Method for predicting cycle failure of soft package lithium ion battery

Abstract

The invention discloses a method for predicting cycle failure of a soft package lithium ion battery, which comprises the following steps of: establishing a multi-dimensional cycle failure judgment condition, and predicting whether the cycle of the battery fails or not according to the multi-dimensional cycle failure judgment condition, the multi-dimensional cycle failure judgment condition at least comprising whether a first coefficient is greater than a first threshold value or not; if the first coefficient is greater than the first threshold value, judging that the battery cycle fails; the step of determining the first coefficient comprises the substeps of determining a constant-voltage charging capacity ratio and direct-current discharging internal resistance, and taking a coupling operation result of the constant-voltage charging capacity ratio and the direct-current discharging internal resistance as the first coefficient.

Description

TECHNICAL FIELD

[0001] The embodiment of the application relates to the technical field of lithium ion batteries, in particular to a method for predicting cycle failure of a soft package lithium ion battery. BACKGROUND

[0002] With the rapid development of new energy vehicles, energy storage power stations, consumer electronics and other fields, soft package lithium ion batteries have become one of the mainstream battery forms due to the advantages of high energy density, strong volume flexibility and light weight. However, in the long-term cycle use process, the electrochemical performance of the soft package lithium ion battery will gradually decay due to the influence of internal active material degradation, electrolyte decomposition, and intensified interface reaction, and eventually lead to cycle failure. Therefore, accurately and timely predicting the cycle failure of the soft package lithium ion battery has become a key technical requirement to ensure its safe and stable operation throughout its life cycle.

[0003] In the prior art, the cycle failure prediction mainly focuses on single parameters such as capacity attenuation and single impedance monitoring, which has the problems of low judgment accuracy, delayed early warning, poor adaptability, and is difficult to adapt to the characteristics of the soft package lithium ion battery. SUMMARY

[0004] The application provides a method for predicting cycle failure of a soft package lithium ion battery to improve the cycle failure prediction accuracy of the battery.

[0005] The embodiment of the application provides a method for predicting cycle failure of a soft package lithium ion battery, which comprises the following steps:

[0006] A multi-dimensional cycle failure judgment condition is established, and whether the battery cycle is failed is predicted according to the multi-dimensional cycle failure judgment condition, wherein the multi-dimensional cycle failure judgment condition at least comprises whether a first coefficient is greater than a first threshold value;

[0007] If the first coefficient is greater than the first threshold value, it is judged that the battery cycle is failed;

[0008] The first coefficient is determined by determining a constant voltage charging capacity proportion and a direct current discharge internal resistance, and taking a coupling operation result of the constant voltage charging capacity proportion and the direct current discharge internal resistance as the first coefficient.

[0009] Optionally, the multi-dimensional cycle failure judgment condition further comprises judging whether a fitting parameter is mutated;

[0010] If at least one of the following conditions is met, it is judged that the battery cycle is failed: the first coefficient is greater than the first threshold value, and the fitting parameter is mutated;

[0011] The fitting parameter at least comprises one or more of positive active material loss and negative active material loss.

[0012] Optionally, the method further comprises:

[0013] determining a thickness change rate and a capacity change rate of the battery, determining a second coefficient according to the thickness change rate and the capacity change rate, and if the second coefficient is greater than a second threshold value, performing a cycle life failure warning of the battery before the cycle life failure of the battery.

[0014] Optionally, the method further comprises:

[0015] obtaining a positive electrode tab temperature and a negative electrode tab temperature, determining a temperature difference gradient of the positive electrode tab temperature and the negative electrode tab temperature, and if the temperature difference gradient is greater than a third threshold value, controlling to stop the battery cycle.

[0016] Optionally, the method further comprises:

[0017] obtaining a high-frequency impedance value in an electrochemical impedance spectroscopy test, determining whether the battery is damaged according to whether the high-frequency impedance value is greater than a fourth threshold value, and if the battery is not damaged, further determining whether the battery cycle is failed.

[0018] Optionally, the multi-dimensional cycle failure judgment condition further comprises: whether the battery has thermal runaway;

[0019] if at least one of the first coefficient being greater than the first threshold value and the battery having thermal runaway is met, it is determined that the battery cycle is failed.

[0020] Optionally, determining the direct-current discharge internal resistance comprises:

[0021] performing 1-10A equal-rate discharge, obtaining a voltage drop and a discharge current in the discharge process, and determining the direct-current discharge internal resistance according to the voltage drop and the discharge current.

[0022] Optionally, determining the first threshold value comprises:

[0023] determining, through a calibration test, a cycle number range in which the battery is in a nonlinear attenuation stage, and a constant-voltage charging capacity proportion and a direct-current discharge internal resistance corresponding to the cycle number range;

[0024] determining the first threshold value through the constant-voltage charging capacity proportion and the direct-current discharge internal resistance corresponding to the cycle number range.

[0025] Optionally, in the calibration test, determining the constant-voltage charging capacity proportion comprises:

[0026] performing constant-current constant-voltage charging on a cycle test battery with 1C, obtaining a constant-voltage charging capacity in the charging process, and taking a ratio of the constant-voltage charging capacity to a total capacity as the constant-voltage charging capacity proportion.

[0027] Optionally, determining the fitting parameters includes: acquiring the charging data of the last week of the cycle battery, forming a post-cycle charging curve, differentiating the post-cycle charging curve to obtain a differential curve, and obtaining the fitting parameters through the differential curve.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a method for predicting the cycle failure of soft-pack lithium-ion batteries. In this method, the constant voltage charging capacity ratio and the DC discharge internal resistance are determined. The coupling calculation result of the constant voltage charging capacity ratio and the DC discharge internal resistance is used as the first coefficient. The first coefficient is used to determine whether the battery has cycle failure. Compared with the failure judgment using a single parameter, which is prone to misjudgment due to environmental interference or stage characteristics, the first coefficient obtained by the two-parameter correlation has a large rate of change between normal (battery) decay and failure edge. Therefore, the accuracy of predicting battery cycle failure by using the first coefficient can be significantly improved. Attached Figure Description

[0029] Figure 1 This is a flowchart of the method for predicting cycle failure of soft-pack lithium-ion batteries in the embodiments;

[0030] Figure 2 This is a flowchart of another method for predicting cycle failure of a pouch lithium-ion battery in the embodiments. Detailed Implementation

[0031] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0032] Figure 1 This is a flowchart of the method for predicting cycle failure of a pouch lithium-ion battery in the embodiments, see reference. Figure 1 The methods include:

[0033] S101. Establish multi-dimensional cycle failure judgment conditions, and predict whether the battery will fail during cycling based on the multi-dimensional cycle failure judgment conditions. The multi-dimensional cycle failure judgment conditions shall include at least: whether the first coefficient is greater than the first threshold.

[0034] In this solution, battery cycle failure refers to a state in which the battery's performance indicators drop below a preset threshold after a certain number of charge-discharge cycles and cannot be recovered. The performance indicators may include capacity decay, internal resistance, etc.

[0035] In this scheme, the multidimensional cycle failure judgment conditions can be determined based on the multidimensional parameters related to battery cycle failure. These multidimensional parameters may include electrochemical parameters (such as the rate of change of voltage at the end of charging), thermal parameters (such as the battery temperature change gradient), mechanical parameters (such as the battery thickness expansion rate), interface parameters (such as the impedance growth rate), and environmental parameters (such as ambient temperature).

[0036] For example, in this solution, a cycle failure judgment condition can be set based on a parameter. For instance, for a pouch battery, the multi-dimensional cycle failure judgment condition may include whether the capacity decay is lower than a preset threshold, whether the aluminum-plastic film bulges, and whether the electrode is misaligned. The battery is judged to have cycle failure if one or more of these cycle failure judgment conditions are met.

[0037] In this scheme, the multidimensional cycle failure judgment condition is set to include at least: whether the first coefficient is greater than the first threshold. The determination of the first coefficient includes determining the constant voltage charging capacity ratio and the DC discharge internal resistance, and taking the coupling calculation result of the constant voltage charging capacity ratio and the DC discharge internal resistance as the first coefficient.

[0038] For example, in this solution, the constant-voltage charging capacity ratio can be determined using charging process data recorded by the Battery Management System (BMS) during operation. For instance, after charging begins, the accumulated charging capacity during the constant-current charging phase, when the charging voltage reaches the cutoff voltage, is recorded as Cap_1; the accumulated charging capacity during the constant-voltage charging phase, when the charging current gradually decreases to the cutoff current and charging stops, is recorded as Cap_2. Then, the constant-voltage charging capacity can be Cap_2 - Cap_1, and the constant-voltage charging capacity ratio is (Cap_2 - Cap_1) / Cap_2.

[0039] For example, in this solution, the DC discharge resistance can be calculated using the discharge current and voltage recorded by the BMS. For instance, the test equipment records the open-circuit voltage OCV of the battery (pack), and records the real-time discharge voltage V_f and real-time discharge current I_f of the battery (pack). According to the principle of voltage division by internal resistance, the DC discharge resistance can be (OCV - V_f) / I_f.

[0040] S102. If the first coefficient is greater than the first threshold, then the battery is determined to have failed cycle.

[0041] In this scheme, the form of the coupling function between the constant voltage charging capacity ratio and the DC discharge internal resistance, and the first threshold, can be determined through calibration experiments, simulation experiments, and empirical values.

[0042] For example, taking the determination through calibration test as an example, the constant voltage charging capacity, total charging capacity, and DC discharge internal resistance corresponding to each preset range of cycle numbers can be obtained respectively. Based on the data used, the coupling function and the first threshold are determined by data fitting.

[0043] For example, obtaining the constant voltage charging capacity, total charging capacity, and DC discharge internal resistance may include:

[0044] The battery is charged using a 1C constant current constant voltage (CC-CV) charging regime (1C is the current corresponding to the battery's rated capacity, such as 10Ah battery with a 1C charging current of 10A) to ensure consistency in the charging process;

[0045] Two key capacity parameters are read from the original charging record: Cap total, which is the total capacity of a single charge; and Cap constant voltage, which is the charging capacity only during the constant voltage phase.

[0046] After obtaining Cap total and Cap constant voltage, calculate the constant voltage charging capacity ratio R constant voltage within this cycle range, R constant voltage = Cap constant voltage / Cap total.

[0047] After charging is complete, perform a high-rate discharge step of 1~10A (high-rate discharge can more obviously expose the differences in battery internal resistance). From the original discharge record, select the stable discharge stage and read the DC discharge internal resistance DCIR discharge (the voltage-current curve can be read and the DCIR discharge can be calculated).

[0048] For example, in this scheme, coupling functions for constant voltage R and DCIR discharge can be established for different cycle number ranges (the rate of change of constant voltage R and DCIR discharge is different in different cycle stages: both rise slowly in the early stage of the cycle and accelerate in the nonlinear decay stage, which can be used to perform staged fitting functions); or, to simplify the form of the coupling function, the fitting function of the nonlinear decay stage can be used as the coupling function of the linear decay stage and the nonlinear decay stage.

[0049] For example, in this solution, different cycle stages can be divided according to the remaining battery capacity (e.g., the healthy stage, the linear decay stage, and the nonlinear decay stage). After determining the coupling function, the coupling function can be determined to meet the requirements based on whether the change law of the first coefficient conforms to the preset law (the preset law can be obtained based on theory or set through experience). If it does not conform, the coupling function is refitted.

[0050] For example, in the linear decay phase, if the first coefficient increases by less than 0.5 for every 50 cycles, and in the nonlinear decay phase, if the first coefficient increases by more than 1 for every 50 cycles, then the coupling function meets the requirements.

[0051] For example, in this scheme, the coupling operation of constant voltage charging capacity ratio and DC discharge internal resistance can be: K_1=f(R_h, DCIR_d), where K_1 represents the first coefficient, R_h represents the constant voltage charging capacity ratio, DCIR_d represents the DC discharge resistance, and f() represents a linear function or a nonlinear function.

[0052] For example, in the nonlinear decay stage, the coupling function can be the product of the constant voltage charging capacity ratio and the DC discharge internal resistance, i.e., K_1=R_h×DCIR_d.

[0053] For example, in this scheme, the first threshold is a threshold determined through calibration. When calculating the first coefficient, the constant voltage charging capacity ratio and DC discharge internal resistance are normalized, and the normalized values ​​are used to calculate the first coefficient. For example, if the constant voltage R is 20% and the DC IR discharge is 70mΩ, then the normalized constant voltage R is 0.2, the DC IR discharge is 0.07 (after converting the units to Ω and then normalizing), and the first coefficient is 0.014.

[0054] For example, in this solution, the range of cycle counts corresponding to different stages of battery life is determined by calibration, and a mapping table between DCIR discharge and the range of cycle counts is established. After calculating the first coefficient, the range of cycle counts corresponding to the first coefficient is determined using the mapping table in conjunction with DCIR discharge, thus determining the range of the first coefficient corresponding to the nonlinear decay stage, and the value of the first threshold is determined according to the accuracy requirements.

[0055] For example, it can be set to detect battery failure at least 50-100 cycles before battery cycle failure. If the minimum value of the first coefficient 50-100 cycles before failure is 14.2, then the first threshold can be set to its lower integer limit of 14 (failure detection can be performed as early as 100 cycles in advance) to ensure prediction accuracy.

[0056] For example, in this solution, the different stages of the battery can be divided according to the remaining battery capacity. It can be defined that when the remaining battery capacity reaches a specified value (e.g., 70%), the battery fails or enters a non-linear degradation stage.

[0057] In this scheme, the battery cycle failure is judged by the first coefficient. It not only utilizes the sensitivity of R constant voltage to abnormal capacity distribution at the charging end, but also uses DCIR discharge to directly characterize the internal structural degradation, effectively avoiding the risk of misjudgment by a single parameter. Especially in the nonlinear decay stage, it can accurately characterize the failure signal and improve the judgment accuracy.

[0058] This embodiment proposes a method for predicting the cycle failure of pouch lithium-ion batteries. In this method, the constant voltage charging capacity ratio and DC discharge internal resistance are determined. The coupling calculation result of the constant voltage charging capacity ratio and DC discharge internal resistance is used as the first coefficient. The first coefficient is used to determine whether the battery has cycle failure. Compared with the failure judgment using a single parameter, which is prone to misjudgment due to environmental interference or stage characteristics, the first coefficient obtained by the two-parameter correlation has a large rate of change between normal (battery) decay and failure edge. Therefore, the accuracy of predicting battery cycle failure by using the first coefficient can be significantly improved.

[0059] Based on any of the aforementioned schemes, in one possible implementation scheme, the multidimensional cycle failure judgment condition further includes: judging whether the fitting parameters have undergone a sudden change;

[0060] If at least one of the following conditions is met: the first coefficient is greater than the first threshold, or the fitted parameters undergo a sudden change, the battery is judged to have cycle failure; the fitted parameters include at least one or more of the following: loss of positive electrode active material and loss of negative electrode active material.

[0061] In this scheme, the loss of positive electrode active material refers to the proportion of active material that cannot participate normally in the Li⁺ intercalation / deintercalation reaction due to structural damage and compositional deterioration during battery cycling.

[0062] In this scheme, the loss of negative electrode active material refers to the proportion of the negative electrode material that cannot be properly intercalated into Li⁺ during battery cycling due to the destruction of the negative electrode material structure and the occupation of active sites.

[0063] For example, in this solution, the loss of positive electrode active material and the loss of negative electrode active material can be determined by the charge-discharge curves recorded by the BMS. Specifically, the voltage and capacity data recorded in real time by the BMS are configured to automatically fit the capacity of the plateau segment.

[0064] For example, in this solution, obtaining the loss of positive electrode active material may include:

[0065] From the voltage-capacity curve of 1C constant current constant voltage charging, select the plateau range of Li⁺ embedding at the positive electrode (i.e., the voltage range corresponding to when the voltage is basically stable), extract the charging capacity of this range, and denot the charging capacity of the current cycle corresponding to this range as Q. +n The initial charge capacity of the cycle is denoted as Q. +0 The positive electrode active material loss L + =[(Q +0 -Q +n ) / Q +0 ]×100%.

[0066] Losses of positive electrode active material can include:

[0067] From the voltage-capacity curve of 1C discharge, select the plateau range where the negative electrode Li⁺ is removed (i.e., the voltage range corresponding to when the voltage is basically stable), extract the discharge capacity of this range, and denot the discharge capacity of the current cycle corresponding to this range as Q. -n The discharge capacity of the initial cycle is denoted as Q. -0 The loss of negative electrode active material is L. - =[( Q -0 - Q -n ) / Q -0 ]×100%.

[0068] As one possible implementation, determining the fitting parameters includes: acquiring the charging data of the last week of the cycled battery, forming a post-cycle charging curve, differentiating the post-cycle charging curve to obtain a differential curve, and obtaining the fitting parameters through the differential curve.

[0069] In this scheme, the charge-discharge curve (charge curve after cycling) is differentiated, i.e., f(V) = DV / DQ. The peak value of the differentiated curve is used to determine the plateau interval, and then the loss of positive electrode active material and the loss of positive electrode active material (as mentioned above, L) are further determined. + L - (The method is the same).

[0070] For example, in this scheme, the threshold used to determine whether the fitted parameters have undergone a sudden change can be determined through calibration experiments.

[0071] For example, calibration tests based on cyclic voltammetry (CV) can be performed to address the loss of positive electrode active material. The process may include:

[0072] The battery was disassembled, and the positive electrode was taken to make a three-electrode test sample (working electrode: positive electrode, reference electrode: Li metal, counter electrode: Li metal).

[0073] CV tests were performed on an electrochemical workstation to extract the oxidation peak (Li⁺ desorption) and reduction peak (Li⁺ insertion) at the cathode. The peak areas were calculated after baseline correction using simulation software. The peak area for the initial cycle (first cycle) was denoted as S. +0 The current circulating peak area is denoted as S. +n L cv+ =[(S +0 - S +n ) / S +0 ]×100%.

[0074] For example, similarly, the process for addressing the loss of positive electrode active material may include:

[0075] Prepare a three-electrode sample using the negative electrode sheet, extract the reduction and oxidation peaks of the negative electrode, and calculate the peak areas; L cv- =[(S -0 -S -n ) / S -0 ]×100%.

[0076] For example, in this solution, it can be determined that the battery is in a specified stage (e.g., a health stage based on the remaining battery capacity), L cv+ The maximum single increase, take L. cv+ A certain multiple of this is used as the threshold for judging whether the loss of the positive electrode active material is abrupt. Similarly, L is taken as... cv- The maximum single increase, take L. cv- A certain multiple of this is used as the mutation threshold for judging whether the loss of negative electrode active material is abrupt.

[0077] Based on any of the aforementioned schemes, in one possible implementation scheme, the method further includes: determining the thickness change rate and capacity change rate of the battery; determining a second coefficient based on the thickness change rate and capacity change rate; and if the second coefficient is greater than a second threshold, then issuing a battery cycle failure warning before the battery cycle failure occurs.

[0078] In this solution, the battery is set as a pouch lithium-ion battery. A thickness sensor can be configured inside the pouch lithium-ion battery (for example, integrated into the upper and lower shells of the battery). The thickness sensor is used to monitor the change in battery thickness in real time, and then the thickness change rate ΔThickness can be calculated.

[0079] In this scheme, the capacity change rate can be determined using charge and discharge data recorded in the BMS. Specifically, the discharge capacity C of the current cycle is obtained. n To obtain the initial discharge capacity C0 (take the corresponding discharge capacity value when charging to full capacity at 1C and discharging to the cutoff voltage of 2.75V at 1C), the capacity change rate Δcap is calculated as follows: Δcap = [(C0 - C0)] n ) / C0]×100%.

[0080] In this scheme, the second threshold can be determined through calibration experiments, which may specifically include:

[0081] The process involves cyclic charging at 1C and discharging at 1~10A, with the thickness change H recorded after each N cycles. n Capacity C n Until the capacity drops to a set value (e.g., 70%).

[0082] Calculate the thickness change rate and capacity change rate for each N cycles, creating a data table of cycle number - second coefficient - remaining battery capacity. Select the minimum value of the second coefficient from the data table when the remaining battery capacity is less than a specified value as the second threshold.

[0083] For example, in this scheme, the second coefficient is set as η, where η = △Thickness / △cap, and the second threshold can be 0.1.

[0084] In this scheme, the unique interfacial side reactions (capacity change rate) and mechanical deformation (thickness change rate) of pouch batteries are comprehensively considered. Since the thickness change of pouch batteries is highly sensitive to internal degradation, and the interference of environmental factors (such as temperature) on the second coefficient can be reduced by calculating the capacity change rate and the thickness change rate, it can provide earlier and more accurate support for battery cycle failure judgment.

[0085] In this scheme, if the second coefficient is greater than the second threshold, a battery cycle failure warning is issued. Based on the battery cycle failure warning, sufficient time can be reserved for maintenance.

[0086] Based on any of the aforementioned schemes, in one possible implementation scheme, the calibration test further includes: obtaining the temperature of the positive tab and the temperature of the negative tab, determining the temperature difference gradient between the positive tab and the negative tab, and controlling the cessation of battery cycling if the temperature difference gradient is greater than a third threshold.

[0087] For example, in this solution, the temperature gradient between the tabs is typically maintained between 1.3 and 2.7°C during normal cycling. When the temperature gradient exceeds 2.7°C, problems such as decreased heat dissipation efficiency, internal short circuits, or material aging may occur.

[0088] For example, in this solution, temperature sensing wires can be fixed to the two tabs of the battery undergoing cycle testing. These wires collect the temperature changes and differences between the positive and negative tabs. Simultaneously, infrared thermal imagers and other equipment can be used to monitor the tab temperature changes in real time, and a comprehensive evaluation can be conducted in conjunction with national standards such as GB31241. If an abnormal temperature difference (temperature gradient exceeding 2.7℃) is detected through the collected data, the cycle test should be immediately stopped, and potential safety hazards such as short circuits and overcharging should be investigated.

[0089] Based on any of the aforementioned schemes, in one possible implementation scheme, the calibration test further includes: obtaining the high-frequency impedance value in the electrochemical impedance spectroscopy (EIS) test, determining whether the battery is damaged based on whether the high-frequency impedance value is greater than a fourth threshold, and if the battery is not damaged, further determining whether the battery cycle has failed.

[0090] For example, in this scheme, the EIS test applies a small-amplitude, wide-frequency sinusoidal AC signal to the battery, records the phase difference and amplitude ratio of the battery's voltage and current responses, and finally generates a Nyquist plot. The high-frequency impedance value corresponds to the x-coordinate of the intersection of the high-frequency segment and the real axis in the Nyquist plot.

[0091] The impedance (high-frequency impedance value) in the high-frequency region of EIS (typically >1kHz) mainly reflects the charge transfer process at the electrode / electrolyte interface, including the electric double layer capacitance (EDL). At high frequencies, the ion migration rate is fast, and the charge and discharge behavior of the electric double layer dominates the impedance. If the high-frequency impedance value suddenly increases by more than 10% of the threshold specified by the manufacturer during EIS testing, it may indicate damage to the internal structure of the battery, electrolyte failure, poor contact between the tab and the current collector, or abnormal electrode compaction density, causing a sudden increase in local ohmic impedance.

[0092] In this scheme, the fourth threshold can be set according to the threshold specified by the manufacturer, and the fourth threshold can be 110% of the threshold specified by the manufacturer.

[0093] In this scheme, the battery is judged to be damaged based on whether the high-frequency impedance value is greater than the fourth threshold. If the battery is not damaged, the battery cycle failure is further judged to ensure the effectiveness of the calibration test process.

[0094] Based on any of the aforementioned schemes, in one possible implementation scheme, determining the constant voltage charging capacity ratio in the calibration test includes: performing constant current and constant voltage charging on the cycle test battery with 1C, obtaining the constant voltage charging capacity during the charging process, and taking the ratio of the constant voltage charging capacity to the total capacity as the constant voltage charging capacity ratio.

[0095] In this scheme, a 1C constant current constant voltage (CC-CV) charging regime is adopted to charge the battery (1C is the current corresponding to the rated capacity of the battery, such as 10Ah battery 1C charging current is 10A) to ensure the consistency of the charging process;

[0096] Two key capacity parameters are read from the original charging record: Cap total, which is the total capacity of a single charge; and Cap constant voltage, which is the charging capacity only during the constant voltage phase.

[0097] After obtaining Cap total and Cap constant voltage, calculate the constant voltage charging capacity ratio R constant voltage within this cycle range, R constant voltage = Cap constant voltage / Cap total.

[0098] Based on any of the aforementioned schemes, in one possible implementation scheme, determining the DC discharge internal resistance in the calibration test includes: performing a high-rate discharge of 1~10A, obtaining the voltage drop and discharge current during the discharge process, and determining the DC discharge internal resistance based on the voltage drop and discharge current.

[0099] In this scheme, a high-rate discharge step of 1~10A is executed. From the original discharge record, the stable discharge stage is selected, and the DC discharge internal resistance DCIR discharge is read (the voltage-current curve can be read and the DCIR discharge can be calculated).

[0100] Based on any of the aforementioned schemes, in one possible implementation scheme, determining the first threshold in the calibration test includes: determining the range of cycle counts in which the battery is in the nonlinear decay stage through the calibration test, as well as the constant voltage charging capacity ratio and DC discharge internal resistance corresponding to the cycle count range; and determining the first threshold through the constant voltage charging capacity ratio and DC discharge internal resistance corresponding to the cycle count range.

[0101] In this scheme, after each complete cycle (1C charging + 1~10A discharging), the constant voltage charging capacity percentage and DC discharge internal resistance are recorded until the battery capacity drops to the failure point (e.g., 70% of the initial capacity).

[0102] Set the remaining capacity of 70%~80% to correspond to the nonlinear decay stage, and determine the corresponding number of cycles, R constant voltage, and DCIR discharge.

[0103] From the calibration test record's cycle count - R constant voltage - DCIR discharge data table, all parameters for the nonlinear decay stage are selected. For each set of parameters in the nonlinear decay stage, R constant voltage × DCIR discharge is calculated, and the minimum value is used as the first threshold.

[0104] Based on any of the aforementioned schemes, in one possible implementation scheme, the multidimensional cycle failure judgment condition further includes: whether the battery has experienced thermal runaway; if at least one of the following conditions is met, namely, a first coefficient greater than a first threshold or the battery experiencing thermal runaway, then the battery is judged to have experienced cycle failure.

[0105] In this solution, determining whether a battery has experienced thermal runaway includes thermal runaway warning and thermal runaway stages. Specifically, the occurrence of thermal runaway can be determined by the battery surface temperature.

[0106] For example, if the battery surface temperature is >80℃ for three consecutive measurements, and the gas sensor detects a CO2 concentration >500ppm (a characteristic gas of SEI membrane decomposition), a thermal runaway warning will be issued; if the battery surface temperature is >200℃, and the sensor detects an open flame or smoke, thermal runaway will be confirmed.

[0107] Figure 2 This is a flowchart of another method for predicting cycle failure of a pouch lithium-ion battery in the embodiments, see reference. Figure 2 Based on any of the aforementioned solutions, in one possible implementation, the method includes:

[0108] S201. Determine the second coefficient based on the thickness change rate and capacity change rate. If the second coefficient is greater than the second threshold, then issue a battery cycle failure warning before the battery cycle failure.

[0109] In this scheme, the battery is a predicted soft-pack lithium-ion battery.

[0110] In this scheme, the second coefficient is η, where η = △Thickness / △cap, and the second threshold is 0.1.

[0111] S202. If at least one of the following conditions is met: the first coefficient is greater than the first threshold, the fitted parameters change abruptly, or the battery experiences thermal runaway, then the battery is determined to have failed cycling.

[0112] In this scheme, the first coefficient is K_1, K_1 = (Cap constant voltage / Cap total) × DCIR discharge, and the first threshold is 14. The fitting parameters include the loss of positive electrode active material and the loss of negative electrode active material, which are determined by the differential of the charge-discharge curve.

[0113] In this solution, the method can be integrated into the BMS (Battery Management System) to perform high-precision battery cycle failure prediction and early warning of battery cycle failure.

[0114] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for predicting cycle failure of pouch lithium-ion batteries, characterized in that, include: Establish multidimensional cycle failure judgment conditions, and predict whether the battery cycle will fail based on the multidimensional cycle failure judgment conditions. The multidimensional cycle failure judgment conditions include at least: whether the first coefficient is greater than the first threshold. If the first coefficient is greater than the first threshold, then the battery is determined to have failed a cycle. Determining the first coefficient includes determining the constant voltage charging capacity ratio and the DC discharge internal resistance, and using the coupling calculation result of the constant voltage charging capacity ratio and the DC discharge internal resistance as the first coefficient.

2. The method for predicting cycle failure of pouch lithium-ion batteries as described in claim 1, characterized in that, The multidimensional cyclic failure judgment condition also includes: judging whether the fitting parameters have undergone a sudden change; If at least one of the following conditions is met: the first coefficient is greater than the first threshold, or the fitting parameter undergoes a sudden change, then the battery is determined to have failed cycling. The fitting parameters include at least one or more of the following: loss of positive electrode active material and loss of negative electrode active material.

3. The method for predicting cycle failure of pouch lithium-ion batteries as described in claim 1, characterized in that, The method also includes: The thickness change rate and capacity change rate of the battery are determined. A second coefficient is determined based on the thickness change rate and capacity change rate. If the second coefficient is greater than a second threshold, a battery cycle failure warning is issued before the battery cycle failure.

4. The method for predicting cycle failure of pouch lithium-ion batteries as described in claim 1, characterized in that, The multidimensional cycle failure judgment criteria also include: whether the battery has experienced thermal runaway; If at least one of the following conditions is met: the first coefficient is greater than the first threshold, or the battery experiences thermal runaway, then the battery is determined to have failed cycling.

5. The method for predicting cycle failure of a pouch lithium-ion battery as described in any one of claims 1 to 4, characterized in that, Determining the first threshold includes: The range of cycle counts in which the battery is in the nonlinear decay stage is determined by calibration tests, as well as the constant voltage charging capacity ratio and DC discharge internal resistance corresponding to the range of cycle counts. The first threshold is determined by the percentage of constant voltage charging capacity and DC discharge internal resistance corresponding to the range of cycle counts.

6. The method for predicting cycle failure of a pouch lithium-ion battery as described in claim 5, characterized in that, In the calibration test, the determination of the DC discharge internal resistance includes: Perform high-rate discharge of 1~10A, obtain the voltage drop and discharge current during the discharge process, and determine the DC discharge internal resistance based on the voltage drop and discharge current.

7. The method for predicting cycle failure of a pouch lithium-ion battery as described in claim 5, characterized in that, In the calibration test, the determination of the constant voltage charging capacity ratio includes: The battery undergoing cyclic testing was charged with constant current and constant voltage at 1C. The constant voltage charging capacity during the charging process was obtained, and the ratio of the constant voltage charging capacity to the total capacity was taken as the constant voltage charging capacity percentage.

8. The method for predicting cycle failure of a pouch lithium-ion battery as described in claim 5, characterized in that, The calibration test also includes: The positive tab temperature and the negative tab temperature are obtained, and the temperature difference gradient between the positive tab temperature and the negative tab temperature is determined. If the temperature difference gradient is greater than a third threshold, the battery cycling is stopped.

9. The method for predicting cycle failure of a pouch lithium-ion battery as described in claim 5, characterized in that, The calibration test also includes: The high-frequency impedance value in the electrochemical impedance spectroscopy test is obtained. The battery is judged to be damaged based on whether the high-frequency impedance value is greater than the fourth threshold. If the battery is not damaged, the battery cycle failure is further judged.

10. The method for predicting cycle failure of a pouch lithium-ion battery as described in claim 2, characterized in that, Determining the fitting parameters includes: The charging data of the last week of the cycle battery is obtained to form a post-cycle charging curve. The post-cycle charging curve is differentiated to obtain a differential curve, and the fitting parameters are obtained through the differential curve.

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