A method and system for detecting lithium plating in a battery

Abstract

The application belongs to the technical field of battery detection, and relates to a battery lithium precipitation detection method and system. The method comprises the following steps: obtaining a first temperature change amount and a first strain change of a to-be-detected battery in a variable-temperature environment, obtaining a thermal strain coefficient of the battery based on the first temperature change amount and the first strain change; obtaining a second temperature change amount and a second strain change of the to-be-detected battery in a storage process after charging, obtaining a battery thermal stress change by using the thermal strain coefficient and the second temperature change amount, obtaining a difference between the second strain change and the battery thermal stress change, and the difference is a mechanical strain; judging whether the mechanical strain abnormally rises in a storage time period, if yes, the to-be-detected battery precipitates lithium, and if not, the to-be-detected battery does not precipitate lithium. In-situ high-sensitivity detection of battery lithium precipitation can be realized by the application.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery detection, and more particularly to a battery lithium precipitation detection method and system. BACKGROUND

[0002] Intelligent large transformers, direct current converter devices and power distribution systems have very high requirements for power quality and safety. As a key component of energy storage or backup power, the safety problem of lithium batteries is a key bottleneck restricting their further development. In particular, the lithium precipitation phenomenon inside lithium batteries is prone to cause thermal runaway and even major safety accidents. Lithium precipitation refers to the deposition of lithium ions on the surface of the negative electrode during the charging process, instead of being fully embedded in the negative electrode material of the battery. This phenomenon usually occurs during the charging process of lithium batteries, especially under conditions of high charging current, low temperature, overcharging or uneven charging. This not only reduces the battery capacity, but also causes lithium dendrite growth, leading to internal short circuit and thermal runaway.

[0003] Currently, through nuclear magnetic resonance, neutron diffraction, scanning electron microscopy (SEM) and other technologies, the deposition and distribution of metal lithium inside the lithium battery can be directly observed, but the equipment is expensive, the operation is complex and the battery structure needs to be destroyed, which cannot be applied to large-scale online monitoring. In addition, the occurrence of lithium precipitation is usually indirectly judged by analyzing the charge-discharge curve, electrochemical impedance spectrum (EIS) and other parameters of the battery, but this method can only determine whether lithium precipitation occurs in the entire battery, cannot give specific location information, and is not real-time. And the electrochemical method represents the average state of the whole battery, compared with the non-electric parameters that can represent the local strain, the sensitivity of the lithium precipitation monitoring is higher. In traditional sensing technology, temperature, stress and acoustic sensors have been used for internal information monitoring of batteries, but they are mostly limited to macroscopic parameters, making it difficult to accurately characterize the micro lithium precipitation behavior. And the traditional method of judging battery lithium precipitation based on stress ignores the thermal strain caused by temperature changes during battery cycling, but the temperature effect of the actual battery often significantly affects its strain signal.

[0004] Therefore, how to realize high-precision in-situ lithium precipitation detection of lithium ion batteries is the key problem at present, and high-precision in-situ lithium precipitation detection can provide a scientific basis for battery health state evaluation, safety management and performance optimization. SUMMARY

[0005] In view of the defects of the prior art, the purpose of the present application is to provide a battery lithium precipitation detection method and system, which aims to solve the problem that lithium ion batteries are difficult to realize high-precision in-situ lithium precipitation detection.

[0006] To achieve the above-mentioned purpose, in a first aspect, the present application provides a battery lithium precipitation detection method, comprising:

[0007] S1 acquires the first temperature change and the first strain change of the battery under test in a variable temperature environment, and obtains the thermal strain coefficient of the battery based on the first temperature change and the first strain change.

[0008] S2 acquires the second temperature change and the second strain change of the battery under test during the storage process after charging. The thermal stress change of the battery is obtained by using the thermal strain coefficient and the second temperature change. The difference between the second strain change and the battery thermal stress change is obtained. The difference is the mechanical strain.

[0009] S3 determines whether the mechanical strain has abnormally increased during the rest period. If so, lithium plating has occurred in the battery under test; otherwise, lithium plating has not occurred.

[0010] This application can obtain more sensitive and accurate lithium plating detection results by using non-electrical parameters that characterize local strain.

[0011] Furthermore, in step S1, the thermal strain coefficient is obtained using the following formula:

[0012]

[0013] in, This represents the first strain change in the battery; The coefficient of thermal strain; This represents the first temperature change.

[0014] Furthermore, in step S2, the expression for the change in battery thermal stress is:

[0015]

[0016] in, For changes in battery thermal stress, The thermal strain coefficient is... This is the second temperature change.

[0017] Furthermore, in step S3, if the strain rise phenomenon of the mechanical strain is not obvious, the following method is used to determine whether lithium plating has occurred in the battery under test:

[0018] exist If any one exists within the time period t Time makes If the test cell is found to have undergone lithium plating, it is determined that lithium plating has occurred.

[0019] If there is no such thing as At time t, the battery under test has not undergone lithium plating;

[0020] in, This represents the change in strain during the suspension period. dt To and a corresponding time variation, represents a resting start time, represents a resting end time.

[0021] Further, before step S1, a plurality of strain sensors are arranged on the inside or surface of the battery to be tested, and the strain sensors are arranged in a two-dimensional grid shape.

[0022] A temperature sensor is arranged at each grid intersection in the two-dimensional grid shape.

[0023] In a second aspect, the application provides a battery lithium precipitation detection system for implementing the detection method of any one of the preceding aspects, and the system comprises:

[0024] a thermal strain coefficient acquisition module, configured to acquire a first temperature variation and a first strain variation of a battery to be tested in a variable temperature environment, and acquire a thermal strain coefficient of the battery based on the first temperature variation and the first strain variation;

[0025] a mechanical strain acquisition module, configured to acquire a second temperature variation and a second strain variation of the battery to be tested in a resting process after charging, acquire a battery thermal stress variation by using the thermal strain coefficient and the second temperature variation, and acquire a difference between the second strain variation and the battery thermal stress variation as a mechanical strain;

[0026] a lithium precipitation discrimination module, configured to judge whether the mechanical strain abnormally rises in a resting time period, and if yes, the battery to be tested has lithium precipitation, and if not, the battery to be tested does not have lithium precipitation.

[0027] In a third aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. When the computer program runs on a processor, the processor executes the detection method described in any one of the possible implementation manners of the first aspect.

[0028] In a fourth aspect, a computer program product is provided, and when the computer program product runs on a processor, the processor executes the detection method described in any one of the possible implementation manners of the first aspect.

[0029] It can be understood that the beneficial effects of the second aspect to the fourth aspect described above can be referred to the related description in the first aspect, and will not be repeated here.

[0030] Overall, compared with the prior art, the above technical solutions conceived by the application have the following beneficial effects:

[0031] (1) The application monitors the strain change caused by the lithium ion battery graphite negative electrode lithium intercalation through a strain sensor, realizes real-time detection of lithium precipitation of the battery by extracting abnormal strain signals caused by lithium precipitation behavior. Compared with the most intuitive nuclear magnetic resonance and neutron diffraction method, which needs to disassemble the battery, its single detection amount is in milligram level, the price is expensive, and it cannot be applied to actual life scenes. However, the method is based on real-time strain detection, low cost, and can be non-destructive detection, which is very suitable for large-scale production and lithium ion batteries used in intelligent power distribution systems and other scenes.

[0032] (2) Based on strain monitoring, the application can detect lithium precipitation behavior in real time and in situ. The occurrence time and evolution process of lithium precipitation are complex and variable, and traditional post-test methods are difficult to capture its dynamic characteristics, which hinders the study of lithium precipitation mechanism and the optimization of fast charging strategy. The dynamic process of lithium deposition-dissolution directly affects the formation of "dead lithium", further causing electrolyte consumption and capacity loss, and affecting the long cycle stability of the battery.

[0033] (3) The strain sensor is arranged in a two-dimensional grid shape on the battery, and the temperature sensor is arranged at each grid intersection of the two-dimensional grid shape. Compared with the single-point layout method, the application can obtain more comprehensive lithium precipitation information of the battery. In addition, more accurate and comprehensive lithium precipitation information is helpful for subsequent analysis of whether the battery to be tested has manufacturing defects or unreasonable size design. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a battery lithium precipitation detection method flowchart provided by an embodiment of the application;

[0035] Figure 2 is a thermal strain coefficient change trend diagram obtained by using different temperature change amounts and corresponding temperature strains provided by an embodiment of the application;

[0036] Figure 3 is a mechanical strain value fitting curve diagram of the battery to be tested in a period of time when the battery to be tested does not precipitate lithium provided by an embodiment of the application;

[0037] Figure 4 is a strain change amount-time ratio fitting curve diagram of the battery to be tested when the battery to be tested does not precipitate lithium provided by an embodiment of the application;

[0038] Figure 5 is a mechanical strain value fitting curve diagram of the battery to be tested when the battery to be tested precipitates lithium provided by an embodiment of the application;

[0039] Figure 6 is a lithium precipitation characteristic parameter value fitting curve diagram of the battery to be tested when the battery to be tested precipitates lithium provided by an embodiment of the application;

[0040] Figure 7is a mechanical stress strain value fitting curve schematic diagram of a battery under test when slight lithium precipitation occurs, provided by an embodiment of the present application.

[0041] Figure 8 is a slight lithium precipitation curve schematic diagram fitted by a lithium precipitation characteristic parameter value when a battery under test has slight lithium precipitation, provided by an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0043] The term "and / or" used herein is a description of an association relationship between associated objects, which means that there can be three relationships, for example, A and / or B can mean that there are three cases of A alone, A and B together, and B alone. The symbol " / " in this paper represents the relationship of or, for example, A / B represents A or B.

[0044] The terms "first" and "second" and the like in the specification and claims herein are used to distinguish different objects, and are not used to describe a specific order of the objects. For example, the first response message and the second response message are used to distinguish different response messages, and are not used to describe a specific order of the response messages.

[0045] In the embodiments of the present application, the words "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Rather, the use of "exemplary" or "for example" is intended to present relevant concepts in a concrete manner.

[0046] In the description of the embodiments of the present application, unless otherwise specified, "a plurality of" means two or more, for example, a plurality of processing units means two or more processing units, and the like; a plurality of elements means two or more elements, and the like.

[0047] The embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

[0048] The lithium precipitation detection method provided in the application belongs to an in-situ detection method. The strain sensor is used to monitor the strain change caused by the intercalation of lithium ions into the graphite negative electrode in the lithium ion battery. The abnormal strain signal caused by the lithium precipitation behavior is extracted to realize real-time monitoring of the lithium precipitation of the battery. The general concept is as follows: during the charging and discharging process of the battery, the intercalation / deintercalation of lithium ions between the graphite layers will cause the interlayer spacing to shrink / enlarge, which is macroscopically manifested as the volume shrinkage / swelling and strain change of the graphite negative electrode. When lithium precipitation occurs, the lithium ions are not intercalated between the graphite layers but deposited on the surface of the graphite, which will cause the strain increase rate and total strain change of this process to be lower than that of the normal intercalation process. In addition, the deposited lithium metal will be re-intercalated into the incompletely intercalated graphite after the charging is completed, resulting in an abnormal strain increment. Through in-situ X-ray diffraction (i.e., in-situ XRD) and nuclear magnetic resonance verification, the strain change characteristics of the two processes are extracted as the criterion for lithium precipitation.

[0049] Specifically, the detection method for lithium precipitation of a battery provided in the application is described through the following embodiments. As shown in the following table, the detection method includes the following steps: Figure 1

[0050] S1: obtaining a first temperature change and a first strain change of the battery to be tested in a variable temperature environment, and obtaining a thermal strain coefficient of the battery based on the first temperature change and the first strain change;

[0051] S2: obtaining a second temperature change and a second strain change of the battery to be tested after charging in a standby process, obtaining a thermal stress change of the battery by using the thermal strain coefficient and the second temperature change, obtaining a difference between the second strain change and the thermal stress change of the battery, and the difference being a mechanical strain;

[0052] S3: judging whether the mechanical strain abnormally rises in the standby time period. If yes, the battery to be tested has lithium precipitation. If no, the battery to be tested does not have lithium precipitation. Specifically, the abnormal rise of the mechanical strain refers to the phenomenon that the strain value of a local area of a material is suddenly significantly higher than that of the surrounding area under the action of mechanical load, which is usually related to internal defects, stress concentration or microstructure inhomogeneity of the material. In this embodiment, the mechanical strain value fitting curve graph of the battery to be tested is analyzed by taking the mechanical strain value fitting curve graph of the battery without lithium precipitation as a reference to obtain the result of whether the mechanical strain of the battery to be tested abnormally rises, and then the lithium precipitation of the battery to be tested is judged.

[0053] The strain sensor and the temperature sensor are respectively arranged on the surface or inside of the battery to be tested in advance. The temperature change and the strain change of the battery to be tested are recorded by using the strain sensor and the temperature sensor.

[0054] ​In step S1, the battery to be tested is placed in a variable temperature environment, for example, the battery to be tested is placed in a temperature environment varying in the range of 0-30℃, and the temperature variation can be irregular or periodic, and the strain variation caused by the temperature variation of the battery to be tested is recorded during the temperature variation. Then the thermal strain coefficient of the battery is calculated according to the strain variation, and the thermal strain coefficient is obtained by using the following formula:

[0055]

[0056] wherein, is the first strain variation of the battery; is the thermal strain coefficient; is the first temperature variation.

[0057] In step S2, the second strain variation is formed by the combination of mechanical strain and thermal stress variation. After the battery to be tested is charged, it is sufficiently rested, and the temperature and strain variation during the resting process are recorded. The thermal strain variation value with temperature recorded in the present embodiment is shown in Table 1:

[0058]

[0059] The data in Table 1 is fitted into a curve graph, as shown in Figure 2 , the thermal strain coefficient obtained by using different temperature variation and the corresponding temperature strain increases linearly with the temperature variation. Figure 2 In the formula, y=20.884x+0.7303 is the linear regression equation of the fitting, x is the strain variation value, y is the thermal strain coefficient value, R 2 is the goodness of fit, indicating that the linearity of the fitting is high.

[0060] The calculation formula of the second strain variation is:

[0061]

[0062] wherein, ε is the second strain variation, is the thermal stress variation of the battery, is the mechanical strain. The thermal stress variation and the mechanical strain of the battery are calculated by the following formulas, respectively:

[0063]

[0064] wherein, is the thermal strain coefficient, is the second temperature variation.

[0065] As shown in Figure 3As shown, this is a fitted curve of the mechanical strain value of the battery under test over a period of time before lithium plating. The curve is smooth, and the strain value gradually decreases. Figure 4 As shown, the ratio of strain change in the cell to time when lithium plating is not yet complete. As the suspension process begins, the curve rises and then gradually flattens out, becoming smooth.

[0066] When lithium plating occurs in the battery under test, lithium ions are not embedded between graphite layers but are deposited on the graphite surface. After charging is completed, the deposited lithium metal will be re-embedded in the unfilled graphite, resulting in abnormal strain increments during the storage period.

[0067] Table 2 below shows some of the total strain changes recorded during the first 6.5 minutes of severe lithium plating in the tested battery, and some of the mechanical strain values ​​obtained according to the aforementioned calculation method:

[0068] Table 2. Parameter values ​​recorded during severe lithium plating.

[0069]

[0070] like Figure 5 The figure shows the fitted curve of mechanical strain values ​​when severe lithium plating occurs; that is, the severe lithium plating curve in the figure is obtained by fitting the mechanical strain values ​​in Table 2. Figure 6 The figure shows a schematic diagram of a severe lithium plating curve obtained by fitting the lithium plating characteristic parameter values. Figure 5 and Figure 6 As can be seen from the data, in cases of severe lithium plating, the graphite reintercalation reaction is prolonged and intense, resulting in a significant abnormal rise in the curve. Therefore, when a significant abnormal rise in mechanical strain occurs during the storage process, it can be clearly determined that the battery under test has experienced severe lithium plating.

[0071] In other preferred embodiments, such as Figure 7 As shown in the figure, the mechanical stress-strain value fitting curve of the battery under test when slight lithium plating occurs is shown. It can be seen from the figure that when the amount of lithium plating in the battery under test is low, the strain rise of mechanical stress is not obvious. It is not even possible to see the rise in the curve from the fitting curve. That is, the curve is similar to the curve when there is no lithium plating. Both are smooth curves and cannot clearly determine whether lithium plating has occurred in the battery under test.

[0072] Therefore, a differential method is introduced to extract the characteristic signal, redefine the characteristic parameters of lithium plating, and determine whether lithium plating has occurred based on these characteristic parameters.

[0073] Specifically, remember t The strain at time 1 is ε 1. Remember t The strain at time 2 is ε 2. The change in strain is:

[0074]

[0075] The time variation is:

[0076]

[0077] The characteristic parameter of lithium precipitation is defined as the ratio of the strain variation of the battery cell to time, i.e. .

[0078] In summary, in the aforementioned step S3, when the strain lifting phenomenon of mechanical stress is not obvious, the following method is used to determine whether the battery under test has lithium precipitation:

[0079] In the time period, if there is any t moment that makes , then it is determined that the battery under test has lithium precipitation;

[0080] If there is no t moment that can make , then the battery under test has no lithium precipitation;

[0081] wherein is the strain variation in the standby time period, dt is the time variation corresponding to , represents the standby start time, represents the standby end time. As shown in Table 3, the strain variation values and the corresponding lithium precipitation characteristic parameter values within the first 6.5 minutes when the battery under test has slight lithium precipitation are recorded in this embodiment

[0082] (The remaining recorded values are too numerous to be displayed here):

[0083] Table 3 Strain variation values and corresponding lithium precipitation characteristic parameter values when lithium precipitation is slight

[0084]

[0085] As shown in Figure 8 , the slight lithium precipitation curve fitted from the lithium precipitation characteristic parameter values is a non-smooth curve, and there is an obvious curve inflection (i.e., abnormal strain lifting) within 0-20 minutes, and the graphite intercalation reaction has a short duration and is relatively mild.

[0086] The above lithium precipitation determination method excludes the influence of thermal stress during battery charging and discharging, avoids interference with lithium precipitation determination, is more sensitive in lithium precipitation detection, and is more accurate in detection results.

[0087] ​In other preferred embodiments, a plurality of strain sensors are arranged on the inside or surface of the battery to be tested, and the strain sensors are arranged in a two-dimensional grid shape, such as a square grid, a hexagonal grid, etc. A temperature sensor is arranged at each intersection of the grid in the two-dimensional grid shape. Compared with the scheme of arranging a sensor at a single point on the battery, this arrangement can simultaneously detect lithium precipitation at multiple locations of the battery, and the location information of the lithium precipitation can help analyze whether the battery has manufacturing defects and whether the size design of the battery is reasonable. Because the location where lithium precipitation occurs does not necessarily have manufacturing defects, but places with manufacturing defects are prone to lithium precipitation. For example, points where the local NP ratio is less than 1 due to uneven slurry coating are prone to lithium precipitation; lithium precipitation is also prone to occur due to uneven rolling of the pole piece, which hinders the migration of lithium ions in the negative electrode; when the size of the battery is too large, the non-uniformity of the battery is aggravated, and the current density at the edge is too large, which also easily induces lithium precipitation. If the local current density of the battery is too large, lithium precipitation is very likely to occur at some locations.

[0088] The lithium precipitation detection technology designed in the present application can improve the reliability and safety of lithium batteries in application scenarios such as smart grids and smart power distribution systems.

[0089] The battery lithium precipitation detection system provided in the present application is described below, and the battery lithium precipitation detection system described below can be correspondingly referred to the battery lithium precipitation detection method described above.

[0090] The present embodiment provides a battery lithium precipitation detection system for implementing the detection method as described above, and the system comprises:

[0091] A thermal strain coefficient acquisition module is configured to acquire a first temperature change and a first strain change of the battery to be tested in a variable temperature environment, and acquire a thermal strain coefficient of the battery based on the first temperature change and the first strain change;

[0092] A mechanical stress acquisition module is configured to acquire a second temperature change and a second strain change of the battery to be tested after charging in a storage process, acquire a thermal stress change of the battery by using the thermal strain coefficient and the second temperature change, and acquire a difference between the second strain change and the thermal stress change of the battery, wherein the difference is a mechanical strain;

[0093] A lithium precipitation discrimination module is configured to determine whether the mechanical strain abnormally rises in the storage time period, and if so, the battery to be tested has lithium precipitation, and if not, the battery to be tested does not have lithium precipitation.

[0094] It can be understood that the detailed function implementation of each unit / module described above can be referred to the description in the foregoing method embodiments, which will not be repeated here.

[0095] It should be understood that the above device is used to execute the method in the above embodiment, the corresponding program module in the device, the implementation principle and technical effect are similar to the description in the above method, the working process of the device can refer to the corresponding process in the above method, and details are not described here.

[0096] Based on the method in the above embodiment, the embodiment of the application provides a computer readable storage medium, the computer readable storage medium stores a computer program, and when the computer program runs on a processor, the processor executes the method in the above embodiment.

[0097] Based on the method in the above embodiment, the embodiment of the application provides a computer program product, when the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0098] It can be understood that the processor in the embodiment of the application can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor, or any conventional processor.

[0099] The method steps in the embodiment of the application can be realized in the form of hardware, or realized in the form of software instructions executed by the processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in a random access memory (RAM), a flash memory, a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM), a register, a hard disk, a mobile hard disk, a CD-ROM or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor, so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC.

[0100] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in or transmitted by a computer readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media sets. The available media can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0101] It can be understood that various numerical numbers involved in the embodiments of the present application are only distinguished for convenience of description, and are not used to limit the scope of the embodiments of the present application.

[0102] Those skilled in the art easily understand that the above only describes the preferred embodiments of the present application and is not used to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting lithium plating in batteries, characterized in that, The method comprises the following steps: S1: obtaining a first temperature change and a first strain change of a battery to be tested in a variable temperature environment, and obtaining a thermal strain coefficient of the battery based on the first temperature change and the first strain change; S2: obtaining a second temperature change and a second strain change of the battery to be tested in a storage process after charging, obtaining a thermal stress change of the battery by using the thermal strain coefficient and the second temperature change, and obtaining a difference between the second strain change and the thermal stress change of the battery, the difference being a mechanical strain; S3: determining whether the mechanical strain abnormally rises in a storage time period, and if yes, the battery to be tested generates lithium precipitation, and if not, the battery to be tested does not generate lithium precipitation.

2. The method of claim 1, wherein the lithium plating is detected by measuring the current and voltage of the battery. In step S1, the thermal strain coefficient is obtained by using the following formula: wherein, is a first strain change of the battery; is a thermal strain coefficient; is a first temperature change amount.

3. The method of claim 1, wherein the step of detecting the lithium plating comprises: In step S2, an expression of the thermal stress change of the battery is as follows: wherein, is a battery thermal stress change, is a thermal strain coefficient, is a second temperature change amount.

4. The method of claim 1, wherein the lithium plating is detected by measuring the current and voltage of the battery. In step S3, when the mechanical strain rising phenomenon is not obvious, the following method is used to determine whether the battery to be tested generates lithium precipitation: In If there is any one t Moment, so that Then determine that the battery to be tested occurs lithium, otherwise it is not determined to occur lithium; wherein, is the amount of strain change over the rest period, dt is the amount of time change corresponding to is the amount of time change corresponding to denotes the rest start time, denotes the rest end time.

5. The method of claim 1, wherein the step of detecting the lithium plating comprises: determining a voltage difference between the first voltage and the second voltage; and determining that the lithium plating has occurred when the voltage difference is less than a predetermined voltage difference. Before step S1, a plurality of strain sensors are arranged in the interior or on the surface of the battery to be tested, and the strain sensors are arranged in a two-dimensional grid shape.

6. The method of claim 5, wherein the step of detecting the lithium precipitation is performed by measuring the voltage of the battery. A temperature sensor is arranged at each grid intersection in the two-dimensional grid shape. 7.A system for detecting lithium plating of a battery, the system comprising: The system is used for implementing the detection method according to any one of claims 1-6, and the system comprises: a thermal strain coefficient obtaining module, configured to obtain a first temperature change and a first strain change of a battery to be tested in a variable temperature environment, and obtain a thermal strain coefficient of the battery based on the first temperature change and the first strain change; a mechanical strain obtaining module, configured to obtain a second temperature change and a second strain change of the battery to be tested in a storage process after charging, obtain a thermal stress change of the battery by using the thermal strain coefficient and the second temperature change, and obtain a difference between the second strain change and the thermal stress change of the battery as a mechanical strain; a lithium precipitation determining module, configured to determine whether the mechanical strain abnormally rises in a storage time period, and if yes, the battery to be tested generates lithium precipitation, and if not, the battery to be tested does not generate lithium precipitation.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: When the computer program runs on the processor, the processor is caused to perform the detection method according to any one of claims 1-6.

9. A computer program product, characterised in that, When the computer program product runs on the processor, the processor is caused to perform the detection method according to any one of claims 1-6.

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