Research on micro-short circuit diagnosis method of lithium iron phosphate battery pack -Lithium - Ion Battery Equipment
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 related to vehicle and energy storage battery packs is a safety hazard during use. In order to diagnose whether micro-short circuits occur in battery packs, Short circuit and determine micro-short circuit cells. This paper proposes a method to diagnose micro-short circuits based on changes in relative charging time of cells. In this method, at the end of charging of the battery pack, the voltage curve of the monomer that reaches the charging cut-off voltage first is used as a benchmark to analyze the charging time of other cells to reach the charging cut-off voltage under the condition that they can continue to charge, and characterize it by the relative charging time. . Since the power of the micro-short-circuit battery continues to be consumed, its relative charging time increases with the number of charges. Based on this characteristic, the relative charging time of each cell in the battery pack is analyzed, and anomalies are detected through box plots. Among the abnormal cells in the test results, the micro-short-circuit cells appear the most frequently. While analyzing the relative charging time, it is also necessary to analyze the impact of DC internal resistance on the diagnostic results, which can improve the accuracy of the diagnostic results. After comparative analysis, the diagnostic results and the actual results are highly consistent. The implementation of this method does not require special testing of the battery pack, and is easy to operate. It can provide method guidance for battery pack safety testing.
Lithium batteries gradually age during long-term use. Micro-short circuit is one of the aging characteristics. If it cannot be detected in time, it will lead to internal short circuit. The deterioration of internal short circuit is usually accompanied by the increase of self-discharge rate and heat, and then occurs. Thermal runaway, causing safety accidents. Micro short circuits inside lithium batteries are easy to trigger. Even if it is not due to manufacturing reasons, excessive charging, discharging, severe vibration and other abuse during use will cause lithium dendrites to form on the surface of the negative electrode, and piercing the separator will also cause micro short circuits inside the lithium battery. Therefore, micro-short circuit diagnosis of lithium batteries is still an urgent problem to be solved in battery management.(Lithium - Ion Battery Equipment)
The existing micro-short circuit detection methods for lithium batteries mainly rely on specific devices and equivalent circuit model detection. Li Kefeng et al. used a pole plate micro-short circuit detection device to apply pressure uniformly in the direction perpendicular to the battery pole plate set. After the positive and negative electrodes were connected under pressure at the hidden danger point of the short circuit, a DC voltage was applied between the two poles to test the voltage between the two poles. Insulation resistance value, if the insulation resistance value of the micro-short-circuit pole piece is lower than the threshold value, it proves that there is a micro-short circuit. However, this method is suitable for single batteries, requires specific equipment, and is not easy to implement. Zheng Yuejiu et al. used the self-discharge current between the two charges of the battery to quantify micro-short circuits. By calculating the remaining rechargeable capacity after two charges, the leakage amount can be obtained, and then the self-discharge current can be obtained, combined with the equivalent circuit of the micro-short circuit cell. model, calculate the internal short-circuit resistance, and determine whether the monomer has a micro-short circuit based on its threshold value. However, this method relies on the setting of the internal short-circuit resistance threshold and is only suitable for diagnosing micro-short circuits in new battery packs. 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 the battery pack using the time and voltage during the charging process of the battery pack. The advantage is that it only requires battery pack charging data, does not require specific detection equipment or sets thresholds for relevant parameters, the calculation amount is small and easy to implement, and micro-short circuit diagnosis can be performed at the single level of the battery pack.
1Diagnosis method of micro short circuit in lithium battery
1.1 Micro-short circuit characteristics of lithium batteries
When the separator of a lithium battery cell is pierced by attached dust or the separator quality is poor and the surface area is reduced or damaged, the positive and negative electrodes of the cell are in direct contact, causing a micro short circuit in the cell. In a battery pack composed of cells connected in series and parallel, compared with normal cells, cells with micro-short circuits will continue to consume power during the charging, discharging, and storage processes, affecting the normal operation of the battery pack.
According to the micro-short circuit characteristics of lithium batteries, when no micro-short circuit occurs in the battery pack, the relative charging time of each cell that does not reach the cut-off voltage first during each charging process is basically unchanged; if there is a micro-short circuit in the battery pack, When a micro short circuit occurs, compared with other normal cells without micro short circuit, the relative charging time of the cell continues to extend with the increase in the number of charges.
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 calculated by the current relative charging time of the monomer and the corresponding relative charging time difference. Obtaining the Kn-n-1,j value can determine the changing trend of the monomer relative to the charging time.
Since the relative charging time of a micro-short-circuit cell increases with the number of charges, the Kn-n-1,j value of a micro-short-circuit cell in the battery pack will be greater than the Kn-n-1 of other normal cells. j value. At the same time, monomers with large internal resistance will have large voltage fluctuations at the end of charging due to fluctuations in each charging time, resulting in large changes in the relative charging time each time, and the Kn-n-1,j value will also change. big. Based on the consistency of the Kn-n-1,j value of each monomer in the entire battery pack, and combined with the impact of internal resistance on the Kn-n-1,j value, it can be determined whether any monomer has a micro-short circuit.
If the Kn-n-1,j value of a certain monomer is abnormal and appears as an abnormal value the most times, then the monomer has a micro short circuit.
The Kn-n-1,j value of a certain monomer is abnormal and appears the most frequently as an outlier. This may be because the monomer has a large DC internal resistance. Screening monomers based on DC internal resistance does not affect the diagnosis results. , and can improve the accuracy of diagnostic results.
2 Application of lithium battery micro-short circuit diagnosis method
2.1 Test objects and steps
The test object is the WXL12S537300A lithium battery module produced by Ladder Wanxiang A123 Co., Ltd. This ladder uses lithium iron phosphate battery packs and consists of 13 modules connected in series. Each module consists of 6 soft pack cells connected in parallel. The rated power of the cells is The capacity is 50A·h and the rated voltage is 3.3V.
During the test, arbinevts600V/300A high-power power lithium battery testing equipment and TU410-5 temperature control box of Harding Technology (Chongqing) Testing Equipment Co., Ltd. were selected. The entire test process is carried out in a temperature-controlled box at 45°C. Leave it for 5 minutes, discharge the battery pack with a current of 300A until any module reaches the cut-off voltage of 2.7V, and then charge the battery pack with a current of 300A until any module reaches the cut-off voltage of 2.7V. One module reaches the cut-off voltage of 3.6V, thus charging and discharging the battery pack. A total of three tests were conducted with the same charge and discharge cycle test steps. The battery pack was kept at a constant temperature of 45°C during the 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 adjacent charging capacities and the voltage of each module at the end of charging. All charging data must be Screening is performed 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 of the battery pack and the last charging capacity and compare it with each other. Select the corresponding charging data with a smaller ΔC value. Since the capacity of two consecutive charges in a normal charge-discharge cycle is basically unchanged, This selection can prevent the relative charging time Δtj of the module from fluctuating greatly each time it is charged;
(2) Calculate the difference ΔU between the voltage of each module at the end of each charge of the battery pack and the voltage of each module at the end of the next charge and compare them with each other. Select the corresponding charging data with a smaller ΔU value. Since the two adjacent times use the same current During charging, the voltage of each module at the end of charging is basically unchanged. This selection can prevent the relative charging time Δtj of the module from fluctuating greatly during each charging;
(3) Count the module serial number that reaches the cut-off voltage first during each charging process of the battery pack, and select the corresponding charging data whose module serial number remains unchanged from all charging data. The charging capacity of the battery pack depends on the module that reaches the charging cut-off voltage first. Because the module that reaches the charging cut-off voltage first changes, the charging capacity and charging time of the battery pack change accordingly. The voltage of each module at the end of charging will change significantly, resulting in large fluctuations 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 cut-off charging voltage is changed, the relative charging time Δtj will be retained. corresponding data.
The battery pack can be charged and discharged normally in the first two tests, but cannot be charged or discharged in the latter part 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 was abnormal because the modules in the battery pack reached the charge cut-off voltage in a short period of time. and discharge cut-off voltage, causing the battery pack to be unable to charge and discharge normally. Therefore, the charging data of the first 28 times of the battery pack are selected for analysis. According to the data selection principles (1) to (3), the charging data that matches the 28 charging data are selected.
2.3 Micro-short circuit analysis of modules in battery packs
Select the module that reaches the charging cut-off voltage first and use its voltage curve as the benchmark to calculate the relative charging time Δtn,j of each module during each charge. The voltage curve of a certain charge of the battery pack 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 of other modules is calculated. j and Kn-n-1,j values, and analyze the consistency of Kn-n-1,j values through box plots.
The DC internal resistance of each module in the three tests was calculated according to the formula. The DC internal resistance curve of each module is shown in Figure 9. In the figure, the DC internal resistance value of module No. 6 is the largest, proving that the Kn- The reason for the abnormality of n-1,j is that the DC internal resistance is large. The box plot is used to analyze the consistency of the DC internal resistance of the modules in the battery pack. As shown in Figure 10, there are no modules with abnormal DC internal resistance in the battery pack. In order to eliminate the influence of DC internal resistance on the diagnosis results, the data of modules whose DC internal resistance value does not exceed 3/4 of the median are selected, that is, the data of modules 6, 7, and 10 with larger DC internal resistance values to ensure Accuracy of diagnostic results.
3Conclusion
Aiming at the issue of safe use during the aging process of battery packs, this article proposes a micro-short circuit diagnosis method for battery packs. After a micro-short circuit occurs in the battery, electric energy will continue to be consumed, causing the relative charging time of the micro-short-circuited cells in the battery pack to increase with the number of charges, thus differing from that of normal cells. Based on this characteristic, this method uses the charging data of the battery pack to calculate the relative charging time of each cell. By analyzing the relative charging time of each cell, it can be determined whether there is a micro-short circuit in the battery pack, and the specific cell of the micro-short circuit can be determined.
When locating micro-short-circuited cells in the battery pack, this method only requires charging data and does not require testing under specific working conditions to diagnose micro-short circuits in the battery pack. It does not affect the normal operation of the battery pack and is simple and easy to operate. It can be used online and can provide certain technical guidance for preventive testing and safety applications of battery packs.
Lithium batteries gradually age during long-term use. Micro-short circuit is one of the aging characteristics. If it cannot be detected in time, it will lead to internal short circuit. The deterioration of internal short circuit is usually accompanied by the increase of self-discharge rate and heat, and then occurs. Thermal runaway, causing safety accidents. Micro short circuits inside lithium batteries are easy to trigger. Even if it is not due to manufacturing reasons, excessive charging, discharging, severe vibration and other abuse during use will cause lithium dendrites to form on the surface of the negative electrode, and piercing the separator will also cause micro short circuits inside the lithium battery. Therefore, micro-short circuit diagnosis of lithium batteries is still an urgent problem to be solved in battery management.(Lithium - Ion Battery Equipment)
The existing micro-short circuit detection methods for lithium batteries mainly rely on specific devices and equivalent circuit model detection. Li Kefeng et al. used a pole plate micro-short circuit detection device to apply pressure uniformly in the direction perpendicular to the battery pole plate set. After the positive and negative electrodes were connected under pressure at the hidden danger point of the short circuit, a DC voltage was applied between the two poles to test the voltage between the two poles. Insulation resistance value, if the insulation resistance value of the micro-short-circuit pole piece is lower than the threshold value, it proves that there is a micro-short circuit. However, this method is suitable for single batteries, requires specific equipment, and is not easy to implement. Zheng Yuejiu et al. used the self-discharge current between the two charges of the battery to quantify micro-short circuits. By calculating the remaining rechargeable capacity after two charges, the leakage amount can be obtained, and then the self-discharge current can be obtained, combined with the equivalent circuit of the micro-short circuit cell. model, calculate the internal short-circuit resistance, and determine whether the monomer has a micro-short circuit based on its threshold value. However, this method relies on the setting of the internal short-circuit resistance threshold and is only suitable for diagnosing micro-short circuits in new battery packs. 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 the battery pack using the time and voltage during the charging process of the battery pack. The advantage is that it only requires battery pack charging data, does not require specific detection equipment or sets thresholds for relevant parameters, the calculation amount is small and easy to implement, and micro-short circuit diagnosis can be performed at the single level of the battery pack.
1Diagnosis method of micro short circuit in lithium battery
1.1 Micro-short circuit characteristics of lithium batteries
When the separator of a lithium battery cell is pierced by attached dust or the separator quality is poor and the surface area is reduced or damaged, the positive and negative electrodes of the cell are in direct contact, causing a micro short circuit in the cell. In a battery pack composed of cells connected in series and parallel, compared with normal cells, cells with micro-short circuits will continue to consume power during the charging, discharging, and storage processes, affecting the normal operation of the battery pack.
According to the micro-short circuit characteristics of lithium batteries, when no micro-short circuit occurs in the battery pack, the relative charging time of each cell that does not reach the cut-off voltage first during each charging process is basically unchanged; if there is a micro-short circuit in the battery pack, When a micro short circuit occurs, compared with other normal cells without micro short circuit, the relative charging time of the cell continues to extend with the increase in the number of charges.
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 calculated by the current relative charging time of the monomer and the corresponding relative charging time difference. Obtaining the Kn-n-1,j value can determine the changing trend of the monomer relative to the charging time.
Since the relative charging time of a micro-short-circuit cell increases with the number of charges, the Kn-n-1,j value of a micro-short-circuit cell in the battery pack will be greater than the Kn-n-1 of other normal cells. j value. At the same time, monomers with large internal resistance will have large voltage fluctuations at the end of charging due to fluctuations in each charging time, resulting in large changes in the relative charging time each time, and the Kn-n-1,j value will also change. big. Based on the consistency of the Kn-n-1,j value of each monomer in the entire battery pack, and combined with the impact of internal resistance on the Kn-n-1,j value, it can be determined whether any monomer has a micro-short circuit.
If the Kn-n-1,j value of a certain monomer is abnormal and appears as an abnormal value the most times, then the monomer has a micro short circuit.
The Kn-n-1,j value of a certain monomer is abnormal and appears the most frequently as an outlier. This may be because the monomer has a large DC internal resistance. Screening monomers based on DC internal resistance does not affect the diagnosis results. , and can improve the accuracy of diagnostic results.
2 Application of lithium battery micro-short circuit diagnosis method
2.1 Test objects and steps
The test object is the WXL12S537300A lithium battery module produced by Ladder Wanxiang A123 Co., Ltd. This ladder uses lithium iron phosphate battery packs and consists of 13 modules connected in series. Each module consists of 6 soft pack cells connected in parallel. The rated power of the cells is The capacity is 50A·h and the rated voltage is 3.3V.
During the test, arbinevts600V/300A high-power power lithium battery testing equipment and TU410-5 temperature control box of Harding Technology (Chongqing) Testing Equipment Co., Ltd. were selected. The entire test process is carried out in a temperature-controlled box at 45°C. Leave it for 5 minutes, discharge the battery pack with a current of 300A until any module reaches the cut-off voltage of 2.7V, and then charge the battery pack with a current of 300A until any module reaches the cut-off voltage of 2.7V. One module reaches the cut-off voltage of 3.6V, thus charging and discharging the battery pack. A total of three tests were conducted with the same charge and discharge cycle test steps. The battery pack was kept at a constant temperature of 45°C during the 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 adjacent charging capacities and the voltage of each module at the end of charging. All charging data must be Screening is performed 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 of the battery pack and the last charging capacity and compare it with each other. Select the corresponding charging data with a smaller ΔC value. Since the capacity of two consecutive charges in a normal charge-discharge cycle is basically unchanged, This selection can prevent the relative charging time Δtj of the module from fluctuating greatly each time it is charged;
(2) Calculate the difference ΔU between the voltage of each module at the end of each charge of the battery pack and the voltage of each module at the end of the next charge and compare them with each other. Select the corresponding charging data with a smaller ΔU value. Since the two adjacent times use the same current During charging, the voltage of each module at the end of charging is basically unchanged. This selection can prevent the relative charging time Δtj of the module from fluctuating greatly during each charging;
(3) Count the module serial number that reaches the cut-off voltage first during each charging process of the battery pack, and select the corresponding charging data whose module serial number remains unchanged from all charging data. The charging capacity of the battery pack depends on the module that reaches the charging cut-off voltage first. Because the module that reaches the charging cut-off voltage first changes, the charging capacity and charging time of the battery pack change accordingly. The voltage of each module at the end of charging will change significantly, resulting in large fluctuations 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 cut-off charging voltage is changed, the relative charging time Δtj will be retained. corresponding data.
The battery pack can be charged and discharged normally in the first two tests, but cannot be charged or discharged in the latter part 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 was abnormal because the modules in the battery pack reached the charge cut-off voltage in a short period of time. and discharge cut-off voltage, causing the battery pack to be unable to charge and discharge normally. Therefore, the charging data of the first 28 times of the battery pack are selected for analysis. According to the data selection principles (1) to (3), the charging data that matches the 28 charging data are selected.
2.3 Micro-short circuit analysis of modules in battery packs
Select the module that reaches the charging cut-off voltage first and use its voltage curve as the benchmark to calculate the relative charging time Δtn,j of each module during each charge. The voltage curve of a certain charge of the battery pack 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 of other modules is calculated. j and Kn-n-1,j values, and analyze the consistency of Kn-n-1,j values through box plots.
The DC internal resistance of each module in the three tests was calculated according to the formula. The DC internal resistance curve of each module is shown in Figure 9. In the figure, the DC internal resistance value of module No. 6 is the largest, proving that the Kn- The reason for the abnormality of n-1,j is that the DC internal resistance is large. The box plot is used to analyze the consistency of the DC internal resistance of the modules in the battery pack. As shown in Figure 10, there are no modules with abnormal DC internal resistance in the battery pack. In order to eliminate the influence of DC internal resistance on the diagnosis results, the data of modules whose DC internal resistance value does not exceed 3/4 of the median are selected, that is, the data of modules 6, 7, and 10 with larger DC internal resistance values to ensure Accuracy of diagnostic results.
3Conclusion
Aiming at the issue of safe use during the aging process of battery packs, this article proposes a micro-short circuit diagnosis method for battery packs. After a micro-short circuit occurs in the battery, electric energy will continue to be consumed, causing the relative charging time of the micro-short-circuited cells in the battery pack to increase with the number of charges, thus differing from that of normal cells. Based on this characteristic, this method uses the charging data of the battery pack to calculate the relative charging time of each cell. By analyzing the relative charging time of each cell, it can be determined whether there is a micro-short circuit in the battery pack, and the specific cell of the micro-short circuit can be determined.
When locating micro-short-circuited cells in the battery pack, this method only requires charging data and does not require testing under specific working conditions to diagnose micro-short circuits in the battery pack. It does not affect the normal operation of the battery pack and is simple and easy to operate. It can be used online and can provide certain technical guidance for preventive testing and safety applications of battery packs.
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