Lithium battery electrolyte shortage early warning method, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a lithium battery electrolyte shortage early warning method, electronic equipment and a storage medium. The method comprises the following steps: establishing a capacity attenuation prediction model of a battery according to actual measurement data of a working condition; according to a film forming reaction formula, establishing a change relationship between the capacity fading prediction model and the negative electrode porosity of the battery to obtain a negative electrode porosity prediction model; obtaining a negative electrode porosity threshold value under the condition of battery failure according to electrochemical simulation or actual measurement; and according to the negative electrode porosity prediction model and the negative electrode porosity threshold, carrying out lithium battery liquid shortage early warning under different working conditions. According to the embodiment of the invention, online lossless liquid shortage early warning is realized.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a lithium battery electrolyte shortage warning method, electronic equipment and storage medium. Background Art

[0002] Lithium battery electrolyte is the ion transport carrier in the battery, composed of lithium salt, organic solvent, and a small amount of additives. The electrolyte plays the role of conducting ions between the positive and negative electrodes of the lithium battery. It is the guarantee for lithium-ion batteries to obtain advantages such as high voltage and high specific energy. The electrolyte has a crucial impact on the reliability of the battery.

[0003] In actual application, the amount of electrolyte in lithium-ion batteries mainly exists in two forms: one is used to fill the pores of the pole piece and the diaphragm to ensure the dynamic performance, and the other is in the form of free liquid. During the battery aging process, as the charge and discharge proceed, the electrolyte is continuously consumed, forming a film at the negative electrode, and the negative electrode porosity gradually decreases. When the negative electrode porosity decreases to a certain extent, the battery capacity will decay faster, affecting the battery life performance. Therefore, liquid shortage warning is very important for battery performance. At present, most of the porosity assessments are based on destructive detection methods, lack of real-time online detection methods, and cannot achieve online liquid shortage warning. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a lithium battery electrolyte shortage warning method, electronic equipment and storage medium to achieve online lossless electrolyte shortage warning.

[0005] In a first aspect, the present application provides a lithium battery electrolyte shortage warning method, the method comprising:

[0006] Establish a battery capacity decay prediction model based on actual operating condition data;

[0007] According to the film formation reaction formula, the relationship between the capacity decay prediction model and the negative electrode porosity change of the battery is established to obtain the negative electrode porosity prediction model;

[0008] Obtaining the negative electrode porosity threshold under battery failure conditions based on electrochemical simulation or actual measurement;

[0009] According to the negative electrode porosity prediction model and the negative electrode porosity threshold, a lithium battery liquid shortage warning is performed under different working conditions.

[0010] In one embodiment of the above-mentioned lithium battery electrolyte shortage warning method,

[0011] The method of establishing a battery capacity attenuation prediction model based on actual operating condition data includes:

[0012] Based on the capacity decay prediction model, the battery is tested under different working conditions to obtain the capacity decay rate of the battery under each working condition;

[0013] A capacity decay prediction model of the battery is established based on the capacity decay rate.

[0014] In one embodiment of the above-mentioned lithium battery electrolyte shortage warning method,

[0015] The SEI film generation rate related to the capacity decay prediction model is obtained based on the correspondence between the film formation reaction formula and the capacity decay prediction model, including:

[0016] According to the capacity loss of 26.8Ah when 1 mol of lithium ions is consumed in the battery electrolyte and the capacity decay prediction model, the lithium ion loss rate of the battery is Q loss *Q / 26.8;

[0017] According to the reaction relationship in the film-forming reaction formula that 2 mol of ethylene carbonate consumes 2 mol of lithium ions to generate 1 mol of SEI film, substituting the lithium ion loss rate into the reaction relationship, we obtain:

[0018]

[0019] Where Q is the battery capacity, Q loss is the capacity decay rate in the capacity decay prediction model.

[0020] In one embodiment of the above-mentioned lithium battery electrolyte shortage warning method,

[0021] The method of obtaining a negative electrode porosity prediction model related to the capacity decay prediction model based on the relationship between the SEI film generation rate and the negative electrode porosity of the battery comprises:

[0022] The relationship between the SEI film generation rate and the battery negative electrode porosity:

[0023]

[0024] in, is the change rate of negative electrode porosity, M SEI is the molar mass of the SEI film, ρ SEI is the density of SEI film, V a is the volume of the negative electrode;

[0025] The prediction model of negative electrode porosity is:

[0026]

[0027] Among them, ε0 is the initial porosity of the negative electrode sheet

[0028] In one embodiment of the above-mentioned lithium battery electrolyte shortage warning method,

[0029] The negative electrode porosity threshold value obtained according to the electrochemical simulation in the case of battery failure includes:

[0030] An electrochemical simulation model was established based on the measured SOC-OCV of the battery and HPPC data at different temperatures;

[0031] According to the electrochemical simulation model, the initial porosity of the negative electrode is gradually reduced to obtain the negative electrode potential curves under different negative electrode porosities;

[0032] The negative electrode potential is confirmed according to the negative electrode potential curve. When the negative electrode potential is less than 0 V, the corresponding negative electrode porosity is a negative electrode porosity threshold.

[0033] In one embodiment of the above-mentioned lithium battery electrolyte shortage warning method,

[0034] The method of performing a lithium battery liquid shortage warning under different working conditions according to the negative electrode porosity prediction model and the negative electrode porosity threshold comprises:

[0035] Calculating a predicted value of the negative electrode porosity corresponding to a required warning condition according to the negative electrode porosity prediction model;

[0036] Determine that the predicted value of the negative electrode porosity is less than the negative electrode porosity threshold value to issue a liquid shortage warning.

[0037] In one embodiment of the above-mentioned lithium battery electrolyte shortage warning method,

[0038] The method further comprises:

[0039] Obtaining a time-dependent negative electrode porosity model under a required warning condition according to the negative electrode porosity prediction model;

[0040] The negative electrode porosity threshold is substituted into the negative electrode porosity model to obtain the liquid shortage time of the battery under the working condition.

[0041] In a second aspect, the present application provides an electronic device comprising a memory, one or more processors, and one or more applications, wherein the one or more applications are stored in the memory, and when the one or more applications are configured to be called by the one or more processors, the one or more processors execute a method as described in any one of the first aspects.

[0042] In a third aspect, the present application provides a computer-readable storage medium storing a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the method as described in any one of the first aspects.

[0043] One or more of the above embodiments of the present application have at least one or more of the following beneficial effects:

[0044] This application establishes a battery capacity decay prediction model based on actual operating condition measured data, establishes a relationship between film formation side reactions and battery capacity decay, and realizes online prediction of negative electrode porosity based on the capacity decay model, thereby realizing lithium ion liquid shortage warning. This method is simple to operate and can realize online lossless liquid shortage warning.

[0045] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The disclosure of the present application will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. In addition, similar numbers in the drawings are used to represent similar components, among which:

[0047] Figure 1 It is a schematic flow chart of the main steps of a battery electrolyte shortage warning method according to an embodiment of the present application;

[0048] Figure 2 It is a schematic flow chart of the main steps of obtaining an electrolyte consumption prediction model and a negative electrode porosity prediction model according to an embodiment of the present application;

[0049] Figure 3 This is a capacity decay rate curve of a certain LFP battery cell in an embodiment of the present application, stored at 30°C 100% SOC for different days;

[0050] Figure 4 The negative electrode porosity threshold process diagram is obtained based on the electrochemical simulation in one embodiment of the present application. DETAILED DESCRIPTION

[0051] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0052] As described in the background technology, the existing detection of electrolyte mostly remains at the stage of destructive detection. For example, the porosity of the positive electrode, negative electrode and isolation membrane is measured by hot pressing, and then the amount of electrolyte required for filling is calculated. However, the state of the isolation membrane in the battery cell will change after hot pressing, resulting in the porosity obtained by this method being much larger than the actual porosity of the battery cell, and the prediction of the electrolyte filling amount is inaccurate. Another method is to determine the amount of electrolyte by measuring the actual liquid absorption of the battery cell. This method does not take into account the electrolyte loss of the battery cell during actual application. The electrolyte of the lithium-ion battery is not static during use, which causes the battery cell to fail due to lack of liquid during use. Based on this, the present application proposes a battery electrolyte lack of liquid warning method to achieve online non-destructive lack of liquid warning.

[0053] See attached Figure 1 , Figure 1 1 is a schematic flow chart of the main steps of a method for early warning of battery electrolyte shortage according to an embodiment of the present application. Figure 1 As shown, a battery electrolyte shortage warning method in an embodiment of the present invention mainly includes the following steps S101 to S103:

[0054] Step S101, establishing a battery capacity decay prediction model according to actual working condition measured data;

[0055] In one embodiment, for a battery that needs to be warned of lack of liquid, a process of establishing a capacity decay prediction model is as follows: first, the capacity decay rate of the battery under each condition is obtained by measuring the battery under different operating conditions, that is, the decay factor under each condition is obtained, and the decay factor is substituted into the capacity decay prediction model, which is the capacity decay prediction model of the battery. For example, the battery operating conditions include different operating temperatures, etc., and the battery with an SOC of 100% at different temperatures (such as 25°C, 45°C, 60°C) is stored for different days, and the cell capacity decay rate Q under the corresponding time is recorded. loss , perform linear fitting or average evaluation to obtain the attenuation factor under each working condition; select a capacity attenuation prediction model, such as the Arrhenius equation Q loss =at^b, where t is the battery operating time, b is the attenuation factor, and a is a constant. Substituting the obtained attenuation factor into the equation, the capacity attenuation prediction model of the battery is obtained.

[0056] Step S102, establishing a capacity decay prediction model and a relationship between the change in the negative electrode porosity of the battery according to the film formation reaction formula, and obtaining a negative electrode porosity prediction model;

[0057] In one embodiment, the relationship between the capacity attenuation prediction model and the negative electrode porosity of the battery is established according to the film formation reaction formula to obtain the negative electrode porosity prediction model, such as Figure 2 As shown, the specific steps include:

[0058] Step S1021, based on the correspondence between the film formation reaction formula and the capacity decay prediction model, obtaining the SEI film formation rate related to the capacity decay prediction model;

[0059] In this example, based on the film-forming reaction formula: 2EC+2Li + +2e - →C2H4+(CH2OCO2Li)2 (EC is ethylene carbonate, the most commonly used electrolyte solvent; (CH2OCO2Li)2 is SEI film), we can get, 2 mol EC, consuming 2 mol Li + , 1 mol of SEI is generated; and according to the theory, 1 mol of Li is consumed + The loss capacity is 26.8Ah. According to the capacity attenuation prediction model, the lithium ion loss rate of the battery is Q loss *Q / 26.8, where Q loss is the capacity decay rate, Q is the battery capacity, then Q loss *Q is the loss capacity when 1 mol Li + When the loss capacity is 26.8Ah, the amount of lithium lost is Q loss *Q / 26.8; Corresponding the lithium ion loss rate to the film formation reaction formula, the SEI film formation rate can be obtained as Where Q is the battery capacity.

[0060] Step S1022, based on the relationship between the SEI film generation rate and the battery negative electrode porosity, obtaining a negative electrode porosity prediction model related to the capacity decay prediction model;

[0061] In this example, negative electrode porosity = negative electrode pore volume V / negative electrode sheet volume V a ;

[0062] Negative electrode volume V a = pole piece length * pole piece width * pole piece thickness;

[0063] Negative electrode pore volume V = negative electrode porosity * negative electrode sheet volume V a ;

[0064] During the use of the battery, the positive electrode porosity and the diaphragm porosity remain basically unchanged, while the negative electrode porosity changes with the growth of the SEI film. The negative electrode porosity change formula is:

[0065]

[0066] in, is the change rate of the negative electrode porosity, α is the specific surface area of ​​the SEI membrane, δ film is the SEI film thickness, C SEI is the SEI concentration, M SEIis the SEI molar mass, here it is 0.162 kg / mol, ρ SEI is the SEI film density, here it is 1690kg / m 3 ;

[0067] Based on the SEI film generation rate obtained in step S1021, Substituting it into the negative electrode porosity change formula, the negative electrode porosity prediction formula is:

[0068]

[0069] Among them, ε0 is the initial porosity of the negative electrode.

[0070] Step S103, obtaining a negative electrode porosity threshold value when the battery fails according to electrochemical simulation or actual measurement;

[0071] In one embodiment, the electrochemical simulation is specifically performed as follows: an electrochemical simulation model is established based on the measured SOC-OCV of a certain battery and HPPC data at different temperatures, and the negative electrode potential curve is obtained at different negative electrode porosities starting from the initial porosity of the negative electrode. When the negative electrode potential is less than 0V, the battery is at risk of lithium plating, and the capacity decay is accelerated in this case, thereby obtaining the negative electrode porosity threshold.

[0072] In one embodiment, the actual measurement method is: based on a large number of storage or cycle diving batteries, the batteries are disassembled at the diving inflection point, and the dodecane absorption method is used to test the negative electrode porosity to obtain the negative electrode porosity at the inflection point, and the negative electrode porosity threshold is obtained.

[0073] Step S104: performing a lithium battery liquid shortage warning under different working conditions according to the negative electrode porosity prediction model and the negative electrode porosity threshold.

[0074] In one embodiment, the method for judging the lack of liquid warning is as follows: the predicted value ε of the negative electrode porosity corresponding to the required warning condition is calculated according to the negative electrode porosity prediction model; the predicted value ε of the negative electrode porosity is compared with the negative electrode porosity threshold ε TH By comparison, when the negative electrode porosity ε<ε TH , then there is a lack of fluid, otherwise, there is no lack of fluid.

[0075] In one embodiment, the liquid shortage warning method: obtain a negative electrode porosity model related to time under a required warning condition according to a negative electrode porosity prediction model; substitute the negative electrode porosity threshold into the negative electrode porosity model to obtain the liquid shortage time of the battery under the condition; that is,

[0076] Because, when the negative electrode porosity ε<ε TH , then there is a lack of fluid, so f(t)=ε TH The obtained time t is the liquid shortage time.

[0077] In a specific embodiment, a 104Ah LFP battery cell with a SOC of 100% at 25°C, 45°C, and 60°C is stored for different days, and the battery cell capacity decay rate at the corresponding time is recorded. A linear fit or average evaluation is performed to obtain the decay factor under each working condition; a capacity decay prediction model is selected, such as the Arrhenius equation Q loss =at^b, where t is the battery operating time, b is the attenuation factor, and a is a constant. Substituting the attenuation factor into the equation, we get the capacity attenuation prediction model Q for the battery. loss =AEa / RT*t^b. This formula can be used to calculate the values ​​of A, Ea, and b through some measured data at 25, 45, and 60°C. R is the gas constant, which is about 8.31 J / (mol-K). When the battery is stored at 100% SOC at 30°C, the temperature T = 273 + 30 = 303K is substituted into the above formula to obtain the relationship between capacity decay and time t at 30°C, that is, Figure 3 As shown in the figure, the capacity decay rate curve of the battery cell at 100% SOC storage for different days at 30°C is shown in the figure. The relationship between the decay of the battery cell and time t under the working condition is Q loss =0.0006t^0.6745. Then the negative electrode porosity prediction formula is:

[0078] Among them, the initial porosity of the negative electrode is 30.7%, Q is 104Ah, M SEI is the SEI molar mass, which is 0.162 kg / mol, ρ SEI is the SEI film density, here it is 1690kg / m 3 ; V a The volume of the negative electrode (electrode length * thickness * width) = 0.000238m 3 .

[0079] According to the electrochemical simulation, the negative electrode porosity threshold value under the condition of lithium battery failure is obtained: the specific operation of the electrochemical simulation is to establish an electrochemical simulation model based on the measured SOC-OCV of the battery and the HPPC data at different temperatures, starting from the initial negative electrode porosity and decreasing by 26.33%, 25%, 20%, 15%, and 10%, to obtain the negative electrode potential curve under different negative electrode porosities, such as Figure 4 As shown in Figure 2, when the negative electrode porosity is 15%, the negative electrode potential is less than 0V, and the battery has local lithium deposition. When the negative electrode porosity is 10%, the negative electrode potential is less than 0V, and there is overall lithium deposition. When the negative electrode potential is less than 0V, the capacity decays faster, so the negative electrode porosity threshold ε is obtained. TH=15%. It should be noted that during the normal charge and discharge process of the battery, there must be enough electrolyte in the porosity of the positive and negative electrodes. However, due to the thickening of the SEI film, the pores of the negative electrode become smaller, the electrolyte filled in the porosity is less, and the ion conductivity is weak. When the porosity is reduced to the point where the lithium salt in the porosity is insufficient to provide lithium ion deintercalation, it means that there is a lack of liquid in the electrode, and the battery will dive. Therefore, by detecting the lithium precipitation point, the lack of liquid can be judged. The porosity of the lithium precipitation point is the result of the thickening of the SEI film due to the consumption of electrolyte. In essence, it is because the electrolyte is consumed too quickly, and the SEI film is formed at the negative electrode. The SEI film thickens, thereby reducing the porosity of the negative electrode. When the negative electrode porosity is reduced to a certain extent, lithium precipitation occurs.

[0080] Therefore, ε = 30.7% - 0.000469*t 0.6745 <15%. In this case, the battery has a small negative electrode porosity, which affects the battery dynamics and deteriorates the battery performance. According to the above formula, t = 5535 days. When the battery is placed at 30°C and 100% SOC for 5535 days, about 15-16 years, the battery performance will drop sharply due to lack of liquid. Based on the above steps S101-S104, the embodiment of the present application establishes a capacity decay prediction model for the battery according to the measured data of the working conditions, establishes a relationship between the film-forming side reaction and the battery capacity decay, and realizes online prediction of the negative electrode porosity based on the capacity decay model, thereby realizing lithium ion liquid shortage warning. The method is simple to operate and can realize lossless liquid shortage warning online.

[0081] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0082] Furthermore, the present invention also provides an electronic device, comprising a memory, one or more processors, and one or more applications, wherein the one or more applications are stored in the memory, and when the one or more applications are configured to be called by the one or more processors, the one or more processors execute the method described in any of the above technical solutions.

[0083] The electronic device in the embodiment of the present invention mainly includes a memory and a processor, the memory can be configured to store a program for executing the method of the above method embodiment, and the processor can be configured to execute the program in the memory, which includes but is not limited to the program for executing the method of the above method embodiment. For ease of explanation, only the part related to the embodiment of the present invention is shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention.

[0084] In an embodiment of the present invention, the electronic device may be a control device device including various electronic components. In some possible implementations, the electronic device may include multiple storage devices and multiple processors. The program for executing the method of the above method embodiment may be divided into multiple subprograms, each of which may be loaded and run by a processor to execute different steps of the method of the above method embodiment. Specifically, each subprogram may be stored in different memories, and each processor may be configured to execute programs in one or more memories to jointly implement the method of the above method embodiment, that is, each processor executes different steps of the method of the above method embodiment to jointly implement the method of the above method embodiment.

[0085] The electronic device is used to perform Figure 1 The method embodiments shown in the drawings are similar in their technical principles, technical problems solved and technical effects produced. Technical personnel in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the electronic device can refer to the contents described in the method embodiments, which will not be repeated here.

[0086] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0087] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the above method embodiment, and the program can be loaded and run by a processor to implement the above method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0088] Further, it should be understood that since the setting of each module is only for illustrating the functional modules of the device of the present invention, the physical devices corresponding to these modules can be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic. It can be understood by those skilled in the art that the modules in the system can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention. Therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0089] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0090] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0091] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A lithium battery electrolyte shortage warning method, characterized in that: The method comprises: Establish a battery capacity decay prediction model based on actual operating condition data; According to the film formation reaction formula, the relationship between the capacity decay prediction model and the negative electrode porosity change of the battery is established to obtain the negative electrode porosity prediction model; Obtaining the negative electrode porosity threshold under battery failure conditions based on electrochemical simulation or actual measurement; According to the negative electrode porosity prediction model and the negative electrode porosity threshold, a lithium battery liquid shortage warning is performed under different working conditions.

2. The lithium battery electrolyte shortage warning method according to claim 1, characterized in that: The method of establishing a battery capacity attenuation prediction model based on actual operating condition data includes: Based on the capacity decay prediction model, the battery is tested under different working conditions to obtain the capacity decay rate of the battery under each working condition; A capacity decay prediction model of the battery is established based on the capacity decay rate.

3. The lithium battery electrolyte shortage warning method according to claim 1, characterized in that: The relationship between the capacity attenuation prediction model and the change in the negative electrode porosity of the battery is established according to the film formation reaction formula to obtain the negative electrode porosity prediction model, including: Based on the corresponding relationship between the film formation reaction formula and the capacity decay prediction model, obtaining the SEI film generation rate related to the capacity decay prediction model; Based on the relationship between the SEI film generation rate and the battery negative electrode porosity, a negative electrode porosity prediction model related to the capacity decay prediction model is obtained.

4. The lithium battery electrolyte shortage warning method according to claim 3, characterized in that: The SEI film generation rate related to the capacity decay prediction model is obtained based on the correspondence between the film formation reaction formula and the capacity decay prediction model, including: According to the capacity loss of 26.8Ah when 1 mol of lithium ions is consumed in the battery electrolyte and the capacity decay prediction model, the lithium ion loss rate of the battery is Q loss *Q / 26.8; According to the reaction relationship in the film-forming reaction formula that 2 mol of ethylene carbonate consumes 2 mol of lithium ions to generate 1 mol of SEI film, substituting the lithium ion loss rate into the reaction relationship, we obtain: SEI film formation rate Among them, Q is the battery capacity, Q loss is the capacity decay rate in the capacity decay prediction model.

5. The lithium battery electrolyte shortage warning method according to claim 4, characterized in that: The method of obtaining a negative electrode porosity prediction model related to the capacity decay prediction model based on the relationship between the SEI film generation rate and the negative electrode porosity of the battery comprises: The relationship between the SEI film generation rate and the battery negative electrode porosity: in, is the change rate of negative electrode porosity, M SEI is the molar mass of the SEI film, ρ SEI is the density of SEI film, V a is the volume of the negative electrode; The prediction model of negative electrode porosity is: Among them, ε0 is the initial porosity of the negative electrode.

6. The lithium battery electrolyte shortage warning method according to claim 1, characterized in that: The negative electrode porosity threshold value obtained according to the electrochemical simulation in the case of battery failure includes: An electrochemical simulation model was established based on the measured SOC-OCV of the battery and HPPC data at different temperatures; According to the electrochemical simulation model, the initial porosity of the negative electrode is gradually reduced to obtain the negative electrode potential curves under different negative electrode porosities; The negative electrode potential is confirmed according to the negative electrode potential curve. When the negative electrode potential is less than 0 V, the corresponding negative electrode porosity is a negative electrode porosity threshold.

7. The lithium battery electrolyte shortage warning method according to any one of claims 1 to 6, characterized in that: The method of performing a lithium battery liquid shortage warning under different working conditions according to the negative electrode porosity prediction model and the negative electrode porosity threshold comprises: Calculating a predicted value of the negative electrode porosity corresponding to a required warning condition according to the negative electrode porosity prediction model; Determine that the predicted value of the negative electrode porosity is less than the negative electrode porosity threshold value to issue a liquid shortage warning.

8. The lithium battery electrolyte shortage warning method according to claim 7, characterized in that: The method further comprises: Obtaining a time-dependent negative electrode porosity model under a required warning condition according to the negative electrode porosity prediction model; The negative electrode porosity threshold is substituted into the negative electrode porosity model to obtain the liquid shortage time of the battery under the working condition.

9. An electronic device, characterized in that: The method comprises a memory, one or more processors, and one or more applications, wherein the one or more applications are stored in the memory, and when the one or more applications are configured to be called by the one or more processors, the one or more processors execute the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: A plurality of program codes are stored, and the program codes are suitable for being loaded and run by a processor to execute the method according to any one of claims 1 to 8.

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