Lithium battery residual life prediction method and device, terminal equipment and storage medium

Abstract

The invention relates to the technical field of battery management, in particular to a lithium battery remaining life prediction method and device, terminal equipment and a storage medium, and the method comprises the steps: determining the working parameters and environmental parameters of a target battery and the cell type of the target battery; determining a corresponding pre-constructed life prediction model according to the type of the battery cell; the life prediction model is constructed according to the working parameters and the environmental parameters; and determining the current cycle charging and discharging times of the target battery, and obtaining the residual life of the target battery according to the life prediction model, the cycle charging and discharging times, the environmental parameters and the working parameters. Therefore, more relevant factors are considered in the life prediction of the lithium battery, the prediction result is more accurate, and the prediction process is more convenient.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a method, device, terminal device and storage medium for predicting the remaining life of a lithium battery. Background Art

[0002] In fact, the aging of batteries during operation is affected by many factors, such as temperature, rate, and depth of discharge (DOD). Due to the complex working conditions of batteries, it is difficult to predict the life of lithium-ion batteries, whether based on data-driven or model-based methods. Physical models require many parameters, and the parameters change with the battery working conditions, so it is difficult to obtain the parameters; data-driven models are limited by dimensions, and the amount of training data increases exponentially with the changes in different conditions, and the amount of calculation is huge. Summary of the invention

[0003] In view of this, an embodiment of the present application provides a method for predicting the remaining life of a lithium battery, comprising:

[0004] Determining operating parameters, environmental parameters and cell types of a target battery;

[0005] Determining a corresponding pre-built life prediction model according to the type of battery cell; the life prediction model is built according to the working parameters and the environmental parameters;

[0006] The current number of charge and discharge cycles of the target battery is determined, and the remaining life of the target battery is obtained according to the life prediction model, the number of charge and discharge cycles, the environmental parameters and the operating parameters.

[0007] In one embodiment, the life prediction model is constructed according to the working parameters and the environmental parameters, including:

[0008] Obtaining the cycle capacity retention rate and the corresponding cycle number of multiple battery cells when they are cyclically charged and discharged under different preset working parameters;

[0009] Fitting is performed according to the cycle capacity retention rate and the corresponding cycle number to obtain a relationship expression between the cycle capacity retention rate and the cycle number of each battery cell under each of the preset working parameters;

[0010] The various relational expressions are combined to calculate the model parameters in the preset aging model to obtain the life prediction model of the battery cell.

[0011] In one embodiment, the operating parameters include charge and discharge rate and discharge depth, and the environmental parameters include ambient temperature;

[0012] The method of combining the relational expressions, calculating the model parameters in the preset aging model, and obtaining the life prediction model of the battery cell includes:

[0013] Obtaining the slope of each relationship expression, and establishing a slope expression of each slope according to the life prediction model;

[0014] The slope expression is: k = α*exp(-Ea / (R*T))*Crate β *DOD;

[0015] Wherein, k is the slope, α is the first coefficient, β is the second coefficient, Ea is the activation energy, R is the ideal gas constant, T is the ambient temperature expressed in Kelvin temperature, exp is the natural exponential function, Crate is the charge and discharge rate, and DOD is the depth of discharge;

[0016] Solving the expressions of the slopes together to obtain the values ​​of the first coefficient, the second coefficient and the activation energy;

[0017] The values ​​of the first coefficient, the second coefficient and the activation energy are substituted into a life prediction formula to obtain the life prediction model.

[0018] In one embodiment, the life prediction formula is expressed as:

[0019] SOH=(α*exp(-Ea / (R*T))*Crate β *DOD)*Crate γ +1;

[0020] In the formula, SOH is the cycle capacity retention rate, γ is the third coefficient, and the third coefficient is determined according to the number of times of each of the relationship expressions.

[0021] In one embodiment, determining the operating parameters, environmental parameters and cell type of the target battery includes:

[0022] Determine the charging voltage and discharging voltage matched with the target battery, determine the average temperature of the environmental parameters of the target battery, and preset the discharge depth of the target battery;

[0023] Determining a charge rate and a discharge rate of the target battery according to the charge voltage and the discharge voltage;

[0024] The battery charging times and battery cell types recorded in the battery management system of the target battery are read.

[0025] In one embodiment, determining the current number of charge and discharge cycles of the target battery, and obtaining the remaining life of the target battery according to the life prediction model, the number of charge and discharge cycles, the environmental parameters, and the operating parameters, includes:

[0026] Determining a preset aging capacity retention rate of the target battery;

[0027] Bringing the aging capacity retention rate, the environmental parameters and the operating parameters into the life prediction model to calculate the number of aging cycle charge and discharge times corresponding to the aging capacity retention rate;

[0028] The remaining life of the target battery is determined according to the difference between the current charge and discharge cycle number and the aging charge and discharge cycle number.

[0029] In one embodiment, determining the current number of charge and discharge cycles of the target battery, and obtaining the remaining life of the target battery according to the life prediction model, the number of charge and discharge cycles, the environmental parameters, and the operating parameters, includes:

[0030] Determine a preset aging capacity retention rate of the target battery, bring the current number of charge and discharge cycles, the environmental parameters and the operating parameters into the life prediction model, and obtain the current capacity retention rate of the target battery;

[0031] The remaining life of the target battery is determined according to the difference between the aged capacity retention rate and the current capacity retention rate.

[0032] In a second aspect, the present application provides a lithium-ion battery remaining life prediction device, comprising:

[0033] A data collection module, used to determine the operating parameters, environmental parameters and cell type of the target battery;

[0034] A model determination module, used to determine a corresponding pre-built life prediction model according to the type of battery cell; the life prediction model is constructed according to the working parameters and the environmental parameters;

[0035] A calculation module is used to determine the current number of charge and discharge cycles of the target battery, and obtain the remaining life of the target battery according to the life prediction model, the number of charge and discharge cycles, the environmental parameters and the operating parameters.

[0036] In a third aspect, the present application provides a terminal device, which includes a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the lithium battery remaining life prediction method.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed on a processor, implements the method for predicting the remaining life of a lithium battery.

[0038] The embodiments of the present application have the following beneficial effects:

[0039] There is no need for complex mathematical modeling or machine learning to obtain a life prediction model. The life prediction model of the present application constructs a life prediction model based on the operating parameters and environmental parameters of the battery. The construction method is simple. In actual use, only calculations need to be performed to obtain the predicted life of the battery. There is no requirement for computing power, which makes it faster to obtain results and has a low threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A schematic diagram of a method flow chart of a lithium battery remaining life prediction method according to an embodiment of the present application is shown;

[0042] Figure 2 A schematic diagram of a life prediction model generation process in an embodiment of the present application is shown;

[0043] Figure 3 A scatter plot of experimental data of cycle number and cycle capacity retention rate of an embodiment of the present application is shown;

[0044] Figure 4 A schematic diagram of a fitting curve of the number of cycles and the cycle capacity retention rate of an embodiment of the present application is shown;

[0045] Figure 5 A schematic diagram of the structure of a lithium battery remaining life prediction device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0047] The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0048] Hereinafter, the terms "including", "having" and their cognates that can be used in various embodiments of the present application are intended only to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or increasing the possibility of one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items. In addition, the terms "first", "second", "third" and the like are only used to distinguish descriptions and cannot be understood as indicating or implying relative importance.

[0049] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present application.

[0050] Cycle life is one of the important indicators of lithium-ion battery performance, but the cycle life test of conventional lithium-ion batteries requires several years of time cost. In addition, electrochemical models or machine learning models are used for prediction, but this prediction method either considers incomplete conditions or requires a large amount of data for training, and these data require a lot of time to obtain. Therefore, the present application provides a lithium battery life prediction method, which obtains the working data, environmental data and battery cell type of the target battery, determines the corresponding life prediction model, and then obtains the remaining life of the target battery through the life prediction model, wherein the life prediction model is constructed according to the working parameters and the environmental parameters, and does not require machine learning models and other models that require computing power to process.

[0051] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0052] Figure 1A flow chart of a lithium battery life prediction method according to an embodiment of the present application is shown. Exemplarily, the lithium battery life prediction method includes the following steps:

[0053] Step S100, determining the operating parameters, environmental parameters and cell type of a target battery.

[0054] The target battery can be a new battery just out of the factory, or a battery that has been recycled or returned to the factory for maintenance or other reasons. For these target batteries, the battery operating parameters, environmental parameters and battery cell type can be obtained by reading battery settings, querying battery versions, etc.

[0055] Among them, the working parameters include the battery's charge and discharge rate and discharge depth.

[0056] The charge and discharge rate of a battery is affected by its operating voltage and the rated voltage of the charger, and can be said to be a known fixed value.

[0057] The depth of discharge refers to how much power a battery has left before it starts to be charged. For example, if you discharge 80% of the battery before charging, that is, charge when the remaining power is 20%, then the depth of discharge is 80%. If you charge when the battery is 50% discharged, then the depth of discharge is 50%.

[0058] For batteries that have just left the factory, the above charge and discharge rate is a fixed value, and the discharge depth can be set based on statistical data or according to the ideal state. For recycled batteries, the discharge depth can be obtained based on the data recorded in the BMS (battery management system) in the battery.

[0059] Environmental parameters refer to the ambient temperature when the battery is working normally. For batteries leaving the factory, the temperature can be obtained based on the actual working scenario or set according to the current room temperature.

[0060] The type of battery cell refers to the model or material of the battery cell in the target battery. Different battery cells will affect the battery life due to different materials or processes, so the type of battery cell also needs to be determined. This data can be determined by querying the model of the target battery.

[0061] Step S200: determining a corresponding pre-built life prediction model according to the type of battery cell.

[0062] In this embodiment, different life prediction models are pre-set for different working parameters, environmental parameters and types of battery cells, and these life prediction models are constructed according to the working parameters and the environmental parameters.

[0063] It is understandable that the lifespans of different battery cells are essentially different. This is an internal factor of the battery and will not affect its own lifespan as a variable. However, environmental parameters and operating parameters are common to different batteries. As variables that affect the lifespan, they are used as factors of the life prediction model to construct the life prediction model.

[0064] Step S300, determining the current charge and discharge cycle times of the target battery, and obtaining the remaining life of the target battery according to the life prediction model, the charge and discharge cycle times, the environmental parameters and the operating parameters.

[0065] After the life prediction model is determined, calculations can be performed. The number of charge and discharge cycles of the target battery also needs to be determined. The number of charge and discharge cycles refers to the number of times the battery is discharged and then charged again. This number can also be obtained from the battery management system. It can be understood that as the number of charge and discharge cycles increases, the battery will gradually wear out. The number of charge and discharge cycles will be used as a dependent variable in the life prediction model for calculation.

[0066] Specifically, after determining the aforementioned working parameters and environmental parameters, the charge and discharge rate, discharge depth, temperature data and number of charge and discharge cycles are determined, and then brought into the model to calculate the predicted life of the battery.

[0067] The battery life is defined by the cycle capacity retention rate. If the battery is brand new and has not been overcharged or discharged, its cycle capacity retention rate is 100%. After the battery has been used for a long time, the cycle capacity retention rate drops to a defined aging value, such as 80%. At this time, the battery is considered to have reached the end of its life. Therefore, the life of a brand new battery can be considered to be the difference between the current cycle capacity retention rate and 80%, or the number of cycles of charge and discharge required to reduce the current cycle capacity retention rate to 80%.

[0068] Therefore, through the above model, the current battery cycle capacity retention rate can be calculated, and then the difference between the cycle capacity retention rate and the specified aging value can be calculated to obtain the battery life. Or according to the model, the aging cycle charge and discharge times when the cycle capacity retention rate of the battery is equal to the specified aging value can be calculated, and then the difference between the current cycle charge and discharge times and the aging cycle charge and discharge times can be calculated to obtain how many cycles of charge and discharge the battery needs to enter the aging state. This value can also be used as the remaining life of the battery.

[0069] The lithium battery life prediction method of this embodiment brings the battery's operating parameters and environmental parameters into the life prediction model to obtain the battery's life. For batteries that have just left the factory, it can be calculated how many cycles of charge and discharge the battery needs to go through before it enters an aging state. For recycled batteries, the current capacity retention rate can be calculated to determine the current battery's aging degree and whether it needs to be replaced. The entire method is fast, convenient, and efficient. It only needs to determine the corresponding data and the result can be obtained through calculation.

[0070] Among them, different battery cell life prediction models only need to be established in sequence and can be used all the time. This embodiment also provides a method for constructing the battery cell life prediction model.

[0071] like Figure 2 As shown, the method includes:

[0072] Step S400, obtaining the cycle capacity retention rate and the corresponding cycle number of multiple battery cells when they are cyclically charged and discharged under different preset working parameters.

[0073] In order to build a life prediction model corresponding to the battery cell, it is necessary to conduct experiments to collect data first. For example, four 25Ah soft-pack batteries can be grouped and the four batteries are numbered A, B, C, and D respectively.

[0074] A was subjected to cyclic charge and discharge in a constant temperature box at a temperature of 25°C, a cyclic charge and discharge current of 0.5C, a discharge depth of 100%, and the cycle capacity retention rate was recorded after 1000 cycles.

[0075] B was subjected to cyclic charge and discharge in a constant temperature box at a temperature of 15°C, a cyclic charge and discharge current of 0.5C, a discharge depth of 100%, and the cycle capacity retention rate was recorded after 1000 cycles.

[0076] C was subjected to cyclic charge and discharge in a constant temperature box at a temperature of 25°C, a cyclic charge and discharge current of 1C, a discharge depth of 100%, and the cycle capacity retention rate was recorded after 1000 cycles.

[0077] D was subjected to cyclic charge and discharge in a constant temperature box at a temperature of 25°C, a cyclic charge and discharge current of 0.5C, a discharge depth of 80%, and the cycle capacity retention rate was recorded after 1000 cycles.

[0078] It can be seen that the working parameters and environmental data of the above four groups are different. Through the above four groups of experiments, four groups of experimental data can be obtained. According to this experimental data, the relationship diagram between the cycle capacity retention rate and the cycle weight can be obtained.

[0079] Step S500, performing fitting according to the cycle capacity retention rate and the corresponding cycle number, to obtain a relationship expression between the cycle capacity retention rate and the cycle number of each battery cell under each of the preset working conditions.

[0080] Specific as Figure 3 As shown, multiple data points can be obtained by obtaining multiple data points. By fitting these data points, mathematical equations corresponding to four aging curves can be obtained, thereby obtaining the following: Figure 4 The fitting curve is shown.

[0081] It can be seen that Figure 3 The limited data obtained through testing only tested 1000 cycles, with the lowest capacity retention rate at 95%. The battery was not tested to reach the set aging range, and the fitted curve can predict the life trend after more than 1000 times. The tester does not need to test the battery to the aging state and then fit it, which saves the amount of test data and test time.

[0082] The interval obtained by fitting can be a linear function or a multi-time equation. In this embodiment, for the convenience of description, it is described in the form of a linear function (y=kx+1).

[0083] In this equation, y is the cycle capacity retention rate, k is the slope, x is the number of cycles, and the intercept is 1 because when x=0, y=1, so the intercept is a known value.

[0084] Because there are 4 sets of experiments, 4 equations can be obtained.

[0085] This embodiment is fitted in the form of a linear function. Depending on the actual data obtained, it can also be fitted into other forms of functions such as a quadratic function or a cubic function. The specific fitting method can be operated with the help of mathematical tools in MATLAB. This embodiment only uses a linear function as an example, and does not mean that the relationship between the cycle capacity retention rate and the number of cycles must be a linear function.

[0086] Step S600, combining various relational expressions, calculating model parameters in a preset aging model, and obtaining a life prediction model for the battery cell.

[0087] Through the simultaneous equations, the life prediction model can be obtained.

[0088] The life prediction model is constructed based on the working parameters and the environmental parameters, which takes into account the impact of factors such as temperature, charge and discharge rate, and depth of discharge on battery health. Therefore, there is a preset life prediction formula:

[0089] SOH=(α*exp(-Ea / (R*T))*Crateβ *DOD)*Crate γ +1;

[0090] In the formula, SOH is the cycle capacity retention rate, α is the first coefficient, β is the second coefficient, Ea is the activation energy, R is the ideal gas constant, T is the operating temperature expressed in Kelvin temperature, exp is a natural exponential function, Crate is the charge and discharge rate, DOD is the discharge depth, and γ is the third coefficient, which is determined according to the number of times of each of the relationship expressions.

[0091] Among them, the α*exp(-Ea / (R*T)) part is used to describe the effect of temperature on battery performance. It is based on the Arrhenius equation, which is usually used to describe the relationship between chemical reaction rate and temperature. Higher temperatures usually accelerate the aging process of the battery, so the health state will decrease, so the temperature and cycle capacity retention rate are negatively correlated. Ea is the activation energy, which indicates the degree of influence of temperature on battery performance. It can be considered as the temperature influence coefficient, which is used to indicate the degree of influence of the -Ea / (R*T) term on the entire formula.

[0092] Crate β *DOD is used to consider the impact of charge and discharge rate (Crate) and depth of discharge (DOD) on battery health. Charge and discharge rate and depth of discharge affect the chemical reaction and mechanical structure of the battery, thus affecting the life of the battery. The charge and discharge rate is usually nonlinearly related to the aging rate, while the discharge depth is linearly related. By introducing exponential and linear terms, the nonlinear impact of these factors on the battery health state can be effectively captured.

[0093] The coefficients α, β, and γ are usually obtained by fitting experimental data and are used to adjust the model to accurately reflect the actual battery performance. Empirical parameters can adjust the flexibility of the model to adapt to the characteristics of different types of batteries.

[0094] The value of γ is not necessarily 1, because the aging curve is not necessarily linear, and there may be nonlinear situations. The form of the γ index can increase the flexibility of the model. For example, the fitting of the above four intervals is a linear function, so γ is 1 here. If the fitting results in four quadratic function curves, γ is 2 here.

[0095] It can be understood that by substituting the coefficients α, β and γ obtained in the above manner into the life prediction formula, a life prediction model is obtained, and this model can be used in the lithium battery life prediction method in the above steps S100 to S300 to calculate the life of the target battery.

[0096] The life prediction model of this embodiment is constructed based on the working parameters of the battery itself and the factors that affect the battery life, such as the temperature. For each type of battery cell, it is only necessary to conduct multiple groups of experiments under different conditions as described above to determine the life prediction model of the battery under certain conditions. For new batteries that have just left the factory, the above life prediction model can be used to give the predicted life of the battery under certain ideal conditions, thereby giving consumers a quantifiable life expectancy. In the scenario of recycling batteries, the remaining life of the battery can be predicted based on the previous working environment of the battery, thereby determining the degree of wear of the battery and determining the next recycling strategy.

[0097] Figure 5 A schematic diagram of the structure of a lithium battery remaining life prediction device according to an embodiment of the present application is shown. Exemplarily, the device includes:

[0098] A data collection module 10, used to determine the operating parameters, environmental parameters and cell type of the target battery;

[0099] A model determination module 20, for determining a corresponding pre-built life prediction model according to the type of battery cell; the life prediction model is constructed according to the operating parameters and the environmental parameters;

[0100] The calculation module 30 is used to determine the current charge and discharge cycle number of the target battery, and obtain the remaining life of the target battery according to the life prediction model, the charge and discharge cycle number, the environmental parameters and the operating parameters.

[0101] The present application provides a terminal device, which includes a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the lithium battery remaining life prediction method.

[0102] The present application provides a computer-readable storage medium storing a computer program, which, when executed on a processor, implements the method for predicting the remaining life of a lithium battery.

[0103] It can be understood that the device of this embodiment corresponds to the method of the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described repeatedly here.

[0104] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU) and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or at least one of other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.

[0105] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory is used to store a computer program, and the processor may execute the computer program accordingly after receiving an execution instruction.

[0106] The computer-readable storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0107] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow diagram, and the combination of boxes in the structure diagram and / or the flow diagram, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0108] In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0109] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.

[0110] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for predicting the remaining life of a lithium battery, characterized in that: include: Determining operating parameters, environmental parameters and cell types of a target battery; Determining a corresponding pre-built life prediction model according to the type of battery cell; the life prediction model is built according to the working parameters and the environmental parameters; The current number of charge and discharge cycles of the target battery is determined, and the remaining life of the target battery is obtained according to the life prediction model, the number of charge and discharge cycles, the environmental parameters and the operating parameters.

2. The method for predicting the remaining life of a lithium battery according to claim 1, characterized in that: The life prediction model is constructed according to the working parameters and the environmental parameters, including: Obtaining the cycle capacity retention rate and the corresponding cycle number of multiple battery cells when they are cyclically charged and discharged under different preset working parameters; Fitting is performed according to the cycle capacity retention rate and the corresponding cycle number to obtain a relationship expression between the cycle capacity retention rate and the cycle number of each battery cell under each of the preset working parameters; The relationship expressions are combined to calculate the model parameters in the preset aging model to obtain the life prediction model of the battery cell.

3. The method for predicting the remaining life of a lithium battery according to claim 2, characterized in that: The working parameters include charge and discharge rate and discharge depth, and the environmental parameters include ambient temperature; The method of combining the relational expressions, calculating the model parameters in the preset aging model, and obtaining the life prediction model of the battery cell includes: Obtaining the slope of each relationship expression, and establishing a slope expression of each slope according to the life prediction model; Solve the expressions of each slope together to obtain the values ​​of the first coefficient, the second coefficient and the activation energy; Substituting the values ​​of the first coefficient, the second coefficient and the activation energy into a life prediction formula to obtain the life prediction model; The slope expression is: k = α*exp(-Ea / (R*T))*Crate β *DOD; Wherein, k is the slope, α is the first coefficient, β is the second coefficient, Ea is the activation energy, R is the ideal gas constant, T is the ambient temperature expressed in Kelvin temperature, exp is the natural exponential function, and Crat e is the charge and discharge rate, and DOD is the depth of discharge.

4. The method for predicting the remaining life of a lithium battery according to claim 3, characterized in that: The expression of the life prediction formula is: SOH=(α*exp(-Ea / (R*T))*Crate β *DOD)*CrateY+1; In the formula, SOH is the cycle capacity retention rate, γ is the third coefficient, and the third coefficient is determined according to the number of times of each of the relationship expressions.

5. The method for predicting the remaining life of a lithium battery according to claim 1, characterized in that: The determining of the operating parameters and environmental parameters of the target battery and the type of battery cell of the target battery includes: Determine the charging voltage and discharging voltage matched with the target battery, determine the average temperature of the environmental parameters of the target battery, and preset the discharge depth of the target battery; Determining a charge rate and a discharge rate of the target battery according to the charge voltage and the discharge voltage; The battery charging times and battery cell types recorded in the battery management system of the target battery are read.

6. The method for predicting the remaining life of a lithium battery according to claim 1, characterized in that: The determining of the current charge and discharge cycle number of the target battery, and obtaining the remaining life of the target battery according to the life prediction model, the charge and discharge cycle number, the environmental parameters and the operating parameters, includes: Determining a preset aging capacity retention rate of the target battery; Bringing the aging capacity retention rate, the environmental parameters and the operating parameters into the life prediction model to calculate the number of aging cycle charge and discharge times corresponding to the aging capacity retention rate; The remaining life of the target battery is determined according to the difference between the current charge and discharge cycle number and the aging charge and discharge cycle number.

7. The method for predicting the remaining life of a lithium battery according to claim 1, characterized in that: The determining of the current charge and discharge cycle number of the target battery, and obtaining the remaining life of the target battery according to the life prediction model, the charge and discharge cycle number, the environmental parameters and the operating parameters, includes: Determine a preset aging capacity retention rate of the target battery, bring the current number of charge and discharge cycles, the environmental parameters and the operating parameters into the life prediction model, and obtain the current capacity retention rate of the target battery; The remaining life of the target battery is determined according to the difference between the aged capacity retention rate and the current capacity retention rate.

8. A lithium-ion battery remaining life prediction device, characterized in that: include: A data collection module, used to determine the operating parameters, environmental parameters and cell type of the target battery; A model determination module, used to determine a corresponding pre-built life prediction model according to the type of battery cell; the life prediction model is constructed according to the working parameters and the environmental parameters; A calculation module is used to determine the current number of charge and discharge cycles of the target battery, and obtain the remaining life of the target battery according to the life prediction model, the number of charge and discharge cycles, the environmental parameters and the operating parameters.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory, the memory stores a computer program, and the processor is used to execute the computer program to implement the lithium battery remaining life prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed on a processor, implements the method for predicting the remaining life of a lithium battery according to any one of claims 1 to 7.

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