition of the battery by users in historical data.
Disclosure of Invention
The present invention provides a battery safety performance testing system and method based on big data, so as to solve the problems proposed in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: a big data-based battery safety performance testing method comprises the following steps:
s1, taking a time point when a battery to be detected starts to be used for the first time as a reference point, acquiring use data corresponding to the time length t from the reference point of each time point of the battery to be detected in historical data, wherein the use data comprises t, the model of the battery to be detected, the temperature corresponding to the battery to be detected at t, the time length when the t is away from the latest use end of the battery to be detected, and the residual electric quantity monitored by a sensor when the latest use end of the battery to be detected,
recording the use data corresponding to t as Qt, wherein the Qt corresponds to an array, qt = [ t, B, tt, ct, dt ],
wherein B represents the model of the battery to be tested, tt represents the temperature corresponding to the battery to be tested at t, ct represents the time length of t from the last use end of the battery to be tested, dt represents the residual electric quantity monitored by the sensor when the last use end of the battery to be tested,
when the battery to be detected is in a working state at t, ct =0 and the value of Dt is equal to the residual electric quantity of the battery to be detected monitored by the sensor at t;
s2, acquiring a relation G (x) between the electric quantity and the placing time of a battery in the same type of the battery to be detected in the database, and a relation G1 (x 1) between the vulcanization rate and the power shortage time of the battery cathode, and acquiring the accumulation LF of the cathode sulfide in the battery to be detected by combining the data acquired in the S1;
s3, acquiring the relation between the falling rate of the active substance on the positive plate and the working temperature of the battery in the same type of the battery to be detected in the database, and acquiring the accumulated falling amount LZ of the active substance on the positive plate in the battery to be detected by combining the data acquired in the S1;
s4, obtaining a theoretical life value A of the battery with the same model as the battery to be tested in the database, and the accumulated amount of sulfide of the negative electrode of the battery and the accumulated amount of active substances in the positive electrode plate of the battery, which respectively correspond to different theoretical service times tL, and predicting the service life of the current service state of the battery to be tested;
s5, calculating the residual service life of the predicted value of the service life of the current service state of the battery to be tested relative to the current time to obtain a battery safety performance test result, managing the battery safety performance test result,
when the obtained remaining service life is more than or equal to the first preset value, the state of the battery to be tested is judged to be normal,
and when the obtained remaining service life is less than a first preset value, judging that the state of the battery to be tested is abnormal, and the battery to be tested has potential safety hazard, and recommending to maintain or replace the battery, wherein the first preset value is a preset constant in the database.
The theoretical life value A of the battery with the same model as the battery to be tested, the accumulative amount of the sulfide of the negative electrode of the battery and the accumulative shedding amount of the active substance in the positive electrode plate of the battery, which respectively correspond to different theoretical service times tL, are preset in the database in advance.
Further, the method for acquiring the relationship between the battery power and the placement duration in the battery with the same model as the battery to be tested in the database in the step S2 comprises the following steps:
s2.1, obtaining battery residual electric quantity dc respectively corresponding to different placing time lengths c in the fully charged batteries with the same model as the battery to be tested in the database in the placing process, and constructing a first data pair (c, dc);
s2.2, taking c =0 as a starting point, taking each first unit time length c1 as a step length, acquiring a first data pair every other first unit time length until a second numerical value in the acquired first data pair appears 0, wherein the first unit time length c1 is a preset constant in a database;
s2.3, constructing a first plane rectangular coordinate system by taking o as an origin, taking the placing time as an x axis and taking the residual electric quantity of the battery as a y axis, and respectively marking coordinate points corresponding to each first data pair in the first plane rectangular coordinate system;
s2.4, performing curve fitting on coordinate points marked in the first plane rectangular coordinate system by using a first relation function model in the database, taking a fitting curve with the minimum sum of distances between the fitting curve and each marked coordinate point in the first plane rectangular coordinate system as a best fitting result, and recording a function G (x) corresponding to the best fitting result as the relation between the battery capacity and the placing time length;
the first relation function model in the database is y = a1 x + a2, a1 is a first coefficient, and a2 is a second coefficient.
In the process of acquiring the relation between the battery electric quantity and the placing time of the battery with the same model as that of the battery to be detected in the database, the invention considers that components in the machine can still continuously consume the electric quantity in the battery in the placing process of the battery, and further the electric quantity in the battery is reduced along with the increase of the placing time, and the acquired G (x) not only quantifies the relation between the battery electric quantity and the placing time, but also provides data reference for the subsequent steps to calculate the corresponding power shortage time of the battery in each placing process.
Further, the method for acquiring the relationship between the vulcanization rate of the negative electrode of the battery and the power shortage duration in the battery with the same model as the battery to be tested in the database in the step S2 comprises the following steps:
s2-1, obtaining battery cathode vulcanization rates eck respectively corresponding to different power-down durations ck in batteries with the same model as the battery to be tested in the database in the power-down idle process of the batteries, constructing a second data pair (ck, eck),
taking a second unit time length c2 preset in the database as a step length, acquiring a second data pair every other second unit time length until the vulcanization amount of the cathode of the battery reaches a preset threshold value,
when ck =0, eck =0 is determined,
when ck = n × c2+0.5 × c2 and n is an integer, determining that eck is equal to a quotient of a difference between a cell negative electrode vulcanization amount corresponding to a power shortage duration of (n + 1) × c2 and a cell negative electrode vulcanization amount corresponding to a power shortage duration of n × c2, which is equal to a total mass of sulfides produced by the cell negative electrode, divided by c 2;
s2-2, constructing a second plane rectangular coordinate system by taking o1 as an origin, taking the power-shortage duration as an x1 axis and taking the battery cathode vulcanization rate as a y1 axis, and respectively marking coordinate points corresponding to each second data pair in the second plane rectangular coordinate system;
s2-3, carrying out curve fitting on coordinate points marked in the second planar rectangular coordinate system by using a second relation function model in the database, taking a fitting curve with the minimum sum of distances between the fitting curve and each marked coordinate point in the second planar rectangular coordinate system as a best fitting result, and recording a function G1 (x 1) corresponding to the best fitting result as the relation between the vulcanization rate of the battery cathode and the power shortage duration;
the second relation function model in the database is y1= a4 tan h (x 1+ a 5) + a6, a4 is a fourth coefficient, a5 is a fifth coefficient, a6 is a sixth coefficient,
。
the method obtains the relationship between the vulcanization rate and the power-lack duration of the negative electrode of the battery in the same type of the battery to be detected in the database, quantifies the relationship between the vulcanization rate and the power-lack duration of the negative electrode of the battery, is convenient to calculate the vulcanization rates of the negative electrode of the battery corresponding to different power-lack durations in the power-lack duration stage in each placing process of the battery to be detected, and provides data reference for calculating the cumulant of the negative electrode sulfide in the battery to be detected in the subsequent steps.
Further, the method for acquiring the accumulation amount of the negative electrode sulfide in the battery to be tested in S2 includes the following steps:
s201, obtaining usage data corresponding to the time length t of each time point of the battery to be tested from the reference point in the historical data, recording a set formed by the obtained usage data as a first set, recording the usage data corresponding to t as Qt, qt = [ t, B, tt, ct, dt ], obtaining the usage data once every first preset time length ty,
judging whether the value of Ct is equal to 0, extracting the use data corresponding to each value of t with Ct not equal to 0, and judging whether the absolute value of the difference value between the values of t corresponding to two adjacent use data in each extracted use data is not equal to ty,
when the absolute value of the difference between the t values respectively corresponding to two adjacent pieces of use data is not equal to ty, acquiring a smaller t value corresponding to the two adjacent pieces of use data, marking the smaller t value as tx, setting a dividing point behind the use data corresponding to tx, and dividing the use data respectively corresponding to each t value with the extracted Ct ≠ 0 through the dividing point to obtain the use data corresponding to different time segments;
when the absolute value of the difference between the t values respectively corresponding to two adjacent pieces of use data does not differ from ty, respectively using the use data respectively corresponding to each t value with the extracted Ct ≠ 0 as the use data corresponding to one time slice;
obtaining the maximum Ct and the corresponding Dt in each corresponding use data in each time segment to obtain a first type data pair corresponding to each time segment, and marking the first type data pair corresponding to the mth time segment as [ Ct m ，Dt m ]；
S202, acquiring a relation G (x) between the electric quantity of the battery and the placing time and a relation G1 (x 1) between the vulcanization rate of the negative electrode of the battery and the electricity shortage time;
s203, obtaining the accumulation LFm of the negative electrode sulfide in the battery to be tested in the mth time segment,
，
wherein x0 represents the value of x corresponding to G (x) =0, default G (x) is monotonously changed when x is not less than 0 and not more than x0, G -1 (x) Is the inverse function of G (x), G -1 （Dt m ) The result of G (x) is Dt m The corresponding x value;
when x0-G -1 （Dt m ）-Ct m If > 0, then H [ x0-G ] is determined -1 （Dt m ），Ct m ]=x0-G -1 （Dt m ）-Ct m ；
When x0-G -1 （Dt m ）-Ct m When the content is less than or equal to 0,
then H [ x0-G ] is determined -1 （Dt m ），Ct m ]=0, and LFm =0;
s204, obtaining the accumulation LF of the negative electrode sulfide in the battery to be tested,
，
wherein m1 represents the total number of time segments corresponding to the battery to be tested.
In the process of obtaining the cumulant of the negative electrode sulfide in the battery to be tested, H [ x0-G ] is obtained -1 （Dt m ），Ct m ]The method is used for judging the corresponding maximum power shortage duration of the battery to be tested in the mth time segment, and further obtaining the accumulation amount of the negative electrode sulfide in the battery to be tested in the mth time segment.
Further, the method for acquiring the accumulated falling amount of the active material of the positive plate in the battery to be tested in S3 comprises the following steps:
s3.1, acquiring the falling amount of the positive plate active substance in the battery to be tested in the third unit time c3 under the condition of different working temperatures in the database, and recording the falling amount of the positive plate active substance in the battery to be tested in the third unit time as LZT0 when the working temperature is T0, so as to obtain a third data pair [ T0, LZT0/c3];
s3.2, constructing a third plane rectangular coordinate system by taking o2 as an origin, a battery working temperature as an x2 axis and a battery anode active material falling rate as a y2 axis, and respectively marking coordinate points corresponding to each third data pair in the third plane rectangular coordinate system;
carrying out curve fitting on coordinate points marked in a third plane rectangular coordinate system by using a third relation function model in the database, taking a fitting curve with the minimum sum of distances between the fitting curve and each marked coordinate point in the third plane rectangular coordinate system as a best fitting result, and marking a function G2 (x 2) corresponding to the best fitting result as the relation between the falling rate of the active substance on the positive plate and the working temperature of the battery in the battery with the same model as that of the battery to be tested;
the total third relational function model of the database is y1= a7 × (x 2+ a 8) + a9, a7 is a seventh coefficient, a8 is an eighth coefficient, a9 is a ninth coefficient,
；
s3.3, obtaining the use data corresponding to the time length t of each time point of the battery to be tested from the reference point in the historical data, calculating the accumulated falling-off quantity LZ of the active substance of the positive plate in the battery to be tested,
，
and tmax represents the maximum value of the time length t of each time point of the battery to be tested from the reference point in the historical data, and TB represents the theoretical working temperature corresponding to the battery to be tested.
In the process of acquiring the accumulated falling amount of the active substance of the positive plate in the battery to be detected, because the use data is acquired at intervals of ty, in the use data monitored by the sensor, tt acquired each time can keep the duration of ty unchanged, and the working temperature of the battery is irregular in the use process, so that in the process of calculating the accumulated falling amount LZ of the active substance of the positive plate in the battery to be detected, the use time of the battery to be detected needs to be segmented according to the acquired use data, the corresponding accumulated falling amount LZ of the active substance in the positive plate in each segment of time is acquired respectively, LZ in the application is not the accumulated falling amount of the active substance corresponding to all temperatures in the positive electrode of the battery under the total use time, but is the difference value of the accumulated falling amount of the active substance in the positive electrode in the actual use process of the battery to be detected under the total use time and the accumulated falling amount of the active substance in the positive electrode of the battery under the theoretical working temperature, in other words, LZ corresponds to the deviation value of the accumulated falling amount of the positive active substance in the actual use process of the positive electrode in the total use process of the battery under the total use process of the total use of the battery under the total use of the theoretical working temperature.
Further, the method for predicting the service life of the current use state of the battery to be tested in S4 includes the following steps:
s4.1, acquiring the accumulated quantity LF of the negative electrode sulfide in the battery to be tested and the accumulated falling quantity LZ of the active substance of the positive electrode plate in the battery to be tested;
s4.2, obtaining batteries with the same model as the battery electrically tested in the database, and respectively corresponding to the preset accumulative amount LF of the negative electrode sulfide by different tL values in the process that the tL is more than or equal to 0 and less than or equal to the theoretical life value A tL And the accumulated falling amount LZ of the active material in the positive electrode plate tL Is denoted as (tL, LF) tL ，LZ tL ）；
S4.3, combining (tL, LF) respectively corresponding to tL in the database when the tL is different values tL ，LZ tL ) Predicting the service life AS of the current service state of the battery,
solving for (LF/tmax) × tL + LF tL =LF A The corresponding value of tL, denoted tL1,
solving for (LZ/tmax) × tL + LZ tL =LZ A The corresponding value of tL, denoted tL2,
wherein, LF A Indicates the accumulation amount of the negative electrode sulfide preset in the database when tL = a,
LZ A represents the cumulative amount of active material shedding in the positive plate preset in the database at tL = a,
AS=min{tL1，tL2}，
wherein min { tL1, tL2} represents the minimum of tL1 and tL 2;
and the life prediction value of the current service state of the battery to be tested relative to the residual service life of the current time is equal to the difference value between the AS and the tmax.
A big data based battery safety performance testing system, the system comprising the following modules:
the data acquisition module takes the time point when the battery to be detected starts to be used for the first time as a reference point, acquires use data corresponding to the time length t of each time point of the battery to be detected from the reference point in historical data,
the sulfide accumulation quantification module is used for acquiring the relation G (x) between the electric quantity and the placing time length of the battery and the relation G1 (x 1) between the vulcanization rate and the power shortage time length of the negative electrode of the battery in the battery with the same model as the battery to be detected in the database, and acquiring the accumulation LF of the negative electrode sulfide in the battery to be detected by combining the data acquired by the data acquisition module;
the active substance falling amount quantifying module is used for acquiring the relation between the falling rate of the active substance on the positive plate and the working temperature of the battery in the battery with the same model as the battery to be tested in the database and acquiring the accumulated falling amount LZ of the active substance on the positive plate in the battery to be tested by combining the data acquired in the data acquiring module;
the battery life prediction module is used for acquiring a theoretical life value A of a battery with the same model as that of the battery to be tested in a database, and the accumulated amount of sulfide of the negative electrode of the battery and the accumulated amount of active substances in the positive electrode plate of the battery, which respectively correspond to different theoretical service times tL, and predicting the service life of the current service state of the battery to be tested;
and the safety performance test management module calculates the residual service life of the service life predicted value of the current service state of the battery to be tested relative to the current time to obtain a battery safety performance test result, and manages the battery safety performance test result.
Furthermore, the data used in the data acquisition module includes t, the model of the battery to be measured, the temperature corresponding to the battery to be measured at t, the time length of t from the last use end of the battery to be measured, and the residual electric quantity monitored by the sensor when the last use end of the battery to be measured.
Furthermore, when the safety performance test management module manages the battery safety performance test result,
when the obtained remaining service life is more than or equal to the first preset value, the state of the battery to be tested is judged to be normal,
and when the obtained remaining service life is less than a first preset value, judging that the state of the battery to be tested is abnormal, and the battery to be tested has potential safety hazard, and recommending to maintain or replace the battery, wherein the first preset value is a preset constant in the database.
Compared with the prior art, the invention has the following beneficial effects: the method analyzes the battery safety performance in the current state of the battery by combining the actual use condition of the battery by a user in historical data, accurately predicts the corresponding residual service life of the battery in different use states by considering the relation between the placing time and the electric quantity change of the battery, the relation between the cathode vulcanization rate and the power shortage time and the relation between the falling rate of the anode active substance and the working temperature of the battery, and realizes effective supervision on the battery safety performance.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic structural diagram of a big data-based battery safety performance testing system according to the present invention;
fig. 2 is a schematic flow chart of a battery safety performance testing method based on big data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1-2, the present invention provides a technical solution: a big data-based battery safety performance testing method comprises the following steps:
s1, taking the time point when the battery to be detected starts to be used for the first time as a reference point, acquiring use data corresponding to the time length t from each time point to the reference point in historical data of the battery to be detected, wherein the use data comprises t, the model of the battery to be detected, the temperature corresponding to the battery to be detected when t is measured, the time length when t is from the last use end of the battery to be detected, and the residual electric quantity monitored by a sensor when the last use end of the battery to be detected,
recording the use data corresponding to t as Qt, wherein the Qt corresponds to an array, qt = [ t, B, tt, ct, dt ],
wherein B represents the model of the battery to be tested, tt represents the temperature corresponding to the battery to be tested at t, ct represents the time length of t from the last use end of the battery to be tested, dt represents the residual electric quantity monitored by the sensor when the last use end of the battery to be tested,
when the battery to be detected is in a working state at t, ct =0 and the value of Dt is equal to the residual electric quantity of the battery to be detected monitored by the sensor at t;
in this embodiment, if the model of the battery to be tested is M01, the time duration t from each time point to the reference point in the history data of the battery to be tested is equal to 200, the corresponding temperature of the battery to be tested is 25 degrees, the corresponding t value of the battery to be tested at the end of the last use is equal to 180, the remaining power monitored by the sensor at the end of the last use of the battery to be tested is equal to 40%,
since 200-180=20, the number of the terminals is increased,
then 200 corresponding usage data Q200= [200, M01, 25, 20, 40% ];
s2, acquiring a relation G (x) between the electric quantity and the placing time of a battery in the same type of the battery to be detected in the database, and a relation G1 (x 1) between the vulcanization rate and the power shortage time of the battery cathode, and acquiring the accumulation LF of the cathode sulfide in the battery to be detected by combining the data acquired in the S1;
the method for acquiring the relation between the battery electric quantity and the placing time length in the battery with the same model as the battery to be tested in the database in the S2 comprises the following steps:
s2.1, obtaining battery residual electric quantity dc respectively corresponding to different placing time lengths c in the fully charged batteries with the same model as the battery to be tested in the database in the placing process, and constructing a first data pair (c, dc);
s2.2, taking c =0 as a starting point, taking each first unit time length c1 as a step length, acquiring a first data pair every other first unit time length until a second numerical value in the acquired first data pair appears 0, wherein the first unit time length c1 is a preset constant in a database;
the first unit time length c1 in the present embodiment is equal to 30 seconds;
s2.3, constructing a first plane rectangular coordinate system by taking o as an origin, taking the placing time as an x axis and taking the residual battery capacity as a y axis, and respectively marking coordinate points corresponding to each first data pair in the first plane rectangular coordinate system;
s2.4, performing curve fitting on coordinate points marked in the first plane rectangular coordinate system by using a first relation function model in the database, taking a fitting curve with the minimum sum of distances between the fitting curve and each marked coordinate point in the first plane rectangular coordinate system as a best fitting result, and recording a function G (x) corresponding to the best fitting result as the relation between the battery capacity and the placing time length;
the first relation function model in the database is y = a1 x + a2, a1 is a first coefficient, and a2 is a second coefficient.
The method for acquiring the relation between the vulcanization rate of the negative electrode of the battery and the power shortage duration of the battery with the same model as the battery to be detected in the database in the S2 comprises the following steps:
s2-1, obtaining battery cathode vulcanization rates eck respectively corresponding to different power-down time durations ck in batteries with the same types as the to-be-tested batteries in the database in the power-down idle process of the batteries, constructing a second data pair (ck, eck),
taking a second unit time length c2 preset in the database as a step length, acquiring a second data pair every other second unit time length until the vulcanization amount of the battery cathode reaches a preset threshold value,
when ck =0, eck =0 is determined,
when ck = n × c2+0.5 × c2 and n is an integer, determining that eck is equal to a quotient of a difference between a cell negative electrode vulcanization amount corresponding to a power shortage duration of (n + 1) × c2 and a cell negative electrode vulcanization amount corresponding to a power shortage duration of n × c2, which is equal to a total mass of sulfides produced by the cell negative electrode, divided by c 2;
s2-2, constructing a second plane rectangular coordinate system by taking o1 as an origin, taking the power-shortage duration as an x1 axis and taking the battery cathode vulcanization rate as a y1 axis, and respectively marking coordinate points corresponding to each second data pair in the second plane rectangular coordinate system;
s2-3, performing curve fitting on coordinate points marked in a second planar rectangular coordinate system by using a second relation function model in the database, taking a fitting curve with the minimum sum of distances to all marked coordinate points in the second planar rectangular coordinate system as a best fitting result, and recording a function G1 (x 1) corresponding to the best fitting result as the relation between the battery cathode vulcanization rate and the insufficient electricity duration;
second relation in the databaseThe functional model is y1= a4 tan h (x 1+ a 5) + a6, a4 is the fourth coefficient, a5 is the fifth coefficient, a6 is the sixth coefficient,
。
the method for acquiring the accumulation amount of the negative electrode sulfide in the battery to be tested in the S2 comprises the following steps:
s201, obtaining usage data corresponding to the time length t of each time point of the battery to be tested from the reference point in the historical data, recording a set formed by the obtained usage data as a first set, recording the usage data corresponding to t as Qt, qt = [ t, B, tt, ct, dt ], obtaining the usage data once every first preset time length ty,
judging whether the value of Ct is equal to 0, extracting the use data corresponding to each value of t with Ct not equal to 0, and judging whether the absolute value of the difference value between the values of t corresponding to two adjacent use data in each extracted use data is not equal to ty,
when the absolute value of the difference between the t values respectively corresponding to two adjacent pieces of use data is not equal to ty, acquiring a smaller t value corresponding to the two adjacent pieces of use data, marking the smaller t value as tx, setting a dividing point behind the use data corresponding to tx, and dividing the use data respectively corresponding to each t value with the extracted Ct ≠ 0 through the dividing point to obtain the use data corresponding to different time segments;
when the absolute value of the difference between the t values respectively corresponding to two adjacent pieces of use data does not differ from ty, respectively using the use data respectively corresponding to each t value with the extracted Ct ≠ 0 as the use data corresponding to one time slice;
obtaining the maximum Ct and the corresponding Dt in each corresponding use data in each time segment to obtain a first type data pair corresponding to each time segment, and marking the first type data pair corresponding to the mth time segment as [ Ct m ，Dt m ]；
S202, acquiring a relation G (x) between the electric quantity of the battery and the placing time and a relation G1 (x 1) between the vulcanization rate of the negative electrode of the battery and the electricity shortage time;
s203, obtaining the accumulation LFm of the cathode sulfide in the battery to be tested in the mth time slice,
，
wherein x0 represents the value of x corresponding to G (x) =0, default G (x) is monotonously changed when x is not less than 0 and not more than x0, G -1 (x) Is the inverse function of G (x), G -1 （Dt m ) The result of G (x) is Dt m The value of x corresponding to time;
when x0-G -1 （Dt m ）-Ct m If > 0, then H [ x0-G ] is determined -1 （Dt m ），Ct m ]=x0-G -1 （Dt m ）-Ct m ；
When x0-G -1 （Dt m ）-Ct m When the content is less than or equal to 0,
then H [ x0-G ] is determined -1 （Dt m ），Ct m ]=0, and LFm =0;
s204, obtaining the accumulation LF of the negative electrode sulfide in the battery to be tested,
，
wherein m1 represents the total number of time segments corresponding to the battery to be tested.
S3, acquiring the relation between the falling rate of the active substance on the positive plate and the working temperature of the battery in the same type of the battery to be detected in the database, and acquiring the accumulated falling amount LZ of the active substance on the positive plate in the battery to be detected by combining the data acquired in the S1;
s4, obtaining a theoretical life value A of the battery with the same model as the battery to be tested in the database, and the accumulative amount of the sulfide of the negative electrode of the battery and the accumulative shedding amount of the active substance in the positive electrode plate of the battery, which respectively correspond to different theoretical service times tL, and predicting the service life of the current service state of the battery to be tested;
the method for acquiring the accumulated falling amount of the active substances of the positive plate in the battery to be tested in the S3 comprises the following steps:
s3.1, acquiring the falling amount of the positive plate active substance in the battery to be detected in a third unit time c3 under the condition of different working temperatures in the database, and recording the falling amount of the positive plate active substance in the battery to be detected in the third unit time as LZT0 when the working temperature is T0 to obtain a third data pair [ T0, LZT0/c3];
s3.2, constructing a third rectangular coordinate system by taking o2 as an origin, the working temperature of the battery as an x2 axis and the falling rate of the active material of the positive electrode of the battery as a y2 axis, and respectively marking coordinate points corresponding to each third data pair in the third rectangular coordinate system;
carrying out curve fitting on coordinate points marked in a third plane rectangular coordinate system by using a third relation function model in the database, taking a fitting curve with the minimum sum of distances between the fitting curve and each marked coordinate point in the third plane rectangular coordinate system as a best fitting result, and marking a function G2 (x 2) corresponding to the best fitting result as the relation between the falling rate of the active substance on the positive plate and the working temperature of the battery in the battery with the same model as that of the battery to be tested;
the total third relational function model of the database is y1= a7 × (x 2+ a 8) + a9, a7 is a seventh coefficient, a8 is an eighth coefficient, a9 is a ninth coefficient,
；
s3.3, obtaining the use data corresponding to the time length t of each time point of the battery to be tested from the reference point in the historical data, calculating the accumulated falling-off quantity LZ of the active substance of the positive plate in the battery to be tested,
，
and tmax represents the maximum value of the time length t of each time point of the battery to be tested from the reference point in the historical data, and TB represents the theoretical working temperature corresponding to the battery to be tested.
The method for predicting the service life of the current service state of the battery to be tested in the S4 comprises the following steps:
s4.1, acquiring the accumulated quantity LF of the negative electrode sulfide in the battery to be tested and the accumulated falling quantity LZ of the active substance of the positive electrode plate in the battery to be tested;
s4.2, obtaining batteries with the same model as the battery electrically tested in the database, and respectively corresponding to the preset accumulative amount LF of the negative electrode sulfide by different tL values in the process that the tL is more than or equal to 0 and less than or equal to the theoretical life value A tL And the accumulated falling amount LZ of the active material in the positive electrode plate tL Is denoted as (tL, LF) tL ，LZ tL ）；
S4.3, combining (tL, LF) respectively corresponding to tL in the database when the tL is different values tL ，LZ tL ) Predicting the service life AS of the current service state of the battery,
solving for (LF/tmax) × tL + LF tL =LF A The corresponding value of tL, denoted tL1,
solving for (LZ/tmax) × tL + LZ tL =LZ A The corresponding value of tL, denoted tL2,
wherein, LF A Indicates the accumulation amount of the negative electrode sulfide preset in the database when tL = a,
LZ A indicates the cumulative amount of active material shedding from the positive electrode plate preset in the database for tL = a,
AS=min{tL1，tL2}，
wherein min { tL1, tL2} represents the minimum of tL1 and tL 2;
and the life prediction value of the current service state of the battery to be tested relative to the residual service life of the current time is equal to the difference value between the AS and the tmax.
S5, calculating the residual service life of the predicted value of the service life of the current service state of the battery to be tested relative to the current time to obtain a battery safety performance test result, managing the battery safety performance test result,
when the obtained remaining service life is more than or equal to the first preset value, the state of the battery to be tested is judged to be normal,
and when the obtained remaining service life is less than a first preset value, judging that the state of the battery to be tested is abnormal, and the battery to be tested has potential safety hazard, and recommending to maintain or replace the battery, wherein the first preset value is a preset constant in the database.
A big data based battery safety performance testing system, the system comprising the following modules:
the data acquisition module takes the time point when the battery to be detected starts to be used for the first time as a reference point, acquires use data corresponding to the time length t of each time point of the battery to be detected from the reference point in historical data,
the sulfide accumulation quantification module is used for acquiring the relation G (x) between the electric quantity and the placing time of the battery and the relation G1 (x 1) between the cathode vulcanization rate and the electricity shortage time of the battery in the battery with the same model as the battery to be detected in the database, and acquiring the accumulation LF of the cathode sulfide in the battery to be detected by combining the data acquired by the data acquisition module;
the active substance falling amount quantifying module is used for acquiring the relation between the falling rate of the active substance on the positive plate and the working temperature of the battery in the battery with the same model as the battery to be tested in the database and acquiring the accumulated falling amount LZ of the active substance on the positive plate in the battery to be tested by combining the data acquired in the data acquiring module;
the battery life prediction module is used for acquiring a theoretical life value A of a battery with the same model as that of the battery to be tested in a database, and the accumulated amount of sulfide of the negative electrode of the battery and the accumulated amount of active substances in the positive electrode plate of the battery, which respectively correspond to different theoretical service times tL, and predicting the service life of the current service state of the battery to be tested;
and the safety performance test management module calculates the residual service life of the service life predicted value of the current service state of the battery to be tested relative to the current time to obtain a battery safety performance test result, and manages the battery safety performance test result.
The data used in the data acquisition module comprises t, the model of the battery to be detected, the temperature corresponding to the battery to be detected at t, the time length of t from the last use end of the battery to be detected and the residual electric quantity monitored by the sensor when the last use end of the battery to be detected.
When the safety performance test management module manages the battery safety performance test result,
when the obtained remaining service life is more than or equal to the first preset value, the state of the battery to be tested is judged to be normal,
and when the obtained remaining service life is less than a first preset value, judging that the state of the battery to be tested is abnormal, and suggesting to maintain or replace the battery if potential safety hazards exist, wherein the first preset value is a preset constant in the database.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, 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, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A battery safety performance testing method based on big data is characterized by comprising the following steps:
s1, taking the time point when the battery to be detected starts to be used for the first time as a reference point, acquiring use data corresponding to the time length t from each time point to the reference point in historical data of the battery to be detected, wherein the use data comprises t, the model of the battery to be detected, the temperature corresponding to the battery to be detected when t is measured, the time length when t is from the last use end of the battery to be detected, and the residual electric quantity monitored by a sensor when the last use end of the battery to be detected,
recording the use data corresponding to t as Qt, wherein the Qt corresponds to an array, qt = [ t, B, tt, ct, dt ],
wherein B represents the model of the battery to be tested, tt represents the temperature corresponding to the battery to be tested at t, ct represents the time length of t from the last use end of the battery to be tested, dt represents the residual electric quantity monitored by the sensor when the last use end of the battery to be tested,
when the battery to be detected is in a working state at t, ct =0 and the value of Dt is equal to the residual electric quantity of the battery to be detected monitored at t through a sensor;
s2, acquiring a relation G (x) between the electric quantity and the placing time of a battery in the same type of the battery to be detected in the database, and a relation G1 (x 1) between the vulcanization rate and the power shortage time of the battery cathode, and acquiring the accumulation LF of the cathode sulfide in the battery to be detected by combining the data acquired in the S1;
s3, acquiring the relation between the falling rate of the active substance on the positive plate and the working temperature of the battery in the same type of the battery to be detected in the database, and acquiring the accumulated falling amount LZ of the active substance on the positive plate in the battery to be detected by combining the data acquired in the S1;
s4, obtaining a theoretical life value A of the battery with the same model as the battery to be tested in the database, and the accumulative amount of the sulfide of the negative electrode of the battery and the accumulative shedding amount of the active substance in the positive electrode plate of the battery, which respectively correspond to different theoretical service times tL, and predicting the service life of the current service state of the battery to be tested;
s5, calculating the residual service life of the predicted value of the service life of the current service state of the battery to be tested relative to the current time to obtain a battery safety performance test result, managing the battery safety performance test result,
when the obtained remaining service life is more than or equal to the first preset value, the state of the battery to be tested is judged to be normal,
when the obtained remaining service life is smaller than a first preset value, judging that the state of the battery to be tested is abnormal, and the potential safety hazard exists, and recommending to maintain or replace the battery, wherein the first preset value is a preset constant in a database;
the method for acquiring the relation between the battery electric quantity and the placing time length in the battery with the same model as the battery to be tested in the database in the S2 comprises the following steps:
s2.1, obtaining battery residual electric quantity dc respectively corresponding to different placing time lengths c in the fully charged batteries with the same model as the battery to be tested in the database in the placing process, and constructing a first data pair (c, dc);
s2.2, taking c =0 as a starting point, taking each first unit time length c1 as a step length, acquiring a first data pair every other first unit time length until a second numerical value in the acquired first data pair appears 0, wherein the first unit time length c1 is a preset constant in a database;
s2.3, constructing a first plane rectangular coordinate system by taking o as an origin, taking the placing time as an x axis and taking the residual electric quantity of the battery as a y axis, and respectively marking coordinate points corresponding to each first data pair in the first plane rectangular coordinate system;
s2.4, performing curve fitting on coordinate points marked in the first plane rectangular coordinate system by using a first relation function model in the database, taking a fitting curve with the minimum sum of distances between the fitting curve and each marked coordinate point in the first plane rectangular coordinate system as a best fitting result, and recording a function G (x) corresponding to the best fitting result as the relation between the battery capacity and the placing time length;
the first relation function model in the database is y = a1 x + a2, a1 is a first coefficient, and a2 is a second coefficient;
the method for acquiring the relation between the vulcanization rate of the negative electrode of the battery and the power shortage duration of the battery with the same model as the battery to be detected in the database in the S2 comprises the following steps:
s2-1, obtaining battery cathode vulcanization rates eck respectively corresponding to different power-down time durations ck in batteries with the same types as the to-be-tested batteries in the database in the power-down idle process of the batteries, constructing a second data pair (ck, eck),
taking a second unit time length c2 preset in the database as a step length, acquiring a second data pair every other second unit time length until the vulcanization amount of the battery cathode reaches a preset threshold value,
when ck =0, eck =0 is determined,
when ck = n × c2+0.5 × c2 and n is an integer, determining that eck is equal to a quotient of a difference between a cell negative electrode vulcanization amount corresponding to a power shortage duration of (n + 1) × c2 and a cell negative electrode vulcanization amount corresponding to a power shortage duration of n × c2, which is equal to a total mass of sulfides produced by the cell negative electrode, divided by c 2;
s2-2, constructing a second plane rectangular coordinate system by taking o1 as an origin, taking the power-shortage duration as an x1 axis and taking the battery cathode vulcanization rate as a y1 axis, and respectively marking coordinate points corresponding to each second data pair in the second plane rectangular coordinate system;
s2-3, performing curve fitting on coordinate points marked in a second planar rectangular coordinate system by using a second relation function model in the database, taking a fitting curve with the minimum sum of distances to all marked coordinate points in the second planar rectangular coordinate system as a best fitting result, and recording a function G1 (x 1) corresponding to the best fitting result as the relation between the battery cathode vulcanization rate and the insufficient electricity duration;
the second relation function model in the database is y1= a4 tan h (x 1+ a 5) + a6, a4 is a fourth coefficient, a5 is a fifth coefficient, a6 is a sixth coefficient,
；
the method for acquiring the accumulation amount of the negative electrode sulfide in the battery to be tested in the S2 comprises the following steps:
s201, obtaining usage data corresponding to the time length t of each time point of the battery to be tested from the reference point in the historical data, recording a set formed by the obtained usage data as a first set, recording the usage data corresponding to t as Qt, qt = [ t, B, tt, ct, dt ], obtaining the usage data once every first preset time length ty,
judging whether the value of Ct is equal to 0, extracting the use data corresponding to each t value with Ct not equal to 0, and judging whether the absolute value of the difference value between the t values corresponding to two adjacent use data in each extracted use data is not equal to ty,
when the absolute value of the difference between the t values respectively corresponding to two adjacent pieces of use data is not equal to ty, acquiring a smaller t value corresponding to the two adjacent pieces of use data, marking the smaller t value as tx, setting a dividing point behind the use data corresponding to tx, and dividing the use data respectively corresponding to each t value with the extracted Ct ≠ 0 through the dividing point to obtain the use data corresponding to different time segments;
when the absolute value of the difference between the t values respectively corresponding to two adjacent pieces of use data does not differ from ty, respectively using the use data respectively corresponding to each t value with the extracted Ct ≠ 0 as the use data corresponding to one time slice;
obtaining the maximum Ct and the corresponding Dt in each corresponding use data in each time segment to obtain a first type data pair corresponding to each time segment, and marking the first type data pair corresponding to the mth time segment as [ Ct m ，Dt m ]；
S202, acquiring a relation G (x) between the electric quantity of the battery and the placing time and a relation G1 (x 1) between the vulcanization rate of the negative electrode of the battery and the electricity shortage time;
s203, obtaining the accumulation LFm of the negative electrode sulfide in the battery to be tested in the mth time segment,
，
wherein x0 represents the value of x corresponding to G (x) =0, default G (x) is monotonously changed when x is not less than 0 and not more than x0, G -1 (x) Is the inverse function of G (x), G -1 （Dt m ) The result of G (x) is Dt m The corresponding x value;
when x0-G -1 （Dt m ）-Ct m If > 0, then H [ x0-G ] is determined -1 （Dt m ），Ct m ]=x0-G -1 （Dt m ）-Ct m ；
When x0-G -1 （Dt m ）-Ct m When the content is less than or equal to 0,
then H [ x0-G ] is determined -1 （Dt m ），Ct m ]=0, and LFm =0;
s204, obtaining the accumulation LF of the negative electrode sulfide in the battery to be tested,
，
wherein m1 represents the total number of time segments corresponding to the battery to be tested;
the method for acquiring the accumulated falling amount of the positive plate active materials in the battery to be tested in the S3 comprises the following steps of:
s3.1, acquiring the falling amount of the positive plate active substance in the battery to be tested in the third unit time c3 under the condition of different working temperatures in the database, and recording the falling amount of the positive plate active substance in the battery to be tested in the third unit time as LZT0 when the working temperature is T0, so as to obtain a third data pair [ T0, LZT0/c3];
s3.2, constructing a third rectangular coordinate system by taking o2 as an origin, the working temperature of the battery as an x2 axis and the falling rate of the active material of the positive electrode of the battery as a y2 axis, and respectively marking coordinate points corresponding to each third data pair in the third rectangular coordinate system;
carrying out curve fitting on coordinate points marked in the third plane rectangular coordinate system by using a third relation function model in the database, taking a fitting curve with the minimum sum of the distances between the fitting curve and each marked coordinate point in the third plane rectangular coordinate system as a best fitting result, and marking a function G2 (x 2) corresponding to the best fitting result as the relation between the falling rate of the active substances on the positive plate and the working temperature of the battery in the battery with the same model as that of the battery to be tested;
the total third relational function model of the database is y1= a7 × (x 2+ a 8) + a9, a7 is a seventh coefficient, a8 is an eighth coefficient, a9 is a ninth coefficient,
；
s3.3, obtaining the historical data of the battery to be testedThe duration t of each time point from the reference point corresponds to the use data respectively, and the accumulated falling quantity LZ of the active substance of the positive plate in the battery to be tested is calculated,
，
tmax represents the maximum value of the time length t of the battery to be tested from each time point to the reference point in the historical data, and TB represents the theoretical working temperature corresponding to the battery to be tested;
the method for predicting the service life of the current service state of the battery to be tested in the S4 comprises the following steps:
s4.1, acquiring the accumulated quantity LF of the negative electrode sulfide in the battery to be tested and the accumulated falling quantity LZ of the active substance of the positive electrode plate in the battery to be tested;
s4.2, obtaining batteries with the same model as the battery electrically tested in the database, and respectively corresponding to the preset accumulative amount LF of the negative electrode sulfide by different tL values in the process that the tL is more than or equal to 0 and less than or equal to the theoretical life value A tL And the accumulated falling amount LZ of the active material in the positive electrode plate tL Is denoted as (tL, LF) tL ，LZ tL ）；
S4.3, respectively corresponding (tL, LF) when tL in the database is different tL ，LZ tL ) Predicting the service life AS of the current service state of the battery,
solving (LF/tmax) × tL + LF tL =LF A The corresponding value of tL, denoted tL1,
solving for (LZ/tmax) × tL + LZ tL =LZ A The corresponding value of tL, denoted tL2,
wherein, LF A Represents the accumulation amount of the negative electrode sulfide preset in the database when tL = a,
LZ A represents the cumulative amount of active material shedding in the positive plate preset in the database at tL = a,
AS=min{tL1，tL2}，
wherein min { tL1, tL2} represents the minimum of tL1 and tL 2;
and the life prediction value of the current service state of the battery to be tested relative to the residual service life of the current time is equal to the difference value between the AS and the tmax.
2. The big data based battery safety performance test system applying the big data based battery safety performance test method according to claim 1, wherein the system comprises the following modules:
the data acquisition module takes the time point when the battery to be detected starts to be used for the first time as a reference point, acquires use data corresponding to the time length t of each time point of the battery to be detected from the reference point in historical data,
the sulfide accumulation quantification module is used for acquiring the relation G (x) between the electric quantity and the placing time of the battery and the relation G1 (x 1) between the cathode vulcanization rate and the electricity shortage time of the battery in the battery with the same model as the battery to be detected in the database, and acquiring the accumulation LF of the cathode sulfide in the battery to be detected by combining the data acquired by the data acquisition module;
the active substance falling amount quantifying module is used for acquiring the relation between the falling rate of the active substance on the positive plate and the working temperature of the battery in the battery with the same model as the battery to be tested in the database and acquiring the accumulated falling amount LZ of the active substance on the positive plate in the battery to be tested by combining the data acquired in the data acquiring module;
the battery life prediction module acquires a theoretical life value A of a battery with the same model as the battery to be detected in the database, and the accumulative amount of a battery negative electrode sulfide and the accumulative shedding amount of an active substance in a battery positive plate which respectively correspond to different theoretical service times tL, and predicts the service life of the current service state of the battery to be detected;
and the safety performance test management module calculates the residual service life of the service life predicted value of the current service state of the battery to be tested relative to the current time to obtain a battery safety performance test result, and manages the battery safety performance test result.
3. The big data-based battery safety performance testing system according to claim 2, wherein: the data used in the data acquisition module comprises t, the model of the battery to be detected, the temperature corresponding to the battery to be detected at t, the time length of t from the last use end of the battery to be detected and the residual electric quantity monitored by the sensor when the last use end of the battery to be detected.
4. The big data-based battery safety performance testing system according to claim 2, wherein: when the safety performance test management module manages the battery safety performance test result,
when the obtained remaining service life is more than or equal to the first preset value, the state of the battery to be tested is judged to be normal,
and when the obtained remaining service life is less than a first preset value, judging that the state of the battery to be tested is abnormal, and the battery to be tested has potential safety hazard, and recommending to maintain or replace the battery, wherein the first preset value is a preset constant in the database.
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