CN114910802A - Battery capacity loss and internal short circuit fault identification method based on feature extraction - Google Patents

Battery capacity loss and internal short circuit fault identification method based on feature extraction Download PDF

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CN114910802A
CN114910802A CN202210429149.6A CN202210429149A CN114910802A CN 114910802 A CN114910802 A CN 114910802A CN 202210429149 A CN202210429149 A CN 202210429149A CN 114910802 A CN114910802 A CN 114910802A
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battery
internal short
short circuit
ucc
charging
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陈思文
孙金磊
唐勇
陈赛汗
仇胜世
刘欣伟
吕凯
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Nanjing University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/52—Testing for short-circuits, leakage current or ground faults
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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Abstract

The invention discloses a battery capacity loss and internal short circuit fault identification method based on feature extraction, which utilizes voltage information acquired in a standing stage and a constant current charging stage after low current pre-charging of an electric automobile and is characterized in that: the slope of a tangent line of a voltage rebound curve in a standing stage after the pre-charging of the battery, a vertical coordinate of the lowest point of a differential capacity curve of the battery and the difference value of the upper end charging electric quantity in the adjacent charging process are used for carrying out fault identification when the aging and the internal short circuit faults coexist, so that the diagnosis of the internal short circuit early fault, the internal short circuit middle fault and the aging fault is realized; and (4) deducing the equivalent resistance of the internal short circuit battery aiming at the identified internal short circuit early fault battery, so as to realize quantitative diagnosis of the internal short circuit fault of the battery. The invention is suitable for single battery and grouped application occasions such as electric automobiles, energy storage systems, electric tools and the like.

Description

Battery capacity loss and internal short circuit fault identification method based on feature extraction
Technical Field
The invention relates to a method for diagnosing a short-circuit fault in a battery, in particular to a method for identifying capacity loss and an internal short-circuit fault of the battery based on feature extraction.
Background
Internal short circuit faults are a significant cause of inducing thermal runaway in lithium ion batteries. According to related researches, the development of the internal short circuit fault into the thermal runaway often needs hundreds of hours, and the development process is not completed at once. The internal short circuit fault often influences the normal use of the battery, and even can cause safety accidents such as battery fire and explosion. From the perspective of battery management, if the internal short-circuit fault can be diagnosed early in the time window, the internal short-circuit fault can be prevented from being upgraded to thermal runaway, and a major safety accident is avoided.
The internal short circuit judgment is usually carried out according to the voltage at the battery end, but the terminal voltage of the battery is reduced due to the aging of the battery, the increase of the internal resistance and the like. In order to diagnose the internal short circuit fault of the battery, the existing research generally adopts various complex algorithms to diagnose, such as a least square method, a neural network method, a support vector machine method, and the like, but the process is often complex and the calculation amount is large.
Disclosure of Invention
The invention aims to provide a battery capacity loss and internal short circuit fault identification method based on feature extraction, and solves the problems that an internal short circuit fault battery is misjudged to be an aged battery with smaller capacity in the discharging process, is misjudged to be a battery with larger capacity in the charging process and the like.
The technical scheme for realizing the purpose of the invention is as follows: a battery capacity loss and internal short circuit fault identification method based on feature extraction comprises the following steps:
step 1, before formal charging of a battery pack, discharging the electric quantity of single batteries, and then connecting the single batteries in series to form a group; applying low-current pre-charging, then standing the battery pack, and judging whether a battery monomer in the middle-stage fault of the internal short circuit exists or not; collecting battery staticCalculating the characteristic parameter f' (u) and the average value of the characteristic parameters of each cell according to the cell voltage data
Figure BDA0003611068170000011
Judging the battery with the internal short circuit in the middle period according to the characteristic parameter; when the monomer characteristic parameter f' (u) exceeds the threshold value
Figure BDA0003611068170000012
If so, judging that the single body is an internal short circuit middle-term fault battery; f' (u) represents the slope of a tangent line of a voltage rebound curve in a standing stage after the pre-charging of the battery;
step 2, performing single constant current charging on the battery pack, calculating to obtain a characteristic parameter DV _ Valley of each monomer by collecting constant current charging data, and performing primary judgment on an aged battery and a normal battery according to the value of the parameter; when the characteristic parameter DV _ Valley is smaller than a threshold k2, judging that the single cell is an undetermined normal battery, otherwise, judging that the single cell is an undetermined aged battery; the DV _ Valley represents the ordinate of the lowest point of the battery differential capacity curve;
step 3, performing constant current charging on the battery pack for multiple times, and calculating according to charging data to obtain a characteristic parameter delta UCC; respectively further judging the to-be-determined normal battery and the to-be-determined aged battery classified in the step 2 according to the value of the delta UCC; for a to-be-determined normal battery, when the delta UCC value is judged to be abnormal, the corresponding single battery is an internal short circuit early fault battery, otherwise, the corresponding single battery is a normal battery; for the battery to be aged, when the delta UCC value is judged to be abnormal, the corresponding single battery is an internal short circuit early fault battery, otherwise, the corresponding single battery is an aged battery; the delta UCC represents the difference value of upper end charging electric quantity in the adjacent charging process;
and 4, aiming at the early-stage fault battery with the internal short circuit identified in the step 3, calculating the equivalent resistance of the internal short circuit battery by using the characteristic parameter delta UCC as a fault characteristic, and realizing quantitative diagnosis of the internal short circuit fault of the battery.
An electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the battery capacity loss and internal short circuit fault identification method based on feature extraction.
A computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the above-described battery capacity loss and internal short fault identification method based on feature extraction.
Compared with the prior art, the invention has the beneficial effects that: (1) the invention provides a battery capacity loss and internal short circuit fault identification method based on feature extraction, which can be used for diagnosing faults when aging and internal short circuit faults coexist according to acquired voltage information in a series battery pack and three proposed feature parameters f' (u), DV _ Valley and delta UCC, so that diagnosis of internal short circuit early faults, internal short circuit medium-term faults and aging faults is realized. For the identified internal short circuit early fault battery, the characteristic parameters are used as fault characteristics to deduce the equivalent resistance of the internal short circuit battery, so as to realize quantitative diagnosis of the internal short circuit fault of the battery; (2) the battery capacity loss and internal short circuit fault identification method based on feature extraction does not need a complex algorithm, and solves the problems of uncertainty, high calculation complexity and the like caused by a complex system and a complex model.
Drawings
Fig. 1 is a graph of the characteristic parameter f' (u) at different SOC points.
Fig. 2 is a DV graph of each battery cell.
Fig. 3 is a diagram illustrating relative DV _ Valley values of the respective battery cells.
Fig. 4 is a flow chart of a short-circuit fault diagnosis in a lithium battery in a series battery.
Detailed Description
A battery capacity loss and internal short circuit fault identification method based on feature extraction comprises the following steps:
step 1, before the battery pack is formally charged, the electric quantity of the single batteries is discharged, and then the single batteries are connected in series to form a group. And applying low current for pre-charging, then standing the battery pack, and judging whether a single battery with a fault in the middle period of the internal short circuit exists or not. Collecting the voltage data of the single body when the battery is in static state, and calculating the characteristic parameter f' (u) and the characteristic parameter average of each single bodyMean value
Figure BDA0003611068170000031
And judging the battery with the internal short circuit middle-term fault according to the characteristic parameter. When the monomer characteristic parameter f' (u) exceeds the threshold value
Figure BDA0003611068170000032
If so, judging that the single body is an internal short circuit middle-term fault battery;
and 2, performing single constant current charging on the battery pack, calculating to obtain a characteristic parameter DV _ Valley of each monomer by collecting constant current charging data, and performing primary judgment on an aged battery and a normal battery according to the value of the parameter. When the characteristic parameter DV _ Valley is smaller than a threshold k2, the single battery is judged to be a pending normal battery, otherwise, the single battery is a pending aging battery, and the characteristic parameter DV _ Valley value of the battery with the capacity of 95% is determined as a value of the threshold k 2;
and 3, carrying out constant current charging on the battery pack for multiple times, and calculating according to charging data to obtain a characteristic parameter delta UCC. And (3) respectively further judging the pending normal batteries and the pending aged batteries classified in the step (2) according to the value of the delta UCC. For a to-be-determined normal battery, when the delta UCC value is judged to be abnormal, the corresponding single battery is an internal short circuit early fault battery, otherwise, the corresponding single battery is a normal battery; for the battery to be aged, when the delta UCC value is judged to be abnormal, the corresponding single battery is an internal short circuit early fault battery, otherwise, the corresponding single battery is an aged battery;
and 4, aiming at the early-stage fault battery with the internal short circuit identified in the step 3, calculating the equivalent resistance of the internal short circuit battery by using the characteristic parameter delta UCC as a fault characteristic, and realizing quantitative diagnosis of the internal short circuit fault of the battery.
Before identifying the short-circuit fault in the battery, the battery is pre-charged with low current, then is kept stand for a period of time, and whether a single battery with the middle-stage short-circuit fault exists is judged according to the collected voltage and current information. And then, performing single constant current charging on the battery, recording voltage and current state information, calculating to obtain a characteristic parameter DV _ Valley, and performing primary judgment on a normal battery and an aged battery according to the characteristic parameter. And thirdly, carrying out constant current charging on the battery pack for multiple times, calculating a characteristic parameter delta UCC according to charging data, and judging an aged battery, a normal battery and an internal short-circuit battery. And finally, calculating the equivalent resistance of the internal short-circuit battery by using the characteristic parameter delta UCC as a fault characteristic, and realizing quantitative diagnosis of the internal short-circuit fault of the battery.
The invention provides three characteristic parameters f' (u), DV _ Valley and delta UCC, and the three parameters are combined to identify three batteries, namely a normal battery, an aged battery and an internal short circuit fault battery.
Further, in step 1 of this embodiment, whether there is a single battery with a middle-stage internal short circuit fault may be determined according to the following steps:
step 1-1, performing 0.5C constant current discharge on each monomer until the lower limit cut-off voltage is 2.75V;
step 1-2, laying aside for 2h, and then connecting in series to form a group;
step 1-3, charging the battery pack to 10% SOC at a constant current of 1C, and then standing for 30 min;
step 1-4, connecting an internal short-circuit resistor;
step 1-5, applying charging pulse with constant current of 0.1C and duration of 1min to the battery pack;
step 1-6, standing for 10min, and then disconnecting the internal short-circuit resistor;
step 2-7, repeating steps 1-3 to 1-6, and charging the battery pack to 30% SOC;
and 1-8, calculating the tangent slope f' (u) of a battery voltage curve segment corresponding to the time from the charging end time to the standing end time. When the battery is switched from the low-current charging to the standing, the battery has weak polarization, the polarization gradually disappears along with the lapse of time, the terminal voltage of the normal battery is reduced and tends to be stable, and the more serious middle-stage fault single body of the internal short circuit is superimposed with larger abnormal self-discharge leakage current in the standing process, so the slope of the tangent line of the battery voltage curve in the corresponding time period is a larger value which is not 0. For discrete data, the characteristic parameter f' (u) can be represented by equation (1).
Figure BDA0003611068170000041
Where k is the number of sampling points, T is the sampling period, u (kt) is the voltage value at the time of the kth sampling period, and u ((k +1) T) is the voltage value at the time of the kth +1 sampling period.
Step 1-9, averaging the characteristic parameters f' (u) of each monomer obtained by calculation to obtain the average value of the characteristic parameters
Figure BDA0003611068170000042
By comparing the individual characteristic parameter f' (u) with a threshold value
Figure BDA0003611068170000043
It is determined whether the battery is in the middle of the internal short circuit. The threshold k1 is used for screening out the middle-stage fault battery with the internal short circuit in the battery pack, and the threshold is a larger value in order to prevent misjudgment and leave a certain margin, wherein the value of k1 is 3. When the cell characteristic parameter f' (u) exceeds a threshold value
Figure BDA0003611068170000044
And then, judging that the single cell is the internal short circuit middle-term fault battery.
Further, step 2 in this embodiment specifically includes:
step 2-1, performing 0.5C constant current discharge on each monomer until the lower limit cut-off voltage is 2.75V;
step 2-2, laying aside the batteries for 2 hours, and then connecting the batteries in series to form a group;
step 2-3, connecting an internal short-circuit resistor;
step 2-4, charging the battery pack to the upper limit cut-off voltage of any monomer at constant current of 4.2V at 0.5C, and standing for 10 minutes;
step 2-5, discharging the battery pack at a constant current of 0.5C until the cut-off voltage of any monomer is 3V, and standing for 20 minutes;
and 2-6, repeating the steps 2-4 to 2-5, and carrying out multiple times of cyclic charge/discharge tests on the battery pack.
And 2-7, collecting constant-current charging data, and calculating characteristic parameters DV _ Valley of each monomer. The DV curve of the battery has a characteristic valley with a large span and represents a charging and discharging voltage platform of the battery. Along with the increase of the cyclic aging of the battery, the abscissa of the valley point moves towards the direction of smaller electric quantity, the ordinate of the valley point moves upwards, and the ordinate of the DV curve is selected as a characteristic parameter for identifying the aging in the series battery pack;
step 2-8, comparing the obtained characteristic parameter DV _ Valley with a threshold k2, determining the battery cell as an undetermined aged battery when DV _ Valley is larger than or equal to k2, and determining the battery cell as an undetermined normal battery when DV _ Valley is smaller than k 2;
further, in this embodiment, the specific method for determining the aged battery, the normal battery, and the internal short-circuited battery in step 3 is as follows:
and 3-1, collecting constant current charging data and calculating a characteristic parameter delta UCC. With the increase of the cycle number, the battery DV curve is continuously shifted to the left, because the leakage current of the battery with the internal short circuit fault causes the Charging Capacity of the battery case to be less and less, the Capacity value between the valley point of the battery DV and the Charging end position of the battery is defined as the Upper Charging Capacity (UCC), therefore, the difference value of the UCC in the adjacent Charging process is taken as a characteristic parameter, and the characteristic parameter is calculated according to the formula (2)
Figure BDA0003611068170000051
Wherein the content of the first and second substances,
Figure BDA0003611068170000052
is the difference value of the UCC in the electricity quantity interval between the ith time and the i +1 time of the charging process of the monomer j,
Figure BDA0003611068170000053
and the value of the electric quantity interval UCC in the ith charging process of the monomer j is shown.
And 3-2, judging the battery to be determined in the step 2 according to the characteristic parameter delta UCC obtained in the step 3-1. The invention adopts the Grabbs criterion as a judgment basis. Firstly, the measured characteristic parameters delta UCC of each monomer are arranged from small to large according to the numerical values: x is the number of 1 ，x 2 ，…，x n (x 1 Minimum, x n Max), where n is the number of characteristic parameters Δ UCC. Then, the average value of the characteristic parameters is calculated
Figure BDA0003611068170000054
And standard deviation S, as shown in formula (3) and formula (4), and statistical data G obtained by calculating characteristic parameters of each monomer m As shown in formula (5).
Figure BDA0003611068170000055
Figure BDA0003611068170000056
Figure BDA0003611068170000057
The critical value G (alpha, n) of the criterion is obtained by table lookup, and then the internal short circuit fault of the unit is identified according to the Grabbs criterion and determined as follows: when G is m G (alpha, n) is regarded as the characteristic parameter x m If the battery is abnormal, the battery corresponding to the parameter is judged as the early fault battery with internal short circuit, and when G is m G (alpha, n) is less than or equal to G, the characteristic parameter x is considered m If the battery is not abnormal, the battery to be determined to be normal can be determined to be a normal battery, and the battery to be determined to be aged can be determined to be an aged battery.
Further, in this embodiment, step 4 is to calculate the short-circuit resistance in the battery, specifically:
step 4-1, calculating the UCC electric quantity of the battery according to the existing constant current charging data, wherein the calculation formula is shown as a formula (6);
Figure BDA0003611068170000061
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003611068170000062
UCC electric quantity value, t, measured for the ith charging process of the battery 1 To start calculating
Figure BDA0003611068170000063
I.e. the moment when the battery reaches the DV feature valley point in the ith charging process, t 2 To end the calculation
Figure BDA0003611068170000064
I.e. the ith end of charge of the battery, V tf (t) is the terminal voltage value of the short-circuit fault battery in the time t, R ISCr Is an internal short-circuit resistor.
Step 4-2, calculating the real UCC electric quantity value in the (i +1) th charging process of the battery, wherein the calculation formula is shown as the formula (7)
Figure BDA0003611068170000065
Wherein the content of the first and second substances,
Figure BDA0003611068170000066
UCC electric quantity value, t, measured for the (i +1) th charging process of the battery 3 To start calculating
Figure BDA0003611068170000067
I.e. the time when the DV characteristic valley point is reached in the i +1 th charging process of the battery, t 4 To end the calculation
Figure BDA0003611068170000068
I.e., the i +1 th charge end time of the battery.
And 4-3, calculating the actual delta UCC electric quantity value of the two adjacent charging processes, thereby calculating the internal short circuit resistance value.
The actual delta UCC electric quantity value of two adjacent charging processes can be calculated according to the formula (6) and the formula (7) and is shown as the formula (8):
Figure BDA0003611068170000069
when the battery has no internal short circuit fault, the internal short circuit equivalent resistance R ISCr Tends to infinity, and the electric quantity difference delta UCC i Then it is 0; according to the equivalent circuit model of the internal short-circuit fault battery, the caused electric quantity difference delta UCC is known i The reason for (a) is the amount of leakage current generated by the leakage current during that period of time, and therefore, the battery Δ UCC i Another expression, as shown in equation (9):
Figure BDA00036110681700000610
therefore, according to the equations (8) and (9), the corresponding difference in the amount of charge for the UCC measurement can be obtained as shown in equation (10):
Figure BDA0003611068170000071
simplified to obtain formula (11)
Figure BDA0003611068170000072
The equivalent internal short circuit resistance value of the internal short circuit fault battery can be calculated according to the formula (11).
The present invention will be specifically described below by taking a certain ternary lithium battery as an example.
Examples
In this embodiment, a ternary lithium battery of ISR18650-2.2Ah is used as an experimental object, six batteries are connected in series to serve as a battery pack, two ends of a battery monomer # a and a battery monomer # b are respectively connected in parallel with a resistor to simulate an internal short circuit, and the method of the present invention is performed at room temperature, as shown in fig. 4, and the specific process is as follows:
before the charging experiment is carried out, the electric quantity of the battery cell needs to be emptied. Performing constant current discharge on the battery by adopting 0.5C multiplying power until the lower limit cut-off voltage is 2.75V; after being placed for 2 hours, the six batteries are connected in series to form a group; then charging the battery pack to 10% SOC at a constant current of 1C and standing for 30 min; then across cell # aThe resistance of 150 omega is incorporated to simulate the short circuit in the early stage, and the resistance of 10 omega is incorporated to simulate the short circuit in the middle stage at the two ends of the battery # b; applying charging pulse with constant current of 0.1C and duration of 1min to the battery pack; standing for 10min, and then disconnecting the internal short-circuit resistor; the low current charging step was repeated until charging to 30% SOC. And after the charging is finished, calculating the tangent slope f' (u) of the battery voltage curve segment corresponding to the charging finishing time to the standing finishing time. The calculated characteristic parameter f' (u) is shown in FIG. 1, in which the curve value of the solid line is shown as
Figure BDA0003611068170000073
As can be seen from the graph, in the experiment of about 1 minute, when the polarization influence gradually disappeared, the absolute values of the characteristic parameter f' (u) of the normal batteries (# c, # e, and # f) and the aged battery (# d) both approached 0. In contrast, the absolute value of the characteristic parameter f' (u) of cell # a (simulated internal short early failure battery) is slightly higher, but the difference is small. While for cell # b (simulated medium short-circuit fault cell), the curve shows a clear anomaly: after 1 minute, the absolute value of the characteristic parameter still has a large value and is less than
Figure BDA0003611068170000074
It can be determined that the cell # b is the medium-short-term faulty battery.
Then, the battery pack is subjected to constant current charging, and the data of the first charging is obtained, so that the DV curve of each single battery is shown in fig. 2. The present invention takes the DV _ Valley value of a 95% aged state battery as a reference value. Then dividing the DV _ Valley value of the cell by the reference value to obtain the DV _ Valley relative value of each cell, wherein the DV _ Valley relative value of each battery cell is shown in fig. 3. As can be seen from the figure, the DV _ Valley relative value of the batteries # a and # d is greater than the standard value "1", so that it is determined as a pending aged battery; the relative value of DV _ Valley of batteries # b, # c, # e and # f is smaller than the standard value "1", and thus determined as a pending normal battery.
Then, through the data of constant current charging of the battery, a characteristic parameter Δ UCC is calculated according to the formula (2), and the corresponding characteristic parameter values of the monomers # a to # b are respectively 0.093, 0.129, 0.020, 0.021, 0.016 and 0.021, calculated mean value of characteristic parameter
Figure BDA0003611068170000081
It was 0.05 and the standard deviation S was 0.044. The critical value G (0.9,6) of the Grubbs criterion is obtained as 1.729 by table look-up, and the values are arranged from small to large in sequence and correspond to x 1 ～x 6 Wherein x is 6 Is a maximum value, x 1 Is the minimum value. Respectively calculating statistical data G according to equation (5) 1 ～G 6 And making a decision according to the grabbs criterion.
Finally, combining the characteristic parameter DV _ Valley result and the characteristic parameter delta UCC result, judging the results of 6 single cells in the battery pack as follows, wherein # a and # b are internal short circuit early fault batteries; # d is an aged cell; # c, # e, and # f are normal batteries. The result is consistent with the expectation, and the effectiveness of the qualitative diagnosis method for the internal short circuit fault is shown.
In conclusion, the method and the device only utilize the measured value of the battery terminal voltage and the battery capacity, adopt three characteristic parameters to identify the fault when the battery aging and the internal short circuit fault coexist, and realize the diagnosis of the internal short circuit early fault, the internal short circuit medium fault and the aging fault. And (3) for the identified internal short circuit early fault battery, the characteristic parameters are used as fault characteristics to deduce the equivalent resistance of the internal short circuit battery, so as to realize quantitative diagnosis of the internal short circuit fault of the battery. The invention does not depend on complex algorithm, and overcomes the problems of uncertainty, high calculation complexity and the like caused by complex systems and complex models.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1. A battery capacity loss and internal short circuit fault identification method based on feature extraction is characterized by comprising the following steps:
step 1, before the battery pack is formally charged, the electric quantity of the single batteries is discharged, and then the single batteries are connected in series to form a group; applying low-current pre-charging, then standing the battery pack, and judging whether a battery monomer in the middle-stage fault of the internal short circuit exists or not; collecting the voltage data of the single body when the battery is still, and calculating the characteristic parameter f' (u) and the average value of the characteristic parameter of each single body
Figure FDA0003611068160000011
Judging the battery with the internal short circuit in the middle period according to the characteristic parameter; when the monomer characteristic parameter f' (u) exceeds the threshold value
Figure FDA0003611068160000012
If so, judging that the single body is an internal short circuit middle-term fault battery; f' (u) represents the slope of a tangent line of a voltage rebound curve in a standing stage after the battery is precharged;
step 2, performing single constant current charging on the battery pack, calculating to obtain a characteristic parameter DV _ Valley of each monomer by collecting constant current charging data, and performing primary judgment on an aged battery and a normal battery according to the value of the parameter; when the characteristic parameter DV _ Valley is smaller than a threshold k2, judging that the single cell is an undetermined normal battery, otherwise, judging that the single cell is an undetermined aged battery; the DV _ Valley represents the ordinate of the lowest point of the battery differential capacity curve;
step 3, performing constant current charging on the battery pack for multiple times, and calculating according to charging data to obtain a characteristic parameter delta UCC; respectively further judging the to-be-determined normal battery and the to-be-determined aged battery classified in the step 2 according to the value of the delta UCC; for a battery to be determined to be normal, when the delta UCC value is determined to be abnormal, the corresponding single battery is an internal short circuit early fault battery, otherwise the corresponding single battery is a normal battery; for the battery to be aged, when the delta UCC value is judged to be abnormal, the corresponding monomer is an internal short circuit early fault battery, otherwise, the corresponding monomer is an aged battery; the delta UCC represents the difference value of upper end charging electric quantity in the adjacent charging process;
and 4, aiming at the early-stage fault battery with the internal short circuit identified in the step 3, calculating the equivalent resistance of the internal short circuit battery by using the characteristic parameter delta UCC as a fault characteristic, and realizing quantitative diagnosis of the internal short circuit fault of the battery.
2. The method for identifying battery capacity loss and internal short circuit fault based on feature extraction as claimed in claim 1, wherein the step 1 is specifically as follows:
step 1-1, performing 0.5C constant current discharge on each monomer until the lower limit cut-off voltage is 2.75V;
step 1-2, laying aside for 2h, and then connecting in series to form a group;
step 1-3, charging the battery pack to 10% SOC at a constant current of 1C, and then standing for 30 min;
step 1-4, connecting an internal short-circuit resistor;
step 1-5, applying charging pulse with constant current of 0.1C and duration of 1min to the battery pack;
step 1-6, standing for 10min, and then disconnecting the internal short-circuit resistor;
step 1-7, repeating steps 1-3 to 1-6, and charging the battery pack to 30% SOC;
step 1-8, calculating a tangent slope f' (u) of a battery voltage curve segment corresponding to the charging ending time to the standing ending time;
step 1-9, averaging the characteristic parameters f' (u) of each monomer obtained by calculation to obtain the average value of the characteristic parameters
Figure FDA0003611068160000021
By comparing the individual characteristic parameter f' (u) with a threshold value
Figure FDA0003611068160000022
Judging whether the battery is in the middle period of internal short circuit; when the monomer characteristic parameter f' (u) exceeds the threshold value
Figure FDA0003611068160000023
And then, judging that the single cell is the internal short circuit middle-term fault battery.
3. The method for identifying battery capacity loss and internal short circuit fault based on feature extraction as claimed in claim 2, wherein, in the steps 1-8, the feature parameter f' (u) can be represented by the following formula for discrete data:
Figure FDA0003611068160000024
where k is the number of sampling points, T is the sampling period, u (kt) is the voltage value at the time of the kth sampling period, and u ((k +1) T) is the voltage value at the time of the kth +1 sampling period.
4. The method for identifying the battery capacity loss and the internal short circuit fault based on the feature extraction as claimed in claim 2, wherein in the steps 1 to 9, the value of k1 is 3.
5. The method for identifying battery capacity loss and internal short circuit fault based on feature extraction as claimed in claim 1, wherein the step 2 is specifically:
step 2-1, performing 0.5C constant current discharge on each monomer until the lower limit cut-off voltage is 2.75V;
step 2-2, laying aside the batteries for 2 hours, and then connecting the batteries in series to form a group;
step 2-3, connecting an internal short-circuit resistor;
step 2-4, charging the battery pack to the upper limit cut-off voltage of any monomer at constant current of 4.2V at 0.5C, and standing for 10 minutes;
step 2-5, discharging the battery pack at a constant current of 0.5C until the cut-off voltage of any monomer is 3V, and standing for 20 minutes;
step 2-6, repeating steps 2-4 to 2-5, and carrying out multiple-cycle charge/discharge tests on the battery pack;
step 2-7, collecting constant current charging data, and calculating characteristic parameters DV _ Valley of each monomer;
and 2-8, comparing the obtained characteristic parameter DV _ Valley with a threshold k2, determining the battery cell as an undetermined aged battery when DV _ Valley is larger than or equal to k2, and determining the battery cell as an undetermined normal battery when DV _ Valley is smaller than k 2.
6. The method for identifying battery capacity loss and internal short circuit fault based on feature extraction as claimed in claim 5, wherein the step 3 is specifically as follows:
step 3-1, collecting constant current charging data and calculating a characteristic parameter delta UCC; defining the electric quantity value between the valley point of the battery DV and the charging end position of the battery as the upper charging electric quantity UCC, so that the difference value of the UCC in the adjacent charging processes is used as a characteristic parameter, and calculating the characteristic parameter according to the following formula
Figure FDA0003611068160000031
Wherein the content of the first and second substances,
Figure FDA0003611068160000032
is the difference value of the UCC in the electricity quantity interval between the ith time and the i +1 time of the charging process of the monomer j,
Figure FDA0003611068160000033
the value of the UCC of the electric quantity interval in the ith charging process of the monomer j is shown;
step 3-2, judging the battery to be determined in the step 2 according to the characteristic parameter delta UCC obtained in the step 3-1; adopting a Grabbs criterion as a judgment basis; firstly, the measured characteristic parameters delta UCC of each monomer are arranged from small to large according to the numerical values: x is the number of 1 ，x 2 ，…，x n Wherein n is the number of characteristic parameters Δ UCC; then, the average value of the characteristic parameters is calculated
Figure FDA00036110681600000312
And standard deviation S, statistical data G obtained by calculating characteristic parameters of each monomer m As shown in formula (3):
Figure FDA0003611068160000034
obtaining the critical value of the criterion by looking up the tableG (α, n), followed by the determination of short-circuit failure within a unit according to the grubbs criterion as follows: when G is m G (alpha, n) is regarded as the characteristic parameter x m If the battery is abnormal, the battery corresponding to the parameter is judged as the early fault battery with internal short circuit, and when G is m G (alpha, n) is less than or equal to G, the characteristic parameter x is considered m If the battery is not abnormal, the battery to be determined to be normal is determined to be a normal battery, and the battery to be determined to be aged is determined to be an aged battery.
7. The method for identifying battery capacity loss and internal short circuit fault based on feature extraction as claimed in claim 6, wherein the step 4 of calculating the internal short circuit resistance specifically comprises:
step 4-1, calculating the UCC electric quantity of the battery according to the existing constant current charging data, wherein the calculation formula is shown as a formula (4);
Figure FDA0003611068160000035
wherein the content of the first and second substances,
Figure FDA0003611068160000036
UCC electric quantity value, t, measured for the ith charging process of the battery 1 To start calculating
Figure FDA0003611068160000037
I.e. the moment when the battery reaches the DV feature valley point in the ith charging process, t 2 To end calculation
Figure FDA0003611068160000038
I.e. the ith end of charge of the battery, V tf (t) is the terminal voltage value of the short-circuit fault battery in the time t, R ISCr Is an internal short-circuit resistor;
step 4-2, calculating the real UCC electric quantity value in the i +1 th charging process of the battery, wherein the calculation formula is shown as the formula (5)
Figure FDA0003611068160000039
Wherein the content of the first and second substances,
Figure FDA00036110681600000310
UCC electric quantity value, t, measured for the (i +1) th charging process of the battery 3 To start calculating
Figure FDA00036110681600000311
I.e. the time when the DV characteristic valley point is reached in the i +1 th charging process of the battery, t 4 To end the calculation
Figure FDA0003611068160000041
I.e. the (i +1) th charging end time of the battery;
4-3, calculating the actual delta UCC electric quantity value of the two adjacent charging processes, thereby calculating the internal short circuit resistance value; calculating the actual delta UCC electric quantity value of two adjacent charging processes according to the formula (4) and the formula (5) as shown in the formula (6):
Figure FDA0003611068160000042
when the battery has no internal short circuit fault, the internal short circuit equivalent resistance R ISCr Tends to infinity, and the electric quantity difference delta UCC i Then 0; according to the equivalent circuit model of the internal short-circuit fault battery, the caused electric quantity difference delta UCC is known i The reason for (a) is the amount of leakage current generated by the leakage current during that period of time, and therefore, the battery Δ UCC i Another expression, as shown in equation (7):
Figure FDA0003611068160000043
therefore, according to the equations (6) and (7), the corresponding difference in the amount of charge for the UCC measurement can be obtained as shown in equation (8):
Figure FDA0003611068160000044
simplified gain type (9)
Figure FDA0003611068160000045
The equivalent internal short circuit resistance value of the internal short circuit fault battery can be calculated according to the formula (9).
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for identifying battery capacity loss and internal short circuit fault based on feature extraction according to any one of claims 1 to 7 when executing the program.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method for identifying a capacity loss and an internal short circuit fault of a battery based on feature extraction according to any one of claims 1 to 7.
CN202210429149.6A 2022-04-22 2022-04-22 Battery capacity loss and internal short circuit fault identification method based on feature extraction Pending CN114910802A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115792522A (en) * 2022-12-08 2023-03-14 青岛艾测科技有限公司 Capacitive load insulation detection method, device and equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115792522A (en) * 2022-12-08 2023-03-14 青岛艾测科技有限公司 Capacitive load insulation detection method, device and equipment
CN115792522B (en) * 2022-12-08 2023-07-28 青岛艾测科技有限公司 Capacitive load insulation detection method, device and equipment

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