Battery lithium precipitation data analysis method and device, and storage medium

Abstract

The application discloses a battery lithium precipitation data analysis method and device and a storage medium. The method comprises the following steps: determining a discharge parameter and a lithium precipitation electric control parameter used for performing a lithium precipitation electric control operation on a to-be-tested battery. The lithium precipitation electric control parameter comprises a detection current used for performing the lithium precipitation electric control operation and at least three lithium precipitation analysis nodes. According to the detection current and the discharge parameter, the lithium precipitation electric control operation is performed on the to-be-tested battery at each lithium precipitation analysis node, and lithium precipitation electric control data corresponding to each lithium precipitation analysis node is obtained. According to all lithium precipitation electric control data and a current capacity corresponding to each lithium precipitation analysis node, sub-analysis data corresponding to each lithium precipitation analysis node in the to-be-tested battery is determined. Then, lithium precipitation-capacity information of the to-be-tested battery is determined. It can be seen that the application can realize the analysis of the cycle life of the battery, reduce the loss of the battery lithium precipitation detection, and improve the analysis efficiency and accuracy of the battery lithium precipitation and capacity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery analysis, in particular to a battery lithium precipitation data analysis method, device and storage medium. BACKGROUND

[0002] During the process of large-rate charging and discharging or high-temperature cycle aging of the battery, with the increase of the cycle number, lithium precipitation and lithium dendrite formation will occur in the negative electrode of the battery, resulting in the decrease of the capacity retention rate of the battery. The lithium precipitation of the battery will affect the normal use, and the battery with excessive lithium precipitation will also cause safety problems. Therefore, detecting the lithium precipitation of the battery has become a technical hotspot in the industry.

[0003] At present, the method for detecting the lithium precipitation of the battery mainly includes disassembling the full battery, analyzing the interface of the electrode sheet, or analyzing the V-Q graph and voltage differential curve dV / dQ-Q graph in the cycle aging of the battery to determine the capacity attenuation reason of the lithium ion battery. However, the three-electrode battery is not easy to make, and disassembling the battery and making the three-electrode battery will cause damage to the battery itself, and also consume a lot of manpower and material resources. Therefore, it is particularly important to provide a method for detecting the lithium precipitation of the battery without disassembling the battery and reducing the damage caused by the detection of the lithium precipitation of the battery. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a battery lithium precipitation data analysis method, device and storage medium, which can detect the relationship between the lithium precipitation and the capacity retention rate of the battery without disassembling the battery, and reduce the damage caused by the detection of the lithium precipitation of the battery.

[0005] In order to solve the above technical problems, the present application discloses a battery lithium precipitation data analysis method, which comprises:

[0006] determining a discharge parameter and a lithium precipitation electric control parameter for performing a lithium precipitation electric control operation on a to-be-tested battery, the lithium precipitation electric control parameter comprising a detection current adopted for performing the lithium precipitation electric control operation and at least three lithium precipitation analysis nodes;

[0007] performing the lithium precipitation electric control operation on the to-be-tested battery at each lithium precipitation analysis node according to the detection current and the discharge parameter, to obtain lithium precipitation electric control data corresponding to each lithium precipitation analysis node; the lithium precipitation electric control data corresponding to each lithium precipitation analysis node comprises a cumulative capacity corresponding to the lithium precipitation analysis node and a target voltage, the cumulative capacity being a changed capacity accumulated by the to-be-tested battery in completing a charge-discharge operation matched with the discharge parameter at the lithium precipitation analysis node, and the target voltage being a voltage corresponding to the to-be-tested battery at the lithium precipitation analysis node when performing the lithium precipitation electric control operation;

[0008] determine target lithium precipitation analysis data corresponding to the battery under test according to all the lithium precipitation control data and the current capacity corresponding to each lithium precipitation analysis node, wherein the target lithium precipitation analysis data comprises sub-analysis data corresponding to each lithium precipitation analysis node;

[0009] determine lithium precipitation-capacity information of the battery under test according to the sub-analysis data corresponding to each lithium precipitation analysis node, wherein the lithium precipitation-capacity information is used to determine association information between battery capacity attenuation and lithium precipitation of the battery under test.

[0010] As an optional implementation, in the first aspect of the present application, the lithium precipitation analysis nodes comprise at least an initial node, an intermediate node and a terminal node, wherein the initial node is a node before the battery under test is initially started for a cyclic charge-discharge operation; the intermediate node is a node with a cycle number of the cyclic charge-discharge operation performed on the battery under test greater than or equal to a preset cycle number, and the number of the intermediate nodes is greater than or equal to 1; and the terminal node is a node corresponding to a current capacity of the battery under test less than or equal to a preset deadline capacity.

[0011] The sub-analysis data corresponding to each lithium precipitation analysis node comprises a node voltage and a node cumulative capacity of the lithium precipitation analysis node.

[0012] As an optional implementation, in the first aspect of the present application, the determination of the target lithium precipitation analysis data corresponding to the battery under test according to all the lithium precipitation control data and the current capacity corresponding to each lithium precipitation analysis node comprises:

[0013] For each lithium precipitation analysis node, a node battery capacity of the battery under test at the lithium precipitation analysis node is determined; a node cumulative capacity corresponding to the lithium precipitation analysis node is determined from all the lithium precipitation control data, and a quotient of the node cumulative capacity and the node battery capacity is calculated to obtain node SOC data corresponding to the lithium precipitation analysis node.

[0014] For each lithium precipitation analysis node, voltage-capacity analysis data corresponding to the lithium precipitation analysis node is determined according to the node SOC data, the node voltage and the node cumulative capacity corresponding to the lithium precipitation analysis node, and the voltage-capacity analysis data of all the lithium precipitation analysis nodes is determined as the target lithium precipitation analysis data corresponding to the battery under test.

[0015] As an optional implementation, in the first aspect of the present application, the determination of the lithium precipitation-capacity information of the battery under test according to the sub-analysis data corresponding to each lithium precipitation analysis node comprises:

[0016] According to the sub-analysis data corresponding to each of the lithium precipitation analysis nodes, determine the phase change characteristic peak corresponding to each of the lithium precipitation analysis nodes and the characteristic peak information corresponding to the phase change characteristic peak, the phase change characteristic peak includes the positive electrode material phase change characteristic peak, the negative electrode material phase change characteristic peak and the negative electrode phase change characteristic peak at low SOC, the negative electrode phase change characteristic peak at low SOC is the phase change characteristic peak corresponding to the SOC value lower than the preset charge threshold;

[0017] Take the phase change characteristic peak and the characteristic peak information corresponding to the initial node as the reference, compare the phase change characteristic peak and the characteristic peak information corresponding to the remaining nodes except the initial node to obtain the offset information of each phase change characteristic peak in each remaining node, the remaining nodes include the intermediate node and the terminal node;

[0018] Analyze all the offset information of each of the remaining nodes to obtain the characteristic peak offset information corresponding to the battery under test as the lithium precipitation-capacity information of the battery under test.

[0019] As an optional implementation, in the first aspect of the present application, the initial start cycle charging and discharging operation of the battery under test includes:

[0020] According to the cell data of the battery under test, determine the cycle electric control parameter corresponding to the cycle charging and discharging operation performed on the battery under test, the cell data includes the battery model and the battery nominal capacity of the battery under test, and the cycle electric control parameter includes at least one of the charging and discharging mode, the cycle current parameter, the charging and discharging interval and the data acquisition interval;

[0021] According to the cycle electric control parameter, perform the cycle charging and discharging operation on the battery under test, wherein the cycle number of the battery under test is updated corresponding to each execution of the cycle charging and discharging operation.

[0022] As an optional implementation, in the first aspect of the present application, the analysis of all the offset information of each of the remaining nodes to obtain the characteristic peak offset information corresponding to the battery under test includes:

[0023] Obtain the same type battery historical data, the same type battery historical data is the lithium precipitation-capacity information corresponding to the same type battery obtained by analyzing the same type battery after performing the lithium precipitation electric control operation on the same type battery;

[0024] For all the offset information of each of the remaining nodes, determine the historical offset information matching all the offset information of the remaining nodes from the same type battery historical data, and the historical offset information of each of the remaining nodes includes the historical characteristic peak information corresponding to each of the characteristic peak information in the remaining node;

[0025] for each of the offset information of each of the remaining nodes, calculating an information difference value between each of the characteristic peak information of the remaining node and the historical characteristic peak information corresponding to the characteristic peak information, to obtain a difference value set of the remaining node, the difference value set of the remaining node including the information difference value between each of the characteristic peak information of the remaining node and the historical characteristic peak information corresponding to the characteristic peak information;

[0026] when it is determined that there is no abnormal difference value in all the difference value sets, determining all the offset information of each of the remaining nodes, the phase transition characteristic peak corresponding to the initial node and the characteristic peak information as the characteristic peak offset information corresponding to the battery to be tested, the abnormal difference value being a value that is not within the standard difference value interval corresponding to the information difference value.

[0027] As an optional implementation, in the first aspect of the present application, when it is determined that there is the abnormal difference value in all the difference value sets, the method further comprises:

[0028] repeating the lithium extraction automatic control operation on the to-be-corrected node corresponding to the abnormal difference value based on a preset correction number, wherein, within any of the preset correction numbers, when it is determined that the information difference value corresponding to the to-be-corrected node is within the standard difference value interval corresponding to the information difference value, updating the characteristic peak information corresponding to the abnormal difference value of the to-be-corrected node to the characteristic peak information corresponding to the current information difference value;

[0029] when the number of times of performing the lithium extraction automatic control operation is greater than the preset correction number, and each of the information difference values obtained after performing the lithium extraction automatic control operation on the to-be-corrected node is the abnormal difference value, generating abnormal information for the to-be-corrected node and removing the to-be-corrected node from all the remaining nodes.

[0030] The second aspect of the present application discloses a battery lithium extraction data analysis device, the device comprises:

[0031] a first determination module for determining a lithium extraction automatic control parameter for performing a lithium extraction automatic control operation on the battery to be tested, the lithium extraction automatic control parameter including a detection current used for performing the lithium extraction automatic control operation and at least three lithium extraction analysis nodes;

[0032] The lithium precipitation analysis module is configured to perform the lithium precipitation control operation on the battery under test at each of the lithium precipitation analysis nodes according to the detection current and the discharge parameter, to obtain lithium precipitation control data corresponding to each of the lithium precipitation analysis nodes; the lithium precipitation control data corresponding to each of the lithium precipitation analysis nodes includes accumulated capacity and a target voltage of the lithium precipitation analysis node, the accumulated capacity being a variation capacity accumulated by the battery under test at the lithium precipitation analysis node when completing a charge-discharge operation matching the discharge parameter, and the target voltage being a voltage corresponding to the battery under test at the lithium precipitation analysis node when the lithium precipitation control operation is performed;

[0033] The first determination module is further configured to determine target lithium precipitation analysis data corresponding to the battery under test according to all the lithium precipitation control data and current capacity corresponding to each of the lithium precipitation analysis nodes, the target lithium precipitation analysis data including sub-analysis data corresponding to each of the lithium precipitation analysis nodes.

[0034] The second determination module is configured to determine lithium precipitation-capacity information of the battery under test according to the sub-analysis data corresponding to each of the lithium precipitation analysis nodes, the lithium precipitation-capacity information being used to determine association information between battery capacity attenuation and lithium precipitation of the battery under test.

[0035] As an optional implementation, in the second aspect of the present application, the lithium precipitation analysis nodes include at least an initial node, an intermediate node, and a termination node, the initial node being a node before the battery under test is initially started for a cycle charge-discharge operation, the intermediate node being a node with a cycle number of the cycle charge-discharge operation performed on the battery under test being greater than or equal to a preset cycle number, and the number of intermediate nodes being greater than or equal to 1, and the termination node being a node corresponding to a current capacity of the battery under test being less than or equal to a preset deadline capacity.

[0036] The sub-analysis data corresponding to each of the lithium precipitation analysis nodes includes node voltage and node accumulated capacity of the lithium precipitation analysis node.

[0037] As an optional implementation, in the second aspect of the present application, the first determination module determines the target lithium precipitation analysis data corresponding to the battery under test according to all the lithium precipitation control data and current capacity corresponding to each of the lithium precipitation analysis nodes in the following manner:

[0038] For each of the lithium precipitation analysis nodes, a node battery capacity of the battery under test at the lithium precipitation analysis node is determined; a node accumulated capacity corresponding to the lithium precipitation analysis node is determined from all the lithium precipitation control data, and a quotient of the node accumulated capacity and the node battery capacity is calculated to obtain node SOC data corresponding to the lithium precipitation analysis node.

[0039] For each of the lithium precipitation analysis nodes, according to the node SOC data, the node voltage and the node cumulative capacity corresponding to the lithium precipitation analysis node, determine the voltage-capacity analysis data corresponding to the lithium precipitation analysis node, and determine the voltage-capacity analysis data of all the lithium precipitation analysis nodes as the target lithium precipitation analysis data corresponding to the battery under test.

[0040] As an optional implementation, in the second aspect of the present application, the manner in which the second determination module determines the lithium precipitation-capacity information of the battery under test according to the sub-analysis data corresponding to each of the lithium precipitation analysis nodes specifically comprises:

[0041] According to the sub-analysis data corresponding to each of the lithium precipitation analysis nodes, determine the phase transition characteristic peak corresponding to each of the lithium precipitation analysis nodes and the characteristic peak information corresponding to the phase transition characteristic peak, the phase transition characteristic peak includes the positive electrode material phase transition characteristic peak, the negative electrode material phase transition characteristic peak and the negative electrode phase transition characteristic peak at low SOC, the negative electrode phase transition characteristic peak at low SOC is the phase transition characteristic peak corresponding to the SOC value lower than the preset charge threshold;

[0042] Take the phase transition characteristic peak and the characteristic peak information corresponding to the initial node as a reference, compare the phase transition characteristic peak and the characteristic peak information corresponding to the remaining nodes except the initial node to obtain the offset information of each phase transition characteristic peak in each of the remaining nodes, the remaining nodes include the intermediate node and the termination node;

[0043] Analyze all the offset information of each of the remaining nodes to obtain the characteristic peak offset information corresponding to the battery under test as the lithium precipitation-capacity information of the battery under test.

[0044] As an optional implementation, in the second aspect of the present application, the initial start-up cycle charging and discharging operation of the battery under test comprises:

[0045] According to the cell data of the battery under test, determine the cycle control parameter corresponding to the execution of the cycle charging and discharging operation on the battery under test, the cell data includes the battery model and the battery nominal capacity of the battery under test, the cycle control parameter includes at least one of the charging and discharging mode, the cycle current parameter, the charging and discharging interval and the data acquisition interval;

[0046] According to the cycle control parameter, execute the cycle charging and discharging operation on the battery under test, wherein, corresponding to the update of the cycle number of the battery under test, execute the cycle charging and discharging operation once.

[0047] As an optional implementation, in the second aspect, the second determining module analyzes all the offset information of each of the remaining nodes to obtain the characteristic peak offset information corresponding to the battery under test in the following manner:

[0048] obtaining homotype battery historical data, the homotype battery historical data being the lithium precipitation-capacity information corresponding to a homotype battery obtained by analyzing the homotype battery after performing the lithium precipitation controlled operation on the homotype battery;

[0049] For all the offset information of each of the remaining nodes, determining historical offset information matching all the offset information of the remaining nodes from the homotype battery historical data, the historical offset information of each of the remaining nodes including historical characteristic peak information corresponding to each of the characteristic peak information in the remaining node;

[0050] For each of the offset information of each of the remaining nodes, calculating an information difference value between each of the characteristic peak information of the remaining node and the historical characteristic peak information corresponding to the characteristic peak information to obtain a difference value set of the remaining node, the difference value set of the remaining node including the information difference value between each of the characteristic peak information of the remaining node and the historical characteristic peak information corresponding to the characteristic peak information;

[0051] When it is determined that there is no abnormal difference value in all the difference value sets, determining all the offset information of each of the remaining nodes, the phase transition characteristic peak corresponding to the initial node, and the characteristic peak information as the characteristic peak offset information corresponding to the battery under test, the abnormal difference value being a value that is not within a standard difference value interval corresponding to the information difference value.

[0052] As an optional implementation, in the second aspect, the second determining module is further configured to:

[0053] When it is determined that there is the abnormal difference value in all the difference value sets, repeatedly performing the lithium precipitation controlled operation on a to-be-corrected node corresponding to the abnormal difference value based on a preset correction number, wherein, within any of the preset correction numbers, when it is determined that the information difference value corresponding to the to-be-corrected node is within a standard difference value interval corresponding to the information difference value, updating the characteristic peak information corresponding to the abnormal difference value to the characteristic peak information corresponding to the current information difference value for the to-be-corrected node;

[0054] When the number of times of performing the lithium precipitation controlled operation is greater than the preset correction number, and each of the information difference values obtained after performing the lithium precipitation controlled operation on the to-be-corrected node is the abnormal difference value, generating abnormal information for the to-be-corrected node and removing the to-be-corrected node from all the remaining nodes.

[0055] The third aspect of the present application discloses another battery lithium precipitation data analysis device, the device comprises:

[0056] A memory storing executable program code;

[0057] A processor coupled to the memory;

[0058] The processor calls the executable program code stored in the memory to execute the battery lithium precipitation data analysis method disclosed in the first aspect of the present application.

[0059] The fourth aspect of the present application discloses a computer readable storage medium, the computer readable storage medium stores computer instructions, the computer instructions are called to execute the battery lithium precipitation data analysis method disclosed in the first aspect of the present application.

[0060] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0061] In the embodiments of the present application, a battery lithium precipitation data analysis method, device and storage medium are provided, the method comprising: determining a discharge parameter and a lithium precipitation electric control parameter for performing a lithium precipitation electric control operation on a to-be-tested battery, the lithium precipitation electric control parameter comprising a detection current used for performing the lithium precipitation electric control operation and at least three lithium precipitation analysis nodes; performing the lithium precipitation electric control operation on the to-be-tested battery at each lithium precipitation analysis node according to the detection current and the discharge parameter to obtain lithium precipitation electric control data corresponding to each lithium precipitation analysis node; the lithium precipitation electric control data corresponding to each lithium precipitation analysis node comprising a cumulative capacity corresponding to the lithium precipitation analysis node and a target voltage, the cumulative capacity being a changed capacity accumulated by the to-be-tested battery completing a charge-discharge operation matched with the discharge parameter at the lithium precipitation analysis node, and the target voltage being a voltage corresponding to the to-be-tested battery at the lithium precipitation analysis node when the lithium precipitation electric control operation is performed; determining target lithium precipitation analysis data corresponding to the to-be-tested battery according to all lithium precipitation electric control data and a current capacity corresponding to each lithium precipitation analysis node, the target lithium precipitation analysis data comprising sub-analysis data corresponding to each lithium precipitation analysis node; and determining lithium-capacity information of the to-be-tested battery according to the sub-analysis data corresponding to each lithium precipitation analysis node, the lithium-capacity information being used to determine associated information between a battery capacity attenuation and a lithium precipitation condition of the to-be-tested battery. It can be seen that, by implementing the present application, the lithium precipitation electric control parameter can be automatically determined, the lithium precipitation electric control operation can be intelligently performed on the to-be-tested battery at the determined multiple lithium precipitation analysis nodes, the lithium precipitation electric control data at each lithium precipitation analysis node can be automatically collected and analyzed, the collection efficiency, analysis efficiency and analysis accuracy of the lithium precipitation electric control data are improved, the sub-analysis data of each lithium precipitation analysis node in the target lithium precipitation analysis data is further analyzed in detail, the lithium-capacity information is finally determined, the lossless lithium precipitation analysis of battery aging is realized without disassembling the battery, and the loss caused by analyzing the lithium precipitation condition of the battery is reduced. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart illustrating a battery lithium plating data analysis method disclosed in an embodiment of the present invention;

[0064] Figure 2 This is a flowchart illustrating another battery lithium plating data analysis method disclosed in an embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram of the structure of a battery lithium plating data analysis device disclosed in an embodiment of the present invention;

[0066] Figure 4 This is a schematic diagram of another battery lithium plating data analysis device disclosed in an embodiment of the present invention;

[0067] Figure 5 This is a dV / dQ-SOC curve of the voltage and capacity of the battery under test at a charge-discharge current of 0.04C, as disclosed in an embodiment of the present invention. Detailed Implementation

[0068] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0070] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combinable with other embodiments.

[0071] The application discloses a battery lithium precipitation data analysis method and device and a storage medium, which can automatically determine lithium precipitation control parameters, intelligently perform lithium precipitation control operations on a to-be-tested battery at determined lithium precipitation analysis nodes, automatically collect and analyze lithium precipitation control data at each lithium precipitation analysis node, improve the collection efficiency, analysis efficiency and analysis accuracy of lithium precipitation control data, further refine analysis of sub-analysis data of each lithium precipitation analysis node in target lithium precipitation analysis data, and finally determine lithium precipitation-capacity information, so that loss caused by analysis of battery lithium precipitation is reduced.

[0072] Embodiment one

[0073] Please refer to Figure 1 , Figure 1 is a flowchart of a battery lithium precipitation data analysis method disclosed by the embodiment of the application. In the flowchart, Figure 1 The battery lithium precipitation data analysis method described in the embodiment of the application can be applied to a battery lithium precipitation data analysis device, and the embodiment of the application is not limited. As shown in Figure 1 The battery lithium precipitation data analysis method can include the following operations:

[0074] 101, determine discharge parameters and lithium precipitation control parameters for performing lithium precipitation control operations on a to-be-tested battery.

[0075] In the embodiment of the application, the lithium precipitation control parameters include a detection current used for performing lithium precipitation control operations and at least three lithium precipitation analysis nodes.

[0076] 102, perform lithium precipitation control operations on the to-be-tested battery at each lithium precipitation analysis node according to the detection current and the discharge parameters, to obtain lithium precipitation control data corresponding to each lithium precipitation analysis node.

[0077] In the embodiment of the application, the lithium precipitation control data corresponding to each lithium precipitation analysis node includes cumulative capacity and a target voltage corresponding to the lithium precipitation analysis node, the cumulative capacity is the accumulated change capacity of the to-be-tested battery at the lithium precipitation analysis node after completing a charge-discharge operation matched with the discharge parameters, and the target voltage is the voltage corresponding to the to-be-tested battery at the lithium precipitation analysis node when the lithium precipitation control operation is performed.

[0078] In the embodiment of the present application, 500 is taken as the cycle number, and 0.04C 100% DOD is performed once at 25 DEG C every 500 cycles of the battery cycle aging, wherein the detection current is 0.04C, and the discharge parameter is 100% DOD. Since the rate used in the lithium precipitation control is very small each time, and the temperature is room temperature, the concentration polarization of the battery is almost not generated, and the structure of the battery to be tested is not damaged.

[0079] 103. Determine the target lithium precipitation analysis data corresponding to the battery to be tested according to all lithium precipitation controlled data and the current capacity corresponding to each lithium precipitation analysis node.

[0080] In the embodiment of the present application, the target lithium precipitation analysis data includes the sub-analysis data corresponding to each lithium precipitation analysis node.

[0081] In the embodiment of the present application, optionally, step 103 includes the following steps.

[0082] For each lithium precipitation analysis node, determine the node battery capacity of the battery to be tested at the lithium precipitation analysis node; determine the node cumulative capacity corresponding to the lithium precipitation analysis node from all lithium precipitation controlled data, and calculate the quotient of the node cumulative capacity and the node battery capacity to obtain the node SOC data corresponding to the lithium precipitation analysis node.

[0083] For each lithium precipitation analysis node, determine the voltage-capacity analysis data corresponding to the lithium precipitation analysis node according to the node SOC data, the node voltage and the node cumulative capacity corresponding to the lithium precipitation analysis node, and determine the voltage-capacity analysis data of all lithium precipitation analysis nodes as the target lithium precipitation analysis data corresponding to the battery to be tested.

[0084] In the embodiment of the present application, specifically, the voltage-capacity analysis data can be obtained by calculating the differential of the node voltage and the node battery capacity respectively to obtain the data correlation information of dV / dQ-Q and dV / dQ-SOC, and further, the data correlation information can be in the form of dV / dQ-Q, dV / dQ-SOC curve, broken line, sector diagram, etc., which is not limited in the embodiment of the present application.

[0085] 104. Determine the lithium precipitation-capacity information of the battery to be tested according to the sub-analysis data corresponding to each lithium precipitation analysis node.

[0086] In the embodiment of the present application, the lithium precipitation-capacity information is used to determine the correlation information between the battery capacity attenuation and the lithium precipitation of the battery to be tested.

[0087] In the embodiment of the present application, the lithium precipitation analysis node at least includes an initial node, an intermediate node and a termination node, the initial node is a node before the initial start cycle charging and discharging operation of the to-be-tested battery, the intermediate node is a node with a cycle number greater than or equal to a preset cycle number in the cycle charging and discharging operation of the to-be-tested battery, and the number of intermediate nodes is greater than or equal to 1, and the termination node is a node corresponding to a current capacity of the to-be-tested battery less than or equal to a preset period capacity.

[0088] The sub-analysis data corresponding to each lithium precipitation analysis node includes the node voltage and the node cumulative capacity of the lithium precipitation analysis node.

[0089] In the embodiment of the present application, the termination node can specifically refer to a node corresponding to a battery capacity retention rate of 65%, the intermediate node can be selected as the first selected node after 800 cycles of the battery cell, and then every 500 cycles is an interval, that is, the 1300th and 1800th cycles are the second and third selected nodes, until the battery capacity retention rate of a certain node is 65%, the node is selected as the termination node. Relatively, if the cycle number of the termination node is between 1300 and 1800, the number of intermediate nodes is 1, which is not limited in the embodiment of the present application.

[0090] In the embodiment of the present application, please refer to Figure 5 , Figure 5 The dV / dQ-SOC curve of the voltage and capacity of the to-be-tested battery under 0.04C charging and discharging current is disclosed in the embodiment of the present application, and the way of determining the voltage-capacity analysis data corresponding to each lithium precipitation analysis node according to the node SOC data, the node voltage and the node cumulative capacity corresponding to the lithium precipitation analysis node can be specifically as follows:

[0091] For the to-be-tested battery, the detailed data of the gradually accumulated capacity and the gradually changed voltage from 0% SOC to 100% SOC is subjected to mathematical differential operation to obtain dV-dQ data, and then the dV-dQ data is taken as Y axis data and SOC is taken as X axis data to draw a dV / dQ-SOC curve, and the relationship between the lithium precipitation data and the capacity retention rate is determined by analyzing the conditions of each characteristic peak in the dV / dQ-SOC curve.

[0092] In the embodiment of the present application, the initial start cycle charging and discharging operation of the to-be-tested battery includes:

[0093] According to the cell data of the to-be-tested battery, the cycle electric control parameters corresponding to the cycle charging and discharging operation of the to-be-tested battery are determined, the cell data includes the battery model and the battery nominal capacity of the to-be-tested battery, and the cycle electric control parameters include at least one of the charging and discharging mode, the cycle current parameter, the charging and discharging interval and the data acquisition interval.

[0094] According to the cyclic electric control parameter, a cyclic charge-discharge operation is performed on the battery to be tested, wherein the cycle number of the battery to be tested is updated each time the cyclic charge-discharge operation is performed.

[0095] In the embodiment of the application, the battery to be tested can be a square aluminum shell wound ternary cell, model M41, and the nominal capacity is 19.5 Ah; further, the cell is cycled in the form of CCCV charging and CC discharging, the cycle current parameter can be 10C cycle current, the cycle is 0% SOC-100% SOC, and the voltage platform is 3-4.2V; the CV segment voltage is 4.2V, the cutoff current is 0.05C; the discharge current is 10C; and the standby time between each charge-discharge interval is 5 minutes. In addition, the cell needs to be fixed using a 4-hole steel clamp before cycling, the initial pre-tightening force is 300kgf (1.5nm), and the test cabinet precision requirement is to record data once every 0.1s.

[0096] In the embodiment of the application, please refer to Figure 5 , specifically, taking the data in the interval of (80%, 100%) of the abscissa and (0.08, 0.1) of the ordinate as an example, the dV / dQ-SOQ curve presented by the 0.04C cycle from right to left is analyzed as follows:

[0097] Among them, Figure 5 The line corresponding to the right 1 (the first from right to left) is the dV / dQ-SOQ curve presented by the 0.04C cycle of the BOL state of the cell without cycling, which has five obvious characteristic peaks from left to right, respectively representing the phase transition of the positive electrode material, the phase transition of the negative electrode material, and the phase transition of the negative electrode material at a low SOC state, which can be a state corresponding to a SOC value lower than 20% or 25%, and the embodiment of the application does not limit it;

[0098] Figure 5 The line corresponding to the right 2 can be the dV / dQ-SOQ curve presented by the 0.04C cycle of the cell after 800 cycles, and the characteristic peak of the phase transition of the negative electrode material at a low SOC state is reduced by one, and the characteristic peak is shifted to the left as a whole, representing that the negative electrode SOC state of the cell has decreased (at this time, the cell has capacity loss after 800 cycles);

[0099] Figure 5 The line corresponding to the left 2 can be the dV / dQ-SOQ curve presented by the 0.04C cycle of the cell after 1300 cycles, and it can be seen that the characteristic peak of the phase transition of the negative electrode material at a low SOC state is difficult to distinguish, while the characteristic peak of the phase transition of the positive electrode material is gradually flat and difficult to identify, and the overall curve is shifted to the left by a large margin compared to before; the characteristic peak can reflect the number of positive and negative lithium ion migration in the cell tab, and the prominence and flatness of the characteristic peak reflect the change in the number of positive and negative lithium ion migration, Figure 5The performance of the middle left 2 line represents that the active lithium ions of the battery cell have been lost in a large amount, that is, lithium precipitation occurs, and it can be known that the capacity retention rate decay speed of the lithium ion loss of the battery cell is obviously accelerated compared with the performance of the right 1 and right 2 curves.

[0100] Finally, Figure 5 The left 1 corresponding to the line of the battery cell has been cycled to the end of life (65% capacity retention rate), at this time, almost all the characteristic peaks have disappeared, and the overall curve is again shifted to the left, indicating that the active lithium ions in the battery cell are lost severely, and serious lithium precipitation occurs, and the capacity retention rate decay speed at this time is extremely fast compared with that at BOL.

[0101] As can be known from the above, compared with the right 1 curve, the positive electrode material phase change characteristic peak of the left 2 curve has no large fluctuation, representing that the positive electrode active lithium ion has been lost in a large amount, that is, lithium precipitation occurs; and the SOC at which the left 2 curve begins to appear the characteristic peak is later than that of the right 1 curve, indicating that irreversible capacity loss occurs in the battery during the cycle charging and discharging process; and it can be concluded that the capacity loss of the battery cell at this time is caused by lithium precipitation, and the capacity retention rate decay speed will be accelerated with the increase of the degree of lithium precipitation of the battery cell.

[0102] It can be seen that the implementation Figure 1 The battery lithium precipitation data analysis method described can automatically determine lithium precipitation control parameters, intelligently perform lithium precipitation control operations on the battery to be tested at the determined multiple lithium precipitation analysis nodes, automatically collect and analyze lithium precipitation control data at each lithium precipitation analysis node, improve the collection efficiency, analysis efficiency and analysis accuracy of the lithium precipitation control data, further refine the sub-analysis data of each lithium precipitation analysis node in the target lithium precipitation analysis data, and finally determine the lithium precipitation-capacity information, so that the lossless lithium precipitation analysis of the battery aging can be performed without disassembling the battery, and the loss caused by the analysis of the lithium precipitation of the battery is reduced.

[0103] Embodiment two

[0104] Please refer to Figure 2 , Figure 2 is a flowchart of another battery lithium precipitation data analysis method disclosed in the embodiments of the present application. Among them, Figure 2 The battery lithium precipitation data analysis method described can be applied to a battery lithium precipitation data analysis device, and the embodiments of the present application are not limited. As Figure 2 shown, the battery lithium precipitation data analysis method can include the following operations:

[0105] 201, determine the discharge parameters and the lithium precipitation control parameters for performing lithium precipitation control operations on the battery to be tested.

[0106] 202. Based on the detection current and discharge parameters, perform lithium plating control operation on the battery under test at each lithium plating analysis node to obtain the lithium plating control data corresponding to each lithium plating analysis node.

[0107] 203. Based on the detection current and discharge parameters, perform lithium plating control operation on the battery under test at each lithium plating analysis node to obtain the lithium plating control data corresponding to each lithium plating analysis node.

[0108] 204. Based on all lithium plating control data and the current capacity corresponding to each lithium plating analysis node, determine the target lithium plating analysis data corresponding to the battery under test.

[0109] For further descriptions of steps 201-204, please refer to the other specific descriptions of steps 101-104 in Embodiment 1. These will not be repeated in the embodiments of the present invention.

[0110] 205. Based on the sub-analysis data corresponding to each lithium plating analysis node, determine the phase transition characteristic peak corresponding to each lithium plating analysis node and the characteristic peak information corresponding to the phase transition characteristic peak.

[0111] In this embodiment of the invention, the phase transition characteristic peaks include the phase transition characteristic peaks of the positive electrode material, the phase transition characteristic peaks of the negative electrode material, and the negative electrode phase transition characteristic peaks under low SOC. The negative electrode phase transition characteristic peaks under low SOC are the phase transition characteristic peaks corresponding to SOC values ​​below a preset charge threshold. The preset charge threshold can be 20%, that is, the negative electrode phase transition characteristic peaks under low SOC can refer to the negative electrode phase transition characteristic peaks under SOC values ​​below 20%.

[0112] 206. Using the phase transition characteristic peak and characteristic peak information corresponding to the initial node as a benchmark, compare the phase transition characteristic peak and characteristic peak information corresponding to the remaining nodes other than the initial node to obtain the offset information of each phase transition characteristic peak in each remaining node.

[0113] In this embodiment of the invention, the remaining nodes include intermediate nodes and termination nodes.

[0114] In this embodiment of the invention, the offset information of each phase transition characteristic peak is used to represent the correlation information between the battery capacity decay and lithium plating of the battery under test under the reference of the initial node as the node offset of each of the remaining nodes.

[0115] 207. Analyze all offset information of each remaining node to obtain the characteristic peak offset information corresponding to the battery under test, which is used as the lithium plating-capacity information of the battery under test.

[0116] It is evident that implementation Figure 2The described battery lithium precipitation data analysis method can automatically analyze all phase change characteristic peaks of each lithium precipitation analysis node and the characteristic peak information of each phase change characteristic peak, so as to obtain the offset of the lithium precipitation analysis node relative to the initial node by comparing each phase change characteristic peak in the initial node, and the offset of all lithium precipitation analysis nodes and the characteristic peak information of the phase change characteristic peak are used to further analyze the lithium precipitation-capacity information of the battery to be tested, thereby improving the analysis accuracy and reliability of the lithium precipitation-capacity information. In addition, the node-by-node analysis method includes the initial cycle use, the intermediate cycle aging, and the terminal node of the battery life depletion of the battery to be tested, and the multi-node analysis includes the complete use cycle and the battery cycle aging information of the battery to be tested, thereby improving the completeness and accuracy of the battery lithium precipitation analysis.

[0117] In an optional embodiment, the above-mentioned analysis of all offset information of each remaining node to obtain the characteristic peak offset information corresponding to the battery to be tested comprises:

[0118] Obtaining the same type battery historical data, the same type battery historical data is the lithium precipitation-capacity information corresponding to the same type battery after performing the lithium precipitation electric control operation on the same type battery;

[0119] For all offset information of each remaining node, determining the historical offset information matched with all offset information of the remaining node from the same type battery historical data, and the historical offset information of each remaining node includes the historical characteristic peak information corresponding to each characteristic peak information in the remaining node;

[0120] For each offset information of each remaining node, calculating the information difference value between each characteristic peak information of the remaining node and the historical characteristic peak information corresponding to the characteristic peak information to obtain a difference value set of the remaining node, and the difference value set of the remaining node includes the information difference value between each characteristic peak information of the remaining node and the historical characteristic peak information corresponding to the characteristic peak information;

[0121] When it is determined that there is no abnormal difference value in all difference value sets, all offset information of each remaining node, the phase change characteristic peak and the characteristic peak information corresponding to the initial node are determined as the characteristic peak offset information corresponding to the battery to be tested, and the abnormal difference value is a value that is not within the standard difference value interval corresponding to the information difference value.

[0122] Optionally, when it is determined that there is an abnormal difference value in all difference value sets, the method further comprises:

[0123] The lithium extraction electric control operation is repeatedly performed on the to-be-corrected node corresponding to the abnormal difference value based on the preset correction number, and when it is determined that the information difference value corresponding to the to-be-corrected node is within the standard difference value range corresponding to the information difference value within any preset correction number, the feature peak information of the to-be-corrected node corresponding to the abnormal difference value is updated to the feature peak information corresponding to the current information difference value.

[0124] When the number of times of performing the lithium extraction electric control operation is greater than the preset correction number, and each information difference value obtained after performing the lithium extraction electric control operation on the to-be-corrected node is an abnormal difference value, an abnormal information for the to-be-corrected node is generated, and the to-be-corrected node is removed from all remaining nodes.

[0125] It should be noted that in each remaining node, after the node data corresponding to the remaining node is collected, historical data is introduced for comparative analysis. For the information difference value outside the standard difference value range corresponding to the information difference value, the lithium extraction electric control, data collection and analysis operations are repeatedly performed on the node according to the preset correction number, until it is determined that the information difference value of the node is within the standard difference value range corresponding to the information difference value / reaches the preset number, and then the cycle aging of the battery under test is continued, until the next remaining node is reached / the termination node is reached, and the entire lithium extraction detection process is terminated.

[0126] It can be seen that in the optional embodiment, when comparing the offset information of each remaining node, the historical data of the same type of battery of the battery under test can be used to compare the offset information of each remaining node, thereby improving the accuracy of the comparison. The information difference value between the current feature peak information and the historical feature peak information can also be automatically calculated. For the information difference value outside the standard difference value range, the lithium extraction electric control operation and related data collection and analysis can also be automatically repeated according to the preset correction number, thereby reducing the probability of data error of the lithium extraction analysis node and improving the accuracy of the subsequent lithium extraction-capacity information.

[0127] Embodiment Three

[0128] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of a battery lithium extraction data analysis device disclosed by the embodiment of the present application. The battery lithium extraction data analysis device can be a battery lithium extraction data analysis terminal, a battery lithium extraction data analysis equipment, a battery lithium extraction data analysis system, or a battery lithium extraction data analysis server. The battery lithium extraction data analysis server can be a local server, a remote server, or a cloud server (also known as a cloud server). When the battery lithium extraction data analysis server is a non-cloud server, the non-cloud server can be connected to the cloud server for communication. The embodiment of the present application does not make any limitation. For example, Figure 3As shown, the lithium precipitation data analysis device of the battery can include a first determination module 301, a lithium precipitation control module 302, and a second determination module 303, wherein:

[0129] The first determination module 301 is configured to determine a discharge parameter and a lithium precipitation control parameter for performing a lithium precipitation control operation on the battery under test, wherein the lithium precipitation control parameter includes a detection current used for performing the lithium precipitation control operation and at least three lithium precipitation analysis nodes.

[0130] The lithium precipitation control module 302 is configured to perform the lithium precipitation control operation on the battery under test at each lithium precipitation analysis node according to the detection current and the discharge parameter, to obtain lithium precipitation control data corresponding to each lithium precipitation analysis node. The lithium precipitation control data corresponding to each lithium precipitation analysis node includes a cumulative capacity of the lithium precipitation analysis node and a target voltage, wherein the cumulative capacity is a changed capacity accumulated by the battery under test at the lithium precipitation analysis node to complete a charge-discharge operation matched with the discharge parameter, and the target voltage is a voltage corresponding to the battery under test at the lithium precipitation analysis node when performing the lithium precipitation control operation.

[0131] The first determination module 301 is further configured to determine target lithium precipitation analysis data corresponding to the battery under test according to all lithium precipitation control data and a current capacity corresponding to each lithium precipitation analysis node, wherein the target lithium precipitation analysis data includes sub-analysis data corresponding to each lithium precipitation analysis node.

[0132] The second determination module 303 is configured to determine lithium precipitation-capacity information of the battery under test according to the sub-analysis data corresponding to each lithium precipitation analysis node, wherein the lithium precipitation-capacity information is used to determine associated information between a battery capacity attenuation and a lithium precipitation condition of the battery under test.

[0133] In the embodiment of the present application, the lithium precipitation analysis nodes include at least an initial node, an intermediate node, and a termination node, wherein the initial node is a node before the battery under test is initially started for a cyclic charge-discharge operation, the intermediate node is a node with a cycle number greater than or equal to a preset cycle number when the battery under test is subjected to the cyclic charge-discharge operation, and the number of intermediate nodes is greater than or equal to 1, and the termination node is a node corresponding to a current capacity of the battery under test less than or equal to a preset period capacity.

[0134] The sub-analysis data corresponding to each lithium precipitation analysis node includes a node voltage and a node cumulative capacity of the lithium precipitation analysis node.

[0135] In the embodiment of the present application, the initial start of the cyclic charge-discharge operation on the battery under test includes:

[0136] According to the cell data of the to-be-tested battery, a cycle control parameter corresponding to a cycle charge-discharge operation performed on the to-be-tested battery is determined, the cell data including a battery model and a nominal capacity of the to-be-tested battery, and the cycle control parameter including at least one of a charge-discharge mode, a cycle current parameter, a charge-discharge interval, and a data collection interval;

[0137] According to the cycle control parameter, the cycle charge-discharge operation is performed on the to-be-tested battery, and the cycle number of the to-be-tested battery is updated each time the cycle charge-discharge operation is performed.

[0138] In the embodiment of the application, the first determination module 301 determines the target lithium precipitation analysis data of the to-be-tested battery according to all lithium precipitation control data and the current capacity corresponding to each lithium precipitation analysis node, and the determination manner specifically includes:

[0139] For each lithium precipitation analysis node, the node battery capacity of the to-be-tested battery at the lithium precipitation analysis node is determined, the node cumulative capacity corresponding to the lithium precipitation analysis node is determined from all lithium precipitation control data, and the quotient of the node cumulative capacity and the node battery capacity is calculated to obtain the node SOC data corresponding to the lithium precipitation analysis node.

[0140] For each lithium precipitation analysis node, the voltage-capacity analysis data corresponding to the lithium precipitation analysis node is determined according to the node SOC data, the node voltage, and the node cumulative capacity corresponding to the lithium precipitation analysis node, and the voltage-capacity analysis data of all lithium precipitation analysis nodes is determined as the target lithium precipitation analysis data corresponding to the to-be-tested battery.

[0141] It can be seen that the implementation Figure 3 The described battery lithium precipitation data analysis device can automatically determine lithium precipitation control parameters, intelligently perform lithium precipitation control operations on the to-be-tested battery at multiple lithium precipitation analysis nodes determined, automatically collect and analyze lithium precipitation control data at each lithium precipitation analysis node, improve the collection efficiency, analysis efficiency, and analysis accuracy of lithium precipitation control data, further refine the sub-analysis data of each lithium precipitation analysis node in the target lithium precipitation analysis data, and ultimately determine the lithium precipitation-capacity information, so that the loss caused by analyzing the lithium precipitation of the battery is reduced without disassembling the battery.

[0142] In an optional embodiment, the second determination module 303 determines the lithium precipitation-capacity information of the to-be-tested battery according to the sub-analysis data corresponding to each lithium precipitation analysis node, and the determination manner specifically includes:

[0143] According to the sub-analysis data corresponding to each lithium precipitation analysis node, determine the phase transition characteristic peak corresponding to each lithium precipitation analysis node and the characteristic peak information corresponding to the phase transition characteristic peak. The phase transition characteristic peak includes the positive electrode material phase transition characteristic peak, the negative electrode material phase transition characteristic peak, and the negative electrode phase transition characteristic peak at low SOC. The negative electrode phase transition characteristic peak at low SOC is the phase transition characteristic peak corresponding to the SOC value lower than the preset charge threshold;

[0144] Take the phase transition characteristic peak and the characteristic peak information corresponding to the initial node as the reference, compare the phase transition characteristic peak and the characteristic peak information corresponding to the remaining nodes except the initial node, and obtain the offset information of each phase transition characteristic peak in each remaining node. The remaining nodes include intermediate nodes and termination nodes.

[0145] Analyze all offset information of each remaining node to obtain the characteristic peak offset information corresponding to the battery under test as the lithium precipitation-capacity information of the battery under test.

[0146] As can be seen, in this optional embodiment, all phase transition characteristic peaks of the lithium precipitation analysis node and the characteristic peak information of each phase transition characteristic peak can be automatically analyzed, thereby obtaining the offset amount of the lithium precipitation analysis node relative to the initial node by comparing each phase transition characteristic peak in the initial node. The offset amount of all lithium precipitation analysis nodes and the characteristic peak information of the phase transition characteristic peak are used to further analyze the lithium precipitation-capacity information of the battery under test, thereby improving the analysis accuracy and reliability of the lithium precipitation-capacity information. In addition, the analysis method of each node includes the initial cycle use of the battery under test, the intermediate cycle aging, and the termination node of the battery life depletion. The multi-node analysis includes the complete use cycle of the battery under test and the battery cycle aging information, thereby improving the completeness and accuracy of the battery lithium precipitation analysis.

[0147] In another optional embodiment, the second determination module 303 analyzes all offset information of each remaining node to obtain the characteristic peak offset information corresponding to the battery under test in the following manner:

[0148] Obtain the same type battery historical data. The same type battery historical data is the lithium precipitation-capacity information corresponding to the same type battery after performing the lithium precipitation electric control operation on the same type battery.

[0149] For all offset information of each remaining node, determine the historical offset information matching the all offset information of the remaining node from the same type battery historical data. The historical offset information of each remaining node includes the historical characteristic peak information corresponding to each characteristic peak information in the remaining node.

[0150] For each offset information of each remaining node, calculate the information difference between each feature peak information of the remaining node and the historical feature peak information corresponding to the feature peak information, to obtain a difference set of the remaining node, the difference set of the remaining node including the information difference between each feature peak information of the remaining node and the historical feature peak information corresponding to the feature peak information;

[0151] When it is determined that there is no abnormal difference in all difference sets, the offset information of each remaining node, the phase transition feature peak corresponding to the initial node and the feature peak information are determined as the feature peak offset information corresponding to the battery to be measured, and the abnormal difference is a value that is not within the standard difference value interval corresponding to the information difference.

[0152] Optionally, when it is determined that there is an abnormal difference in all difference sets, the lithium extraction automatic control operation is repeatedly performed on the to-be-corrected node corresponding to the abnormal difference based on a preset correction number, and when it is determined that the information difference corresponding to the to-be-corrected node is within the standard difference value interval corresponding to the information difference within any preset correction number, the feature peak information corresponding to the abnormal difference of the to-be-corrected node is updated to the feature peak information corresponding to the current information difference.

[0153] When the number of times of performing the lithium extraction automatic control operation is greater than the preset correction number, and each information difference obtained after performing the lithium extraction automatic control operation on the to-be-corrected node is an abnormal difference, an abnormal information for the to-be-corrected node is generated and the to-be-corrected node is removed from all remaining nodes.

[0154] It can be seen that in the optional embodiment, when comparing and analyzing the offset information of each remaining node, the historical data of the same type of battery of the battery to be measured can be used to compare the offset information of each remaining node, thereby improving the accuracy of the comparison. The information difference between the current feature peak information and the historical feature peak information can also be automatically calculated. For the information difference outside the standard difference value interval, the lithium extraction automatic control operation on the lithium extraction analysis node and the related data collection and analysis can be automatically repeated according to the set preset correction number, thereby reducing the probability of data error of the lithium extraction analysis node and improving the accuracy of the subsequent lithium extraction-capacity information.

[0155] Embodiment Four

[0156] Please refer to Figure 4 , Figure 4 is another structure diagram of a battery lithium extraction data analysis device disclosed in the embodiment of the present application. As shown in Figure 4 , the battery lithium extraction data analysis device can include:

[0157] a memory 401 storing executable program codes;

[0158] A processor 402 coupled to the memory 401;

[0159] The processor 402 invokes the executable program code stored in the memory 401 to perform the steps in the battery lithium precipitation data analysis method described in the embodiment one or the embodiment two of the present application.

[0160] Embodiment five

[0161] The embodiment of the present application discloses a computer readable storage medium, which stores computer instructions, and the computer instructions are used to perform the steps in the battery lithium precipitation data analysis method described in the embodiment one or the embodiment two of the present application when invoked.

[0162] Embodiment six

[0163] The embodiment of the present application discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the battery lithium precipitation data analysis method described in the embodiment one or the embodiment two.

[0164] The above described device embodiments are only schematic, wherein the modules described as separate components can or can not be physically separate, and the components shown as modules can or can not be physical modules, i.e., can be located in one place, or can be distributed on multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0165] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and the necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in terms of the contribution to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

[0166] Finally, it should be noted that: the battery lithium precipitation data analysis method, device and storage medium disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing lithium plating data in batteries, characterized in that, The method comprises: determining a discharge parameter and a lithium precipitation controlled parameter for performing a lithium precipitation controlled operation on a battery to be tested, the lithium precipitation controlled parameter comprising a detection current for performing the lithium precipitation controlled operation and at least three lithium precipitation analysis nodes; performing the lithium precipitation controlled operation on the battery to be tested at each of the lithium precipitation analysis nodes according to the detection current and the discharge parameter, to obtain lithium precipitation controlled data corresponding to each of the lithium precipitation analysis nodes; the lithium precipitation controlled data corresponding to each of the lithium precipitation analysis nodes comprises a cumulative capacity and a target voltage of the lithium precipitation analysis node, the cumulative capacity being a changed capacity accumulated at the lithium precipitation analysis node when the battery to be tested completes a charge-discharge operation matching the discharge parameter, and the target voltage being a voltage corresponding to the battery to be tested at the lithium precipitation analysis node when the lithium precipitation controlled operation is performed; determining target lithium precipitation analysis data corresponding to the battery to be tested according to all the lithium precipitation controlled data and a current capacity corresponding to each of the lithium precipitation analysis nodes, the target lithium precipitation analysis data comprising sub-analysis data corresponding to each of the lithium precipitation analysis nodes; determining lithium precipitation-capacity information of the battery to be tested according to the sub-analysis data corresponding to each of the lithium precipitation analysis nodes, the lithium precipitation-capacity information being used to determine associated information between a battery capacity attenuation and a lithium precipitation condition of the battery to be tested; the lithium precipitation analysis nodes comprise at least an initial node, an intermediate node and a termination node; the determining of the lithium precipitation-capacity information of the battery to be tested according to the sub-analysis data corresponding to each of the lithium precipitation analysis nodes comprises: determining a phase transition characteristic peak corresponding to each of the lithium precipitation analysis nodes and feature peak information corresponding to the phase transition characteristic peak according to the sub-analysis data corresponding to each of the lithium precipitation analysis nodes, the phase transition characteristic peak comprising a positive electrode material phase transition characteristic peak, a negative electrode material phase transition characteristic peak and a negative electrode phase transition characteristic peak at a low SOC, the negative electrode phase transition characteristic peak at the low SOC being a phase transition characteristic peak corresponding to an SOC value lower than a preset charge threshold; comparing the phase transition characteristic peak and the feature peak information corresponding to each of the remaining nodes except the initial node with the phase transition characteristic peak and the feature peak information corresponding to the initial node as a reference, to obtain offset information of each of the phase transition characteristic peaks in each of the remaining nodes, the remaining nodes comprising the intermediate node and the termination node; analyzing all the offset information of each of the remaining nodes to obtain feature peak offset information corresponding to the battery to be tested as the lithium precipitation-capacity information of the battery to be tested. 2.The battery lithium plating data analysis method of claim 1, wherein, the initial node is a node before an initial start of a cycle charge-discharge operation on the battery to be tested; the intermediate node is a node with a cycle number of the cycle charge-discharge operation on the battery to be tested greater than or equal to a preset cycle number, and the number of the intermediate nodes is greater than or equal to 1; the termination node is a node corresponding to a current capacity of the battery to be tested less than or equal to a preset deadline capacity; the sub-analysis data corresponding to each of the lithium precipitation analysis nodes comprises a node voltage and a node cumulative capacity of the lithium precipitation analysis node. 3.The battery lithium plating data analysis method of claim 1, wherein, The method further comprises: determining the target lithium precipitation analysis data corresponding to the battery under test according to all the lithium precipitation controlled data and the current capacity corresponding to each lithium precipitation analysis node; For each lithium precipitation analysis node, determining the node battery capacity of the battery under test at the lithium precipitation analysis node; determining the node cumulative capacity corresponding to the lithium precipitation analysis node from all the lithium precipitation controlled data, and calculating the quotient of the node cumulative capacity and the node battery capacity to obtain the node SOC data corresponding to the lithium precipitation analysis node; 4.The battery lithium plating data analysis method of claim 1, wherein, For each lithium precipitation analysis node, determining the voltage-capacity analysis data corresponding to the lithium precipitation analysis node according to the node SOC data, the node voltage and the node cumulative capacity corresponding to the lithium precipitation analysis node, and determining the voltage-capacity analysis data of all the lithium precipitation analysis nodes as the target lithium precipitation analysis data corresponding to the battery under test. The method further comprises: determining the cycle controlled parameters corresponding to the cycle charge-discharge operation performed on the battery under test according to the cell data of the battery under test, wherein the cell data comprises the battery model and the nominal capacity of the battery under test, and the cycle controlled parameters comprise at least one of the charge-discharge mode, the cycle current parameter, the charge-discharge interval and the data acquisition interval; 5.The battery lithium plating data analysis method of claim 1, wherein, performing the cycle charge-discharge operation on the battery under test according to the cycle controlled parameters, wherein the cycle number of the battery under test is updated each time the cycle charge-discharge operation is performed. The method further comprises: obtaining the same-type battery historical data, wherein the same-type battery historical data is the lithium precipitation-capacity information corresponding to the same-type battery obtained by performing the lithium precipitation controlled operation on the same-type battery and analyzing the same-type battery; For each of the residual nodes, determining the historical offset information matching all the offset information of the residual node from the same-type battery historical data, wherein the historical offset information of each residual node comprises the historical characteristic peak information corresponding to each of the characteristic peak information in the residual node; For each of the residual nodes, calculating the information difference between each of the characteristic peak information in the residual node and the historical characteristic peak information corresponding to the characteristic peak information to obtain the difference set of the residual node, wherein the difference set of the residual node comprises the information difference between each of the characteristic peak information in the residual node and the historical characteristic peak information corresponding to the characteristic peak information; 6.The battery lithium plating data analysis method of claim 5, wherein, When it is determined that there is no abnormal difference in all the difference sets, determining all the offset information of each of the residual nodes, the phase change characteristic peak corresponding to the initial node and the characteristic peak information as the characteristic peak offset information corresponding to the battery under test, wherein the abnormal difference is a value that is not within the standard difference value interval corresponding to the information difference. When it is determined that there is the abnormal difference in all the difference sets, the method further comprises: The lithium extraction automatic control operation is repeatedly performed on the to-be-corrected node corresponding to the abnormal difference value based on a preset correction number, and when it is determined that the information difference value corresponding to the to-be-corrected node is within a standard difference value range corresponding to the information difference value within any of the preset correction numbers, the feature peak information corresponding to the abnormal difference value is updated to the feature peak information corresponding to the current information difference value. When the number of times of performing the lithium extraction automatic control operation is greater than the preset correction number, and each information difference value obtained after the lithium extraction automatic control operation is performed on the to-be-corrected node is the abnormal difference value, abnormal information is generated for the to-be-corrected node, and the to-be-corrected node is removed from all the remaining nodes. 7.A battery lithium plating data analysis device, characterized by, The device comprises: The first determination module is configured to determine a discharge parameter and a lithium extraction automatic control parameter used for performing a lithium extraction automatic control operation on a to-be-tested battery, the lithium extraction automatic control parameter comprising a detection current used for performing the lithium extraction automatic control operation and at least three lithium extraction analysis nodes; The lithium extraction automatic control module is configured to perform the lithium extraction automatic control operation on the to-be-tested battery at each lithium extraction analysis node according to the detection current and the discharge parameter, to obtain lithium extraction automatic control data corresponding to each lithium extraction analysis node, the lithium extraction automatic control data corresponding to each lithium extraction analysis node comprising a cumulative capacity corresponding to the lithium extraction analysis node and a target voltage, the cumulative capacity being a changed capacity accumulated by the to-be-tested battery at the lithium extraction analysis node when completing a charge-discharge operation matching the discharge parameter, and the target voltage being a voltage corresponding to the to-be-tested battery at the lithium extraction analysis node when the lithium extraction automatic control operation is performed; The first determination module is further configured to determine target lithium extraction analysis data corresponding to the to-be-tested battery according to all the lithium extraction automatic control data and a current capacity corresponding to each lithium extraction analysis node, the target lithium extraction analysis data comprising sub-analysis data corresponding to each lithium extraction analysis node; The second determination module is configured to determine lithium extraction-capacity information of the to-be-tested battery according to the sub-analysis data corresponding to each lithium extraction analysis node, the lithium extraction-capacity information being used to determine association information between a battery capacity attenuation and a lithium extraction condition of the to-be-tested battery; The lithium extraction analysis nodes comprise at least an initial node, an intermediate node and a terminal node; The sub-analysis data corresponding to each lithium extraction analysis node comprises a node voltage and a node cumulative capacity of the lithium extraction analysis node; The second determination module determines the lithium extraction-capacity information of the to-be-tested battery according to the sub-analysis data corresponding to each lithium extraction analysis node in the following manner: According to the sub-analysis data corresponding to each lithium extraction analysis node, a phase change characteristic peak corresponding to each lithium extraction analysis node and feature peak information corresponding to the phase change characteristic peak are determined, the phase change characteristic peak comprising a positive electrode material phase change characteristic peak, a negative electrode material phase change characteristic peak and a negative electrode phase change characteristic peak at a low SOC, the negative electrode phase change characteristic peak at the low SOC being a phase change characteristic peak corresponding to an SOC value lower than a preset charge threshold. The offset information of each phase transition characteristic peak in each of the remaining nodes is obtained by comparing the phase transition characteristic peaks and the characteristic peak information corresponding to the remaining nodes except the initial node, the remaining nodes including the intermediate node and the terminal node; The characteristic peak offset information corresponding to the battery under test is obtained by analyzing all the offset information of each of the remaining nodes, as the lithium precipitation-capacity information of the battery under test. 8.A battery lithium plating data analysis device, characterized by, The device comprises: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the battery lithium precipitation data analysis method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, which are invoked to execute the battery lithium precipitation data analysis method according to any one of claims 1-6.

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