Battery lithium precipitation risk detection method and device, storage medium and electronic equipment

Abstract

The application provides a battery lithium precipitation risk detection method and device, a storage medium and an electronic device. The actual working condition and the cycle time of a battery to be detected can be obtained. The cycle time is substituted into a life attenuation prediction model and a negative electrode impedance growth rate model corresponding to the battery and the actual working condition, respectively, to obtain a target capacity attenuation rate and a target negative electrode impedance growth rate of the battery under the actual working condition and the cycle time. According to the target capacity attenuation rate and the target negative electrode impedance growth rate, a lithium precipitation risk result of the battery under the actual working condition and the cycle time is determined. The lithium precipitation risk result includes a first risk detection result, and the first risk detection result represents that the battery has no lithium precipitation risk. The method provided in the application can quickly and accurately detect the lithium precipitation risk.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, storage medium, and electronic device for detecting the risk of lithium plating in batteries. Background Technology

[0002] Lithium plating refers to the phenomenon where, during battery charging, lithium ions cannot be inserted into the negative electrode material in time, resulting in the deposition of metallic lithium on the negative electrode surface. This phenomenon typically occurs when charging is too fast or when the battery is in a low-temperature environment. The consequences of lithium plating are multifaceted: First, it reduces the actual usable capacity of the battery because the deposited lithium cannot participate in normal charge-discharge cycles; second, lithium plating accelerates battery aging and shortens its lifespan; most seriously, lithium plating can puncture the separator, causing an internal short circuit and triggering thermal runaway, posing a significant safety hazard to the device. Therefore, lithium plating testing is necessary to assess battery safety performance.

[0003] Existing methods for detecting lithium plating mainly include two approaches: disassembly and voltage plateau analysis. While the former allows direct observation of lithium plating on the negative electrode surface, it is destructive, unsuitable for online monitoring, and costly. The latter relies on voltage changes during resting periods to determine the presence of lithium plating; however, the voltage changes caused by lithium plating are extremely small and easily affected by other factors, resulting in low accuracy. This, in turn, impacts the safety performance evaluation results of lithium-ion batteries. Summary of the Invention

[0004] The technical problem to be solved by this application is to provide a method, apparatus, storage medium, and electronic device for detecting the risk of lithium plating absence in batteries, which can quickly and accurately perform lithium plating absence risk detection. The specific solution is as follows:

[0005] A method for detecting lithium plating risk in batteries, comprising:

[0006] Obtain the actual operating conditions and cycle time of the battery to be tested;

[0007] Substitute the cycle time into the life decay prediction model and the negative electrode impedance growth rate model corresponding to the battery and its actual operating conditions, respectively, to obtain the target capacity decay rate and the target negative electrode impedance growth rate of the battery under the actual operating conditions and the cycle time.

[0008] Based on the target capacity decay rate and the target negative electrode impedance growth rate, the lithium plating risk result of the battery under the actual operating conditions and the cycle time is determined; the lithium plating risk result includes a first risk detection result, which indicates that the battery does not have a lithium plating risk.

[0009] Optionally, the process of constructing the lifetime decay prediction model in the above method includes:

[0010] Acquire aging test data of the test battery under test conditions, wherein the test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested;

[0011] The lifespan degradation prediction model is constructed based on the capacity decay rate at each test time in the aging test data of the test battery.

[0012] Optionally, the process of constructing the negative electrode impedance growth rate model includes:

[0013] Obtain the negative electrode impedance of the test battery at various test times under the test conditions; the test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested;

[0014] The negative electrode impedance growth rate of the test battery under the test conditions is calculated based on the negative electrode impedance of the test battery at each test time under the test conditions.

[0015] The negative electrode impedance growth rate model is constructed based on the negative electrode impedance growth rate of the test battery under the test conditions.

[0016] Optionally, in the above method, obtaining the negative electrode impedance of the test battery at various test times under test conditions includes:

[0017] Based on the battery measurement data of the test battery at different test times, the negative electrode impedance of the test battery at each test time is obtained; wherein, the battery measurement data is obtained by aging test of the test battery under the test conditions, and the battery measurement data includes the negative electrode mechanical impedance of the test battery in the first structure, the battery active material area of ​​the test battery in the first structure, the negative electrode chemical impedance of the battery in the second structure, and the battery active material area of ​​the battery in the second structure.

[0018] Optionally, in the above method, determining the lithium plating risk of the battery under the actual operating conditions and the cycle time based on the target capacity decay rate and the target negative electrode impedance growth rate includes:

[0019] If the target capacity decay rate is greater than the target negative electrode impedance growth rate, then the lithium plating risk result of the battery is determined as the first risk detection result.

[0020] A battery lithium plating risk detection device, comprising:

[0021] The acquisition unit is used to acquire the actual operating conditions and cycle time of the battery to be tested;

[0022] An execution unit is used to substitute the cycle time into the life decay prediction model and the negative electrode impedance growth rate model corresponding to the battery and its actual operating conditions, respectively, to obtain the target capacity decay rate and the target negative electrode impedance growth rate of the battery under the actual operating conditions and the cycle time.

[0023] The determining unit is configured to determine the lithium plating risk result of the battery under the actual operating conditions and the cycle time based on the target capacity decay rate and the target negative electrode impedance growth rate; the lithium plating risk result includes a first risk detection result, which indicates that the battery does not have a lithium plating risk.

[0024] Optionally, the execution unit in the aforementioned apparatus includes:

[0025] The first acquisition subunit is used to acquire aging test data of the test battery under test conditions, wherein the test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested.

[0026] The first construction subunit is used to construct the life decay prediction model based on the capacity decay rate of each first test time in the aging test data of the test battery.

[0027] Optionally, the execution unit in the aforementioned apparatus includes:

[0028] The second acquisition subunit is used to acquire the negative electrode impedance of the test battery at various test times under the test conditions; the test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested;

[0029] The calculation subunit is used to calculate the negative electrode impedance growth rate of the test battery under the test conditions based on the negative electrode impedance of the test battery at each test time under the test conditions.

[0030] The second construction subunit is used to construct the negative electrode impedance growth rate model based on the negative electrode impedance growth rate of the test battery under the test conditions.

[0031] A storage medium comprising stored instructions, wherein, when the instructions are executed, the device in which the storage medium resides executes the battery lithium plating risk detection method as described above.

[0032] An electronic device includes a memory and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors as described above for detecting the risk of lithium plating in batteries.

[0033] Based on the above, this application provides a method, apparatus, storage medium, and electronic device for detecting lithium plating risk in batteries. The method includes: acquiring the actual operating conditions and cycle time of the battery to be tested; substituting the cycle time into a lifespan degradation prediction model and a negative electrode impedance growth rate model corresponding to the battery and its actual operating conditions, respectively, to obtain the target capacity decay rate and the target negative electrode impedance growth rate of the battery under the actual operating conditions and the cycle time; determining the lithium plating risk result of the battery under the actual operating conditions and the cycle time based on the target capacity decay rate and the target negative electrode impedance growth rate; the lithium plating risk result includes a first risk detection result, which indicates that the battery does not have a lithium plating risk. Applying the method provided in this application embodiment can quickly and accurately detect the absence of lithium plating risk, ensuring the normal operation of the battery. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0035] Figure 1 This application provides a flowchart of a method for detecting the risk of lithium plating in batteries.

[0036] Figure 2 A flowchart illustrating the construction process of a lifetime decay prediction model provided in this application;

[0037] Figure 3 A flowchart illustrating the construction process of a negative electrode impedance growth rate model provided in this application;

[0038] Figure 4 This application provides a schematic diagram of a battery equivalent circuit.

[0039] Figure 5 A flowchart of a battery lithium-free risk detection process provided in this application;

[0040] Figure 6 A schematic diagram illustrating the rate of change of capacity retention and negative electrode impedance growth rate of a battery under preset operating conditions provided in this application;

[0041] Figure 7 A schematic diagram of the capacity retention rate and negative electrode impedance growth rate of a battery under preset operating conditions provided in this application;

[0042] Figure 8 This application provides a schematic diagram of the structure of a battery lithium-free risk detection device.

[0043] Figure 9 This is a schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0046] This application provides a method for detecting the risk of lithium plating failure in batteries. This method can be applied to electronic devices, such as computers, tablets, smartphones, and smart wearable devices. The method flowchart is shown below. Figure 1 As shown, it specifically includes:

[0047] S101: Obtain the actual operating conditions and cycle time of the battery to be tested.

[0048] In this embodiment, the actual operating conditions and cycle time of the battery can be obtained through software systems such as a battery management system; alternatively, the actual operating conditions and cycle time of the battery can be received from the user through an interactive interface or communication interface.

[0049] In this embodiment, the actual operating conditions may include any temperature, charging rate and discharging rate. For example, it may be 30°C, charging rate 0.5C and discharging rate 0.5C.

[0050] Optionally, the battery can be any type of lithium battery, for example, a lithium iron phosphate battery.

[0051] S102: Substitute the cycle time into the life decay prediction model and the negative electrode impedance growth rate model corresponding to the battery and its actual operating conditions, respectively, to obtain the target capacity decay rate and the target negative electrode impedance growth rate of the battery under actual operating conditions and cycle time.

[0052] In this embodiment, the lifetime decay prediction model can be expressed as lnQ loss =ln(At^z), where A is a constant, b is the decay factor, t is the cycle time, and Q... loss This represents the capacity decay rate of the battery.

[0053] In this embodiment, the negative electrode impedance growth rate model can be lnΔR. 负 =ln(at^b), where a and b are both constants, and the negative electrode impedance growth rate ΔR 负 = (Negative electrode internal resistance at cycle time t - negative electrode internal resistance at BOL) / negative electrode internal resistance at BOL, where the negative electrode internal resistance at BOL is the negative electrode internal resistance of the battery in its initial state.

[0054] S103: Determine the lithium plating risk results of the battery under actual operating conditions and cycle time based on the target capacity decay rate and the target negative electrode impedance growth rate; the lithium plating risk results include the first risk detection result, which indicates that the battery does not have a lithium plating risk.

[0055] In this embodiment, a capacity decay rate curve can be plotted based on the target capacity decay rate of the battery under actual operating conditions and cycle time, and a negative electrode impedance growth rate curve can be plotted based on the target negative electrode impedance growth rate of the battery under actual operating conditions and cycle time. Then, by comparing the capacity decay rate curve and the negative electrode impedance growth rate curve, the lithium plating risk result of the battery can be obtained.

[0056] By applying the method provided in the embodiments of this application, the lithium plating risk result of the battery can be obtained based on the capacity decay rate and the negative electrode impedance growth rate. It can quickly and accurately detect the risk of non-lithium plating, realize online detection, eliminate the need for destructive disassembly, and ensure timely detection.

[0057] In one embodiment provided in this application, based on the above-described scheme, optionally, the construction process of the lifetime degradation prediction model is as follows: Figure 2 As shown, it includes:

[0058] S201: Obtain aging test data of the test battery under test conditions. The test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested.

[0059] In this embodiment, the aging test data can be obtained by aging the battery under the test conditions. The aging test data can include real-time discharge capacity and capacity retention rate at multiple test times. The test time can be any cycle or performance test time point in the aging test process.

[0060] For example, the battery can be cycled (aged) at 30°C with a charge / discharge rate of 0.5C, and a short-term performance test (RPT) can be performed every few cycles to obtain aging test data. The RPT includes calibrating the capacity by cycling at 0.33C for 3 weeks to determine the capacity retention rate, and adjusting the state of charge (SOC) to 50% to measure the DC internal resistance (DCR). Specific DCR test conditions can be as follows: after resting at 25°C and 50% SOC for 2 minutes, discharge at 2C for 10 seconds, and then rest for another 30 minutes.

[0061] In this way, under the same test conditions, the capacity retention rate and DC internal resistance corresponding to each test time can be obtained, thus saving test time.

[0062] S202: Based on the capacity decay rate at each test time in the aging test data of the test battery, a lifespan decay prediction model is constructed.

[0063] In this embodiment, the capacity decay rate Q loss =1 - Capacity retention rate = (Discharge capacity at a specific test time - BOL discharge capacity) / BOL discharge capacity, which is also the lifetime degradation prediction model lnQ loss =lnAt^z=ln(1-capacity retention rate)=(discharge capacity at cycle time - BOL discharge capacity) / BOL discharge capacity, where BOL represents the initial battery state; therefore, the capacity decay rate at each test time can be determined by the capacity retention rate at each test time, and thus a lifetime decay prediction model lnQ can be constructed based on the capacity decay rate at each test time. loss .

[0064] In one embodiment provided in this application, based on the above-described scheme, optionally, the construction process of the negative electrode impedance growth rate model is as follows: Figure 3 As shown, it includes:

[0065] S301: Obtain the negative electrode impedance of the test battery at various test times under the test conditions; the test conditions are the same as the actual conditions, and the type identification of the test battery is the same as the type identification of the battery to be tested.

[0066] In this embodiment, the test time can be a test time longer than the preset test duration, for example, it can be 30 days later.

[0067] S302: Calculate the negative electrode impedance growth rate of the test battery under test conditions based on the negative electrode impedance of the test battery at various test times under test conditions.

[0068] In this embodiment, the process of calculating the negative electrode impedance growth rate of the test battery under the test condition based on the negative electrode impedance at each test time is as follows:

[0069] Negative impedance growth rate ΔR 负 = (Negative electrode internal resistance at a specific test time - BOL negative electrode internal resistance) / BOL negative electrode internal resistance.

[0070] S303: The negative electrode impedance growth rate model is constructed based on the negative electrode impedance growth rate of the test battery under test conditions.

[0071] In this embodiment, the negative electrode impedance growth rate model can be lnΔR. 负 .

[0072] In one embodiment provided in this application, based on the above-described scheme, optionally, obtaining the negative electrode impedance of the test battery at various test times under test conditions includes:

[0073] Based on the battery measurement data of the test battery at different test times, the negative electrode impedance of the test battery at each test time is obtained. The battery measurement data is obtained by aging test of the test battery under test conditions. The battery measurement data includes the negative electrode mechanical impedance of the test battery in the first structure, the battery active material area of ​​the test battery in the first structure, the negative electrode chemical impedance of the battery in the second structure, and the battery active material area of ​​the battery in the second structure.

[0074] In this embodiment, battery measurement data can be obtained for at least two test periods, such as battery measurement data for test periods of 400 cycles and 600 cycles.

[0075] Optionally, the first structure can be a metal-cased battery structure, such as an aluminum-cased battery structure, i.e., a wound battery structure; the second structure can be a card-type battery structure.

[0076] In this embodiment, the capacity retention rate of the battery varies at different test times. The battery measurement data at each test time may include the full cell impedance, positive parameter impedance, negative parameter impedance, positive electrode mechanical component impedance, negative electrode mechanical component impedance, positive electrode chemical impedance, negative electrode chemical impedance, and battery active material area of ​​the battery in the first structure, as well as the full cell impedance, positive parameter impedance, negative parameter impedance, positive electrode mechanical component impedance, negative electrode mechanical component impedance, positive electrode chemical impedance, negative electrode chemical impedance, and battery active material area of ​​the battery in the second structure.

[0077] Optionally, the mechanical impedance of each component of the battery under the metal casing battery structure can be measured first. Then, the battery can be disassembled in a pre-set humidity environment to remove the positive and negative electrode plates and assemble them into a card-type three-electrode battery (positive electrode, negative electrode and reference electrode). That is, the battery is placed in a card-type battery structure. Then, the DCR of the card-type three-electrode battery under test conditions is tested, and the negative electrode impedance of the metal casing battery is calculated based on the equivalent circuit of the battery under different structures.

[0078] See Figure 4 The equivalent circuit of the battery under different structures is shown; specifically, for the card-type three-electrode battery: r cell = r1+r c + r2+r a Card-type three-electrode battery positive electrode DCR r 正= r1+r c Card-type three-electrode battery negative electrode DCR r 负= r2+r a Among them, r 1、 r 2、 r c、 r a These are the positive electrode mechanical impedance, negative electrode mechanical impedance, positive electrode chemical impedance, and negative electrode chemical impedance of a card-type three-electrode battery, where r 1、 r2 can be measured with a multimeter. 正、 r 负 These are the positive and negative impedance parameters of the card's three-electrode battery, obtained through DCR testing.

[0079] For metal-cased batteries: R cell =R1+R2+R c +R a R 1、 R 2、 R c、 R a These are the positive electrode mechanical impedance, negative electrode mechanical impedance, positive electrode chemical impedance, and negative electrode chemical impedance of the metal-cased battery. Since the chemical impedance of the metal-cased battery cannot be directly measured, in this embodiment, the measured chemical impedance per unit area of ​​the three-electrode battery is amplified to the wound battery (metal-cased battery) according to the area and parallel branches (number of folds) to obtain the complete negative electrode DCR of the metal-cased battery.

[0080] Specifically, for n identical negative electrode layers connected in parallel, the chemical impedance of a single electrode needs to be divided by n, then Ra = .

[0081] Furthermore, the metal-cased battery negative electrode DCR R 负 Calculated using the following formula:

[0082]

[0083] Where ra is the negative electrode chemical resistance of the card battery, S2 is the area of ​​the active material of the metal-cased battery (which can be 550 mm * 82.5 mm), S1 is the area of ​​the active material of the card battery (which can be 32 mm * 42 mm), and n is the number of folds of the positive electrode of the metal-cased battery (which can be 108 folds). R1 ​​and R2 are the mechanical impedances of the positive and negative electrodes of the metal-cased battery, respectively, measured with a multimeter.

[0084] In this way, by separating the chemical impedance from the total DCR, the same set of ra for the three electrodes can be quickly transferred to wound batteries of different sizes / folds. As long as the active material system and coating density remain unchanged, there is no need to repeatedly disassemble to obtain the three electrodes, thus improving overall versatility and testing efficiency.

[0085] Based on the above method, the negative electrode impedance can be determined by obtaining the mechanical impedance of the negative electrode of the metal-cased battery, the chemical impedance of the negative electrode of the card battery, the active material area of ​​the metal-cased battery, the active material area of ​​the card battery, and the fold number of the positive electrode of the metal-cased battery. Thus, based on ΔR... 负 =(R 负’ -R 负 ) / R 负 Calculate the negative electrode impedance growth rate.

[0086] In one embodiment provided in this application, based on the above-described scheme, optionally, the lithium plating risk of the battery under actual operating conditions and cycle time is determined according to the target capacity decay rate and the target negative electrode impedance growth rate, including:

[0087] If the target capacity decay rate is greater than the target negative electrode impedance growth rate, then the lithium plating risk result of the battery is determined as the first risk detection result.

[0088] The inventors discovered that lithium plating occurs during charging and discharging because increased negative electrode polarization leads to a decrease in the negative electrode potential, reaching the lithium plating potential. According to the formula for negative electrode potential V = V0 - η = V0 - iRa, where V0 is the equilibrium potential, η is the overpotential, i is the charging current, and Ra is the internal resistance of the negative electrode polarization, the charging regime was developed based on a fast-charging strategy established by BOL. During use, as the battery ages, the charging rate remains constant, and the charging current is adjusted in real-time according to the rated capacity decay. The current i is related to capacity decay. For example, if the battery capacity is 100Ah and the 1C rate current is 100A, if the battery capacity decays to 90Ah, the 1C rate current becomes 90A. If the capacity decay rate is greater than the rate of increase in the negative electrode internal resistance, the negative electrode potential will not decrease during aging, and there is no risk of lithium plating in this case. If the capacity decay rate is less than the negative electrode internal resistance growth rate, the negative electrode potential may or may not decrease during aging. In this case, it is impossible to determine whether lithium plating will occur, and the battery's safety performance cannot be guaranteed. Therefore, based on this, determining the capacity decay rate 'a' and the negative electrode internal resistance growth rate 'b' can determine whether lithium plating will occur. When a ≥ b, there is no risk of lithium plating.

[0089] The above method requires no complex equipment, is simple to calculate, and is correlated with actual operating conditions. It can directly reflect the aging state of the battery in actual use and is applicable to different types of lithium-ion batteries. It enables online detection and early warning of lithium plating, thus providing a basis for timely adjustments to the charging strategy.

[0090] In some embodiments, the lithium plating risk result also includes a second risk detection result. If the target capacity decay rate is not greater than the target negative electrode impedance growth rate, then the lithium plating risk result of the battery is determined as the second risk detection result, which characterizes the presence of lithium plating risk in the battery. That is, when a < b, it is the second risk detection result, indicating the presence of lithium plating risk. It should be noted that the lithium plating risk detection result at this time is uncertain and can be further judged in conjunction with other parameters and methods to further confirm whether lithium plating risk exists.

[0091] To clearly explain the implementation principle of this solution, the following explanation uses an aluminum-cased wound battery as an example. (See [link to relevant documentation]). Figure 5 The flowchart provided in this application describes a battery lithium-free risk detection process, which specifically includes the following steps:

[0092] Step 1: Aging of aluminum-cased coiled batteries under specific operating conditions. Based on partial measured data, a lifespan degradation prediction model Qloss=kt^z is established, where t is the battery operating time, z is the degradation factor (usually 0.5), and k is a constant. The modeling approach for the lifespan degradation prediction model is as follows: Qloss=kt^0.5 at different temperatures, record the cell capacity degradation rate at the corresponding time, and the capacity degradation rate a=(BOL capacity - EOL capacity) / BOL capacity, where BOL is the initial battery state and EOL is the battery state after aging.

[0093] Step 2: The aluminum-cased coil battery is aged under specific operating conditions to multiple test times (SOH), such as SOH1 and SOH2, where SOH = discharge capacity / initial discharge capacity (BOL). Then, the mechanical impedance of each component of the aluminum-cased coil battery is measured.

[0094] Step 3: Disassemble the aluminum-cased coiled battery from Step 2 into a card-type three-electrode battery in a low-humidity environment, such as a drying room or glove box. Measure the mechanical impedance of each component of the card-type three-electrode battery. Adjust the charge of the card-type three-electrode battery to a fixed state of charge (SOC) and test the negative electrode impedance using a charge / discharge device.

[0095] Step 4: Based on the equivalent circuit of aluminum-cased coil battery and card three-electrode battery, the negative electrode impedance in aluminum-cased coil battery is obtained by calculation formula.

[0096] Step 5: Based on the negative electrode impedances obtained from SOH1 and SOH2 of the aluminum-cased coiled battery, establish a prediction model for the negative electrode impedance growth in the aluminum-cased coiled battery, lnΔR. 负s =ln(at^b), where a and b are both constants.

[0097] Step 6: Based on the actual test time and operating conditions, input the corresponding lifetime degradation prediction model and negative electrode impedance growth prediction model to determine the capacity degradation rate *a* and the negative electrode internal resistance growth rate *b*, and determine whether lithium plating has occurred. When *a* ≥ *b*, there is no risk of lithium plating; when *a* < *b*, there may be a risk of lithium plating.

[0098] To clearly illustrate the process and principle of the battery lithium-free risk detection method in this application, the following example is provided:

[0099] The lithium iron phosphate (LFP) battery was subjected to 0.5C charge and 0.5C discharge cycle testing at 30°C. A short-term performance test (RPT) was performed every 200 cycles (approximately 54 days). The RPT included the following steps: calibrating the capacity by performing 3 charge-discharge cycles at a rate of 0.33C, and measuring the discharge rate (DCR) after adjusting the state of charge (SOC) to 50%. The DCR test conditions were: at 25°C, with the battery at 50% SOC, it was first rested for 2 minutes, then discharged at 2C for 10 seconds, and finally rested for another 30 minutes.

[0100] Based on previous cycle data, the battery capacity decay follows the formula Q. loss =At^b, where A is a constant and b is the decay factor. Based on experimental results, the capacity decay prediction model for this battery under the above operating conditions is obtained as lnQ. loss =0.1039lnt-3.8144, where Qloss is defined as 1−SOH, i.e., Q loss = (Real-time discharge capacity − BOL discharge capacity) / BOL discharge capacity.

[0101] At 30°C, after 400 cycles (approximately 54 days) and 600 cycles (approximately 105 days), the capacity retention rates were measured to be 93.4% for SOH1 and 91.7% for SOH2, respectively. It should be noted that SOH1 and SOH2 represent the capacity retention states at two different testing times.

[0102] Then, discharge the batteries in their initial state (BOL) and aged state (SOH1, SOH2) at a rate of 0.5C to 2.5V. Continue discharging at a rate of 0.05C until the voltage reaches 2.5V again, ensuring the battery is completely discharged. Carefully disassemble the battery and remove the positive and negative electrodes in a strictly humidity-controlled environment (humidity < -30℃). Assemble a card-type three-electrode battery using one positive electrode, one negative electrode, and two separators, with a reference electrode placed in between. Cycle the battery for 3 weeks at a rate of 0.33C, defining the discharge capacity of the last week as the battery's rated capacity C0. Fully charge the battery at a constant current and constant voltage rate of 0.33C. Discharge the battery again at a rate of 0.33C to 50% SOC. At 25℃, discharge for 10 seconds at a current intensity twice the rated capacity 2C0 to measure the DCR of the negative electrode at 50% SOC. Use a multimeter to measure the mechanical impedance of each component (such as the positive electrode, negative electrode, reference electrode, etc.) of the card-type three-electrode battery.

[0103] Next, based on the equivalent circuits of the aluminum-cased coil battery and the card-type three-electrode battery, the negative electrode impedances under BOL, SOH1, and SOH2 are calculated and shown in Table 1.

[0104] Table 1

[0105]

[0106] The chemical impedance in the table includes the membrane ion resistance, membrane resistance, charge transfer resistance, and diffusion resistance. These impedance parameters can be obtained through DCR testing or calculation. The mechanical impedance includes the impedance of the poles, tabs, etc. These impedance parameters can be obtained by measuring with a multimeter.

[0107] After 400 cycles at 30°C, the capacity retention rate (SOH1) was 93.4%, the negative electrode internal resistance was 0.54 mΩ, and the negative electrode growth rate was 2.63%. After 600 cycles, the capacity retention rate (SOH2) was 91.7%, the negative electrode internal resistance was 0.55 mΩ, and the negative electrode growth rate was 6.32%. See also... Figure 6 The relationship between the RPT capacity retention and DCR growth rate of aluminum-cased coiled batteries at 30°C with cycle time is shown.

[0108] Under this operating condition, the negative electrode DCR growth rate satisfies at^b, therefore, the negative electrode DCR growth rate model ΔR under this operating condition is obtained. 负 =(R 负’ -R 负 ) / R 负 ,ln△R 负 =0.3028lnt-5.8646. Based on the lifetime decay prediction model lnQ loss =0.1039lnt-3.8144 and the negative DCR growth rate prediction model ln△R 负 =0.077lnt-3.6767, plot the capacity decay rate curve and the negative electrode DCR growth rate curve, as follows. Figure 7 As shown, under 30°C cycling conditions, the capacity decay rate consistently exceeds the negative electrode internal resistance growth rate, indicating that the battery will not undergo lithium plating. In other embodiments, if the capacity decay rate is less than the negative electrode internal resistance growth rate, it indicates a potential risk of lithium plating, prompting a warning, adjustment of the charge / discharge strategy, or further assessment using other parameters and methods to determine the presence of lithium plating risk. In practical applications, under current operating conditions, the capacity decay rate must exceed the negative electrode internal resistance growth rate for the battery to operate normally, ensuring battery performance and safety. Compared to the lithium plating risk detection method in this embodiment, the method for detecting no lithium plating risk is more practical and reliable.

[0109] and Figure 1 Corresponding to the method, embodiments of this application also provide a battery lithium-free risk detection device, used for detecting lithium plating risk. Figure 1 The specific implementation of the method is shown in the schematic diagram of the device. Figure 8 As shown, it includes:

[0110] The acquisition unit 801 is used to acquire the actual operating conditions and cycle time of the battery to be tested;

[0111] The execution unit 802 is used to substitute the cycle time into the life decay prediction model and the negative electrode impedance growth rate model corresponding to the battery and its actual operating conditions, respectively, to obtain the target capacity decay rate and the target negative electrode impedance growth rate of the battery under actual operating conditions and cycle time.

[0112] The determination unit 803 is used to determine the lithium plating risk result of the battery under actual operating conditions and cycle time based on the target capacity decay rate and the target negative electrode impedance growth rate; the lithium plating risk result includes the first risk detection result, which indicates that the battery does not have a lithium plating risk.

[0113] In one embodiment provided in this application, based on the above-described solution, optionally, the acquisition unit 801 includes:

[0114] The first acquisition subunit is used to acquire aging test data of the test battery under test conditions. The test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested.

[0115] The first construction subunit is used to construct a lifespan degradation prediction model based on the capacity degradation rate of each first test time in the aging test data of the test battery.

[0116] In one embodiment provided in this application, based on the above-described solution, optionally, the execution unit 802 includes:

[0117] The second acquisition subunit is used to acquire the negative electrode impedance of the test battery at various test times under the test conditions; the test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested.

[0118] The calculation subunit is used to calculate the negative electrode impedance growth rate of the test battery under the test conditions based on the negative electrode impedance of the test battery at each test time under the test conditions.

[0119] The second construction subunit is used to construct a negative electrode impedance growth rate model based on the negative electrode impedance growth rate of the test battery under test conditions.

[0120] In one embodiment provided in this application, based on the above-described solution, optionally, the second acquisition subunit includes:

[0121] The execution module is used to obtain the negative electrode impedance of the test battery at each test time based on the battery measurement data of the test battery at different test times. The battery measurement data is obtained by aging test of the test battery under test conditions. The battery measurement data includes the negative electrode mechanical impedance of the test battery in the first structure, the battery active material area of ​​the test battery in the first structure, the negative electrode chemical impedance of the battery in the second structure, and the battery active material area of ​​the battery in the second structure.

[0122] In one embodiment provided in this application, based on the above-described solution, optionally, the determining unit 803 includes:

[0123] The first determining sub-unit is used to determine the lithium plating risk result of the battery as the first risk detection result if the target capacity decay rate is greater than the target negative electrode impedance growth rate.

[0124] The specific principles and execution processes of each unit and module in the battery lithium-free risk detection device disclosed in the above embodiments of this application are the same as those of the battery lithium-free risk detection method disclosed in the above embodiments of this application. Please refer to the corresponding parts of the battery lithium-free risk detection method provided in the above embodiments of this application, and they will not be repeated here.

[0125] This application embodiment also provides a storage medium, which includes stored instructions, wherein, when the instructions are executed, the device where the storage medium is located executes the above-described battery lithium plating risk detection method.

[0126] This application also provides an electronic device, the structural schematic diagram of which is shown below. Figure 9 As shown, it specifically includes a memory 901 and one or more instructions 902, wherein one or more instructions 902 are stored in the memory 901 and configured to be executed by one or more processors 903 to perform the following operations:

[0127] Obtain the actual operating conditions and cycle time of the battery to be tested;

[0128] Substitute the cycle time into the life decay prediction model and the negative electrode impedance growth rate model corresponding to the battery and its actual operating conditions, respectively, to obtain the target capacity decay rate and the target negative electrode impedance growth rate of the battery under the actual operating conditions and the cycle time.

[0129] Based on the target capacity decay rate and the target negative electrode impedance growth rate, the lithium plating risk result of the battery under the actual operating conditions and the cycle time is determined; the lithium plating risk result includes a first risk detection result, which indicates that the battery does not have a lithium plating risk.

[0130] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0131] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0132] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0133] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0134] The above provides a detailed description of a battery lithium-free risk detection method provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A battery lithium precipitation-free risk detection method, characterized in that, include: Obtain the actual operating conditions and cycle time of the battery to be tested; Substitute the cycle time into the life decay prediction model and the negative electrode impedance growth rate model corresponding to the battery and its actual operating conditions, respectively, to obtain the target capacity decay rate and the target negative electrode impedance growth rate of the battery under the actual operating conditions and the cycle time. Based on the target capacity decay rate and the target negative electrode impedance growth rate, the lithium plating risk of the battery under the actual operating conditions and the cycle time is determined; The lithium plating risk result includes a first risk detection result, which indicates that the battery does not have a lithium plating risk; The process of constructing the lifetime decay prediction model includes: Acquire aging test data of the test battery under test conditions, wherein the test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested; The lifespan degradation prediction model is constructed based on the capacity decay rate at each test time in the aging test data of the test battery. The construction process of the negative electrode impedance growth rate model includes: Obtain the negative electrode impedance of the test battery at various test times under the test conditions; the test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested; The negative electrode impedance growth rate of the test battery under the test conditions is calculated based on the negative electrode impedance of the test battery at each test time under the test conditions. The negative electrode impedance growth rate model is constructed based on the negative electrode impedance growth rate of the test battery under the test conditions. The acquisition of the negative electrode impedance of the test battery at various test times under test conditions includes: Based on the battery measurement data of the test battery at different test times, the negative electrode impedance of the test battery at each test time is obtained; wherein, the battery measurement data is obtained by aging test of the test battery under the test conditions, and the battery measurement data includes the negative electrode mechanical impedance of the test battery in the first structure, the battery active material area of ​​the test battery in the first structure, the negative electrode chemical impedance of the battery in the second structure, and the battery active material area of ​​the battery in the second structure.

2. The method of claim 1, wherein, The step of determining the lithium plating risk of the battery under the actual operating conditions and the cycle time based on the target capacity decay rate and the target negative electrode impedance growth rate includes: If the target capacity decay rate is greater than the target negative electrode impedance growth rate, then the lithium plating risk result of the battery is determined as the first risk detection result.

3. A battery lithium precipitation risk-free detection device, characterized in that, include: The acquisition unit is used to acquire the actual operating conditions and cycle time of the battery to be tested; An execution unit is used to substitute the cycle time into the life decay prediction model and the negative electrode impedance growth rate model corresponding to the battery and its actual operating conditions, respectively, to obtain the target capacity decay rate and the target negative electrode impedance growth rate of the battery under the actual operating conditions and the cycle time. The determining unit is used to determine the lithium plating risk result of the battery under the actual operating conditions and the cycle time based on the target capacity decay rate and the target negative electrode impedance growth rate. The lithium plating risk result includes a first risk detection result, which indicates that the battery does not have a lithium plating risk; The execution unit includes: The first acquisition subunit is used to acquire aging test data of the test battery under test conditions, wherein the test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested. The first construction subunit is used to construct the life decay prediction model based on the capacity decay rate of each first test time in the aging test data of the test battery. The execution unit includes: The second acquisition subunit is used to acquire the negative electrode impedance of the test battery at various test times under the test conditions; the test conditions are the same as the actual conditions, and the type identifier of the test battery is the same as the type identifier of the battery to be tested; The calculation subunit is used to calculate the negative electrode impedance growth rate of the test battery under the test conditions based on the negative electrode impedance of the test battery at each test time under the test conditions. The second construction subunit is used to construct the negative electrode impedance growth rate model based on the negative electrode impedance growth rate of the test battery under the test conditions. The second acquisition subunit includes: The execution module is used to obtain the negative electrode impedance of the test battery at each test time based on the battery measurement data of the test battery at different test times. The battery measurement data is obtained by aging test of the test battery under test conditions. The battery measurement data includes the negative electrode mechanical impedance of the test battery in the first structure, the battery active material area of ​​the test battery in the first structure, the negative electrode chemical impedance of the battery in the second structure, and the battery active material area of ​​the battery in the second structure.

4. A storage medium, characterized by The storage medium includes stored instructions, wherein, when the instructions are executed, the device containing the storage medium is controlled to perform the battery lithium plating risk detection method as described in any one of claims 1-2.

5. An electronic device, comprising: It includes a memory, and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors as described in any one of claims 1-2.

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