Method and device for detecting lithium precipitation of lithium battery

Abstract

The invention provides a lithium battery lithium precipitation detection method, device and equipment and a computer readable storage medium. The lithium battery lithium precipitation detection method comprises the following steps: charging and discharging a to-be-detected lithium battery with a preset current, and collecting related parameters of the to-be-detected lithium battery in the charging and discharging process; obtaining the current charge-discharge cycle number of the lithium battery to be tested; calculating a plurality of detection scores for detecting whether the lithium battery to be detected separates out lithium or not based on the current charge-discharge cycle times and the related parameters; and determining a fusion detection score based on the plurality of detection scores, and determining that lithium precipitation exists in the to-be-detected lithium battery when the fusion detection score exceeds a fusion detection threshold.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and more specifically to a lithium battery lithium deposition detection method, device and equipment, and a computer-readable storage medium. Background Art

[0002] As global environmental and energy issues become increasingly severe, new energy vehicles have attracted widespread attention due to their energy-saving, high efficiency, low carbon and environmental protection advantages. In recent years, the electric vehicle industry, such as pure electric vehicles and hybrid electric vehicles, has risen rapidly and has become an inevitable trend in the development of the automobile industry. Power batteries are one of the core components of new energy vehicles. Lithium batteries have become the mainstream choice of power batteries for new energy vehicles due to their many advantages such as light weight, small size, high energy density, high charge and discharge efficiency, and long cycle life. They are also widely used in many other fields besides new energy vehicles, such as mobile communications, digital technology, energy storage, etc.

[0003] During the charging or use of lithium batteries, due to some abnormal factors, lithium plating may occur, that is, lithium ions fall off from the positive electrode of the battery, but cannot be evenly and completely embedded in the negative electrode of the battery, but partially precipitate in the form of lithium metal, forming gray or white lithium dendrites on the surface of the negative electrode. The lithium plating phenomenon will reduce the number of available lithium ions in the lithium battery, thereby reducing the battery performance and shortening the battery cycle life. In addition, as the lithium dendrites generated by lithium plating continue to grow, the lithium dendrites may pierce the diaphragm between the positive and negative electrodes, causing the positive and negative electrodes to overlap, causing a micro-short circuit inside the battery and further thermal runaway, resulting in serious safety accidents such as battery combustion and explosion. Therefore, it is necessary to monitor the lithium plating of lithium batteries, which has been a problem since the emergence of lithium batteries. Usually, the detection of lithium plating is achieved by disassembling the battery cell, but this method is highly destructive and inefficient, and is not suitable for large-scale implementation on vehicles. Therefore, a non-destructive and efficient lithium plating detection method is needed. Summary of the invention

[0004] The present disclosure provides a method, an apparatus and a device, a computer-readable storage medium, and a computer program product that can efficiently and non-destructively perform lithium deposition detection on a lithium battery.

[0005] According to one aspect of an embodiment of the present disclosure, a lithium battery lithium deposition detection method is provided, comprising: charging and discharging a lithium battery to be tested with a preset current, and collecting relevant parameters of the lithium battery to be tested during the charging and discharging process; obtaining a current number of charge and discharge cycles of the lithium battery to be tested; calculating a plurality of detection scores for detecting whether the lithium battery to be tested has lithium deposition based on the current number of charge and discharge cycles and the relevant parameters; and determining a fusion detection score based on the plurality of detection scores, and determining that lithium deposition has occurred in the lithium battery to be tested when the fusion detection score exceeds a fusion detection threshold.

[0006] According to an example of an embodiment of the present disclosure, the relevant parameters include the voltage and battery capacity of the lithium battery to be tested during the charging and discharging process, and the calculation of multiple detection scores for detecting whether the lithium battery to be tested has lithium deposition based on the current number of charge and discharge cycles and the relevant parameters includes: determining the differential voltage curve of the lithium battery to be tested at the current number of charge and discharge cycles based on the voltage and the battery capacity; extracting the valley capacity value at a predetermined valley of the differential voltage curve; obtaining multiple historical valley capacity values ​​at the predetermined valley of the differential voltage curve of multiple historical charge and discharge cycles of the lithium battery to be tested; and determining a first detection score for detecting whether the lithium battery to be tested has lithium deposition based on the valley capacity value of the lithium battery to be tested, the multiple historical valley capacity values, and a first detection threshold corresponding to the current number of charge and discharge cycles.

[0007] According to an example of an embodiment of the present disclosure, the first detection threshold corresponding to the current number of charge and discharge cycles is determined by the following method: performing charge and discharge cycles for the current number of charge and discharge cycles on multiple test lithium batteries, wherein the multiple test lithium batteries are lithium batteries having the same battery cells as the lithium battery to be tested; for each of the multiple test lithium batteries: determining a differential voltage curve for each charge and discharge cycle of the test lithium battery; extracting a valley capacity value at the predetermined valley of the differential voltage curve for each charge and discharge cycle to obtain multiple valley capacity values ​​of the test lithium battery; calculating multiple statistical indicators of the test lithium battery based on the multiple valley capacity values; integrating the multiple statistical indicators to generate an integrated statistical indicator of the test lithium battery; and determining the first detection threshold based on the integrated statistical indicator of each test lithium battery of the multiple test lithium batteries.

[0008] According to an example of an embodiment of the present disclosure, determining a first detection score for detecting whether the lithium battery to be tested has lithium plating based on the valley capacity value, the multiple historical valley capacity values, and the first detection threshold corresponding to the current number of charge and discharge cycles includes: calculating multiple statistical indicators of the lithium battery to be tested based on the valley capacity value of the lithium battery to be tested and the multiple historical valley capacity values; integrating the multiple statistical indicators to generate an integrated statistical indicator of the lithium battery to be tested; and determining the first detection score based on the integrated statistical indicator of the lithium battery to be tested and the first detection threshold.

[0009] According to an example of an embodiment of the present disclosure, the multiple statistical indicators include at least one of the following: variance, standard deviation, mean absolute deviation, coefficient of variation, Z score, correlation coefficient, mean absolute error, and data quantile.

[0010] According to an example of an embodiment of the present disclosure, the relevant parameters include the voltage and battery capacity of the lithium battery to be tested during the charging and discharging process, and the calculation of multiple detection scores for detecting whether the lithium battery to be tested has lithium deposition based on the current number of charge and discharge cycles and the relevant parameters includes: determining the differential capacity curve of the lithium battery to be tested at the current number of charge and discharge cycles based on the voltage and the battery capacity; obtaining the differential capacity curve of the lithium battery to be tested at the first charge and discharge cycle and the historical charge and discharge cycle numbers other than the first charge and discharge cycle; calculating the area difference between the differential capacity curve of the current charge and discharge cycle number and the first charge and discharge cycle at a predetermined high voltage section and the horizontal coordinate of the curve; calculating the historical area difference between the differential capacity curve of the historical charge and discharge cycle number and the first charge and discharge cycle at a predetermined high voltage section and the horizontal coordinate of the curve; determining the second detection score based on the area difference, the historical area difference, the current number of charge and discharge cycles, the historical number of charge and discharge cycles, and a second detection threshold corresponding to the current number of charge and discharge cycles.

[0011] According to an example of an embodiment of the present disclosure, the second detection threshold corresponding to the current number of charge and discharge cycles is determined by the following method: performing charge and discharge cycles for the current number of charge and discharge cycles on a test lithium battery, wherein the test lithium battery is a lithium battery having the same battery cell as the lithium battery to be tested; determining a differential capacity curve for each charge and discharge cycle of the test lithium battery; calculating the area difference between the differential capacity curve of each charge and discharge cycle after the first charge and discharge cycle of the test lithium battery and the differential capacity curve of the first charge and discharge cycle and the horizontal coordinate of the curve at a predetermined high voltage section to obtain multiple area differences; performing linear fitting on the multiple area differences and the corresponding number of charge and discharge cycles to obtain a first fitting curve, and obtaining a first slope and a first intercept of the first fitting curve as the second detection threshold.

[0012] According to an example of an embodiment of the present disclosure, determining the second detection score based on the area difference, the historical area difference, the current number of charge and discharge cycles, and the second detection threshold corresponding to the historical number of charge and discharge cycles includes: determining the first measurement slope and the first measurement intercept of the first straight line with the current number of charge and discharge cycles and the area difference, the historical number of charge and discharge cycles, and the historical area difference as two points respectively; determining the first component of the second detection score based on the first measurement slope and the first slope in the second detection threshold; determining the second component of the second detection score based on the first measurement intercept and the first intercept in the second detection threshold; and determining the second detection score based on the first component of the second detection score and the second component of the second detection score.

[0013] According to an example of an embodiment of the present disclosure, the relevant parameters include the maximum battery capacity and internal resistance of the lithium battery to be tested at the current number of charge and discharge cycles, and the calculation of multiple detection scores for detecting whether the lithium battery to be tested has lithium plating based on the current number of charge and discharge cycles and the relevant parameters includes: obtaining the maximum battery capacity and internal resistance of the battery to be tested at the first charge and discharge cycle; determining the third detection score based on the maximum battery capacity and internal resistance at the current number of charge and discharge cycles, the maximum battery capacity and internal resistance at the first charge and discharge cycle, and a third detection threshold corresponding to the current number of charge and discharge cycles.

[0014] According to an example of an embodiment of the present disclosure, the third detection threshold corresponding to the current number of charge and discharge cycles is determined by the following method: performing charge and discharge cycles for the current number of charge and discharge cycles on a test lithium battery, wherein the test lithium battery is a lithium battery having the same battery cells as the lithium battery to be tested; determining the internal resistance and maximum battery capacity of the test lithium battery in each charge and discharge cycle to obtain multiple internal resistances and multiple maximum battery capacities; performing linear fitting on the multiple internal resistances and the multiple maximum battery capacities to obtain a second fitting curve, and obtaining a second slope and a second intercept of the second fitting curve as the third detection threshold.

[0015] According to an example of an embodiment of the present disclosure, a third detection score for detecting whether the lithium battery to be tested has lithium plating is determined based on the maximum battery capacity and internal resistance at the current number of charge and discharge cycles, the maximum battery capacity and internal resistance at the first charge and discharge cycle, and a third detection threshold corresponding to the current number of charge and discharge cycles, including: determining a second measurement slope and a second measurement intercept of a second straight line with the maximum battery capacity and internal resistance at the current number of charge and discharge cycles and the maximum battery capacity and internal resistance at the first charge and discharge cycle as two points respectively; determining a first component of the third detection score based on the second measurement slope and the second slope in the third detection threshold; determining a second component of the third detection score based on the second measurement intercept and the second intercept in the third detection threshold; and determining the third detection score based on the first component of the third detection score and the second component of the third detection score.

[0016] According to an example of an embodiment of the present disclosure, determining a fused detection score based on the multiple detection scores includes weighting each of the multiple detection scores to determine the fused detection score, and wherein the fused detection threshold is determined by weighting multiple detection thresholds corresponding to the multiple detection scores.

[0017] According to an example of an embodiment of the present disclosure, the lithium battery to be tested is a power battery of a vehicle, and obtaining the current number of charge and discharge cycles of the lithium battery to be tested includes: determining the current number of charge and discharge cycles of the lithium battery to be tested based on the current total mileage of the vehicle.

[0018] According to another aspect of an embodiment of the present disclosure, a lithium battery lithium deposition detection device is provided, the device comprising: a charge and discharge unit, configured to charge and discharge a lithium battery to be tested with a preset current, and collect relevant parameters of the lithium battery to be tested during the charge and discharge process; an acquisition unit, configured to acquire the current number of charge and discharge cycles of the lithium battery to be tested; a detection unit, configured to calculate multiple detection scores for detecting whether the lithium battery to be tested has lithium deposition based on the current number of charge and discharge cycles and the relevant parameters; and a determination unit, configured to determine a fusion detection score based on the multiple detection scores, and determine that the lithium battery to be tested has lithium deposition when the fusion detection score exceeds a fusion detection threshold.

[0019] According to another aspect of an embodiment of the present disclosure, a lithium battery lithium plating detection device is provided, comprising: one or more processors; and one or more memories, wherein the memories store computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the one or more processors execute the methods described in the above aspects.

[0020] According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the processor executes a method as described in any one of the above-mentioned aspects of the present disclosure.

[0021] According to another aspect of an embodiment of the present disclosure, a computer program product is provided, which includes computer-readable instructions, and when the computer-readable instructions are executed by a processor, the processor executes a method as described in any one of the above aspects of the present disclosure.

[0022] By using the lithium battery lithium deposition detection method, device and equipment, computer-readable storage medium and computer program product according to the above-mentioned various aspects of the present disclosure, a multi-dimensional lithium battery lithium deposition detection system can be constructed, which can construct multi-dimensional features of lithium batteries based on data collected during the charge and discharge cycle of lithium batteries to obtain multi-dimensional lithium deposition detection scores, and fuse the multi-dimensional lithium deposition detection scores to provide a comprehensive score for lithium battery lithium deposition, so as to achieve efficient and non-destructive lithium battery lithium deposition detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other purposes, features and advantages of the embodiments of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0024] Figure 1 The basic architecture of the lithium plating detection system according to an embodiment of the present disclosure is shown.

[0025] Figure 2 A flow chart of a lithium plating detection method according to an embodiment of the present disclosure is shown.

[0026] Figure 3 A plurality of differential voltage curves of a tested lithium battery according to an example of an embodiment of the present disclosure are shown.

[0027] Figure 4 The valley capacity values ​​of a plurality of differential voltage curves of a tested lithium battery according to an example of an embodiment of the present disclosure are shown.

[0028] FIG. 5A to FIG. 5H The bar graphs respectively show the variance, standard deviation, mean absolute deviation, coefficient of variation, Z score, correlation coefficient, mean absolute error, and data quantile of the valley capacity values ​​of the differential voltage curves of multiple tested lithium batteries according to the examples of the embodiments of the present disclosure.

[0029] Figure 6A bar chart showing integrated statistical indicators of valley capacity values ​​of differential voltage curves of multiple tested lithium batteries according to an example of an embodiment of the present disclosure.

[0030] Figure 7 A plurality of differential capacity curves of tested lithium batteries according to examples of embodiments of the present disclosure are shown.

[0031] Figure 8 A predetermined high voltage section of a differential capacity curve of a certain subsequent charge and discharge cycle and a first charge and discharge cycle according to an example of an embodiment of the present disclosure is shown.

[0032] Fig. 9 A first fitting curve of a plurality of area differences corresponding to various charge and discharge cycle numbers according to an example of an embodiment of the present disclosure is shown.

[0033] Fig.10 A second fitting curve of multiple internal resistances corresponding to various charge and discharge cycle numbers according to an example of an embodiment of the present disclosure is shown.

[0034] Fig.11 A schematic structural diagram of a lithium deposition detection device according to an embodiment of the present disclosure is shown.

[0035] Fig.12 A schematic diagram showing the architecture of an exemplary computing device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0037] As shown in the present disclosure and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. In general, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0038] Although the present disclosure makes various references to certain modules in the apparatus according to the embodiments of the present disclosure, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the apparatus and method can use different modules.

[0039] Flowcharts are used in this disclosure to illustrate the operations performed by the methods and devices according to the embodiments of the present disclosure. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0040] In the embodiments of the present disclosure, a lithium battery generally refers to a battery that uses lithium metal or an alloy as a positive electrode or negative electrode material and uses a non-aqueous electrolyte solution, for example, common lithium battery positive electrode materials such as lithium iron phosphate, lithium manganate, lithium cobalt oxide, nickel cobalt lithium manganate ternary materials, etc. Generally speaking, lithium batteries can be divided into lithium ion batteries and lithium metal batteries, wherein lithium ion batteries do not contain metallic lithium and are usually rechargeable, while lithium metal batteries refer to batteries that use lithium metal as an electrode, and are usually disposable batteries. In the embodiments of the present disclosure, a lithium ion battery is used as an example for description, but this does not mean that the described features or functions cannot be used for lithium metal batteries, unless explicitly stated or there is a contradiction. In addition, in the embodiments of the present disclosure, a lithium ion battery in an electric vehicle is often used as an example for description, but this does not mean that the described features or functions are only used in the field of electric vehicles, but can be used in any other fields such as mobile communications, digital technology, etc. involving lithium batteries, unless explicitly stated or there is a contradiction. In addition, in the embodiment of the present disclosure, the lithium battery may include one or more lithium battery cells. The embodiment of the present disclosure does not limit the number of battery cells of the lithium battery. In the following, "lithium battery" and "lithium battery cell" can be used interchangeably.

[0041] In the disclosed embodiments, the number of charge and discharge cycles refers to the number of charge and discharge cycles of a lithium battery. Among them, a charge and discharge cycle refers to the process of completing a complete discharge and charge of a lithium battery. For example, if a lithium battery currently has 100% power, it is discharged to zero power, and then charged to 100% power again, then the lithium battery has undergone a charge and discharge cycle, and its charge and discharge cycle number increases by 1. The service life of a lithium battery can be measured by the number of charge and discharge cycles. For example, the service life of a conventional lithium battery can be 300 to 500 charge and discharge cycles, 1000 to 1200 charge and discharge cycles, or even higher. Generally, the capacity of a lithium battery will decay as the number of charge and discharge cycles increases, thereby affecting the performance of the lithium battery.

[0042] When a lithium battery is charged, lithium ions are deintercalated from the positive electrode, moved to the negative electrode and embedded in the negative electrode; when it is discharged, lithium ions are deintercalated from the negative electrode, moved to the positive electrode and embedded in the positive electrode. During the charging or use of a lithium battery, if some abnormal factors occur, lithium plating may occur, that is, lithium ions fall off from the positive electrode of the battery, but cannot be evenly and completely embedded in the negative electrode of the battery, but are partially precipitated in the form of lithium metal, forming gray or white lithium dendrites on the surface of the negative electrode. For example, lithium plating may occur during low-temperature charging, high-power charging, overcharging, or when the battery cell is deformed, the battery diaphragm is wrinkled, or the electrolyte is reduced, the negative electrode structure is damaged, etc. As mentioned above, lithium plating will reduce the battery life and may cause serious safety accidents such as battery combustion and explosion, so it is necessary to monitor the lithium plating phenomenon. Existing lithium plating detection methods are usually achieved by disassembling battery cells, but this method is highly destructive and inefficient, and is not suitable for large-scale implementation on vehicles.

[0043] In view of this, the present disclosure provides a multi-dimensional feature fusion method that can achieve efficient and non-destructive lithium plating detection. It constructs multi-dimensional features based on data collected during the charging and discharging process of lithium batteries, and establishes a corresponding multi-dimensional feature fusion algorithm, so that a comprehensive evaluation of lithium plating of lithium batteries can be achieved based on multi-dimensional features.

[0044] The basic architecture of the lithium deposition detection system according to the embodiment of the present disclosure is as follows: Figure 1As shown, it includes a data acquisition module 110, a multi-dimensional feature analysis module 120 and a multi-dimensional feature fusion module 130. The data acquisition module 110 is used to collect relevant parameters of the lithium battery to be tested during the charge and discharge cycle, such as current, voltage, charge and discharge rate, charge and discharge time, temperature, etc. The multi-dimensional feature analysis module 120 constructs features for the collected relevant parameters from different dimensions to determine a plurality of detection scores for detecting whether the lithium battery to be tested has lithium deposition. For example, according to the lithium deposition detection method of the embodiment of the present disclosure, the first detection score can be determined using the differential voltage curve (dV / dQ) of the lithium battery to be tested, the second detection score can be determined using the differential capacity curve (dQ / dV) of the lithium battery to be tested, and the third detection score can be determined using the maximum battery capacity and internal resistance of the lithium battery to be tested, etc., as will be further described in detail below. According to the lithium plating detection method of the embodiment of the present disclosure, the electrochemical impedance spectrum (EIS) of the lithium battery to be tested, the battery health state (SOH) decay, the charge and discharge temperature, the capacity tracking algorithm, the Arrhenius algorithm, etc. can also be used to determine more detection scores. The embodiment of the present disclosure does not specifically limit the method for determining the detection score and the number of detection scores. The multi-dimensional feature fusion module 130 fuses multiple detection scores to determine the fused detection score, and judges whether lithium plating occurs in the lithium battery to be tested based on a pre-set fusion detection threshold, as will be further described in detail below.

[0045] Refer to the following Figure 2 A lithium plating detection method according to an embodiment of the present disclosure is described. Figure 2 FIG. 2 shows a flow chart of a lithium deposition detection method 200 according to an embodiment of the present disclosure. Figure 2As shown, in step S210, the lithium battery to be tested is charged and discharged with a preset current, and the relevant parameters of the lithium battery to be tested are collected during the charging and discharging process. Here, the preset current can be any suitable current value determined according to actual detection needs or empirical parameters, and the embodiment of the present disclosure does not specifically limit this. When the lithium battery to be tested is charged and discharged to collect the relevant parameters of the lithium battery, the lithium battery can be subjected to a complete charge and discharge cycle, but the embodiment of the present disclosure is not limited thereto, and more or fewer charge and discharge cycles can also be performed. For example, when the lithium battery of an electric vehicle is subjected to lithium deposition detection using the lithium deposition detection method according to the embodiment of the present disclosure, the current power of the vehicle may be any value, and the lithium battery can be discharged to zero power from the power of the arbitrary value, and then charged to full power again, and the relevant parameters of the lithium battery are collected during this period, and the number of charge and discharge cycles is less than 1 at this time. In addition, the relevant parameters of the lithium battery collected may include the voltage, current, charge and discharge rate, charging time, battery capacity, temperature, etc. of the lithium battery during the charging and discharging process, as will be further described in detail below. During the charging and discharging process of the lithium battery, various parameters related to the lithium battery, such as voltage and current, can be collected at a predetermined collection interval, wherein the smaller the collection interval, the more relevant parameters of the lithium battery are obtained, thereby providing a more accurate lithium deposition detection, but correspondingly, more relevant parameters will also require more analysis time, resulting in an increase in detection time. An appropriate predetermined collection interval can be determined according to actual detection requirements, specifically, the predetermined collection interval can be determined based on a trade-off between detection accuracy and detection time, for example, it can be collected once per second, once per minute, etc., and the embodiments of the present disclosure do not impose specific restrictions on this.

[0046] In step S220, the current number of charge and discharge cycles of the lithium battery to be tested is obtained, that is, the number of charge and discharge cycles that the lithium battery has undergone since its production. For example, the current number of charge and discharge cycles can be determined based on the current capacity of the lithium battery to be tested and its design capacity. As mentioned above, as the number of charge and discharge cycles of the lithium battery increases, its battery capacity will gradually decay. The current number of charge and discharge cycles of the lithium battery can be determined based on the decay characteristics of the battery capacity and the current capacity and design capacity of the lithium battery to be tested. In the case where the lithium battery to be tested is a power battery of a vehicle, the current number of charge and discharge cycles of the lithium battery to be tested can also be determined based on the current total mileage of the vehicle. For example, the current number of charge and discharge cycles can be calculated based on the maximum cruising range of the vehicle and the current total mileage.

[0047] In step S230, multiple detection scores for detecting whether the lithium battery to be tested has lithium deposition can be calculated based on the current number of charge and discharge cycles of the lithium battery to be tested and the collected relevant parameters. In the embodiment of the present disclosure, multiple detection scores can be determined using different features in the collected relevant parameters to evaluate whether lithium deposition has occurred in the lithium battery to be tested from multiple dimensions. And then, in step S240, a fusion detection score can be determined based on the multiple detection scores. For example, the fusion detection score can be determined by weighting each of the multiple detection scores. When the fusion detection score exceeds the fusion detection threshold, it can be determined that lithium deposition has occurred in the lithium battery to be tested. At this time, an alarm message can be generated and output. For example, an alarm message can be sent to the driver of the vehicle containing the lithium battery to be tested to remind the driver of the lithium battery of the lithium deposition phenomenon and the possible risks.

[0048] According to an example of an embodiment of the present disclosure, a differential voltage curve can be constructed using relevant parameters of a lithium battery to be tested collected during the charging and discharging process, and a first detection score for detecting lithium precipitation can be calculated based on the characteristics of the differential voltage curve. Wherein, the differential voltage curve (dV / dQ) refers to the voltage change per unit capacity during the charging and discharging process of a lithium battery, and is often used to analyze the performance of a lithium battery. In this example, the collected relevant parameters may include the voltage and battery capacity of the lithium battery to be tested during the charging and discharging process, and the differential voltage curve of the lithium battery to be tested at the current number of charge and discharge cycles is determined based on the voltage and battery capacity. The differential voltage curve usually has multiple peaks and troughs, which can reflect the characteristics of the lithium battery to a certain extent. In this example, the capacity value (referred to as the trough capacity value) at the predetermined trough of the differential voltage curve of the lithium battery to be tested can be extracted to determine the first detection score for detecting lithium precipitation. Wherein, the predetermined trough can be, for example, the second trough of the differential voltage curve, and the embodiment of the present disclosure does not specifically limit this, and the predetermined trough can be determined according to actual detection requirements and empirical parameters.

[0049] In addition, multiple historical valley capacity values ​​at the predetermined valley of the differential voltage curve of multiple historical charge and discharge cycles of the lithium battery to be tested can be obtained to determine the lithium plating detection score together with the valley capacity value of the current charge and discharge cycle number of the lithium battery to be tested. Taking the lithium plating detection of electric vehicles as an example, the lithium plating detection method according to the embodiment of the present disclosure can be implemented as an independent lithium plating detection device, or can be integrated into an existing charging device or integrated into the electric vehicle body as a lithium plating detection component. In each charging and discharging process of the electric vehicle, the relevant parameters of the lithium battery can be collected and stored. When the lithium plating detection is currently performed on the lithium battery to be tested, these historical storage parameters can be obtained and analyzed to obtain the differential voltage curves of multiple historical charge and discharge cycles of the lithium battery to be tested, and then extract multiple historical valley capacity values ​​at the predetermined valley of these differential voltage curves. Next, the first detection score for detecting whether the lithium battery to be tested is lithium plating can be determined based on the valley capacity value at the current charge and discharge cycle number of the lithium battery to be tested, the multiple historical valley capacity values ​​obtained, and the first detection threshold corresponding to the current charge and discharge cycle number.

[0050] In this example of the embodiment of the present disclosure, the first detection threshold corresponding to different numbers of charge and discharge cycles may be, for example, pre-set, for example, pre-set in a lithium deposition detection device or in a charging device or electric vehicle body that integrates a lithium deposition detection component. The pre-set first detection threshold may be determined by performing multiple charge and discharge cycle tests on a plurality of lithium batteries for testing (referred to as test lithium batteries) having the same cells as the lithium battery to be tested, as described below. Note that the "current number of charge and discharge cycles" mentioned below refers to the current number of charge and discharge cycles of the lithium battery to be tested determined in step S220 above.

[0051] Take determining the first detection threshold corresponding to the current number of charge and discharge cycles of the lithium battery to be tested as an example for explanation. First, charge and discharge cycles of the current number of charge and discharge cycles are performed on multiple test lithium batteries. For example, if the current number of charge and discharge cycles of the lithium battery to be tested determined above is 350, then 350 charge and discharge cycles can be performed on each test lithium battery. For each test lithium battery, in each of these charge and discharge cycles, relevant parameters of the test lithium battery including voltage, capacity, etc. are collected, and the differential voltage curve of each charge and discharge cycle is determined accordingly, and the valley capacity value at the predetermined valley of the differential voltage curve is extracted to obtain multiple valley capacity values ​​of each test lithium battery in the multiple test lithium batteries.

[0052] Combination Figure 3 The example in the example is as follows, Figure 3The figure shows a plurality of differential voltage curves of a lithium battery according to an example of an embodiment of the present disclosure, wherein the gray curves of different color depths represent the differential voltage curves of different charge and discharge cycles, and the thicker part of the curve represents that a plurality of differential voltage curves overlap. For example, if the current number of charge and discharge cycles is 350, 350 differential voltage curves can be determined. Figure 3 It can be seen from the figure that there are multiple troughs in these differential voltage curves. For example, Figure 3 The capacity value at the valley indicated by the arrow in the middle can be extracted from the 350 differential voltage curves to obtain 350 valley capacity values. Plotting these valley capacity values ​​with the number of charge and discharge cycles as the horizontal axis and the valley capacity value as the vertical axis, we can get the following: Figure 4 As shown in the scatter plot, it can be seen that the distribution of these trough capacity values ​​is discrete and has no obvious regularity. Further statistical calculations of these trough capacity values ​​using statistical methods can determine multiple statistical indicators, where the multiple statistical indicators can include, for example, variance, standard deviation, mean absolute deviation, coefficient of variation, Z score, correlation coefficient, mean absolute error, data quantile, and any other applicable statistical indicators, which are not specifically limited in the embodiments of the present disclosure.

[0053] The lithium deposition conditions of the multiple test lithium batteries used in determining the first detection threshold are known. For example, the lithium deposition conditions of these test lithium batteries can be determined by disassembling and inspecting them. Afterwards, multiple statistical indicators of the valley capacity value of the differential voltage curve of each test lithium battery are determined respectively by the above method, such as variance, standard deviation, etc. For each statistical indicator, for example, the detection threshold for judging whether lithium deposition occurs in the test lithium battery can be determined based on the classification of the statistical indicator values ​​of these test lithium batteries, and the reliability of the detection threshold can be analyzed in combination with the actual lithium deposition conditions of these test lithium batteries.

[0054] Combination Figure 5A-5H The examples in this section illustrate this. FIG. 5A to FIG. 5H The variance, standard deviation, mean absolute deviation, coefficient of variation, Z score, correlation coefficient, mean absolute error, and data quantile of the trough capacity values ​​of the differential voltage curves of multiple test lithium batteries (12 are shown in the figure as an example) according to the example of the embodiment of the present disclosure are respectively shown in the bar graph, wherein the test lithium batteries C2, C3, C4, C11, and C12 do not have lithium deposition, while the remaining test lithium batteries have lithium deposition. Figure 5A Taking the variance in as an example, after determining the variance of the valley capacity values ​​of the 12 tested lithium batteries, we can plot Figure 5A The histogram in the figure is obtained, and for example, the variance detection threshold for judging whether lithium plating occurs in the tested lithium battery is determined by classifying the 12 variance values, such as Figure 5AAs shown by the horizontal straight line in . When the variance of the tested lithium battery is greater than the variance detection threshold, it can be considered that the tested lithium battery has lithium deposition, and when the variance of the tested lithium battery is less than the variance detection threshold, it can be considered that the tested lithium battery does not have lithium deposition. Combined with the actual lithium deposition situation of the tested lithium battery, it can be concluded that the variance detection threshold determined in this way has three errors in judging whether lithium deposition occurs, that is, the detection accuracy is 75%. Similarly, when using standard deviation, mean absolute deviation, coefficient of variation, Z score, correlation coefficient, mean absolute error, and data quantile for detection, the detection accuracy is 75%, 83.33%, 75%, 66.6%, 75%, 75%, and 58.3%, respectively.

[0055] In addition, in order to further improve the accuracy of lithium plating detection, in this example of the embodiment of the present disclosure, multiple statistical indicators of each tested lithium battery can be integrated to generate an integrated statistical indicator, and the first detection threshold can be determined based on the integrated statistical indicators of each tested lithium battery. For example, a random forest algorithm can be used to integrate multiple statistical indicators of multiple tested lithium batteries to determine the first detection threshold. The random forest algorithm is a classification algorithm that includes multiple decision trees, which can integrate the results of multiple weak classifiers to determine a better final classification result. In this example, the random forest algorithm can be used to Figures 5A-5H The eight statistical indicators of each tested lithium battery in the integrated statistical indicators are integrated to generate an integrated statistical indicator, and then the first detection threshold for judging whether lithium deposition occurs is determined. The result is as follows: Figure 6 shown. Figure 6 A bar graph showing integrated statistical indicators of the valley capacity values ​​of the differential voltage curves of multiple tested lithium batteries according to an example of an embodiment of the present disclosure is shown. Figure 6 As mentioned above, the tested lithium batteries C2, C3, C4, C11, and C12 do not have lithium deposition, while the other tested lithium batteries have lithium deposition. The horizontal straight line represents the determined first detection threshold. It can be seen that when using the first detection threshold to determine whether lithium deposition occurs, an error only occurs at the lithium battery cell 12, and the detection accuracy rate is increased to 91.7%, which is much higher than that of Figures 5A-5H The detection accuracy using a single statistical indicator is shown. It should be noted that the above description is made by taking the random forest algorithm as an example, but the embodiments of the present disclosure are not limited thereto, and any other appropriate classification algorithm may also be used.

[0056] In the above manner, multiple statistical indicators of the valley capacity values ​​of the differential voltage curves of the current charge and discharge cycle numbers of multiple test lithium batteries are integrated, and a first detection threshold for detecting lithium plating corresponding to the current charge and discharge cycle number is determined based on the integrated statistical indicators. Similarly, the first detection threshold corresponding to different charge and discharge cycle numbers can be determined and stored in the same manner, for example, in a lithium plating detection device or a charging device or electric vehicle body that integrates a lithium plating detection component, for use in the lithium plating detection process of the lithium battery to be tested.

[0057] As described above, the first detection score for detecting whether the lithium battery to be tested has lithium precipitated can be determined based on the valley capacity value at the current charge and discharge cycle number of the lithium battery to be tested, the multiple historical valley capacity values ​​obtained, and the first detection threshold corresponding to the current charge and discharge cycle number. Specifically, multiple statistical indicators of the lithium battery to be tested, such as variance, standard deviation, etc., can be calculated based on the valley capacity value of the current charge and discharge cycle number of the lithium battery to be tested and the multiple historical valley capacity values. The multiple statistical indicators of the lithium battery to be tested are integrated, for example, using a random forest algorithm to integrate to generate an integrated statistical indicator, and the integrated statistical indicator is compared with the first detection threshold. Simply, for example, when the integrated statistical indicator of the lithium battery to be tested is greater than the first detection threshold, it can be determined that the lithium battery to be tested has lithium precipitated; otherwise, it can be determined that the lithium battery to be tested does not have lithium precipitated. Further, the first detection score for detecting lithium precipitated can also be determined based on the integrated statistical indicator of the lithium battery to be tested and the first detection threshold for subsequent multi-dimensional feature fusion. For example, the first detection score may be determined based on a percentage of an integrated statistical indicator of the lithium battery to be tested and a first detection threshold, a relative error, etc., and the embodiments of the present disclosure do not impose specific limitations on this.

[0058] According to another example of an embodiment of the present disclosure, a differential capacity curve can be constructed using relevant parameters of the lithium battery to be tested collected during the charge and discharge process, and a second detection score for detecting lithium plating can be calculated based on the characteristics of the differential capacity curve. Among them, the differential capacity curve (dQ / dV) refers to the capacity change per unit voltage during the charge and discharge process of the lithium battery, and is often used to analyze the performance of the lithium battery. In this example, the collected relevant parameters may include the voltage and battery capacity of the lithium battery to be tested during the charge and discharge process, and the differential capacity curve of the lithium battery to be tested at the current number of charge and discharge cycles is determined based on the voltage and battery capacity.

[0059] In addition, the differential capacity curve of the lithium battery to be tested at the first charge and discharge cycle and the number of historical charge and discharge cycles other than the first charge and discharge cycle can be obtained, so as to determine the lithium plating detection score together with the differential capacity curve at the current number of charge and discharge cycles. Taking the lithium plating detection of electric vehicles as an example, the lithium plating detection method according to the embodiment of the present disclosure can be implemented as an independent lithium plating detection device, or can be integrated into an existing charging device or integrated into the electric vehicle body as a lithium plating detection component. During each charge and discharge process of the electric vehicle, the relevant parameters of the lithium battery can be collected and stored. Then, when the lithium plating detection is currently performed on the lithium battery to be tested, these historically stored parameters can be obtained and analyzed to obtain the differential capacity curve of the first charge and discharge cycle of the lithium battery to be tested and the number of historical charge and discharge cycles other than the first charge and discharge cycle.

[0060] Since the high-voltage section of the differential capacity curve can reflect the characteristics of the lithium battery to a certain extent, in this example, the predetermined high-voltage section of the differential capacity curve of the current charge and discharge cycle number, the historical charge and discharge cycle number, and the first charge and discharge cycle can be extracted, and the area difference between the differential capacity curve of the current charge and discharge cycle number and the first charge and discharge cycle at the predetermined high-voltage section and the horizontal coordinate of the curve, and the historical area difference between the differential capacity curve of the historical charge and discharge cycle number and the first charge and discharge cycle at the predetermined high-voltage section and the horizontal coordinate of the curve. Afterwards, the second detection score for detecting lithium precipitation can be determined based on the calculated area difference, the historical area difference, and the second detection threshold corresponding to the current charge and discharge cycle number. Among them, the predetermined high-voltage section can be a predetermined part on the differential capacity curve, such as a part with a voltage greater than 4V, but the embodiment of the present disclosure does not make specific restrictions on this, and the predetermined high-voltage section can be determined according to actual detection requirements and empirical parameters.

[0061] In this example of the embodiment of the present disclosure, the second detection threshold corresponding to different numbers of charge and discharge cycles may be pre-set, for example, pre-set in a lithium deposition detection device or a charging device or an electric vehicle body in which a lithium deposition detection component is integrated. The pre-set second detection threshold may be determined by performing multiple charge and discharge cycle tests on a test lithium battery having the same battery cell as the lithium battery to be tested, as described below.

[0062] The following is an example of determining the second detection threshold value corresponding to the current number of charge and discharge cycles of the lithium battery to be tested. First, the test lithium battery is subjected to charge and discharge cycles of the current number of charge and discharge cycles. For example, if the current number of charge and discharge cycles of the lithium battery to be tested as determined above is 350, then the test lithium battery can be subjected to 350 charge and discharge cycles. In each of these charge and discharge cycles, relevant parameters including voltage and capacity of the test lithium battery are collected, and the differential capacity curve of each charge and discharge cycle is determined accordingly, such as Figure 7 shown. Figure 7 The figure shows a plurality of differential capacity curves of a tested lithium battery according to an example of an embodiment of the present disclosure, wherein the gray curves of different color depths represent the differential capacity curves of different charge and discharge cycles, and the thicker part of the curve represents that the plurality of differential capacity curves overlap. Figure 7 In FIG. 1 , a predetermined high voltage section of the differential capacity curve is shown in a black frame, and the curve section with a voltage greater than 4V is taken as an example for explanation.

[0063] Next, the area difference between the differential capacity curve of each charge and discharge cycle after the first charge and discharge cycle of the tested lithium battery and the differential capacity curve of the first charge and discharge cycle at a predetermined high voltage section and the abscissa of the curve is determined to obtain a plurality of area differences. Figure 8 The example in the example is as follows, Figure 8 The predetermined high voltage section of the differential capacity curve of a certain subsequent charge-discharge cycle and the first charge-discharge cycle according to an example of an embodiment of the present disclosure is shown. Figure 8 In the figure, the dotted line represents the difference curve of the predetermined high voltage section of the differential capacity curve of the subsequent charge-discharge cycle and the first charge-discharge cycle, then the area enclosed by the difference curve and the abscissa (in Figure 8 (shown by shading in the figure) is the area difference between the differential capacity curve of the subsequent charge and discharge cycle and the differential capacity curve of the first charge and discharge cycle at the predetermined high voltage section and the horizontal coordinate of the curve. In a similar manner, the area difference between each charge and discharge cycle number and the differential capacity curve of the first charge and discharge cycle at the predetermined high voltage section and the horizontal coordinate can be determined to obtain multiple area differences. For example, in the case where the current charge and discharge cycle number of the lithium battery to be tested is 350 as determined above, 350 differential capacity curves and corresponding 350 area differences can be determined, of which the area difference corresponding to the first charge and discharge cycle is 0. Linear fitting of these area differences and their corresponding charge and discharge cycle numbers can be obtained as follows: Fig. 9 The first fitting curve shown in FIG. 1 can determine the first slope and the first intercept of the first fitting curve as the second detection threshold for detecting lithium plating corresponding to the current number of charge and discharge cycles.

[0064] In the above manner, by linearly fitting the area difference between the differential capacity curve of each charge and discharge cycle number of the tested lithium battery and the differential capacity curve of the first charge and discharge cycle at a predetermined high voltage section and the horizontal axis, a second detection threshold for detecting lithium deposition corresponding to the current charge and discharge cycle number is determined. Similarly, the second detection threshold corresponding to different charge and discharge cycle numbers can be determined and stored in the same manner, for example, in a lithium deposition detection device or a charging device or electric vehicle body that integrates a lithium deposition detection component, for use in the lithium deposition detection process of the lithium battery to be tested.

[0065] As described above, the second detection score for detecting lithium plating can be determined based on the area difference corresponding to the current number of charge and discharge cycles of the lithium battery to be tested, the historical area difference corresponding to the historical number of charge and discharge cycles, the current number of charge and discharge cycles, the historical number of charge and discharge cycles, and the second detection threshold corresponding to the current number of charge and discharge cycles. Specifically, the first measurement slope and the first measurement intercept of the first straight line can be determined as two points (current number of charge and discharge cycles, area difference) and (historical number of charge and discharge cycles, historical area difference). Simply, for example, when the first measurement slope and the first measurement intercept are both greater than the first slope and the first intercept in the second detection threshold, it can be determined that the lithium battery to be tested has lithium plating; otherwise, it can be determined that the lithium battery to be tested does not have lithium plating. Further, the first component of the second detection score can be determined based on the first measurement slope and the first slope in the second detection threshold, the second component of the second detection score can be determined based on the first measurement intercept and the first intercept in the second detection threshold, and the second detection score can be determined based on the first component and the second component of the second detection score for subsequent multi-dimensional feature fusion. For example, the first component and the second component of the second detection score can be determined based on the percentage of the first measurement slope and the first measurement intercept of the lithium battery to be tested and the first slope and the first intercept in the second detection threshold, the relative error, etc., and the second detection score can be determined, for example, by averaging, multiplying, etc. the first component and the second component. The embodiment of the present disclosure does not impose specific restrictions on the method of determining the second detection score and its components.

[0066] According to another example of the embodiment of the present disclosure, the relevant parameters of the lithium battery to be tested collected during the charging and discharging process may include the maximum battery capacity and internal resistance of the lithium battery to be tested at the current number of charge and discharge cycles. Among them, the maximum battery capacity refers to the capacity reached by the lithium battery to be tested when it is fully charged. Since the battery performance of the lithium battery will decay with the increase of the number of charge and discharge cycles, the maximum battery capacity corresponding to different numbers of charge and discharge cycles is different. The internal resistance measurement method of the lithium battery can generally include a DC internal resistance measurement method and an AC internal resistance measurement method. The embodiment of the present disclosure does not limit the specific method of the internal resistance measurement. Taking the DC internal resistance measurement method as an example for explanation, the lithium battery to be tested can be charged or discharged at a constant current within a predetermined time period with a predetermined current value. For example, the lithium battery to be tested is discharged at a constant current with a predetermined current value I within a predetermined time period △T, and the internal resistance of the lithium battery to be tested can be calculated according to the following formula:

[0067]

[0068] Among them, U t Indicates the voltage value at the beginning of discharge, U t+△T Represents the voltage value after the discharge △T time. The internal resistance of the lithium battery can be determined by measuring in the above manner at any time point in the current charge and discharge cycle. Ideally, the battery capacity can be measured at 50% of the maximum battery capacity, and the discharge time can be, for example, only 10 seconds, but the embodiment of the present disclosure does not specifically limit the time point of the internal resistance measurement and the charge and discharge time, and any other appropriate measurement time point and charge and discharge time can also be used.

[0069] In addition, the maximum battery capacity and internal resistance of the lithium battery to be tested at the first charge and discharge cycle can be obtained. Taking the lithium deposition detection of electric vehicles as an example, the lithium deposition detection method according to the embodiment of the present disclosure can be implemented as an independent lithium deposition detection device, or can be integrated into an existing charging device or integrated into the electric vehicle body as a lithium deposition detection component. In each charging and discharging process of the electric vehicle, the relevant parameters of the lithium battery can be collected and stored. When the lithium deposition detection is currently performed on the lithium battery to be tested, these historical storage parameters can be obtained and analyzed to obtain the maximum battery capacity and internal resistance at the first charge and discharge cycle. Afterwards, the third detection score for lithium deposition detection can be determined based on the maximum battery capacity and internal resistance at the current charge and discharge cycle number, the maximum battery capacity and internal resistance at the first charge and discharge cycle, and the third detection threshold corresponding to the current charge and discharge cycle number.

[0070] In this example of the embodiment of the present disclosure, the third detection threshold corresponding to different numbers of charge and discharge cycles may be pre-set, for example, pre-set in a lithium deposition detection device or a charging device or an electric vehicle body in which a lithium deposition detection component is integrated. The pre-set third detection threshold may be determined by performing multiple charge and discharge cycle tests on a test lithium battery having the same battery cell as the lithium battery to be tested, as described below.

[0071] Take determining the second detection threshold value corresponding to the current number of charge and discharge cycles of the lithium battery to be tested as an example for explanation. First, the test lithium battery is subjected to charge and discharge cycles of the current number of charge and discharge cycles. For example, if the current number of charge and discharge cycles of the lithium battery to be tested determined above is 350, then the test lithium battery can be subjected to 350 charge and discharge cycles. In each of these charge and discharge cycles, the maximum battery capacity and internal resistance of the test lithium battery are determined, thereby obtaining multiple internal resistances and multiple maximum battery capacities. Linear fitting of multiple internal resistances and corresponding multiple maximum battery capacities can be obtained as follows: Fig.10 The second fitting curve shown in FIG. The second slope and the second intercept of the second fitting curve can be determined as the third detection threshold for detecting lithium plating corresponding to the current number of charge and discharge cycles.

[0072] In the above manner, by linearly fitting the internal resistance and the maximum battery capacity of each charge and discharge cycle number of the tested lithium battery, the third detection threshold for detecting lithium plating corresponding to the current charge and discharge cycle number is determined. Similarly, the third detection threshold corresponding to different charge and discharge cycle numbers can be determined and stored in the same manner, for example, in a lithium plating detection device or a charging device or an electric vehicle body that integrates a lithium plating detection component, for use in the lithium plating detection process of the lithium battery to be tested.

[0073] As described above, the third detection score for detecting whether lithium deposition occurs can be determined based on the maximum battery capacity and internal resistance at the current number of charge and discharge cycles, the maximum battery capacity and internal resistance at the first charge and discharge cycle, and the third detection threshold corresponding to the current number of charge and discharge cycles. Specifically, the second measurement slope and the second measurement intercept of the second straight line can be determined as two points (the maximum battery capacity of the current number of charge and discharge cycles, the internal resistance of the current number of charge and discharge cycles), and (the maximum battery capacity of the first charge and discharge cycle, the internal resistance of the first charge and discharge cycle). Simply, for example, when the second measurement slope and the second measurement intercept are both greater than the second slope and the second intercept in the third detection threshold, it can be determined that lithium deposition exists in the lithium battery to be tested; otherwise, it can be determined that lithium deposition does not exist in the lithium battery to be tested. Further, the first component of the third detection score can be determined based on the second measurement slope and the second slope in the third detection threshold, the second component of the third detection score can be determined based on the second measurement intercept and the second intercept in the third detection threshold, and the third detection score can be determined based on the first component and the second component of the third detection score for subsequent multi-dimensional feature fusion. For example, the first component and the second component of the third detection score can be determined based on the percentage of the second measurement slope and the second measurement intercept of the lithium battery to be tested to the second slope and the second intercept in the third detection threshold, the relative error, etc., and the third detection score can be determined, for example, by averaging, multiplying, etc. the first component and the second component. The embodiment of the present disclosure does not specifically limit the method for determining the third detection score and its components. In this example, since the time △T required to determine the internal resistance can be very short, for example, it can be only 10 seconds or less, a faster lithium plating detection can be provided based on the third detection score in this example.

[0074] The above describes specific examples of determining the first detection score using the differential voltage curve of the lithium battery to be tested, determining the second detection score using the differential capacity curve of the lithium battery to be tested, and determining the third detection score using the maximum battery capacity and internal resistance of the lithium battery to be tested. In addition, the lithium plating detection method according to the embodiment of the present disclosure can also use the electrochemical impedance spectrum (EIS) of the lithium battery to be tested, the battery health state (SOH) decay, the charge and discharge temperature, etc. to determine more detection scores. The embodiment of the present disclosure does not specifically limit the method for determining the detection score and the number of detection scores.

[0075] After determining a plurality of detection scores, in step S240, a fusion detection score may be determined based on the plurality of detection scores, for example, the fusion detection score may be determined by weighting each of the plurality of detection scores. Afterwards, the fusion detection score of the lithium battery to be tested at the current charge and discharge cycle is compared with the corresponding fusion detection threshold. In an example of an embodiment of the present disclosure, the fusion detection threshold corresponding to different charge and discharge cycle numbers may be, for example, pre-set in a lithium deposition detection device or in a charging device or electric vehicle body in which a lithium deposition detection component is integrated. Specifically, the fusion detection threshold may be determined by weighting a plurality of detection thresholds corresponding to a plurality of detection scores. For example, the fusion detection threshold may be determined by weighting and summing the above-mentioned first detection threshold, the second detection threshold, the third detection threshold, and the like. However, the embodiments of the present disclosure are not limited thereto, and any other appropriate method may be used to determine the fusion detection threshold based on a plurality of detection thresholds. When the fusion detection score exceeds the fusion detection threshold, it may be determined that the lithium battery to be tested has lithium deposition; otherwise, it may be determined that the lithium battery to be tested does not have lithium deposition. When it is determined that the lithium battery to be tested has lithium deposition, an alarm message may be generated and output. For example, an alarm message may be sent to the driver of the vehicle containing the lithium battery to be tested to remind the driver of the lithium deposition phenomenon in the lithium battery and the possible risks.

[0076] The above describes the lithium battery lithium deposition detection method according to the embodiment of the present disclosure. Using the lithium battery lithium deposition detection method according to the embodiment of the present disclosure, a multi-dimensional lithium battery lithium deposition detection system can be constructed, which can construct a multi-dimensional feature of the lithium battery based on the data collected during the charge and discharge cycle of the lithium battery to obtain a multi-dimensional lithium deposition detection score, and fuse the multi-dimensional lithium deposition detection scores, thereby providing a comprehensive score for lithium battery lithium deposition, and realizing efficient and non-destructive lithium battery lithium deposition detection. In addition, different detection options can be provided to users according to actual lithium deposition detection needs. For example, if the user wants to obtain a more accurate lithium deposition detection result and does not require too much detection time, the lithium deposition detection can be performed based on the above-mentioned first detection score and the second detection score. For another example, if the user wants to quickly obtain the lithium deposition detection result, the lithium deposition detection can be performed based on the above-mentioned third detection score. The lithium battery lithium deposition detection method according to the embodiment of the present disclosure can be applied to multiple fields such as electric vehicles, mobile communications, and digital technology involving lithium batteries. Taking electric vehicles as an example, the lithium deposition detection method according to the embodiment of the present disclosure can be implemented as an independent lithium deposition detection device, or can be integrated into an existing charging device or into the electric vehicle body as a lithium deposition detection component. Then, each time the electric vehicle is connected to the lithium deposition detection device or each time it is charged, the vehicle battery can be efficiently and non-destructively tested for lithium deposition.

[0077] Refer to the following Fig.11A lithium plating detection device according to an embodiment of the present disclosure is described. Fig.11 FIG. 1 shows a schematic diagram of the structure of a lithium deposition detection device 1100 according to an embodiment of the present disclosure. Fig.11 As shown, the lithium deposition detection device may include a charge and discharge unit 1110, an acquisition unit 1120, a detection unit 1130, and a determination unit 1140. In addition to these four components, the lithium deposition detection device 1100 may also include more components, but since these components are not related to the embodiments of the present disclosure, the detailed description of their contents is omitted here. In addition, the details of the functions of the lithium deposition detection device 1100 are the same as those described above. Figure 2 The details of the steps of the described lithium plating detection method 200 are similar, so for the sake of brevity, repeated description of some contents is omitted here.

[0078] The charge and discharge unit 1110 is configured to charge and discharge the lithium battery to be tested with a preset current, and collect the relevant parameters of the lithium battery to be tested during the charge and discharge process. Here, the preset current can be any suitable current value determined according to actual detection needs or empirical parameters, and the embodiment of the present disclosure does not specifically limit this. When charging and discharging the lithium battery to be tested to collect the relevant parameters of the lithium battery, the charge and discharge unit 1110 can perform a complete charge and discharge cycle on the lithium battery, but the embodiment of the present disclosure is not limited thereto, and more or fewer charge and discharge cycles can also be performed. For example, when the lithium battery of an electric vehicle is tested for lithium deposition using the lithium deposition detection method according to the embodiment of the present disclosure, the current power of the vehicle may be any value, and the lithium battery can be discharged to zero power from the power of the arbitrary value, and then charged to full power again, and the relevant parameters of the lithium battery are collected during this period, and the number of charge and discharge cycles is less than 1 at this time. In addition, the relevant parameters of the lithium battery collected may include the voltage, current, charge and discharge rate, charging time, battery capacity, temperature, etc. of the lithium battery during the charge and discharge process, as will be further described in detail below.

[0079] The acquisition unit 1120 is configured to acquire the current number of charge and discharge cycles of the lithium battery to be tested, that is, the number of charge and discharge cycles that the lithium battery has undergone since its production. For example, the acquisition unit 1120 can determine the current number of charge and discharge cycles based on the current capacity of the lithium battery to be tested and its design capacity. As mentioned above, as the number of charge and discharge cycles of the lithium battery increases, its battery capacity will gradually decay. The current number of charge and discharge cycles of the lithium battery can be determined based on the decay characteristics of the battery capacity using the current capacity and design capacity of the lithium battery to be tested. In the case where the lithium battery to be tested is a power battery of a vehicle, the acquisition unit 1120 can also determine the current number of charge and discharge cycles of the lithium battery to be tested based on the current total mileage of the vehicle. For example, the current number of charge and discharge cycles can be calculated based on the maximum cruising range of the vehicle and the current total mileage.

[0080] The detection unit 1130 is configured to calculate multiple detection scores for detecting whether the lithium battery to be tested has lithium precipitated based on the current number of charge and discharge cycles of the lithium battery to be tested and the collected relevant parameters. In the embodiment of the present disclosure, the detection unit 1130 can use different features in the collected relevant parameters to determine multiple detection scores to evaluate whether the lithium battery to be tested has lithium precipitated from multiple dimensions, as described above with reference to Figures 2 to 10 Specifically described. The determination unit 1140 is configured to determine a fusion detection score based on the multiple detection scores. For example, the fusion detection score can be determined by weighting each of the multiple detection scores. When the fusion detection score exceeds the fusion detection threshold, the determination unit 1140 can determine that lithium plating exists in the lithium battery to be tested. At this time, an alarm message can be generated and output. For example, an alarm message can be sent to the driver of the vehicle containing the lithium battery to be tested to remind the driver of the lithium battery of the lithium plating phenomenon and the possible risks.

[0081] The above describes a lithium battery lithium deposition detection device according to an embodiment of the present disclosure. Using the lithium battery lithium deposition detection device according to an embodiment of the present disclosure, a multi-dimensional lithium battery lithium deposition detection system can be constructed, which can construct multi-dimensional features of lithium batteries based on data collected during the charge and discharge cycle of lithium batteries to obtain multi-dimensional lithium deposition detection scores, and fuse the multi-dimensional lithium deposition detection scores to provide a comprehensive score of lithium battery lithium deposition, thereby achieving efficient and non-destructive lithium battery lithium deposition detection.

[0082] In addition, the device according to the embodiment of the present disclosure (for example, a lithium battery lithium deposition detection device, etc.) can also be used with the aid of Fig.12 The exemplary computing device architecture shown is implemented. Fig.12 A schematic diagram showing the architecture of an exemplary computing device according to an embodiment of the present disclosure is shown. Fig.12 As shown, the computing device 1200 may include a bus 1210, one or more CPUs 1220, a read-only memory (ROM) 1230, a random access memory (RAM) 1240, a communication port 1250 connected to a network, an input / output component 1260, a hard disk 1270, etc. The storage device in the computing device 1200, such as the ROM 1230 or the hard disk 1270, can store various data or files used for computer processing and / or communication and program instructions executed by the CPU. The computing device 1200 may also include a user interface 1280. Of course, Fig.12 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Fig.12 One or more components in the computing device shown. The device according to the embodiment of the present disclosure may be configured to execute the lithium battery lithium deposition detection method according to the above embodiment of the present disclosure, or to implement the lithium battery lithium deposition detection device according to the above embodiment of the present disclosure.

[0083] The embodiments of the present disclosure may also be implemented as a computer-readable storage medium. Computer-readable instructions are stored on a computer-readable storage medium according to an embodiment of the present disclosure. When the computer-readable instructions are executed by a processor, the lithium battery lithium deposition detection method according to the embodiment of the present disclosure described with reference to the above figures may be executed. Computer-readable storage media include, but are not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0084] According to an embodiment of the present disclosure, a computer program product or a computer program is also provided, the computer program product or the computer program including computer-readable instructions, the computer-readable instructions being stored in a computer-readable storage medium. A processor of a computer device can read the computer-readable instructions from the computer-readable storage medium, and the processor executes the computer-readable instructions, so that the computer device executes the lithium battery lithium deposition detection method described in the above-mentioned various embodiments.

[0085] The program part of the technology can be considered as a "product" or "manufactured product" in the form of executable code and / or related data, which is participated in or realized by computer-readable media. Tangible and permanent storage media can include any memory or storage used by any computer, processor, or similar device or related module. For example, various semiconductor memories, tape drives, disk drives or any similar devices that can provide storage functions for software.

[0086] All software or parts of it may sometimes be communicated over a network, such as the Internet or other communications network. Such communications can load software from one computer device or processor to another. Therefore, another medium capable of transmitting software elements may also be used as a physical connection between local devices, such as light waves, radio waves, electromagnetic waves, etc., transmitted through cables, optical cables or air. Physical media used to carry carriers, such as cables, wireless connections or optical cables and the like, can also be considered as media that carry software. As used herein, unless limited to tangible "storage" media, other terms referring to computer or machine "readable media" refer to media that participate in the process of executing any instructions by the processor.

[0087] The present application uses specific words to describe examples of the embodiments of the present application. For example, "first / second example", "another example", etc. refer to a certain feature, structure or characteristic related to at least one example of the present application. Therefore, it should be emphasized and noted that "example" or "another example" mentioned twice or more in different positions in this specification does not necessarily refer to the same example. In addition, some features, structures or characteristics in one or more examples of the present application can be appropriately combined.

[0088] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0089] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0090] The above is an explanation of the present invention and should not be considered as a limitation thereof. Although several exemplary embodiments of the present invention have been described, it will be readily appreciated by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined in the claims. It should be understood that the above is an explanation of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. A lithium battery lithium deposition detection method, comprising: Charging and discharging the lithium battery to be tested with a preset current, and collecting relevant parameters of the lithium battery to be tested during the charging and discharging process; Obtaining the current number of charge and discharge cycles of the lithium battery to be tested; Calculating a plurality of detection scores for detecting whether the lithium battery to be tested has lithium deposition based on the current number of charge and discharge cycles and the relevant parameters; as well as A fusion detection score is determined based on the multiple detection scores, and when the fusion detection score exceeds a fusion detection threshold, it is determined that lithium deposition exists in the lithium battery to be tested.

2. The method according to claim 1, wherein: The relevant parameters include the voltage and battery capacity of the lithium battery to be tested during the charging and discharging process, and wherein the multiple detection scores for detecting whether the lithium battery to be tested has lithium deposition are calculated based on the current number of charge and discharge cycles and the relevant parameters, including: Determine a differential voltage curve of the lithium battery to be tested at the current charge and discharge cycle number based on the voltage and the battery capacity; extracting a valley capacity value at a predetermined valley of the differential voltage curve; Acquire a plurality of historical valley capacity values ​​at the predetermined valley of the differential voltage curve of a plurality of historical charge and discharge cycles of the lithium battery to be tested; A first detection score for detecting whether the lithium battery to be tested has lithium deposition is determined based on the valley capacity value of the lithium battery to be tested, the multiple historical valley capacity values, and a first detection threshold corresponding to the current number of charge and discharge cycles.

3. The method according to claim 2, wherein: The first detection threshold corresponding to the current number of charge and discharge cycles is determined by the following method: Performing charge and discharge cycles of the current number of charge and discharge cycles on a plurality of test lithium batteries, wherein the plurality of test lithium batteries are lithium batteries having the same cells as the lithium battery to be tested; For each test lithium battery in the plurality of test lithium batteries: Determining a differential voltage curve for each charge and discharge cycle of the test lithium battery; Extracting the valley capacity value at the predetermined valley of the differential voltage curve of each charge and discharge cycle to obtain multiple valley capacity values ​​of the tested lithium battery; Calculating a plurality of statistical indicators of the tested lithium battery based on the plurality of valley capacity values; Integrating the plurality of statistical indicators to generate an integrated statistical indicator of the tested lithium battery; as well as The first detection threshold is determined based on the integrated statistical indicator of each tested lithium battery of the plurality of tested lithium batteries.

4. The method according to claim 2, wherein: Determining a first detection score for detecting whether the lithium battery to be tested has lithium deposition based on the valley capacity value, the multiple historical valley capacity values, and a first detection threshold corresponding to the current number of charge and discharge cycles includes: Calculating a plurality of statistical indicators of the lithium battery to be tested based on the valley capacity value of the lithium battery to be tested and the plurality of historical valley capacity values; Integrating the multiple statistical indicators to generate an integrated statistical indicator of the lithium battery to be tested; The first detection score is determined based on the integrated statistical indicator of the lithium battery to be tested and the first detection threshold.

5. The method according to claim 3, wherein: The multiple statistical indicators include at least one of the following: variance, standard deviation, mean absolute deviation, coefficient of variation, Z score, correlation coefficient, mean absolute error, and data quantile.

6. The method according to claim 1, wherein: The relevant parameters include the voltage and battery capacity of the lithium battery to be tested during the charging and discharging process, and wherein the multiple detection scores for detecting whether the lithium battery to be tested has lithium deposition are calculated based on the current number of charge and discharge cycles and the relevant parameters, including: Determine a differential capacity curve of the lithium battery to be tested at the current charge and discharge cycle number based on the voltage and the battery capacity; Obtaining a differential capacity curve of the lithium battery to be tested at the first charge-discharge cycle and the number of historical charge-discharge cycles other than the first charge-discharge cycle; Calculating the area difference between the differential capacity curve of the current charge and discharge cycle number and the first charge and discharge cycle at a predetermined high voltage section and the abscissa of the curve; Calculating the historical area difference between the differential capacity curve of the historical charge and discharge cycle number and the first charge and discharge cycle and the abscissa of the curve at a predetermined high voltage section; The second detection score is determined based on the area difference, the historical area difference, the current charge and discharge cycle number, the historical charge and discharge cycle number, and a second detection threshold corresponding to the current charge and discharge cycle number.

7. The method according to claim 6, wherein: The second detection threshold corresponding to the current number of charge and discharge cycles is determined by the following method: Performing charge and discharge cycles for the current number of charge and discharge cycles on the test lithium battery, wherein the test lithium battery is a lithium battery having the same battery cell as the lithium battery to be tested; Determining a differential capacity curve for each charge and discharge cycle of the test lithium battery; Calculating the area difference between the differential capacity curve of each charge and discharge cycle after the first charge and discharge cycle of the tested lithium battery and the differential capacity curve of the first charge and discharge cycle and the abscissa of the curve at a predetermined high voltage section to obtain a plurality of area differences; A linear fit is performed on the multiple area differences and the corresponding charge and discharge cycle numbers to obtain a first fitting curve, and a first slope and a first intercept of the first fitting curve are obtained as the second detection threshold.

8. The method according to claim 7, wherein: Determining the second detection score based on the area difference, the historical area difference, the current number of charge-discharge cycles, and a second detection threshold corresponding to the historical number of charge-discharge cycles includes: Determine a first measurement slope and a first measurement intercept of a first straight line using the current charge-discharge cycle number and the area difference, and the historical charge-discharge cycle number and the historical area difference as two points respectively; determining a first component of the second detection score based on the first measurement slope and a first slope in the second detection threshold; determining a second component of the second detection score based on the first measurement intercept and a first intercept in the second detection threshold; and The second detection score is determined based on the first component of the second detection score and the second component of the second detection score.

9. The method according to claim 1, wherein: The relevant parameters include the maximum battery capacity and internal resistance of the lithium battery to be tested at the current number of charge and discharge cycles, and wherein the multiple detection scores for detecting whether the lithium battery to be tested has lithium deposition are calculated based on the current number of charge and discharge cycles and the relevant parameters include: Obtaining the maximum battery capacity and internal resistance of the battery to be tested at the first charge and discharge cycle; The third detection score is determined based on the maximum battery capacity and internal resistance at the current charge and discharge cycle number, the maximum battery capacity and internal resistance at the first charge and discharge cycle, and a third detection threshold corresponding to the current charge and discharge cycle number.

10. The method according to claim 9, wherein: The third detection threshold corresponding to the current number of charge and discharge cycles is determined by the following method: Performing charge and discharge cycles for the current number of charge and discharge cycles on the test lithium battery, wherein the test lithium battery is a lithium battery having the same battery cell as the lithium battery to be tested; Determining the internal resistance and maximum battery capacity of the tested lithium battery in each charge and discharge cycle to obtain multiple internal resistances and multiple maximum battery capacities; Linear fitting is performed on the multiple internal resistances and the multiple maximum battery capacities to obtain a second fitting curve, and a second slope and a second intercept of the second fitting curve are obtained as the third detection threshold.

11. The method according to claim 10, wherein: Determining a third detection score for detecting whether the lithium battery to be tested has lithium deposition based on the maximum battery capacity and internal resistance at the current charge and discharge cycle number, the maximum battery capacity and internal resistance at the first charge and discharge cycle, and a third detection threshold corresponding to the current charge and discharge cycle number includes: Determine a second measurement slope and a second measurement intercept of a second straight line using the maximum battery capacity and internal resistance at the current charge and discharge cycle number and the maximum battery capacity and internal resistance at the first charge and discharge cycle as two points respectively; determining a first component of the third detection score based on the second measurement slope and a second slope in the third detection threshold; determining a second component of the third detection score based on the second measurement intercept and a second intercept in the third detection threshold; and The third detection score is determined based on the first component of the third detection score and the second component of the third detection score.

12. The method according to claim 1, wherein: determining a fused detection score based on the plurality of detection scores comprises weighting individual detection scores in the plurality of detection scores to determine the fused detection score, and The fusion detection threshold is determined by weighting multiple detection thresholds corresponding to the multiple detection scores.

13. The method according to claim 1, wherein: The lithium battery to be tested is a power battery of a vehicle, and obtaining the current number of charge and discharge cycles of the lithium battery to be tested includes: The current number of charge and discharge cycles of the lithium battery to be tested is determined based on the current total mileage of the vehicle.

14. A lithium battery lithium deposition detection device, the device comprising: A charge and discharge unit, configured to charge and discharge the lithium battery to be tested with a preset current, and collect relevant parameters of the lithium battery to be tested during the charge and discharge process; An acquisition unit, configured to acquire the current charge and discharge cycle number of the lithium battery to be tested; A detection unit, configured to calculate a plurality of detection scores for detecting whether the lithium battery to be tested has lithium deposition based on the current number of charge and discharge cycles and the relevant parameters; as well as The determination unit is configured to determine a fusion detection score based on the multiple detection scores, and determine that lithium plating exists in the lithium battery to be tested when the fusion detection score exceeds a fusion detection threshold.

15. A lithium battery lithium deposition detection device, comprising: one or more processors; as well as One or more memories, wherein the memories store computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 13.

16. A computer-readable storage medium having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by a processor, the processor is caused to execute the method according to any one of claims 1 to 13.

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