CN111006834A - Method for real-time monitoring and evaluation of battery collision damage based on sensor signals - Google Patents
Method for real-time monitoring and evaluation of battery collision damage based on sensor signals Download PDFInfo
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- CN111006834A CN111006834A CN201911345247.6A CN201911345247A CN111006834A CN 111006834 A CN111006834 A CN 111006834A CN 201911345247 A CN201911345247 A CN 201911345247A CN 111006834 A CN111006834 A CN 111006834A
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M7/00—Vibration-testing of structures; Shock-testing of structures
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Abstract
The embodiment of the invention discloses a method for monitoring and evaluating collision damage of a battery in real time based on sensor signals, which comprises the following steps: acquiring electrical property data of the lithium ion power battery before collision; performing a collision test by using a battery collision test bed, and acquiring a sensor collision signal; performing damage characterization test on the collided batteries, and classifying the batteries according to damage degrees; carrying out data processing on the sensor signals and extracting digital features; and finally, performing supervised machine learning training and testing by taking the signal characteristics as input and the battery classification labels as targets to obtain an optimal classification model. The embodiment of the invention also discloses a simple test bed for single 18650 battery collision. By adopting the invention, the collision force and the vibration signal which are monitored in real time are utilized to obtain the damage degree of the same battery after collision, and the battery which does not show the failure characteristic but has potential safety hazard is found in time.
Description
Technical Field
The invention relates to the field of lithium ion power batteries, in particular to a method for monitoring and evaluating collision damage of a battery in real time based on sensor signals.
Background
New energy electric automobile is more and more accepted by people because of environmental protection, support intelligent driving system etc. however more collision accidents also appear thereupon, as electric automobile energy storage system's key component, lithium ion battery's collision security is the first time to rush. When a vehicle is involved in a collision accident, the battery will burn or even explode immediately in most cases, and in some cases, although the battery does not directly show obvious phenomena, a fire will occur in the case of the subsequent use or standing of the electric vehicle, and the latter is called a "hidden battery". At present, the research on collision failure is less, and most of the research is related to the instant failure during collision, while the related documents about the failure after the failure of the battery caused by the hidden trouble caused by collision are few, and the failure mechanism of the battery has no direct and definite conclusion.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a method for monitoring and evaluating collision damage of a battery in real time based on sensor signals. The damage degree of the battery after the collision can be judged by using the sensor signal.
In order to solve the above technical problem, an embodiment of the present invention provides a method for real-time monitoring and evaluating collision damage of a battery based on a sensor signal, including the following steps:
acquiring electrical property data of the lithium ion power battery before collision, and acquiring voltage, capacity and internal resistance information of the battery through standard circulation, rapid charge-discharge circulation and the like;
performing a collision test by using a battery collision test bed, and collecting force and vibration sensor collision signals;
performing damage characterization test on the batteries after collision, and comparing the performances of the batteries before and after collision to divide the batteries into three types, namely safety, hidden danger and failure;
carrying out data processing on the force and vibration signals, and extracting digital characteristics;
and finally, performing supervised machine learning training and testing by taking the signal characteristics as input and the battery classification labels as targets to obtain an optimal classification model.
Further, for obtaining the electrical property data of the lithium ion power battery before collision, the pre-test is required as follows:
standard cycle testing: when the standard cycle is stable, the next test can be carried out, and the stability is judged by the method that the difference of the two adjacent discharge capacities is less than 3 percent of the nominal capacity;
and (3) rapid charge-discharge cycle test: 2-rate or more rapid charge-discharge cycle;
internal resistance measurement: after the battery is discharged, the internal resistance during standing needs to be accurate to two decimal points with the unit of m omega by the adopted battery internal resistance measuring instrument.
Full charge of electric quantity before the test: the charging method before the test refers to the charging method of GB/T31485-2015 single storage battery, namely, the constant current charging is carried out at 1C until the cut-off voltage is reached, and then the constant voltage charging is carried out until the current is reduced to 0.05C.
Furthermore, the damage characterization test for the lithium ion power battery after collision comprises two aspects of physical visual inspection and electrical property test, and the specific test is as follows:
physical visual inspection: checking whether the electrolyte leaks, smokes, fires, explodes and the like after the collision of the battery;
standard cycle testing: continuing to test the battery without the failure phenomenon, and terminating the test if the related failure phenomenon occurs;
and (3) rapid charge-discharge cycle test: the battery which completes the circulation is tested, the charging and discharging multiplying power is consistent with that during pretreatment, and the test is stopped if the failure phenomenon occurs;
internal resistance measurement: and (5) measuring when the battery is kept standing after discharging, wherein the measuring tool is the same as the pretest.
Full charge-standard discharge cycle test: charging according to a GB/T31485-2015 monomer storage battery charging method, namely charging to cut-off voltage by using a 1C constant current, then charging to constant voltage until the current is reduced to 0.05C, then discharging to cut-off voltage by using a standard multiplying power, and terminating the test if a failure phenomenon occurs;
the battery pre-crash performance test and the battery damage characterization test do not need to be checked in the exact order described above, but the order of the same test before/after crash should correspond one-to-one.
Further, the description of the classification of the post-impact battery into three categories according to the pre/post-impact performance comparison is accurately explained as follows:
a safety battery: the electrical performance of the battery after the collision is within the fluctuation range of the normal operation of the battery;
hidden danger battery: the electrical property attenuation quantity of the battery after collision exceeds the fluctuation range of the normal operation of the battery, but the failure characteristic does not appear;
a failed battery: the battery after the collision has failure characteristics such as electrolyte leakage, ignition or explosion.
Further, the specific classification of the three types of safety, hidden danger and failure batteries is as follows:
as long as the battery has electrolyte leakage, smoke, fire or explosion phenomena after collision or in damage characterization tests, the battery is classified as a failed battery;
the battery damage characterization test evaluates three aspects, namely voltage, capacity and internal resistance. And when the performances of the three aspects are in a normal range, the battery is classified as a safe battery, and when any performance does not reach the standard, the battery is classified as a hidden danger battery. The following is a specific method for evaluating the electrical property up to the standard:
voltage U'i：U0≤U′i≤U1，U0To discharge the cut-off voltage, U1Cut off voltage for charging
Standard cyclic discharge capacity C'si：C′si≥1.03Csi，CsiFor standard cyclic discharge capacity at pretest
Quick cycle discharge capacity C'ri：C′ri≥1.03Cri，CriFor rapid cycling discharge capacity at pretest
Full charge-standard discharge capacity C'fi：C′fi≥1.03Cfi，CfiTo pre-test fullnessInternal resistance R 'of charge-standard discharge capacity'i：min{R1，R2，...，Rn}≤R′i≤max{R1，R2，...，Rn}
Wherein, i is 1, 2, 3 … n, n is the total number of the tested batteries, and i is the ordinal number of the batteries.
The embodiment of the invention has the following beneficial effects: through a collision test, a relevant sensor is used for acquiring collision signals, processing data, extracting characteristics, comparing the performance of a power battery before/after collision, dividing the collided battery into three types, namely a safety battery, a hidden danger battery and a failure battery, and establishing a correlation model of force and vibration signals and the damage degree category of the battery by applying a general machine learning algorithm. The method can predict the damage degree of the battery under the collision in real time according to the force and the vibration signal during the collision for the same battery modeled, thereby finding the battery with hidden danger in time and ensuring the safety.
Drawings
FIG. 1 is a general method flow diagram of the present invention;
FIG. 2 is a table of lithium ion battery performance parameters for an example of the present invention;
FIG. 3 is a schematic view of an experimental apparatus for a crash test according to the present invention;
FIG. 4 is a flow chart of a battery damage characterization test phase of the present invention;
FIG. 5 is the results of predictive model training, testing, and synthesis according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Reference is made to the general method flow diagram shown in figure 1.
The method for monitoring and evaluating the collision damage of the battery in real time based on the sensor signal, disclosed by the embodiment of the invention, takes a lithium cobaltate battery as an application object for explanation, and the performance parameters of the battery are shown in figure 2, and basically comprises the following steps:
firstly, preprocessing a lithium ion power battery, and recording electrical property data of the lithium ion power battery: the battery was pretreated as follows in the following order: 1. standard cycle test, 2 rapid charge-discharge cycle test, 3 internal resistance measurement, and 4 constant-current and constant-voltage charging before the test. The charge/discharge voltage, current and capacity of the battery to be measured are collected once per second, and the internal resistance is measured once after the rapid discharge is finished.
And a second step of performing a collision test by using a battery collision test bed, and acquiring signals generated during collision by using an impact sensor and a vibration sensor: the collision test stand used in this example uses a total of 6 different types of cylindrical stainless steel masses falling to cause impact as shown in fig. 3(a), and as shown in fig. 3(b), the masses are: 2kg, 2.25kg, 2.5kg, 2.75kg, 3kg, 3.5 kg. The battery was placed as shown in fig. 3(c) and two types of crash junctions were used to simulate the contact of the battery case with the battery deformed under a crash. The sensor acquisition frequency was 50 kHz. When the collision test is carried out, the single batteries are in a standard discharge state, and the cylindrical mass block is released at the 30 th second of discharge.
Thirdly, performing damage characterization test on the collided lithium ion power battery, and classifying the collided battery according to damage degree: the test at this stage comprises two aspects of physical and electrical property tests, and the specific test comprises the following steps: 1. physical visual inspection, 2 standard cycle test, 3 rapid charge-discharge cycle test, 4 internal resistance measurement, and 5 full charge-standard discharge cycle test.
The test and damage assessment process is shown in fig. 4, and the batteries after collision are classified into three categories, namely safety, hidden danger and failure.
As long as the battery has electrolyte leakage, smoke, fire or explosion phenomena after collision or in damage characterization tests, the battery is classified as a failed battery;
the battery damage characterization test evaluates three aspects, namely voltage, capacity and internal resistance. And when the performances of the three aspects are in a normal range, the battery is classified as a safe battery, and when any performance does not reach the standard, the battery is classified as a hidden danger battery. The following is a specific method for evaluating the electrical property up to the standard:
voltage U'i：2.75V≤U′i≤4.25V
Standard cyclic discharge capacity C'si：C′si≥1.03Csi，CsiFor standard cyclic discharge capacity at pretest
Quick cycle discharge capacity C'ri：C′ri≥1.03Cri，CriFor rapid cycling discharge capacity at pretest
Full charge-standard discharge capacity C'fi：C′fi≥1.03Cfi，CfiFor full charge-standard discharge capacity at pretest
Internal resistance R'i：min{R1，R2，...，Rn}≤R′i≤max{R1，R2，...，Rn}
Wherein, i is 1, 2, 3 … n, n is the total number of the tested batteries, and i is the ordinal number of the batteries.
And fourthly, carrying out data processing on the sensor signals, intercepting effective signal segments, and extracting digital features: the number of digital signatures of the force signal is 5: maximum, variance, kurtosis value, skewness value, integral area of force signal and time; the digital signature of the vibration signal is 4 in total: maximum absolute value, variance, kurtosis value, skewness value.
And fifthly, performing supervised machine learning training and testing by taking the signal characteristics as input and the battery classification labels as targets to obtain an optimal classification model suitable for the battery: FIG. 5 shows the training, testing and synthesis results of the best model, represented by the confusion matrix, with an overall accuracy of 98.2%.
The model may use the impact force and vibration to predict the extent of damage to the battery in real time under the impact.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (6)
1. A method for real-time monitoring and evaluating collision damage of a battery based on sensor signals is characterized by comprising the following steps:
acquiring electrical property data of the lithium ion power battery before collision;
performing a collision test by using a battery collision test bed, and acquiring a sensor collision signal;
performing damage characterization test on the collided batteries, and classifying the batteries according to damage degrees;
carrying out data processing on the sensor signals and extracting digital features;
and finally, performing supervised machine learning training and testing by taking the signal characteristics as input and the battery classification labels as targets to obtain an optimal classification model.
2. The method for real-time monitoring and evaluating battery collision damage based on sensor signals according to claim 1, wherein the acquiring pre-collision electrical performance data of the lithium ion power battery comprises: voltage, capacity, internal resistance. The data are from standard cycle, rapid charge-discharge cycle, internal resistance test while standing.
3. The method for real-time monitoring and evaluating battery collision damage based on sensor signals according to claim 1, wherein the collected sensor collision signals are signals directly reflecting collision strength, and comprise impact force and vibration signals.
4. The method for real-time monitoring and assessing battery collision damage based on sensor signals according to claim 1, wherein the step of performing damage characterization tests on the collided battery includes: physical visual inspection, standard cycle test, rapid charge-discharge cycle test, constant-voltage constant-current charge-standard discharge cycle test and internal resistance measurement when the battery is in standing.
5. The method for real-time monitoring and evaluation of battery collision damage based on sensor signals according to any of claims 1-4, wherein the step of classifying the batteries according to their degree of damage comprises classifying the batteries after collision into three categories:
a safety battery: the electrical performance of the battery after the collision is within the fluctuation range of the normal operation of the battery;
hidden danger battery: the electrical property attenuation quantity of the battery after collision exceeds the fluctuation range of the normal operation of the battery, but the failure characteristic does not appear;
a failed battery: the battery after the collision has failure characteristics such as electrolyte leakage, ignition or explosion.
6. The method for real-time monitoring and evaluating collision damage of batteries based on sensor signals as claimed in claim 5, wherein the specific classification rules of the safety, hidden danger and failed batteries are as follows:
as long as the battery has electrolyte leakage, smoke, fire or explosion phenomena during collision or damage characterization test, the battery is classified as a failed battery;
the battery damage characterization test evaluates three aspects, namely voltage, capacity and internal resistance, when the performances of the three aspects are in a normal range, the battery is classified as a safe battery, and when any one of the performances does not reach the standard, the battery is classified as a hidden danger battery;
the evaluation method for the electrical property reaching the standard comprises the following steps:
voltage U'i：U0≤U′i≤U1，U0To discharge the cut-off voltage, U1A charge cut-off voltage;
standard cyclic discharge capacity C'si：C′si≥1.03Csi，CsiIs the standard cycle discharge capacity at the time of pretest;
quick cycle discharge capacity C'ri：C′ri≥1.03Cri，CriIs the rapid cycle discharge capacity at the time of pretest;
full charge-standard discharge capacity C'fi：C′fi≥1.03Cfi，CfiFull charge-standard discharge capacity at pretest;
internal resistance R'i：min{R1，R2，…，Rn}≤R′i≤max{R1，R2，…，Rn}；
Wherein, i is 1, 2, 3 … n, n is the total number of the tested batteries, and i is the ordinal number of the batteries.
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Application publication date: 20200414 |