21700 battery

Research on the diagnosis method of micro-short circuit of 21700 battery pack

Abstract Lithium batteries are widely used in new energy electric vehicles and energy storage due to their superior performance. However, the micro-short circuit problem of lithium batteries is a safety hazard in the use of battery packs for vehicles and energy storage. In order to diagnose whether a micro-short circuit occurs in the battery pack and determine the monomer with a micro-short circuit, this paper proposes a method for diagnosing micro-short circuits based on the change of the relative charging time of the monomer. At the end of charging of the battery pack, this method uses the voltage curve of the monomer that first reaches the charging cut-off voltage as a benchmark to analyze the charging time of other monomers to reach the charging cut-off voltage under the condition that they can continue to charge, and characterizes it with the relative charging time. Due to the continuous consumption of electric energy of the micro-short-circuited battery, its relative charging time increases with the number of charging times. According to this characteristic, the relative charging time of each monomer in the battery pack is analyzed, and anomaly detection is performed through a box plot. Among the abnormal monomers in the detection results, the one with the most repeated occurrences is the micro-short-circuited monomer. While analyzing the relative charging time, it is necessary to analyze the influence of the DC internal resistance on the diagnosis result, thereby improving the accuracy of the diagnosis result. After comparative analysis, the diagnostic results and actual results are highly consistent. The implementation of this method does not require special testing of the battery pack, and the operation is convenient, which can provide method guidance for battery pack safety detection.

Keywords battery pack; micro short circuit; relative charging time; box diagram

Lithium batteries gradually age during long-term use, and micro short circuit is one of the aging characteristics. If it cannot be detected in time, it will lead to internal short circuit, and the deterioration of internal short circuit is usually accompanied by the increase of self-discharge rate and heat, and then thermal runaway occurs, causing safety accidents. The internal micro short circuit of lithium batteries is easy to trigger. Even if it is not due to manufacturing reasons, excessive charging and discharging, severe vibration and other abuses during use will cause lithium dendrites to form on the negative electrode surface, piercing the diaphragm and causing micro short circuits inside the lithium battery. Therefore, the micro short circuit diagnosis of lithium batteries is still an urgent problem to be solved in battery management.

The existing micro short circuit detection method of lithium batteries is mainly through specific devices and equivalent circuit models. Li Kefeng et al. used a micro-short circuit detection device for the electrode group to uniformly apply pressure in a direction perpendicular to the battery electrode group. After the positive and negative electrodes were connected under pressure, a DC voltage was applied between the two electrodes to test the insulation resistance value between the two electrodes. If the insulation resistance of the micro-short circuit electrode is lower than the threshold, it proves that there is a micro-short circuit. However, this method is applicable to single cells and requires a specific device, which is not easy to implement. Zheng Yuejiu et al. quantified the micro-short circuit by the self-discharge current between the end of two battery charges. The residual rechargeable capacity after two charges can be calculated to obtain the leakage amount, and then the self-discharge current is obtained. Combined with the equivalent circuit model of the micro-short circuit monomer, the internal short circuit resistance is calculated, and the threshold value is used to determine whether the monomer has a micro-short circuit. However, this method relies on the setting of the internal short circuit resistance threshold, which is only applicable to the diagnosis of micro-short circuits in new battery packs, and the diagnostic error for aging battery packs is large.

In view of the difficulties of the above two methods, this paper proposes a micro-short circuit diagnosis method for battery packs using the time and voltage during the battery pack charging process. Its advantages are that only the battery pack charging data is required, no specific detection equipment or thresholds of related parameters are required, the calculation amount is small and easy to implement, and micro-short circuit diagnosis can be performed at the monomer level of the battery pack.

1 Diagnostic method of lithium battery micro-short circuit

1.1 Micro-short circuit characteristics of lithium batteries

When the diaphragm of the lithium battery monomer is pierced by attached dust, the diaphragm quality is poor and the surface area is reduced or damaged, the positive and negative electrodes of the monomer are in direct contact, resulting in a micro-short circuit in the monomer. In a battery pack composed of monomers connected in series and parallel, compared with normal monomers, the monomers with micro-short circuits will continue to consume electrical energy during charging, discharging and storage, affecting the normal operation of the battery pack.

As shown in Figure 1, 6 voltage curves are charging voltage curves of the same 18650 lithium iron phosphate monomer battery at different SOHs, with a rated capacity of 1A·h, a rated voltage of 3.3V, and a charging cut-off voltage of 3.65V. The six charging voltage curves in Figure 1 are simulated as the voltage curves of a battery pack during a certain charge. The battery pack is composed of six cells connected in series, which are numbered from 1 to 6.

Figure 1 LiFePO4 battery charging curve

For a battery pack composed of these six cells connected in series, the charging is stopped when the No. 6 cell first reaches the charging cut-off voltage of 3.65V at tn,0, while the other five cells are not fully charged. If a cell that is not fully charged in the battery pack can be charged separately, the relative charging time Δtn,i of the cell can be obtained, and the calculation formula is

(1)

Where n is the number of charges; tn,0 is the time when the nth charging of the battery pack ends; tn,i is the time when the No. i cell that has not reached the cut-off voltage at the end of the battery pack charging ends and is charged to the cut-off voltage.

In the actual charging process of the battery pack, it is impossible to charge a single cell separately, so the accurate remaining charging time of the cell that has not reached the charging cut-off voltage at the end of charging cannot be obtained, but it can be obtained by taking the voltage curve of the cell that first reaches the charging cut-off voltage as a reference. In Figure 1, the charging voltage curve of the No. 6 cell that first reaches the cut-off voltage is used as a reference. The relative charging time Δtn,j can be calculated through the voltage of other cells in the battery pack at the end of charging and the reference charging voltage curve, which represents the remaining charging time of the cell. The calculation formula is

(2)

Where n is the number of charges; j is the sequence number of the cell; tn,j is the charging time corresponding to the voltage point on the reference charging curve at the nth charge when the voltage is the same as the voltage at the end of charging of the No. j cell.

According to the micro-short circuit characteristics of lithium batteries, when there is no micro-short circuit in the battery pack, the relative charging time of each cell that does not reach the cut-off voltage first during each charging process remains basically unchanged; when there is a micro-short circuit in the battery pack, compared with other normal cells that do not have micro-short circuits, the relative charging time of the cell continues to increase with the increase in the number of charging times.

1.2 Diagnosis of micro short circuit in battery pack

After obtaining the relative charging time of each charge of the monomer in the battery pack, the relative charging time difference between two adjacent charges of the monomer can be calculated, and the Kn-n-1,j value can be calculated by the current relative charging time of the monomer and the corresponding relative charging time difference. The change trend of the relative charging time of the monomer can be determined. The calculation formula of the Kn-n-1,j value is

(3)

Since the relative charging time of the micro short circuit monomer increases with the increase of the number of charging times, the Kn-n-1,j value of the micro short circuit monomer in the battery pack will be greater than the Kn-n-1,j value of other normal monomers. At the same time, the monomer with larger internal resistance has a larger voltage fluctuation at the end of charging due to the fluctuation of each charging time, which makes the relative charging time change greatly each time, and the Kn-n-1,j value will also increase. According to the consistency of the Kn-n-1,j values of each monomer in the entire battery pack and the influence of the internal resistance on the Kn-n-1,j value, it can be judged whether there is a micro short circuit in the monomer. The specific judgment method is as follows:

(1) Analyze the consistency of the Kn-n-1,j values of each monomer through a box plot;

(2) Count the monomer numbers with abnormal Kn-n-1,j values;

(3) Calculate the DC internal resistance of each monomer and perform consistency analysis through a box plot;

(4) Analyze the consistency of the DC internal resistance of monomers with abnormal Kn-n-1,j values to determine whether there are monomers with abnormal or large DC internal resistance;

(5) Screen the monomers according to the DC internal resistance and repeat steps (1) to (2) to perform Kn-n-1,j value consistency analysis;

If the Kn-n-1,j value of a monomer is abnormal and appears the most times as an abnormal value, then the monomer has a micro short circuit.

If the Kn-n-1,j value of a monomer is abnormal and appears the most times as an abnormal value, it may be because the DC internal resistance of the monomer is large. Screening the monomers according to the DC internal resistance does not affect the diagnosis result and can improve the accuracy of the diagnosis result.

2 Application of lithium battery micro-short circuit diagnosis method

2.1 Test object and steps

The test object is the WXL12S537300A lithium battery module produced by Ladder Wanxiang A123 Co., Ltd. The Ladder uses a 21700 battery pack consisting of 13 modules in series, each module consists of 6 soft-pack monomers in parallel, the rated capacity of the monomer is 50A·h, and the rated voltage is 3.3V.

During the test, arbinevts600V/300A high-power power lithium battery test equipment and Harding Technology (Chongqing) Test Equipment Co., Ltd. TU410-5 temperature control box were used. The entire test process was carried out in a temperature control box at 45℃, and it was left to stand for 5 minutes. The battery pack was discharged with a current of 300A until any module reached a cut-off voltage of 2.7V, and then the battery pack was charged with a current of 300A until any module reached a cut-off voltage of 3.6V, thereby performing a charge and discharge cycle on the battery pack. A total of three tests were conducted using the same charge and discharge cycle test steps. The battery pack was kept at a constant temperature of 45°C during the static process of two consecutive tests.

2.2 Selection of test data

Select the charging data from all the test data. When calculating the relative charging time Δtj of each module of the battery pack, take into account the consistency of the difference between the two consecutive charging capacities and the voltage of each module at the end of charging. All charging data should be screened to ensure the accuracy of micro-short circuit diagnosis without affecting the results. The data selection principles are as follows:

(1) Calculate the difference ΔC between each charging capacity and the last charging capacity of the battery pack and compare them with each other. Select the corresponding charging data with a smaller ΔC value. Since the two consecutive charging capacities in the normal charge and discharge cycle are basically unchanged, the selected charging data can be Prevent the relative charging time Δtj of the module from fluctuating greatly during each charging;

(2) Calculate the difference ΔU between the voltage of each module at the end of each charging of the battery pack and the voltage of each module at the end of the next charging, and compare them with each other. Select the corresponding charging data with a smaller ΔU value. Since the voltage of each module at the end of charging is basically unchanged when the same current is used for two consecutive times, this selection can prevent the relative charging time Δtj of the module from fluctuating greatly during each charging;

(3) Count the module number that first reaches the cut-off voltage during each charging process of the battery pack, and select the corresponding charging data with unchanged module number from all charging data. The charging capacity of the battery pack depends on the module that first reaches the charging cut-off voltage. Because the module that first reaches the charging cut-off voltage changes, the charging capacity and charging time of the battery pack will change accordingly. At the end of charging, the voltage of each module will change significantly, resulting in a large fluctuation in the relative charging time Δtj. If the charging data of the battery pack still meets the data selection principles (1) to (2) after the module that first reaches the charging cut-off voltage changes, the corresponding data will be retained.

The battery pack can be charged and discharged normally in the first two tests, but cannot be charged and discharged in the later stage of the third test. As shown in Figure 2, in the cyclic charge and discharge voltage curves of each module in the third test of the battery pack, the voltage curve after the 29th charge is abnormal. Because the modules in the battery pack reach the charge cut-off voltage and discharge cut-off voltage in a short time, the battery pack cannot be charged and discharged normally. Therefore, the charging data of the first 28 times of the battery pack are selected for analysis. Among the 28 charging data, the charging data that meets the requirements are selected according to the data selection principles (1) to (3).

Figure 2 Voltage curves of each module in the third test

Figure 3 shows the charging capacity of the battery pack in each of the three tests. The difference ΔC between each charging capacity and the last charging capacity is calculated based on each charging capacity. The charging capacity difference curve in the three tests is shown in Figure 4, where the ordinate is the difference between the two adjacent charging capacities and the abscissa is the corresponding number of two adjacent charging times. In the three tests, the charging capacity value changes little during the last two tests, and ΔC is basically kept within ±2A·h. Taking -2A·h≤ΔC≤2A·h as the charging data selection condition, and combining Figures 3 and 4, the 2nd, 3rd, 6th to 9th charging data in Test 1, all data in Test 2, and the 19th to 25th and 27th charging data in Test 3 are selected.

Figure 3 Battery pack charging capacity curve

Figure 4 Battery pack ΔC curve

While analyzing the battery pack ΔC, the ΔU of each module of the battery pack is analyzed. Figure 5 shows the voltage curve of each module at the end of each charge in the three tests of the battery pack. ΔU can be calculated, and the ΔU curve of each module is shown in Figure 6. Among the 13 modules of the battery pack, the ΔU of most modules does not exceed ±10mV. Taking -10mV≤ΔU≤10mV as the charging data selection condition, and combining Figures 5 and 6, the 6th to 9th charging data in Test 1, the 11th to 17th charging data in Test 2, and the 20th to 25th charging data in Test 3 are selected.

Figure 5 Voltage curve of each module at the end of charging

Figure 6 ΔU curve of each module in the battery pack

Taking into account the ΔC of the battery pack and the ΔV of each module, the specific charging data selected are shown in Table 1.

Table 1 Statistics of selected charging data

2.3 Micro-short circuit analysis of modules in the battery pack

Select the module that first reaches the charging cut-off voltage, and use its voltage curve as a reference to calculate the relative charging time Δtn,j of each module during each charging. The voltage curve of a battery pack during a certain charge is shown in Figure 7. During the charging process, module No. 10 reaches the charging cut-off voltage first. Based on the charging voltage curve of module No. 10, the relative charging time Δtn,j and Kn-n-1,j values of other modules are calculated, and the consistency of Kn-n-1,j values is analyzed through a box plot.

Figure 7 Voltage curve of each module during charging

The box plot of Kn-n-1,j values of each module is shown in Figure 8. There are multiple abnormal values in the figure. The module numbers with abnormal Kn-n-1,j values are shown in Table 2. The modules with abnormal Kn-n-1,j values are modules 2 and 6. There are two reasons for the abnormal Kn-n-1,j values, namely micro short circuit and large internal resistance. The internal resistance of each module is calculated for further analysis.

Figure 8 Box plot of Kn-n-1,j values of each module in two consecutive charging tests

Table 2 Statistics of module numbers with abnormal Kn-n-1,j values

The DC internal resistance of each module is calculated by the static voltage and charging voltage before each test [11]. The DC internal resistance calculation formula is

Wherein, k is the number of tests; U1-k,j is the static voltage of module j in the kth test; U2-k,j is the voltage 10s after the start of charging of module j in the kth test; Ik is the charging current of the kth test.

The DC internal resistance of each module in the three tests is calculated according to formula (4). The DC internal resistance curves of each module are shown in Figure 9. In the figure, the DC internal resistance value of module 6 is the largest, which proves that the reason for the abnormal Kn-n-1,j of module 6 is the large DC internal resistance value. The DC internal resistance consistency of the modules in the battery pack is analyzed using the box plot. As shown in Figure 10, there is no module with abnormal DC internal resistance in the battery pack. In order to eliminate the influence of DC internal resistance on the diagnostic results, the module data with DC internal resistance values not exceeding 3/4 of the median, that is, the data of modules 6, 7, and 10 with larger DC internal resistance values, are selected to ensure the accuracy of the diagnostic results.

Figure 9 DC internal resistance of each module

Figure 10 DC internal resistance box diagram of each module

For the remaining modules, Kn-n-1,

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