Detection method and detection device

Abstract

The application provides a detection method and a detection device, and relates to the technical field of batteries. In the method, K sets of parameter values are used for charging simulation of a battery. A first parameter value in each set of parameter values is a constant, and a second parameter value is a variable. The first parameter value is used for a first region of the battery, and the second parameter value is used for a second region of the battery. The parameter values are related to the impedance of the battery. K sets of curves are generated. A first curve in each set of curves is a negative electrode voltage change curve of the first region, and a second curve is a negative electrode voltage change curve of the second region. An association between the minimum negative electrode voltage and the second parameter value is established according to the K sets of curves. The second parameter value when the minimum negative electrode voltage reaches a first threshold value is determined according to the association. The difference between the second parameter value and the first parameter value is calculated as a threshold value of the impedance unevenness degree. By controlling the impedance unevenness degree of the battery to be less than the threshold value of the impedance unevenness degree, the probability of lithium precipitation of the battery can be reduced.

Description

Technical Field

[0001] This application relates to the field of battery technology, and more specifically, to a detection method and a detection device. Background Technology

[0002] With the development of new energy technologies, batteries are being used in a wider range of fields, such as as a power source to power vehicles, in order to reduce the use of non-renewable resources.

[0003] Lithium plating is a common occurrence in batteries during charging. This occurs when lithium ions, after being extracted from the positive electrode, cannot be inserted into the negative electrode and instead deposit on its surface, forming a grayish layer. Lithium plating not only degrades battery performance and shortens cycle life but also limits fast-charging capacity and can potentially lead to catastrophic consequences such as combustion or explosion. Therefore, reducing the probability of lithium plating in batteries is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] This application provides a detection method and detection device that can reduce the probability of lithium plating in batteries.

[0005] Firstly, a detection method is provided, comprising: performing charging simulation on a battery based on K sets of parameter values ​​to obtain K sets of curves, wherein each set of parameter values ​​in the K sets includes the same first parameter value and different second parameter values, the first parameter value and the second parameter value correspond to the same parameter, which is related to the impedance of the battery, the first parameter value is applied to a first region of the battery, the second parameter value is applied to a second region of the battery, K is greater than or equal to 2, each set of curves in the K sets includes the same first curve and different second curves, the first curve is the change curve of the negative electrode voltage corresponding to the first region, and the second curve is the change curve of the negative electrode voltage corresponding to the second region; determining K minimum negative electrode voltage values ​​and K second parameter values ​​corresponding to each of the K minimum negative electrode voltage values ​​based on the K sets of curves; determining the correlation between the minimum negative electrode voltage values ​​and the second parameter values ​​based on the K minimum negative electrode voltage values ​​and the K second parameter values, and determining the second parameter value corresponding to when the minimum negative electrode voltage value is equal to a first threshold value based on the correlation.

[0006] The second parameter value is a variable, while the first parameter value is a constant. Since the parameters corresponding to these values ​​are related to the battery's impedance, and the first parameter value applies to the first region while the second parameter value applies to the second region, the impedance non-uniformity between the first and second regions can be characterized by the difference between the first and second parameter values. That is, if the first and second parameter values ​​are different, there is an impedance difference between the first and second regions; if the first and second parameter values ​​are the same, there is no impedance difference between the first and second regions. Accordingly, each set of parameter values ​​corresponds to a degree of impedance non-uniformity. In the K sets of curves obtained by the detection device through charging simulation of the battery based on K sets of parameter values, each curve corresponds to a degree of impedance non-uniformity. The impact of this impedance non-uniformity on lithium plating can be reflected by the negative electrode voltage value. That is, during charging, the lower (more negative) the minimum negative electrode voltage, the easier it is to fall below the voltage value required for lithium ion plating, and the higher the probability of lithium plating in the battery. Therefore, the detection device determines the aforementioned correlation through K sets of curves, and determines the second parameter value corresponding to the first threshold based on this correlation and the first threshold. The difference between the second parameter value corresponding to the first threshold and the first parameter value can be represented as the impedance non-uniformity threshold. By controlling the actual impedance non-uniformity of the battery to be less than this impedance non-uniformity threshold, the probability of lithium plating in the battery can be reduced. In summary, the above method can reduce the probability of lithium plating in the battery.

[0007] One possible implementation is that the parameter is either a battery manufacturing process parameter or a battery operating parameter. The battery manufacturing process parameter is related to the battery's impedance. Thus, the industry can control the battery's manufacturing process parameters during the production stage to ensure that the actual impedance non-uniformity is less than an impedance non-uniformity threshold, thereby reducing the probability of lithium plating. Similarly, the battery operating parameter is related to the battery's impedance. Thus, the industry can control the battery's operating parameters during the charging stage to ensure that the actual impedance non-uniformity is less than an impedance non-uniformity threshold, thereby reducing the probability of lithium plating.

[0008] One possible implementation involves battery process parameters including any one or more of the following: separator pore blockage rate, lithium salt concentration, compaction density, maximum lithium intercalation capacity, minimum lithium intercalation capacity, porosity, tortuosity, electrode thickness, electrochemical reaction rate constant, lithium-ion solid-phase diffusion coefficient, lithium-ion liquid-phase diffusion coefficient, or coating weight. All of these parameters are process parameters related to battery impedance. During battery production, the industry can control one or more of these parameters to ensure that the actual impedance non-uniformity of the battery is less than the impedance non-uniformity threshold, thereby reducing the probability of lithium plating.

[0009] One possible implementation involves using battery operating parameters including temperature difference or the difference in deformation rate between different regions of the separator. These parameters are all operating parameters related to the battery's impedance. During the battery charging phase, the industry can control any of these parameters to ensure that the actual impedance non-uniformity of the battery is less than a threshold threshold, thereby reducing the probability of lithium plating.

[0010] One possible implementation is that the first threshold is less than or equal to 0. A minimum negative electrode voltage of less than or equal to 0 means that the probability of lithium plating in the battery is greater than the probability threshold. Therefore, the impedance non-uniformity threshold determined based on the aforementioned first threshold can effectively reduce the probability of lithium plating in the battery.

[0011] One possible implementation is to set a first threshold greater than 0. When the first threshold is greater than 0, it means that the minimum negative electrode voltage is greater than 0, implying that the battery has no risk of lithium plating, which can further reduce the probability of lithium plating in the battery.

[0012] One possible implementation is that both the first and second regions correspond to the negative electrode of the battery, or both correspond to the positive electrode of the battery, or both correspond to the separator of the battery. By dividing the first and second regions within a single location, it is possible to control the degree of impedance non-uniformity at that single location, thereby improving the accuracy of controlling the impedance non-uniformity of the battery.

[0013] One possible implementation is that the first region is located in a different position than the second region, where the position represents the positive electrode, negative electrode, or separator of the battery. By setting different parameter values ​​for different positions, this allows for control over the overall impedance non-uniformity of the battery, reducing process complexity. For example, it eliminates the need to consider impedance non-uniformity between different regions within a single location.

[0014] One possible implementation is that the K sets of parameter values ​​include the first set of parameter values, where the first parameter value and the second parameter value in the first set of parameter values ​​are the same.

[0015] One possible implementation is that when the values ​​of the first parameter and the second parameter are different, the value of the second parameter is greater than the value of the first parameter.

[0016] One possible implementation is that when the values ​​of the first parameter and the second parameter are different, the value of the second parameter is T times the value of the first parameter, where T is greater than 1.

[0017] Secondly, a detection device is provided, comprising a processing unit, configured to: perform charging simulation on a battery based on K sets of parameter values, obtaining K sets of curves respectively, wherein each set of parameter values ​​in the K sets includes the same first parameter value and different second parameter values, the first parameter value and the second parameter value correspond to the same parameter, which is related to the impedance of the battery, the first parameter value is applied to a first region of the battery, the second parameter value is applied to a second region of the battery, K is greater than or equal to 2, each set of curves in the K sets includes the same first curve and different second curves, the first curve is the change curve of the negative electrode voltage corresponding to the first region, and the second curve is the change curve of the negative electrode voltage corresponding to the second region; determine K minimum negative electrode voltage values ​​and K second parameter values ​​corresponding to each of the K minimum negative electrode voltage values ​​based on the K sets of curves; determine the correlation between the minimum negative electrode voltage values ​​and the second parameter values ​​based on the K minimum negative electrode voltage values ​​and the K second parameter values, and determine the second parameter value corresponding to when the minimum negative electrode voltage value is equal to a first threshold value based on the correlation.

[0018] One possible implementation is that the parameter is of the type of battery process parameters or battery operating parameters.

[0019] One possible implementation is that the battery process parameters include any one or more of the following: separator pore blockage rate, lithium salt concentration, compaction density, maximum lithium intercalation, minimum lithium intercalation, porosity, tortuosity, electrode thickness, electrochemical reaction rate constant, lithium ion solid-phase diffusion coefficient, lithium ion liquid-phase diffusion coefficient, or coating weight.

[0020] One possible implementation is that the battery's operating parameters include temperature difference or the difference in deformation rate between different regions of the separator.

[0021] One possible implementation is that the first threshold is less than or equal to 0.

[0022] One possible implementation is to have a first threshold greater than 0.

[0023] One possible implementation is that both the first region and the second region correspond to the negative electrode of the battery, or both the first region and the second region correspond to the positive electrode of the battery, or both the first region and the second region correspond to the separator of the battery.

[0024] One possible implementation is that the location of the first region is different from that of the second region, where the location is the positive electrode, negative electrode, or separator of the battery.

[0025] One possible implementation is that the K sets of parameter values ​​include the first set of parameter values, where the first parameter value and the second parameter value in the first set of parameter values ​​are the same.

[0026] One possible implementation is that when the values ​​of the first parameter and the second parameter are different, the value of the second parameter is greater than the value of the first parameter.

[0027] One possible implementation is that when the values ​​of the first parameter and the second parameter are different, the value of the second parameter is T times the value of the first parameter, where T is greater than 1.

[0028] Thirdly, a detection device is provided, which includes a processor and a memory. The memory is used to store a program, and the processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method in the first aspect or its various implementations described above.

[0029] Fourthly, a computer-readable storage medium is provided for storing a computer program that causes a computer to perform the methods described in the first aspect or its implementations. Attached Figure Description

[0030] Figure 1 A schematic flowchart of the detection method 100 according to an embodiment of this application is shown.

[0031] Figure 2 This is a schematic diagram of battery A and battery B being charged in parallel.

[0032] Figure 3 This is a schematic diagram of standard curve 1 and standard curve 2.

[0033] Figure 4 It is a geometric schematic diagram of an electrochemical simulation model.

[0034] Figure 5 It is another geometric schematic diagram of an electrochemical simulation model.

[0035] Figure 6 This is a schematic diagram of the change curves of the negative electrode voltage in regions 1 and 2 respectively.

[0036] Figure 7 This is a schematic diagram of a charging simulation.

[0037] Figure 8 This is a schematic diagram showing the relationship between the minimum change in negative electrode voltage and the multiple of Rct.

[0038] Figure 9 This is another schematic diagram of charging simulation.

[0039] Figure 10 This is another schematic diagram of charging simulation.

[0040] Figure 11 This is another schematic diagram of charging simulation.

[0041] Figure 12This is another schematic diagram of charging simulation.

[0042] Figure 13 A schematic block diagram of a detection device according to an embodiment of this application is shown.

[0043] Figure 14 A schematic diagram of the hardware structure of the detection device according to an embodiment of this application is shown. Detailed Implementation

[0044] The embodiments of this application are described below with reference to the accompanying drawings and examples. The following descriptions and drawings of the embodiments are used to illustrate the principles of this application by way of example, but should not be used to limit the scope of this application, that is, this application is not limited to the described embodiments.

[0045] In the description of this application, unless otherwise stated, "a plurality of" means two or more. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0046] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The terms "comprising" and "having," and any variations thereof, in this specification, claims, and the foregoing description of the drawings, are intended to cover non-exclusive inclusion.

[0047] In this application, the reference to "embodiment" means that a specific feature or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0048] As mentioned earlier, lithium plating not only degrades battery performance and shortens cycle life, but also limits fast-charging capacity and can potentially lead to catastrophic consequences such as combustion and explosion. The occurrence of lithium plating is closely related to the degree of impedance non-uniformity within the battery; that is, the higher the degree of impedance non-uniformity, the higher the probability of lithium plating. Specifically, differences in impedance across different regions of the battery lead to inconsistent charging speeds, resulting in an uneven distribution of lithium intercalation. This uneven distribution alters the battery's local equilibrium potential, creating a potential difference within the battery. This potential difference becomes the driving force for local currents, further distributing the current unevenly and ultimately inducing lithium plating. However, currently, there is no technology to evaluate the correlation between the degree of impedance non-uniformity and the probability of lithium plating. Without this correlation, the industry struggles to effectively control the degree of impedance non-uniformity, potentially exceeding certain thresholds and thus increasing the probability of lithium plating.

[0049] In view of this, embodiments of this application provide a detection method and a detection device. First, a charging simulation of the battery is performed based on K sets of parameter values ​​to obtain K sets of curves. Each set of parameter values ​​includes the same first parameter value and different second parameter values. The first parameter value and the second parameter value correspond to the same parameter, which is related to the battery impedance. The first parameter value is applied to the first region of the battery, and the second parameter value is applied to the second region of the battery. K is greater than or equal to 2. Each set of curves includes the same first curve and different second curves. The first curve is the change curve of the negative electrode voltage corresponding to the first region, and the second curve is the change curve of the negative electrode voltage corresponding to the second region. Then, based on the K sets of curves, K minimum negative electrode voltage values ​​and K second parameter values ​​corresponding to each of the K minimum negative electrode voltage values ​​are determined. The correlation between the minimum negative electrode voltage values ​​and the second parameter values ​​is determined based on the K minimum negative electrode voltage values ​​and the K second parameter values. Finally, based on the correlation, the second parameter value corresponding to when the minimum negative electrode voltage value is equal to a first threshold value is determined.

[0050] Since the parameters mentioned above are related to the battery's impedance, and the first parameter applies to the first region while the second parameter applies to the second region, the impedance non-uniformity between the first and second regions can be characterized by the difference between the first and second parameter values. That is, if the first and second parameter values ​​are different, there is an impedance difference between the first and second regions; if the first and second parameter values ​​are the same, there is no impedance difference between the first and second regions. Accordingly, each set of parameter values ​​corresponds to a degree of impedance non-uniformity. In the K sets of curves obtained by the detection device through charging simulation of the battery based on the K sets of parameter values, each curve corresponds to a degree of impedance non-uniformity. The impact of this impedance non-uniformity on lithium plating can be reflected by the negative electrode voltage value. That is, during charging, the lower (more negative) the minimum negative electrode voltage, the easier it is to fall below the voltage value required for lithium ion plating, and the higher the probability of lithium plating in the battery. Therefore, the detection device determines the aforementioned correlation through K sets of curves, and determines the second parameter value corresponding to the first threshold based on this correlation and the first threshold. The difference between the second parameter value corresponding to the first threshold and the first parameter value can be represented as the impedance non-uniformity threshold. By controlling the actual impedance non-uniformity of the battery to be less than this impedance non-uniformity threshold, the probability of lithium plating in the battery can be reduced. In summary, the above method can reduce the probability of lithium plating in the battery.

[0051] The aforementioned testing methods can be applied to both the production and operational stages of battery devices. A battery device may include at least one battery cell assembly for providing voltage and capacity. A battery cell assembly may include multiple battery cells connected in series, parallel, or a combination of these cells via a busbar. A battery cell can be a rechargeable battery, meaning a battery cell that can be reactivated by charging after discharge to continue its use. Furthermore, battery cells can be lithium-ion batteries, lithium iron phosphate batteries, nickel-cobalt-manganese ternary batteries, nickel-cobalt-aluminum ternary batteries, sodium-ion lithium batteries, sodium-ion batteries, sodium-lithium-ion batteries, lithium metal batteries, sodium metal batteries, lithium-sulfur batteries, magnesium-ion batteries, nickel-metal hydride batteries, nickel-cadmium batteries, lead-acid batteries, and negative electrodeless batteries, etc., without limitation. Additionally, the type of battery cell can also be a stacked cell, a pouch cell, a prismatic cell, or a cylindrical cell, etc.

[0052] A battery cell assembly is typically formed by arranging multiple battery cells. As an example, a battery cell assembly is a battery module, which is formed by arranging and fixing multiple battery cells together to form a single module. As another example, a battery module can be formed by bundling multiple battery cells together with cable ties.

[0053] The battery device can also be a battery pack, which includes a housing and one or more individual battery cells housed within the housing.

[0054] Figure 1 A schematic flowchart of a detection method 100 according to an embodiment of this application is shown. Optionally, the execution entity of method 100 may be a detection device. Method 100 includes: S110. Perform charging simulation on the battery based on K sets of parameter values ​​to obtain K sets of curves. Each set of parameter values ​​in K sets includes the same first parameter value and different second parameter values. The first parameter value and the second parameter value correspond to the same parameter, which is related to the impedance of the battery. The first parameter value is applied to the first region of the battery, and the second parameter value is applied to the second region of the battery. K is greater than or equal to 2. Each set of curves in K sets includes the same first curve and different second curves. The first curve is the change curve of the negative electrode voltage corresponding to the first region, and the second curve is the change curve of the negative electrode voltage corresponding to the second region.

[0055] S120. Determine the K minimum negative electrode voltage values ​​and the K second parameter values ​​corresponding to each of the K minimum negative electrode voltage values ​​based on the K sets of curves.

[0056] S130. Determine the correlation between the minimum negative electrode voltage and the second parameter value based on the K minimum negative electrode voltage values ​​and the K second parameter values, and determine the second parameter value corresponding to the minimum negative electrode voltage being equal to the first threshold value based on the correlation.

[0057] The second parameter value is a variable, while the first parameter value is a constant. Since the parameters corresponding to these values ​​are related to the battery's impedance, and the first parameter value applies to the first region while the second parameter value applies to the second region, the impedance non-uniformity between the first and second regions can be characterized by the difference between the first and second parameter values. That is, if the first and second parameter values ​​are different, there is an impedance difference between the first and second regions; if the first and second parameter values ​​are the same, there is no impedance difference between the first and second regions. Accordingly, each set of parameter values ​​corresponds to a degree of impedance non-uniformity. In the K sets of curves obtained by the detection device through charging simulation of the battery based on K sets of parameter values, each curve corresponds to a degree of impedance non-uniformity. The impact of this impedance non-uniformity on lithium plating can be reflected by the negative electrode voltage value. That is, during charging, the lower (more negative) the minimum negative electrode voltage, the easier it is to fall below the voltage value required for lithium ion plating, and the higher the probability of lithium plating in the battery. Therefore, the detection device determines the aforementioned correlation through K sets of curves, and determines the second parameter value corresponding to the first threshold based on this correlation and the first threshold. The difference between the second parameter value corresponding to the first threshold and the first parameter value can be represented as the impedance non-uniformity threshold. By controlling the actual impedance non-uniformity of the battery to be less than this impedance non-uniformity threshold, the probability of lithium plating in the battery can be reduced. In summary, the above method can reduce the probability of lithium plating in the battery.

[0058] One possible example is that the impedance non-uniformity of a battery can be represented by the impedance difference between different regions. An example of impedance is charge transfer resistance (Rct), which is obtained through electrochemical impedance spectroscopy (EIS). Specifically, the larger the impedance difference between different regions, the greater the impedance non-uniformity of the battery. For example, when the impedance difference between the first and second regions is a first difference, it corresponds to a first degree of impedance non-uniformity; when the impedance difference between the first and second regions is a second difference, it corresponds to a second degree of impedance non-uniformity; if the first difference is greater than the second difference, then the first degree of impedance non-uniformity is greater than the second degree of impedance non-uniformity. The value of Rct is affected by various parameters, as described below.

[0059] One possible example is that the impedance non-uniformity of a battery can also be characterized by the difference between parameter values ​​(both corresponding to the same parameter) in different regions, where the parameter value corresponds to a parameter related to the battery's impedance. The larger the difference between the parameter values ​​(both corresponding to the same parameter) in different regions, the greater the impedance non-uniformity of the battery. For example, when the difference between the parameter values ​​(both corresponding to the same parameter) in the first region and the second region is the first difference, it corresponds to the first impedance non-uniformity; when the difference is the second difference, it corresponds to the second impedance non-uniformity. If the first difference is greater than the second difference, then the first impedance non-uniformity is greater than the second impedance non-uniformity.

[0060] The first parameter value is applied to the first region, which can be understood as setting the value of a specific parameter (the parameter corresponding to the first parameter value) in the first region to the first parameter value. The second parameter value is applied to the second region, which can be understood as setting the value of a specific parameter (the parameter corresponding to the second parameter value) in the second region to the second parameter value. By using the first and second parameter values, the impedance difference between the first and second regions can be controlled, thus creating the degree of impedance non-uniformity between them. The larger the difference between the first and second parameter values, the greater the impedance non-uniformity between the first and second regions. Since the first parameter value is constant, the second parameter value is variable, and the second parameter value is different in each set of parameter values, each set of parameter values ​​corresponds to a degree of impedance non-uniformity, thus forming K degrees of impedance non-uniformity.

[0061] The testing device performs charging simulation on the battery based on K sets of parameter values, and obtains K sets of curves respectively. This can be understood as the testing device using each set of parameter values ​​to perform charging simulation on the battery. The charging conditions selected by the testing device during the charging simulation can be one or a combination of several of the following: constant current charging, constant voltage charging, constant power charging, pulse charging, variable current charging, variable voltage charging, and variable power charging. Finally, the first curve and the second curve corresponding to the set of parameter values ​​are obtained.

[0062] For example, the K sets of parameter values ​​include parameter value group 1 and parameter value group 2. Both parameter value group 1 and parameter value group 2 include the same first parameter value. The second parameter value in parameter value group 1 is different from the second parameter value in parameter value group 2. The detection device first sets the value of a specific parameter in the first region to the first parameter value in parameter value group 1 and sets the value of a specific parameter in the second region to the second parameter value in parameter value group 1. Then, a charging simulation is performed, and the changing trend of the negative electrode voltage in the first and second regions is observed to obtain the first curve and the second curve corresponding to parameter value group 1. Then, the detection device sets the value of the specific parameter in the second region to the second parameter value in parameter value group 2, performs a charging simulation again, and observes the changing trend of the negative electrode voltage in the first and second regions to obtain the first curve and the second curve corresponding to parameter value group 2. This process is repeated to obtain K sets of curves, each set of curves corresponding to a set of parameter values.

[0063] When the values ​​of the first and second parameters are the same, the impedance non-uniformity between the first and second regions is 0, and the first and second curves are identical. When the values ​​of the first and second parameters are different, the impedance non-uniformity between the first and second regions is not 0. Different impedances lead to different trends in the change of negative electrode voltage, thus the first and second curves are different. Correspondingly, when the minimum negative electrode voltage corresponding to each curve is different, this application can select the minimum value as the final minimum negative electrode voltage. The final minimum negative electrode voltage can be used to characterize the impedance non-uniformity between the first and second regions. For example, using the minimum negative electrode voltage of the impedance-uniform region as a benchmark, the impedance non-uniformity can be characterized by comparing the minimum negative electrode voltage of the impedance-non-uniform region with the benchmark value. Here, K sets of curves correspond to K minimum negative electrode voltage values ​​(the aforementioned final minimum negative electrode voltage). Unless otherwise specified, the minimum negative electrode voltage mentioned below refers to the aforementioned final minimum negative electrode voltage.

[0064] After obtaining K sets of curves, the detection device determines the K minimum negative electrode voltage values ​​and the K second parameter values ​​corresponding to the K minimum negative electrode voltage values. It then determines the correlation relationship based on the K minimum negative electrode voltage values ​​and the K second parameter values. In other words, the detection device draws curves based on the K minimum negative electrode voltage values ​​and the K second parameter values ​​to obtain the aforementioned correlation relationship. Subsequently, it determines the second parameter value corresponding to the first threshold value through this correlation relationship and the first threshold value, and finally determines the aforementioned impedance non-uniformity threshold value.

[0065] One possible implementation is that the above parameters are either battery process parameters or battery operating parameters.

[0066] Battery manufacturing processes are related to battery impedance. Therefore, the industry can control these processes during production to ensure the actual impedance non-uniformity is below a threshold, thus reducing the probability of lithium plating. Battery operating parameters are also related to battery impedance. Therefore, the industry can control these parameters during charging to ensure the actual impedance non-uniformity is below a threshold, further reducing the probability of lithium plating.

[0067] One possible implementation is that the battery process parameters include any one or more of the following: separator pore blockage rate, lithium salt concentration, compaction density, maximum lithium intercalation, minimum lithium intercalation, porosity, tortuosity, electrode thickness, electrochemical reaction rate constant, lithium ion solid-phase diffusion coefficient, lithium ion liquid-phase diffusion coefficient, or coating weight.

[0068] The parameters mentioned above are all process parameters related to battery impedance. During the battery production stage, the industry can control one or more of these parameters to ensure that the actual impedance non-uniformity of the battery is less than the impedance non-uniformity threshold, thereby reducing the probability of lithium plating in the battery.

[0069] Diaphragm porosity refers to the percentage of the original effective pore volume (or pore area) of the diaphragm after it has been blocked by by-reaction products, electrode particles, electrolyte decomposition products, etc., out of the total original effective pore volume (or total effective pore area). The difference in diaphragm porosity is related to the non-uniformity of the diaphragm coating; the more non-uniform the coating, the greater the difference in porosity.

[0070] Lithium salt concentration refers to the molar concentration of lithium salt in the battery electrolyte, usually expressed in moles per liter (mol / L) or molar concentration (M).

[0071] Press density (PD) refers to the density of an electrode after a cold-pressing process. It is an important indicator of the degree of electrode compaction, and PD has a significant impact on the electrochemical performance of a battery. The unit of PD is g / cm³.

[0072] Maximum lithium intercalation capacity refers to the maximum number of lithium ions that can be intercalated (or absorbed) per unit mass or unit amount of substance in the electrode material of a battery. It is usually expressed as theoretical specific capacity (mAh / g) or the molar ratio of lithium to carbon. Both the positive and negative electrodes have a maximum lithium intercalation capacity.

[0073] Minimum lithium intercalation capacity refers to the minimum number of lithium ions that can be intercalated (or absorbed) per unit mass or unit amount of substance in the electrode material of a battery. It is usually expressed as theoretical specific capacity or the molar ratio of lithium to carbon. Both the positive and negative electrodes have a minimum lithium intercalation capacity.

[0074] Porosity refers to the proportion of pore volume within an electrode to its total volume, reflecting the degree of pore development within the electrode. Porosity determines electrolyte wetting and lithium-ion liquid-phase transport capabilities, and is closely related to battery impedance, ion transport uniformity, and the risk of lithium plating during fast charging. Porosity exists in both the positive and negative electrodes.

[0075] Tortivity is a dimensionless physical parameter that describes the deviation of the actual transport path of lithium ions in the electrode pores from a straight line. Tortivity exists in both the positive and negative electrodes.

[0076] Electrode thickness refers to the geometric dimension of the active material layer in the direction perpendicular to the electrode plane. Its size directly affects the length of the lithium-ion solid-phase diffusion path and the degree of polarization. The greater the electrode thickness, the greater the resistance to lithium-ion transport, and the higher the battery impedance and fast-charging polarization. Uneven thickness distribution will exacerbate local reaction differences and increase the risk of lithium plating. Electrode thickness exists in both the positive and negative electrodes.

[0077] The electrochemical reaction rate constant (k) is a kinetic parameter characterizing the rate of charge transfer reactions at the electrode interface, and its magnitude directly determines the speed of the electrochemical reaction at the electrode. A larger electrochemical reaction rate constant indicates easier charge transfer at the interface, lower charge transfer resistance, and better electrode kinetic performance; conversely, a smaller constant hinders the interface reaction, increases impedance, and exacerbates polarization, increasing the risk of lithium plating. Both the positive and negative electrodes possess an electrochemical reaction rate constant.

[0078] The solid-phase diffusion coefficient of lithium ion (D_s) characterizes the diffusion and migration rate of lithium ions within the solid phase of the electrode active material and is a key parameter determining the solid-phase ion transport capability of the electrode. A larger solid-phase diffusion coefficient indicates smoother lithium ion transport within the particles, lower electrode polarization and impedance, and better fast-charging performance; conversely, a smaller coefficient leads to restricted ion transport, exacerbated local polarization, and increased risk of lithium plating. Both the positive and negative electrodes exhibit a lithium-ion solid-phase diffusion coefficient.

[0079] The liquid-phase diffusion coefficient of lithium ion (D_L) characterizes the diffusion and migration ability of lithium ions in the electrolyte and is a crucial parameter determining the liquid-phase ion transport efficiency of a battery. A higher D_L indicates smoother lithium ion transport within the electrolyte through the electrode pores, resulting in lower polarization and impedance. Conversely, a lower D_L hinders liquid-phase ion transport, exacerbating localized reaction inhomogeneities and increasing the risk of lithium plating during fast charging.

[0080] Coating weight (CW) refers to the weight of the active material coated on the current collector. It is an important parameter for measuring the active material loading on the electrode. The unit of CW is mg / cm².

[0081] One possible implementation involves using battery operating parameters that include temperature difference or the difference in deformation rate between different regions of the separator.

[0082] The parameters mentioned above are all operating parameters related to the battery's impedance. During the battery charging phase, the industry can control any of these parameters to ensure that the actual impedance non-uniformity of the battery is less than the impedance non-uniformity threshold, thereby reducing the probability of lithium plating.

[0083] Temperature difference refers to the temperature variation between different locations within a battery. For example, if the ambient temperature of the negative electrode is temperature 1 and the ambient temperature of the positive electrode is temperature 2, the difference between temperature 1 and temperature 2 will result in uneven impedance within the battery. Similarly, if the ambient temperature of the negative electrode is temperature 1 and the ambient temperature of the separator is temperature 3, the difference between temperature 1 and temperature 3 will also result in uneven impedance within the battery.

[0084] The membrane deformation rate is a physical parameter that measures the relative change in the membrane's dimensions (length / thickness) caused by immersion in electrolyte or expansion and contraction during charging and discharging at the positive and negative electrodes. The membrane deformation rate can be defined as the percentage change in dimensionality of the membrane after immersion in electrolyte or expansion and contraction during charging and discharging at the positive and negative electrodes, relative to its original dimensions.

[0085] The deformation rate of the separator affects the battery impedance through the following mechanisms: 1. Factors affecting the ionic resistance of the membrane: Excessive expansion → pore structure deformation / closure: The expansion of the membrane due to liquid absorption will compress the micropores, reduce porosity, increase the tortuosity of the pores, hinder lithium ion migration, and directly increase the membrane's bulk resistance. Excessive shrinkage → pore collapse: Thermal shrinkage or electrolyte evaporation causes the membrane to shrink, which will also reduce ionic conductivity and increase internal resistance.

[0086] 2. Affects the interfacial impedance between the electrode and the diaphragm: Expansion / contraction leads to a decrease in the flatness of the diaphragm and a worse fit with the electrode, resulting in poor local contact and increasing the interfacial impedance. The increase in interfacial impedance will exacerbate polarization and reduce the charging and discharging efficiency.

[0087] The varying deformation rates of different regions of the separator can lead to impedance non-uniformity within the battery. For example, different amounts of separator deformation result in variations in porosity and tortuosity, altering the liquid-phase transport path of lithium ions and causing different levels of resistance to ion transport. This results in uneven distribution of ohmic impedance and lithium-ion liquid-phase diffusion impedance, ultimately leading to impedance non-uniformity within the battery. Therefore, the deformation rate of different regions of the separator can be detected during the battery charging phase. If differences in deformation rate exist, the charging rate can be reduced to decrease electrode expansion, thereby reducing the compressive deformation of the separator and ensuring that the actual impedance non-uniformity of the battery is less than the impedance non-uniformity threshold. Furthermore, this application supports feeding back the deformation rate of different regions of the separator to the cell design stage to increase the allowance for expansion and reduce deformation.

[0088] One possible implementation is that the first threshold is less than or equal to 0. A minimum negative electrode voltage of less than or equal to 0 means that the probability of lithium plating in the battery is greater than the probability threshold. Therefore, the impedance non-uniformity threshold determined based on the aforementioned first threshold can effectively reduce the probability of lithium plating in the battery.

[0089] One possible implementation is to set a first threshold greater than 0. When the first threshold is greater than 0, it means that the minimum negative electrode voltage is greater than 0, implying that the battery has no risk of lithium plating, which can further reduce the probability of lithium plating in the battery.

[0090] One possible implementation is that both the first region and the second region correspond to the negative electrode of the battery, or both the first region and the second region correspond to the positive electrode of the battery, or both the first region and the second region correspond to the separator of the battery.

[0091] By dividing a single location into a first region and a second region, it is possible to control the degree of impedance non-uniformity at that single location, thereby improving the accuracy of controlling the impedance non-uniformity of the battery.

[0092] One possible implementation is that the location of the first region is different from that of the second region, where the location is the positive electrode, negative electrode, or separator of the battery.

[0093] By setting different parameter values ​​for different locations, the overall impedance non-uniformity of the battery can be controlled, reducing process complexity. For example, it is not necessary to consider the impedance non-uniformity between different areas within a single location.

[0094] For example, the first region belongs to the negative electrode of the battery, and the second region belongs to the positive electrode of the battery. Alternatively, the first region may belong to the negative electrode of the battery, and the second region may belong to the battery separator. (This is repeated four times in the original text.)

[0095] One possible implementation is that the K sets of parameter values ​​include the first set of parameter values, where the first parameter value and the second parameter value in the first set of parameter values ​​are the same.

[0096] By setting the same first and second parameter values, there is no impedance difference between the first and second regions. In this case, the battery can be considered to be in a state of uniform impedance during the charging simulation. Under uniform impedance, the obtained negative electrode voltage change curve can be used as a reference curve, representing the battery's performance under ideal conditions. Based on the first set of parameter values, adjusting the second parameter value in the remaining parameter sets can introduce different impedance differences. By comparing the curves corresponding to the remaining K-1 sets of parameter values ​​with the reference curve corresponding to the first set of parameter values, the influence of impedance non-uniformity on the negative electrode voltage can be observed more clearly. By comparing the curves corresponding to different sets of parameter values, the influence of impedance non-uniformity on the negative electrode voltage can be quantified.

[0097] One possible implementation is that when the values ​​of the first parameter and the second parameter are different, the value of the second parameter is greater than the value of the first parameter.

[0098] By increasing the value of the second parameter to be greater than that of the first parameter, a clear impedance difference is introduced between the first and second regions of the battery. This allows for a clear observation of the impact of impedance non-uniformity on battery performance during simulation. Because the impedance of the second region is greater, the corresponding negative electrode voltage variation curve will be more pronounced, facilitating a more accurate capture and analysis of the effect of impedance non-uniformity on the battery's negative electrode voltage. Furthermore, the increased value of the second parameter may make lithium plating more significant, thus enabling a clearer correlation model between impedance non-uniformity and lithium plating.

[0099] Optionally, when the values ​​of the first and second parameters differ, the value of the second parameter should be less than the value of the first parameter. By making the value of the second parameter less than the value of the first parameter, a clear impedance difference is introduced between the first and second regions of the battery, allowing the impact of impedance non-uniformity on battery performance to be clearly observed during simulation. Since the impedance of the first region is greater, the change curve of the negative electrode voltage corresponding to the first region will be more significant, which helps to more accurately capture and analyze the impact of impedance non-uniformity on the negative electrode voltage of the battery.

[0100] One possible implementation is that when the values ​​of the first parameter and the second parameter are different, the value of the second parameter is T times the value of the first parameter, where T is greater than 1.

[0101] By setting the second parameter value to T times the first parameter value, the impedance difference between the first and second regions can be amplified, allowing for a clearer reflection of the impact of impedance non-uniformity on battery performance. Since the second parameter value is greater than the first parameter value, the impedance of the second region is higher, leading to a more significant change in the negative electrode voltage in this region. This significant voltage change helps to more easily capture and analyze the impact of impedance non-uniformity on battery performance, especially the minimum change in negative electrode voltage. During battery charging, impedance non-uniformity can lead to non-uniform current distribution within the battery, thus exacerbating lithium plating. By amplifying the impedance difference, the occurrence of lithium plating can be accelerated, allowing for faster observation and analysis of its impact on battery performance. Amplifying the impedance difference also allows for a clearer observation of the relationship between impedance non-uniformity and negative electrode voltage, facilitating the establishment of a more accurate model. Setting the second parameter value to T times the first parameter value significantly amplifies the impedance difference, accelerates the detection of lithium plating, improves simulation efficiency and model accuracy, and reduces the number of parameter value sets.

[0102] For example, the K parameter value groups include parameter value group 1, parameter value group 2, parameter value group 3 and parameter value group 4. The first parameter value and the second parameter value in parameter value group 1 are the same. The second parameter value in parameter value group 2 is 1.5 times the first parameter value. The second parameter value in parameter value group 3 is 2 times the first parameter value. The second parameter value in parameter value group 4 is 2.5 times the first parameter value.

[0103] The technical principles of method 100 are further described below.

[0104] The technical principle of Method 100 is mainly based on the following steps: 1. Obtain the standard curve set.

[0105] The standard curve set includes standard curve 1 and standard curve 2. Standard curve 1 shows the change in negative electrode voltage of battery A during charging, and standard curve 2 shows the change in negative electrode voltage of battery B during charging. Battery A and battery B are identical, but their impedances differ. Using the standard curve set, this application can determine the change in negative electrode voltage of the battery under non-uniform impedance conditions and construct an electrochemical simulation model based on this change in negative electrode voltage.

[0106] For a description of how to obtain the standard curve set, please refer to [link / reference needed]. Figure 2 .

[0107] Figure 2 This is a schematic diagram of battery A and battery B being charged in parallel. (Reference) Figure 2 Ammeter A1 measures the current of battery A as a function of its state of charge (SOC). Ammeter A2 measures the current of battery B as a function of SOC. Ammeter A3 measures the overall current change curve of batteries A and B when they are charged in parallel. Voltmeter V1 measures the terminal voltage of batteries A and B. Since batteries A and B are charged in parallel, the terminal voltages of batteries A and B are the same. Battery A is placed at ambient temperature 1, and battery B is placed at ambient temperature 2. Ambient temperature 1 is any temperature between -40℃ and 80℃, and ambient temperature 2 is any temperature between -40℃ and 80℃. Ambient temperature 1 and ambient temperature 2 are different. Because ambient temperature 1 and ambient temperature 2 are different, the impedance of battery A is different from that of battery B, and the curves of the negative terminal voltage change of battery A and battery B will also be different. With the above architecture, battery A and battery B can be regarded as different parts of a virtual battery. The impedance of the virtual battery is non-uniform, which is caused by the different ambient temperatures of the different parts of the virtual battery.

[0108] Figure 2 In this example, batteries A and B are identical, both being three-electrode batteries. A three-electrode battery is a battery structure that introduces a reference electrode into a traditional two-electrode battery (positive and negative electrodes), primarily used for studying electrochemical performance. The aforementioned reference electrode can be used to determine the negative electrode voltage. The reference electrode materials in batteries A and B can be one or more of the following: lithium sheet, lithium-plated copper wire, LixAl, LixSn, Li2Bi, Li4Ti5O12, LiFePO4, silver wire, and platinum wire; there are no restrictions on the choice.

[0109] Figure 3 This is a schematic diagram of standard curve 1 and standard curve 2. (Reference) Figure 3From top to bottom, the first curve represents the battery's terminal voltage change curve. Since batteries A and B are charged in parallel, their terminal voltage change trends are the same, therefore, the terminal voltage change curves of battery A and battery B overlap to form a single curve. The second curve is standard curve 1, and the third curve is standard curve 2. Standard curve 1 and standard curve 2 are different because batteries A and B are located at different ambient temperatures. By setting a temperature difference, the impedance of battery A and battery B are different; in other words, there is impedance non-uniformity within the virtual batteries corresponding to batteries A and B. This application obtains the aforementioned standard curve 1 and standard curve 2 by monitoring the voltage change between the reference electrode and negative electrode of batteries A and B as charging time, respectively.

[0110] 2. Determine the electrochemical simulation model.

[0111] The electrochemical simulation model can be used to simulate battery charging. A description of the geometric model of this electrochemical simulation model can be found in [link to relevant documentation]. Figure 4 The electrochemical simulation model is software-independent and can be used to calculate the partial differential equations of the battery within a pseudo-two-dimensional (P2D) model framework.

[0112] Figure 4 This is a geometric schematic diagram of an electrochemical simulation model. (Reference) Figure 4 The negative electrode of the virtual battery includes regions 1 and 2, the separator of the virtual battery includes regions 3 and 4, the positive electrode of the virtual battery includes regions 5 and 6, the copper side of the virtual battery serves as the grounding terminal, and the aluminum side of the virtual battery serves as the current inflow terminal. The double-headed arrows indicate that adjacent upper and lower regions cannot communicate with each other, and electrons and ions cannot pass through the boundary between the upper and lower regions. In other words, regions 1, 3, and 5 can be regarded as battery A, and regions 2, 4, and 6 can be regarded as battery B.

[0113] Figure 4 The electrochemical simulation model described herein can simulate the parallel charging of batteries A and B. This application allows for the setting of an initial set of model parameters, which is iterated to obtain a final set of model parameters. The final set of model parameters ensures that the curves obtained from the charging simulation are consistent with the standard curves, or that the error between them is less than an error threshold. The initial and final set of model parameters include the same model parameters, which include one or more of the following: membrane pore blockage rate, lithium salt concentration, compaction density, maximum lithium intercalation capacity, minimum lithium intercalation capacity, porosity, tortuosity, electrode thickness, electrochemical reaction rate constant, lithium-ion solid-phase diffusion coefficient, lithium-ion liquid-phase diffusion coefficient, and coating weight.

[0114] For a description of the initial and final model parameter sets, please refer to Table 1.

[0115]

[0116] In Table 1, for example, both the initial model parameter set and the final model parameter set include the maximum lithium intercalation amount of the positive electrode, the minimum lithium intercalation amount of the positive electrode, the maximum lithium intercalation amount of the negative electrode, the minimum lithium intercalation amount of the negative electrode, the porosity of the positive electrode, the porosity of the negative electrode, the tortuosity of the positive electrode, the tortuosity of the negative electrode, the thickness of the positive electrode, the thickness of the negative electrode, the electrochemical reaction rate constant of the positive electrode, the electrochemical reaction rate constant of the negative electrode, the solid-phase diffusion coefficient of lithium ions in the positive electrode, the solid-phase diffusion coefficient of lithium ions in the negative electrode, and the liquid-phase diffusion coefficient of lithium ions.

[0117] The values ​​of the model parameters in the initial model parameter set differ from those in the final model parameter set. Furthermore, the values ​​in the final model parameter set are obtained through iterative iterations of the values ​​in the initial model parameter set. For example, the maximum lithium intercalation capacity of the cathode is 0.9 in the initial model parameter set, but after continuous iterations, the final model parameter set shows a maximum lithium intercalation capacity of 0.85.

[0118] Using a set of standard curves, this application can iterate through the initial model parameter set to obtain the final model parameter set. After obtaining the final model parameter set, the above electrochemical simulation model is applied to a two-electrode battery, see [link to relevant documentation]. Figure 5 .

[0119] Figure 5 This is another geometric schematic diagram of an electrochemical simulation model. (Reference) Figure 5 ,and Figure 4 In contrast, adjacent upper and lower regions can communicate with each other, and electrons and ions can pass through the boundaries between regions.

[0120] In this embodiment of the application, the area ratio between different regions can be set arbitrarily. For example, the area ratio of region 1 to region 2 is any ratio between 1:0.1 and 100; the area ratio of region 3 to region 4 is any ratio between 1:0.1 and 100; and the area ratio of region 5 to region 6 is any ratio between 1:0.1 and 100.

[0121] based on Figure 5In the geometric schematic diagram described above, different parameter values ​​are set for any two regions to create a scenario of non-uniform impedance. For example, region 1 is set with parameter value a1, and region 2 is set with parameter value a2. Parameter values ​​a1 and a2 are different, and both parameter values ​​a1 and a2 correspond to the maximum lithium intercalation amount of the negative electrode. The negative electrode voltage change curves for region 1 and region 2 are measured respectively. For example, region 1 is set with parameter value b1, and region 6 is set with parameter value b2. Both parameter values ​​b1 and b2 correspond to porosity. The negative electrode voltage change curves for region 1 and region 6 are measured respectively. In this embodiment, a probe can be placed in region 2 to measure the negative electrode voltage change curve for region 6, and a probe can be placed in region 1 to measure the negative electrode voltage change curve for region 1. For example, region 3 is set with parameter value c1, and region 4 is set with parameter value c2. Parameter values ​​c1 and c2 both correspond to the membrane pore blockage rate. The negative electrode voltage change curves corresponding to region 3 and region 4 are measured respectively. In this embodiment, a probe is placed in region 2 to measure the negative electrode voltage change curve corresponding to region 4, and a probe is placed in region 1 to measure the negative electrode voltage change curve corresponding to region 3. For example, region 1 is set with parameter value d1, and region 3 is set with parameter value d2. Parameter values ​​d1 and d2 both correspond to temperature. The negative electrode voltage change curves corresponding to region 1 and region 3 are measured respectively. In this embodiment, a probe is placed in region 1 to measure the negative electrode voltage change curve corresponding to region 1, and a probe is placed in region 3 to measure the negative electrode voltage change curve corresponding to region 3.

[0122] Figure 6 This is a schematic diagram showing the change curves of the negative electrode voltage in regions 1 and 2, respectively. (Reference) Figure 6 From top to bottom, the first curve represents the battery's terminal voltage change curve. The terminal voltage change curves corresponding to region 1 and region 2 overlap to form a single curve. The second curve is the negative electrode voltage change curve corresponding to region 1, and the third curve is the negative electrode voltage change curve corresponding to region 2. Regions 1 and 2 are each set with different parameter values, and these different parameter values ​​correspond to different temperatures. Therefore, the negative electrode voltage change curves of regions 1 and 2 are different.

[0123] In this embodiment, multiple sets of parameter values ​​can be set and charging simulations can be performed separately to obtain multiple sets of curves. The minimum value of the negative electrode voltage is 0 as the observation standard to determine the parameter value corresponding to the minimum value of 0.

[0124] The following description uses specific examples. It should be noted that the content described below uses some of the parameters mentioned above as examples, rather than providing an example of all parameters. However, the processing logic involved in the content described below is general.

[0125] Example A1-Rct The values ​​of the first and second parameters both correspond to Rct. Rct can be understood as the combined effect of the aforementioned multiple or all parameters; that is, different combinations of parameters result in different Rct values.

[0126] For Region 1 (First Region) and Region 2 (Second Region), five sets of values ​​can be set, as shown in Table 2.

[0127] Table 2

[0128] As shown in Table 2: Group 0: The Rct of region 1 is 1Rct1, and the Rct of region 2 is 1Rct1; Group 1: The Rct of region 1 is 1Rct1, and the Rct of region 2 is 2Rct1; Group 2: The Rct of region 1 is 1Rct1, and the Rct of region 2 is 3Rct1; Group 3: The Rct of region 1 is 1Rct1, and the Rct of region 2 is 4Rct1; Group 4: The Rct of region 1 is 1Rct1, and the Rct of region 2 is 5Rct1.

[0129] Based on Table 2, the detection device uses the five sets of parameter values ​​to... Figure 5 The virtual batteries were subjected to charging simulations to obtain their respective sets of curves, which can be found in [reference needed]. Figure 7 .

[0130] Figure 7 This is a schematic diagram of a charging simulation. For example: Figure 7 (a) describes the charging condition selected by the detection device when performing charging simulation on the virtual battery. In this charging condition, the charging rate decreases stepwise with the increase of SOC. In the low SOC stage (0~0.4), a high rate and large current are used to achieve rapid energy replenishment. In the medium and high SOC stage (0.4~1.0), electrode polarization is controlled by step-down current reduction. While ensuring fast charging efficiency, the risk of lithium plating on the negative electrode is effectively suppressed. This is a typical charging strategy that balances fast charging performance and battery safety.

[0131] Figure 7(b) describes the current change curve corresponding to group 1. The current change curve in region 1 is different from that in region 2. During the full SOC charging stage, the safe charging rate corresponding to 2Rct1 is lower than that corresponding to normal Rct1, and the curve fluctuation is more obvious. During the full charge (SOC=1) stage, the current curve corresponding to 2Rct1 shows obvious current spikes, reflecting a higher risk of lithium plating.

[0132] Figure 7 In (c), from top to bottom, the first curve is the terminal voltage variation curve, and the terminal voltage variation curves corresponding to each group are the same. The second to sixth curves are the negative electrode voltage variation curves corresponding to Rct1, 2Rct1, 3Rct1, 4Rct1, and 5Rct1, respectively. Comparing the curves, it can be seen that the larger Rct is, the lower the corresponding minimum negative electrode voltage, the closer it is to the 0V lithium-ion deposition critical potential, and the higher the risk of lithium deposition. The negative electrode voltage corresponding to 5Rct1 is significantly lower than the negative electrode voltage corresponding to the baseline 1Rct. This result indicates that the uneven distribution of Rct inside the battery leads to significant differences in the negative electrode voltage in different regions, with lower negative electrode voltage and a higher risk of lithium deposition in high-impedance regions.

[0133] For a description of the minimum negative voltage in each negative voltage variation curve, please refer to Table 3.

[0134] Table 3

[0135] In Table 3, examples are provided: Group 0: The negative electrode voltage change curves corresponding to Region 1 and Region 2 are the same, and the minimum negative electrode voltage is 0.0169V for both.

[0136] Group 1: The final minimum negative voltage is 0.0057V, and the minimum change in negative voltage is -11.2mV.

[0137] Group 2: The final minimum negative voltage is -0.0042V, and the minimum change in negative voltage is -21.1mV.

[0138] Group 3: The final minimum negative voltage is -0.0109V, and the minimum change in negative voltage is -27.8mV.

[0139] Group 4: The final minimum negative voltage is -0.0139V, and the minimum change in negative voltage is -30.8mV.

[0140] Based on Table 3, the detection device can determine the relationship between the minimum change in negative electrode voltage and the Rct multiple. (See Table 3 for details.) Figure 8 .

[0141] Figure 8 This is a schematic diagram showing the relationship between the minimum change in negative electrode voltage and the multiple of Rct. (Reference) Figure 8 As the Rct factor increases, the minimum change value of the negative electrode voltage tends to increase. When the minimum change value of the negative electrode voltage is -16.9mV (i.e., the minimum negative electrode voltage is 0V), the corresponding Rct factor is between 2 and 3.

[0142] It should be noted that the charging conditions in Example A also apply to Examples A2 to A5 below.

[0143] Example A2—CW and PD For Region 1 (first region) and Region 2 (second region), two sets of parameter values ​​are set, as shown in Table 4.

[0144] Table 4

[0145] As shown in Table 4: Group A: CW of region 1 is 20 (mg / cm²), PD of region 2 is 3.4 (g / cm³), CW of region 2 is 20 (mg / cm²), PD of region 2 is 3.4 (g / cm³). Group B: CW of region 1 is 20 (mg / cm²), PD of region 2 is 3.4 (g / cm³), CW of region 2 is 20.004 (mg / cm²), and PD of region 2 is 3.43 (g / cm³).

[0146] Based on Table 4, the detection device can determine the appropriate parameters based on the two sets of parameter values. Figure 5 The virtual batteries were subjected to charging simulations to obtain their respective sets of curves, which can be found in [reference needed]. Figure 9 .

[0147] Figure 9 This is another schematic diagram of a charging simulation. An example is shown below: Figure 9 Figure (a) shows the current variation curves for group B. The current variation curves in region 1 and region 2 are different. As can be seen from the figure, during the full SOC charging stage, the safe charging rate for region 2 is lower than that for region 1, and the curve fluctuations are more obvious. During the full charge (SOC=1) stage, the current variation curve in region 2 shows a significant current spike, indicating a higher risk of lithium plating.

[0148] Figure 9(b) shows the negative electrode voltage change curve corresponding to group B. From top to bottom, the first curve is the terminal voltage change curve. The terminal voltage change curves corresponding to region 1 and region 2 are the same. The second to third curves are the negative electrode voltage change curves corresponding to region 1 (corresponding to the gray curve) and region 2 (corresponding to the black curve), respectively. In the fully charged stage (SOC=1), the negative electrode voltage corresponding to region 2 is significantly lower, and the risk of lithium plating is greater.

[0149] Figure 9 The results (a) and (b) show that the CW fluctuation deviation in the coating process can cause a certain risk of lithium plating, and the fluctuation tolerance of the coating process needs to be controlled.

[0150] Example A3—Temperature Difference For Region 1 (First Region) and Region 2 (First Region), a set of temperature differences is set, as shown in Table 5.

[0151] Table 5

[0152] As shown in Table 5, the temperature in region 1 is 53.5℃, and the temperature in region 2 is 28.4℃, with a temperature difference of 25.1℃ between them.

[0153] Based on Table 5, the detection device can perform... Figure 5 The virtual battery charging simulation described above can be found in [reference needed]. Figure 10 .

[0154] Figure 10 This is another schematic diagram of a charging simulation. An example is shown below: Figure 10 Figure (a) shows the current variation curve corresponding to Group 1. The current variation curves in Region 1 and Region 2 are different. As shown in the figure, during the full SOC charging stage, the safe charging rate corresponding to Region 2 is lower than that corresponding to Region 1, and the curve fluctuations are more obvious. During the full charge (SOC=1) stage, the current variation curve in Region 2 shows a significant current spike, indicating a higher risk of lithium plating.

[0155] Figure 10 Figure (b) shows the negative electrode voltage variation curves corresponding to group 1. From top to bottom, the first curve is the terminal voltage variation curve, and the second to third curves are the negative electrode voltage variation curves corresponding to region 1 and region 2, respectively. As can be seen from the figure, during the SOC charging stage, the negative electrode voltage in the low-temperature region is lower than that in the high-temperature region throughout the entire process, and the gap widens significantly in the high SOC stage (0.8~1.0). The negative electrode voltage in the low-temperature region is closer to the 0V critical potential for lithium ion deposition, and the risk of lithium deposition is significantly higher than that in the high-temperature region.

[0156] Figure 10 Results (a) and (b) demonstrate the risk of lithium plating caused by temperature difference. This application can support controlling the charging rate to optimize the temperature rise rate or controlling the flow rate of the heat dissipation medium to optimize the cooling effect, and keep the temperature difference within a safe range.

[0157] Example A4—Lithium Salt Concentration For Region 1 (first region) and Region 2 (second region), a set of lithium salt concentration differences is set, as shown in Table 6.

[0158] Table 6

[0159] As shown in Table 6, the lithium salt concentration in region 1 is 1100 (mol·L⁻¹). -1 The lithium salt concentration in region 2 is 800 (mol·L⁻¹). -1 The lithium salt concentration difference between the two is 300 (mol·L⁻¹). -1 ).

[0160] Based on Table 6, the detection device can perform... Figure 5 The virtual battery charging simulation described above can be found in [reference needed]. Figure 11 .

[0161] Figure 11 This is another schematic diagram of a charging simulation. An example is shown below: Figure 11 (a) shows the current change curve corresponding to group 1. The current change curve of region 1 is different from that of region 2. As can be seen from the figure, the safe charging rate that region 2 can withstand is higher than that that region 1 can withstand throughout the entire process, and the peak rate is also higher. The polarization of region 2 increases significantly in the medium and high SOC stage, and even a negative rate (current reversal) phenomenon occurs.

[0162] Figure 11 Figure (b) shows the negative electrode voltage variation curves corresponding to Group 1. From top to bottom, the first curve is the terminal voltage variation curve, and the second to third curves are the negative electrode voltage variation curves corresponding to Region 2 and Region 1, respectively. As can be seen from the figure, the two curves almost overlap in the low and medium SOC stage, but show obvious differentiation in the high SOC (0.8~1.0) stage: the negative electrode voltage of Region 1 is slightly lower than that of Region 2 throughout the entire process, and is closer to the 0V critical potential for lithium ion deposition, indicating a higher risk of lithium deposition.

[0163] Figure 11Results (a) and (b) demonstrate the deterioration of local anodic potential caused by uneven lithium salt concentration distribution. The greater the lithium salt concentration difference, the greater the risk of lithium plating. Through the above methods, this application can formulate standards for controlling uneven lithium salt concentration, such as controlling the size of the battery cell and the design value of the electrolyte lithium salt concentration.

[0164] Example A5—Coating Pore Clogging Rate For regions 3 (first region) and 4 (second region), a set of diaphragm pore blockage rate values ​​are set, as shown in Table 7.

[0165] Table 7

[0166] As shown in Table 7, the membrane pore-clogging rate in region 3 is 0.8, and the membrane pore-clogging rate in region 4 is 0.5. The difference between the two membrane pore-clogging rates is 0.3.

[0167] Based on Table 7, the detection device can perform... Figure 5 The virtual battery charging simulation described above can be found in [reference needed]. Figure 12 .

[0168] Figure 12 This is another schematic diagram of a charging simulation. An example is shown below: Figure 12 Figure (a) shows the current variation curve corresponding to group 1. The current variation curves in region 3 and region 4 are different. As shown in the figure, the uneven distribution of the membrane pore blockage rate leads to severe uneven current distribution inside the battery: region 4, due to its lower impedance, bears a much higher charging current than region 3 during the full SOC stage, becoming the main charging region; region 3, due to its higher impedance, experiences severe current shunting, and its charging capacity is continuously suppressed. The impedance difference between the two regions drives the current to shift from region 3 to region 4, forming an initial current imbalance; this current imbalance further leads to uneven lithium intercalation, causing the negative electrode potential of region 4 to drop rapidly, forming a potential difference with region 3; this potential difference drives the current to flow from region 3 to region 4 in the reverse direction.

[0169] Figure 12 (b) shows the negative electrode voltage change curves corresponding to regions 3 and 4 respectively. The negative electrode voltage change curves corresponding to region 1 and region 2 are different. During the full SOC charging stage, the negative electrode voltage of region 3 is lower than that of region 4 throughout the entire process, and the difference is significant in the medium and high SOC stages. The negative electrode voltage of region 3 is closer to the 0V critical potential for lithium ion deposition, and the risk of lithium deposition is significantly higher.

[0170] Figure 12Results (a) and (b) demonstrate the local deterioration of anodic potential caused by uneven distribution of membrane pore blockage rate. The greater the difference in membrane pore blockage rate, the greater the risk of lithium plating. Through the above method, this application can formulate control standards for uneven membrane pore blockage rate, such as controlling the application method of membrane coating, such as optimizing spraying to dot coating.

[0171] The above examples are described with the premise that regions 1 and 2 are both located at the negative electrode or regions 3 and 4 are both located at the membrane. However, the content can also be applied to scenarios where different regions are located in different positions.

[0172] The detection method of this application embodiment has been described above; the detection apparatus of this application embodiment is described below. The detection apparatus can execute detection method 100.

[0173] Figure 13 A schematic block diagram of a detection device according to an embodiment of this application is shown. Figure 13 As shown, the detection device includes a processing unit 1310, which is used to: perform charging simulation on the battery according to K sets of parameter values ​​to obtain K sets of curves. Each set of parameter values ​​in the K sets includes the same first parameter value and different second parameter values. The first parameter value and the second parameter value correspond to the same parameter, which is related to the impedance of the battery. The first parameter value is applied to the first region of the battery, and the second parameter value is applied to the second region of the battery. K is greater than or equal to 2. Each set of curves in the K sets includes the same first curve and different second curves. The first curve is the change curve of the negative electrode voltage corresponding to the first region, and the second curve is the change curve of the negative electrode voltage corresponding to the second region. Based on the K sets of curves, determine K minimum negative electrode voltage values ​​and K second parameter values ​​corresponding to each of the K minimum negative electrode voltage values. Based on the K minimum negative electrode voltage values ​​and the K second parameter values, determine the correlation between the minimum negative electrode voltage values ​​and the second parameter values, and determine the second parameter value corresponding to the minimum negative electrode voltage value equal to a first threshold value based on the correlation.

[0174] The detection device can perform the corresponding operations in detection method 100, which will not be described in detail here for the sake of brevity.

[0175] Figure 14 A schematic diagram of the hardware structure of a detection device according to an embodiment of this application is shown. The detection device includes a memory 1401, a processor 1402, a communication interface 1403, and a bus 1404. The memory 1401, the processor 1402, and the communication interface 1403 are interconnected via the bus 1404.

[0176] The memory 1401 can be a read-only memory (ROM), a static storage device, or a random access memory (RAM). The memory 1401 can store programs, and when the program stored in the memory 1401 is executed by the processor 1402, the processor 1402 and the communication interface 1403 are used to execute the various steps of the detection method 100.

[0177] The processor 1402 may be a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), graphics processing unit (GPU), or one or more integrated circuits, for executing related programs to achieve the functions required by the units in the apparatus of this application embodiment, or to execute the detection method 100.

[0178] The processor 1402 can also be an integrated circuit chip with signal processing capabilities. During implementation, each step of the detection method 100 can be completed through integrated logic circuits in the processor 1402 or through software instructions.

[0179] Processor 1402 can also be a general-purpose processor, digital signal processor (DSP), ASIC, field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly implemented by the hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 1401. Processor 1402 reads information from memory 1401 and, in conjunction with its hardware, completes the functions required by the units included in the detection device of the embodiments of this application, or executes detection method 100.

[0180] The communication interface 1403 uses a transceiver device, such as, but not limited to, a transceiver, to enable communication between the detection device and other devices or communication networks.

[0181] Bus 1404 may include a pathway for transmitting information between various components of the detection device (e.g., memory 1401, processor 1402, communication interface 1403).

[0182] Although Figure 14 The detection device described only shows the memory, processor, and communication interface; however, those skilled in the art should understand that in actual implementation, Figure 14 The detection device may also include other components necessary for normal operation. Furthermore, those skilled in the art should understand, depending on specific needs, Figure 14 The detection device may also include hardware components to implement other additional functions. Furthermore, those skilled in the art should understand that... Figure 14 The detection device may also include only the components necessary to implement this application, and need not include... Figure 14 All the devices shown.

[0183] This application also provides a computer-readable storage medium for storing a computer program for performing the methods described in the various embodiments of this application.

[0184] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0185] This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the above-described detection method.

[0186] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features, but these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A detection method, characterized in that, include: Charging simulations were performed on the battery based on K sets of parameter values, resulting in K sets of curves. Each set of parameter values ​​in the K sets includes the same first parameter value and different second parameter values. The first parameter value and the second parameter value correspond to the same parameter, which is related to the impedance of the battery. The first parameter value is applied to the first region of the battery, and the second parameter value is applied to the second region of the battery. K is greater than or equal to 2. Each set of curves in the K sets includes the same first curve and different second curves. The first curve is the change curve of the negative electrode voltage corresponding to the first region, and the second curve is the change curve of the negative electrode voltage corresponding to the second region. Based on the K sets of curves, determine the K minimum negative electrode voltage values ​​and the K corresponding values ​​of the second parameter for each of the K minimum negative electrode voltage values; The correlation between the minimum negative electrode voltage and the second parameter values ​​is determined based on the K minimum negative electrode voltage values ​​and the K second parameter values, and the second parameter value corresponding to the minimum negative electrode voltage being equal to the first threshold value is determined based on the correlation.

2. The method according to claim 1, characterized in that, The type of the parameter is either the battery's process parameter or the battery's operating parameter.

3. The method according to claim 2, characterized in that, The battery's process parameters include any one or more of the following: Membrane pore blockage rate, lithium salt concentration, compaction density, maximum lithium intercalation, minimum lithium intercalation, porosity, tortuosity, electrode thickness, electrochemical reaction rate constant, lithium ion solid-phase diffusion coefficient, lithium ion liquid-phase diffusion coefficient, or coating weight.

4. The method according to claim 2 or 3, characterized in that, The battery's operating parameters include temperature difference or the difference in deformation rate between different regions of the separator.

5. The method according to any one of claims 1 to 3, characterized in that, The first threshold is less than or equal to 0.

6. The method according to any one of claims 1 to 3, characterized in that, The first region and the second region both correspond to the negative electrode of the battery, or the first region and the second region both correspond to the positive electrode of the battery, or the first region and the second region both correspond to the separator of the battery.

7. The method according to any one of claims 1 to 3, characterized in that, The location of the first region is different from the location of the second region, wherein the location is the positive electrode, negative electrode, or separator of the battery.

8. The method according to any one of claims 1 to 3, characterized in that, The K sets of parameter values ​​include the first set of parameter values, where the first parameter value and the second parameter value in the first set of parameter values ​​are the same.

9. The method according to claim 8, characterized in that, When the values ​​of the first parameter and the second parameter are different, the value of the second parameter is greater than the value of the first parameter.

10. The method according to claim 8, characterized in that, When the values ​​of the first parameter and the second parameter are different, the value of the second parameter is T times the value of the first parameter, where T is greater than 1.

11. A detection device, characterized in that, Includes a processing unit, the processing unit being used for: Charging simulations were performed on the battery based on K sets of parameter values, resulting in K sets of curves. Each set of parameter values ​​in the K sets includes the same first parameter value and different second parameter values. The first parameter value and the second parameter value correspond to the same parameter, which is related to the impedance of the battery. The first parameter value is applied to the first region of the battery, and the second parameter value is applied to the second region of the battery. K is greater than or equal to 2. Each set of curves in the K sets includes the same first curve and different second curves. The first curve is the change curve of the negative electrode voltage corresponding to the first region, and the second curve is the change curve of the negative electrode voltage corresponding to the second region. Based on the K sets of curves, determine the K minimum negative electrode voltage values ​​and the K corresponding values ​​of the second parameter for each of the K minimum negative electrode voltage values; The correlation between the minimum negative electrode voltage and the second parameter values ​​is determined based on the K minimum negative electrode voltage values ​​and the K second parameter values, and the second parameter value corresponding to the minimum negative electrode voltage being equal to the first threshold value is determined based on the correlation.

12. The apparatus according to claim 11, characterized in that, The type of the parameter is either the battery's process parameter or the battery's operating parameter.

13. The apparatus according to claim 12, characterized in that, The battery's process parameters include any one or more of the following: Membrane pore blockage rate, lithium salt concentration, compaction density, maximum lithium intercalation, minimum lithium intercalation, porosity, tortuosity, electrode thickness, electrochemical reaction rate constant, lithium ion solid-phase diffusion coefficient, lithium ion liquid-phase diffusion coefficient, or coating weight.

14. The apparatus according to claim 12 or 13, characterized in that, The battery's operating parameters include temperature difference or the difference in deformation rate between different regions of the separator.

15. The apparatus according to any one of claims 11 to 13, characterized in that, The first threshold is less than or equal to 0.

16. The apparatus according to any one of claims 11 to 13, characterized in that, The first region and the second region both correspond to the negative electrode of the battery, or the first region and the second region both correspond to the positive electrode of the battery, or the first region and the second region both correspond to the separator of the battery.

17. The apparatus according to any one of claims 11 to 13, characterized in that, The location of the first region is different from the location of the second region, wherein the location is the positive electrode, negative electrode, or separator of the battery.

18. The apparatus according to any one of claims 11 to 13, characterized in that, The K sets of parameter values ​​include the first set of parameter values, where the first parameter value and the second parameter value in the first set of parameter values ​​are the same.

19. The apparatus according to claim 18, characterized in that, When the values ​​of the first parameter and the second parameter are different, the value of the second parameter is greater than the value of the first parameter.

20. The apparatus according to claim 18, characterized in that, When the values ​​of the first parameter and the second parameter are different, the value of the second parameter is T times the value of the first parameter, where T is greater than 1.

21. A detection device, characterized in that, include: Memory, used to store programs; A processor for executing a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method of any one of claims 1 to 10.

22. A computer-readable storage medium, characterized in that, Used to store a computer program that causes a computer to perform the method according to any one of claims 1 to 10.

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