Methods, devices, and equipment for improving the cycle life of power batteries based on formation depth

Abstract

This application discloses a method, apparatus, and device for improving the cycle life of power batteries based on formation depth, relating to the field of power battery technology. The method includes: determining a formation depth model based on charging cutoff voltage, charging rate, and cycle number; conducting charge-discharge tests on the power battery at different formation depths based on the formation depth model to obtain charge-discharge test data; fitting the charge-discharge test data to determine a power battery cycle life evaluation model based on formation depth; determining the optimal formation depth based on the power battery cycle life evaluation model; and determining the charge-discharge regime of the power battery based on the optimal formation depth to improve the cycle life of the power battery. Through the above method, a power battery cycle life evaluation model is established, which can quickly and effectively predict the cycle life of the power battery, thereby determining the optimal formation depth, optimizing battery design, optimizing the charge-discharge regime, and thus effectively improving the cycle life of the power battery.

Description

Technical Field

[0001] This application relates to the field of power battery technology, and in particular to a method, apparatus and equipment for improving the cycle life of power batteries based on formation depth. Background Technology

[0002] With the transformation of the global energy structure, the demand for new energy vehicles and efficient, reliable energy storage systems is increasing, leading to a continuous rise in the demand for high-performance power batteries. Cycle life is a crucial parameter for evaluating the techno-economic viability of power batteries and a key indicator for assessing battery performance and lifespan. The cycle life of power batteries directly impacts the operating costs and economic benefits of electric vehicles and energy storage systems. Researching ways to improve battery cycle life and overall battery performance to meet market demands and enhance user experience has significant practical implications and long-term impacts on promoting battery technology development, ensuring safety, protecting the environment, reducing costs, and driving the sustainable development of the new energy industry.

[0003] The cycle life of a power battery is influenced by a variety of factors, including battery design, charge / discharge regime, usage conditions, manufacturing process, and maintenance measures. The underlying mechanisms are complex and challenging to study. Traditional power battery cycle life testing requires continuous charge-discharge cycles until the battery reaches its end-of-life condition, which is time-consuming and costly.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a method, apparatus and equipment for improving the cycle life of power batteries based on the formation depth, which aims to solve the technical problems of long test cycle and high cost in the traditional power battery cycle life test in the prior art.

[0006] To achieve the above objectives, this application provides a method for improving the cycle life of power batteries based on formation depth, the method comprising:

[0007] Based on the charging cutoff voltage, charging rate, and number of cycles, a deep model is determined.

[0008] Based on the formation depth model, charge and discharge tests were conducted on the power battery at different formation depths to obtain charge and discharge test data.

[0009] By fitting the charge and discharge test data, a cycle life assessment model for power batteries with formation depth is determined.

[0010] Based on the power battery cycle life assessment model, the optimal formation depth is determined;

[0011] The charging and discharging regime of the power battery is determined based on the optimal formation depth in order to improve the cycle life of the power battery.

[0012] In one embodiment, the step of determining the transformation depth model based on the charging cutoff voltage, charging rate, and number of cycles includes:

[0013] Obtain the first influence model between charging cutoff voltage and formation depth, the second influence model between charging rate and formation depth, and the third influence model between cycle number and formation depth;

[0014] Multiplying the first influence model, the second influence model, and the third influence model together yields the deep model.

[0015] In one embodiment, before obtaining the steps of obtaining the first influence model between the charging cutoff voltage and the formation depth, the second influence model between the charging rate and the formation depth, and the third influence model between the number of cycles and the formation depth, the method further includes:

[0016] Based on the influence of the charging cut-off voltage on the formation depth, a linear function influence model is determined. The independent variable of the linear function influence model is the charging cut-off voltage, the coefficient of the linear function influence model is the charging cut-off voltage proportional coefficient, and the constant term of the linear function influence model is the charging cut-off voltage offset coefficient.

[0017] Based on the formation depth test data, the linear function influence model was fitted to determine the optimal charging cut-off voltage proportional coefficient and the optimal charging cut-off voltage offset coefficient.

[0018] Based on the optimal charging cutoff voltage proportional coefficient, the optimal charging cutoff voltage offset coefficient, and the linear function influence model, the first influence model between the charging cutoff voltage and the formation depth is determined.

[0019] In one embodiment, before obtaining the steps of obtaining the first influence model between the charging cutoff voltage and the formation depth, the second influence model between the charging rate and the formation depth, and the third influence model between the number of cycles and the formation depth, the method further includes:

[0020] Based on the influence of charging rate on formation depth, the first exponential function influence model is determined. The base of the first exponential function influence model is the natural constant, and the exponent of the first exponential function influence model is the product of the square of the charging rate and the negative of the charging rate exponential decay coefficient.

[0021] Based on the formation depth test data, the first exponential function influence model was fitted to determine the optimal charging rate exponential decay coefficient.

[0022] Based on the optimal charging rate exponential decay coefficient and the first exponential function influence model, a second influence model between charging rate and formation depth is determined.

[0023] In one embodiment, before obtaining the steps of obtaining the first influence model between the charging cutoff voltage and the formation depth, the second influence model between the charging rate and the formation depth, and the third influence model between the number of cycles and the formation depth, the method further includes:

[0024] Based on the impact of the number of cycles on the formation depth, the second exponential function influence model is determined. The base of the second exponential function influence model is the number of cycles, the exponent of the second exponential function influence model is the cycle number decay exponent, and the coefficient of the second exponential function influence model is the cycle number baseline proportional coefficient.

[0025] Based on the formation depth test data, the influence model of the second exponential function was fitted to determine the optimal cycle number decay index and the optimal cycle number benchmark ratio coefficient.

[0026] Based on the optimal cycle number decay index, the optimal cycle number baseline ratio coefficient, and the second exponential function influence model, a third influence model between the cycle number and the formation depth is determined.

[0027] In one embodiment, the step of fitting charge-discharge test data to determine the cycle life assessment model of the power battery with the formation depth includes:

[0028] An empirical model of the relationship between formation depth and cycle life of power battery is obtained. The base of the empirical model is formation depth, the exponent is the formation depth influence coefficient, the coefficient is the negative of the baseline cycle life coefficient, and the constant term is the baseline cycle life coefficient.

[0029] Based on charge-discharge test data corresponding to different formation depths, the empirical model of the exponential function is fitted to determine the optimal formation depth influence coefficient and the optimal baseline cycle life coefficient.

[0030] Based on the optimal formation depth influence coefficient, the optimal benchmark cycle life coefficient, and the exponential function empirical model, a cycle life assessment model for power batteries with formation depth is obtained.

[0031] In one embodiment, the step of determining the optimal formation depth based on a cycle lifetime assessment model includes:

[0032] Based on the formation depth-based cycle life assessment model for power batteries, the cycle life of power batteries is predicted, and the cycle life prediction data corresponding to the formation depth is obtained.

[0033] The optimal cycle lifetime data is determined from the cycle lifetime prediction data, and the formation depth corresponding to the optimal cycle lifetime data is taken as the optimal formation depth.

[0034] In one embodiment, the charge-discharge test data includes at least battery charge-discharge performance data, battery cycle stability data, battery internal resistance and impedance spectrum data, battery temperature characteristics data, battery aging characteristics data, battery safety performance data, battery material characteristics data, battery manufacturing process data, battery microstructure data, and battery thermal performance data.

[0035] Furthermore, to achieve the above objectives, this application also proposes a power battery cycle life improvement device based on formation depth, which includes:

[0036] The fitting module is used to determine the deep model based on the charging cutoff voltage, charging rate, and number of cycles.

[0037] The testing module is used to perform charge and discharge tests on power batteries at different formation depths based on the formation depth model, and obtain charge and discharge test data.

[0038] The fitting module is also used to fit charge and discharge test data to determine the cycle life assessment model with the formation depth.

[0039] The enhancement module is used to determine the optimal formation depth based on the cycle life assessment model;

[0040] The enhancement module is also used to determine the charge and discharge regime of the power battery based on the optimal formation depth, so as to improve the cycle life of the power battery.

[0041] In addition, to achieve the above objectives, this application also proposes a power battery cycle life improvement device based on formation depth. The power battery cycle life improvement device based on formation depth includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the power battery cycle life improvement method based on formation depth as described above.

[0042] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium, and stores a computer program on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for improving the cycle life of power batteries based on formation depth as described above.

[0043] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the above-described method for improving the cycle life of a power battery based on formation depth.

[0044] This application provides a method for improving the cycle life of power batteries based on formation depth. The method determines a formation depth model based on charging cutoff voltage, charging rate, and cycle count. Based on the formation depth model, charge-discharge tests are conducted on the power battery at different formation depths to obtain charge-discharge test data. The charge-discharge test data is fitted to determine a cycle life evaluation model for the power battery based on formation depth. Based on the cycle life evaluation model, the optimal formation depth is determined. Based on the optimal formation depth, the charge-discharge regime of the power battery is determined to improve its cycle life. This application combines the principles and laws of battery performance changes during use to study the influence of formation depth on the cycle life of power batteries. It fits empirical formulas to establish a cycle life evaluation model for power batteries, which can quickly and effectively predict the cycle life of power batteries, shorten the testing cycle, reduce costs, and determine the optimal formation depth based on the cycle life prediction data. This optimizes battery design and charge-discharge regimes, thereby effectively improving the cycle life of power batteries and solving the technical problems of long testing cycles and high costs in traditional power battery cycle life testing. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating an embodiment of the method for improving the cycle life of power batteries based on formation depth in this application.

[0048] Figure 2 This is a flowchart illustrating Embodiment 2 of the method for improving the cycle life of power batteries based on formation depth in this application;

[0049] Figure 3 This is a schematic diagram of the module structure of the power battery cycle life improvement device based on formation depth according to an embodiment of this application;

[0050] Figure 4 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the method for improving the cycle life of power batteries based on formation depth in the embodiments of this application.

[0051] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0053] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0054] The main solution of this application embodiment is as follows: A formation depth model is determined based on the charging cutoff voltage, charging rate, and number of cycles; based on the formation depth model, charge-discharge tests are performed on the power battery at different formation depths to obtain charge-discharge test data; the charge-discharge test data is fitted to determine a power battery cycle life assessment model based on the formation depth; the optimal formation depth is determined based on the power battery cycle life assessment model; and the charge-discharge regime of the power battery is determined based on the optimal formation depth to improve the cycle life of the power battery.

[0055] This application provides a solution that combines the principles and patterns of battery performance changes during use to study the impact of formation depth on the cycle life of power batteries. By fitting empirical formulas, a cycle life assessment model for power batteries is established, which can quickly and effectively predict the cycle life of power batteries, shorten the testing cycle, reduce costs, and determine the optimal formation depth based on the cycle life prediction data. This optimizes battery design and charge / discharge regimes, thereby effectively improving the cycle life of power batteries and solving the technical problems of long testing cycles and high costs in traditional power battery cycle life testing.

[0056] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a power battery cycle life improvement device based on formation depth. This embodiment does not specifically limit it in this regard. The following uses a power battery cycle life improvement device based on formation depth as an example to describe this embodiment and the following embodiments.

[0057] This application provides a method for improving the cycle life of power batteries based on formation depth, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for improving the cycle life of power batteries based on formation depth in this application.

[0058] In this embodiment, the method for improving the cycle life of a power battery based on formation depth includes steps S10 to S50:

[0059] Step S10: Based on the charging cutoff voltage, charging rate, and number of cycles, determine the transformation depth model;

[0060] It should be noted that the depth of formation (DOF) refers to the state of charge reached by the battery during its first charge. The formation process is a crucial step in battery manufacturing, involving both the initial charge and discharge. An appropriate DOF can activate the electrochemical reactions within the battery, forming a stable solid electrolyte interface (SEI film), reducing internal resistance, and preventing irreversible consumption of electrolyte and lithium ions during subsequent cycles, directly impacting the cycle life of the power battery. Furthermore, the DOF affects battery performance, including battery capacity, internal resistance, charge / discharge efficiency, and battery consistency. The specific mechanism of action is as follows:

[0061] 1) Increased formation depth typically leads to lower battery internal resistance because deeper formation helps form a more complete and stable SEI (Solid Electrolyte Interphase) film, thereby reducing battery internal resistance and affecting charge / discharge efficiency. Traditional low-current pre-charge formation methods contribute to the formation of a dense and stable SEI film. The quality of the SEI film directly affects the battery's cycle performance, self-discharge, and safety performance. Different formation processes result in SEI films with varying degrees of density, which in turn affects battery performance to varying degrees. However, prolonged low-current charging increases the impedance of the SEI film, thus affecting the battery's rate discharge performance. Furthermore, the long low-current pre-charge formation time directly impacts production efficiency.

[0062] 2) A higher formation depth allows for full utilization of the battery's active materials, and under polarization, the battery's initial capacity may be higher.

[0063] 3) Higher formation rates may lead to faster aging of battery materials, especially under high temperatures or other harsh conditions;

[0064] 4) Inconsistent formation depth may lead to a decline in the performance of some cells in the battery pack, affecting the overall performance and lifespan of the battery pack.

[0065] It is evident that the formation depth is one of the key factors affecting the cycle life of power batteries. Therefore, finding the optimal formation depth during battery production and use is crucial for improving the overall performance and reliability of batteries, reducing production costs, and extending the cycle life of power batteries.

[0066] Additionally, it should be noted that the formation depth is usually related to parameters such as the charging cut-off voltage, charging rate, and number of cycles. The formation depth model is a mathematical model that describes the relationship between the formation depth and the charging cut-off voltage, charging rate, and number of cycles. The charging cut-off voltage, charging rate, and number of cycles can be adjusted using the fitted formation depth model to achieve different formation depths.

[0067] In one feasible implementation, step S10 may include steps S101 to S102:

[0068] Step S101: Obtain the first influence model between charging cutoff voltage and formation depth, the second influence model between charging rate and formation depth, and the third influence model between cycle number and formation depth.

[0069] It should be noted that the first influence model between charging cut-off voltage and formation depth is the mathematical model describing the relationship between formation depth and charging cut-off voltage, which is the mathematical model corresponding to the law of influence of charging cut-off voltage on formation depth; the second influence model between charging rate and formation depth is the mathematical model describing the relationship between formation depth and charging rate, which is the mathematical model corresponding to the law of influence of charging rate on formation depth; and the third influence model between cycle number and formation depth is the mathematical model describing the relationship between formation depth and cycle number, which is the mathematical model corresponding to the law of influence of cycle number on formation depth.

[0070] Step S102: Multiply the first influence model, the second influence model, and the third influence model to obtain the transformed deep model.

[0071] It should be noted that the transformation into a depth model can take the following form:

[0072] DOF = f(V) cut-off I charge n cycles )

[0073] =f1(V cut-off )×f2(I charge )×f3(n cycles )

[0074] In the formula, f does not refer to a specific mathematical function, but rather to the relationship between the depth of DOF and the charging cutoff voltage V. cut-off Charging rate I charge and the number of loops n cycles The complex relationships between them are represented by f1, f2, and f3, which are the first, second, and third influence models describing the effects of charging cutoff voltage, charging rate, and number of cycles on formation depth, respectively.

[0075] Understandably, since the effects of charging cutoff voltage, charging rate, and cycle number on formation depth may involve complex electrochemical, thermodynamic, physical mechanisms, and mechanical properties of the battery, the first, second, and third influence models usually need to be determined based on actual test conditions.

[0076] In one feasible implementation, steps A11 to A13 are included before step S101:

[0077] Step A11: Based on the influence of the charging cut-off voltage on the formation depth, determine the linear function influence model. The independent variable of the linear function influence model is the charging cut-off voltage, the coefficient of the linear function influence model is the charging cut-off voltage proportional coefficient, and the constant term of the linear function influence model is the charging cut-off voltage offset coefficient.

[0078] It should be noted that the charging cut-off voltage usually refers to the upper limit of the battery voltage when fully charged, determined by the battery's chemical composition and design, and is typically provided by the battery manufacturer. The charging cut-off voltage is generally related to the battery's fully charged state. A higher charging cut-off voltage allows for full utilization of the active materials inside the battery, resulting in a higher initial capacity under polarization. However, an excessively high charging cut-off voltage may lead to more intense chemical reactions inside the battery, resulting in more electrolyte decomposition and gas generation, thus affecting the formation depth and impacting battery performance and cycle life. Therefore, for the first influence model between charging cut-off voltage and formation depth, this embodiment considers a linear relationship with voltage. A linear function influence model is first set up, using the charging cut-off voltage as the independent variable, the charging cut-off voltage proportionality coefficient as the coefficient, and the charging cut-off voltage offset coefficient as the constant term, as shown below:

[0079] f0(V cut-off )=a1·V cut-off +b1

[0080] In the formula, f0(V cut-off ) represents the influence model of a linear function, V cut-off Here, a1 is the charging cutoff voltage, a1 is the charging cutoff voltage proportionality coefficient, used to describe the strength of the relationship between the charging cutoff voltage and battery performance, and b1 is the charging cutoff voltage offset coefficient, used to adjust the starting point of the relationship between the charging cutoff voltage and battery performance.

[0081] Step A12: Based on the formation depth test data corresponding to different charging cut-off voltages, fit the linear function influence model to determine the optimal charging cut-off voltage proportional coefficient and the optimal charging cut-off voltage offset coefficient.

[0082] It should be noted that the formation depth test data refers to the data related to formation depth obtained under different charging cut-off voltages, different charging rates, and different cycle numbers. This includes charge / discharge rate, charge / discharge efficiency, battery capacity, battery voltage, battery capacity retention, battery internal resistance, formation current, formation time, and formation temperature. The optimal charging cut-off voltage proportionality coefficient is the most suitable charging cut-off voltage proportionality coefficient obtained through fitting, and the optimal charging cut-off voltage offset coefficient is the most suitable charging cut-off voltage offset coefficient obtained through fitting.

[0083] It is understandable that by using the formation depth test data corresponding to different charging cut-off voltages, the above linear function influence model can be fitted to find the best fitting parameters. At this time, the best fitting parameters are the optimal charging cut-off voltage proportional coefficient and the optimal charging cut-off voltage offset coefficient.

[0084] It should be understood that statistical methods can be used to fit the formation depth test data. The statistical method can be the least squares method, and an appropriate method can be selected according to actual needs. This embodiment does not make specific limitations on this.

[0085] Step A13: Based on the optimal charging cut-off voltage proportional coefficient, the optimal charging cut-off voltage offset coefficient, and the linear function influence model, determine the first influence model between the charging cut-off voltage and the formation depth.

[0086] It is understandable that by substituting the optimal charging cutoff voltage proportional coefficient and the optimal charging cutoff voltage offset coefficient into the linear function influence model, the fitted first influence model f1(V) can be obtained. cut-off This allows the first influence model to be used to determine the depth model.

[0087] In one feasible implementation, steps B11 to B13 are included before step S101:

[0088] Step B11: Based on the influence of charging rate on formation depth, determine the first exponential function influence model. The base of the first exponential function influence model is the natural constant, and the exponent of the first exponential function influence model is the product of the square of the charging rate and the negative of the charging rate exponential decay coefficient.

[0089] It should be noted that the charging rate (or charging ratio) represents the multiple of a battery's rated capacity when charged at a certain current. During battery cycling, different charging rates lead to different capacity decay rates. The charging rate affects heat accumulation and electrolyte decomposition during charging. Higher charging rates may result in more heat accumulation and electrolyte decomposition, leading to increased battery temperature, accelerated battery aging, and impacting battery cycle stability and formation depth. Therefore, for the second influence model between charging rate and formation depth, this embodiment sets up a first exponential function influence model, using an exponential function with the natural constant e as the base and the product of the square of the charging rate and the negative of the exponential decay coefficient of the charging rate as the exponent, as shown below:

[0090]

[0091] In the formula, f0(I charge ) represents the first exponential function influence model, a2 is the exponential decay coefficient of the charging rate, which is an influence coefficient related to the battery thermal effect, I charge Let be the charging rate, and exp be an exponential function with the natural constant e as the base.

[0092] Step B12: Based on the formation depth test data, fit the first exponential function influence model to determine the optimal charging rate exponential decay coefficient.

[0093] It should be noted that the optimal charging rate exponential decay coefficient is the most suitable charging rate exponential decay coefficient obtained through fitting.

[0094] It is understandable that by using the formation depth test data corresponding to different charging rates, the above-mentioned first exponential function influence model can be fitted to find the best fitting parameters. At this time, the best fitting parameters are the best charging rate exponential decay coefficients.

[0095] It should be understood that statistical methods can be used to fit the formation depth test data. The statistical method can be the least squares method, and an appropriate method can be selected according to actual needs. This embodiment does not make specific limitations on this.

[0096] Step B13: Based on the optimal charging rate exponential decay coefficient and the first exponential function influence model, determine the second influence model between charging rate and formation depth.

[0097] It is understandable that substituting the optimal charging rate exponential decay coefficient into the first exponential function influence model yields the fitted second influence model f2(I). chrge This allows the second influence model to be used to determine the depth model.

[0098] In one feasible implementation, steps C11 to C13 are included before step S101:

[0099] Step C11: Based on the influence of the number of cycles on the transformation depth, determine the second exponential function influence model. The base of the second exponential function influence model is the number of cycles, the exponent of the second exponential function influence model is the cycle number decay exponent, and the coefficient of the second exponential function influence model is the cycle number baseline proportional coefficient.

[0100] It should be noted that, generally speaking, a cycle is defined as the process of charging a power battery to its rated capacity and then discharging it to its cutoff voltage, i.e., experiencing one full charge and full discharge cycle. The number of cycles refers to the number of charge-discharge cycles a battery undergoes, and it is directly related to battery aging and cycle life. After multiple charge-discharge cycles, the internal structure and chemical composition of a battery change, leading to a gradual decline in performance. Therefore, for the third influence model between the number of cycles and the depth of formation, this embodiment sets up a second exponential function influence model, using an exponential function with the number of cycles as the base, the cycle decay exponent as the exponent, and the cycle number baseline proportionality coefficient as the coefficient, as shown below:

[0101]

[0102] In the formula, f0(n) cycles The second exponential function influence model is represented by a3, which is the baseline proportional coefficient for the number of cycles, and b3 is the cycle decay index, which reflects the severity of the impact of the number of cycles on the battery cycle life. Usually, b3 is negative, indicating that the battery cycle life will decrease as the number of cycles increases.

[0103] Step C12: Based on the formation depth test data, fit the second exponential function influence model to determine the optimal cycle number decay index and the optimal cycle number baseline ratio coefficient.

[0104] It should be noted that the optimal cycle count decay index is the most suitable cycle count decay index obtained by fitting, and the optimal cycle count baseline ratio coefficient is the most suitable cycle count baseline ratio coefficient obtained by fitting.

[0105] It is understandable that by using the transformation depth test data corresponding to different number of cycles, the above-mentioned second exponential function influence model is fitted to find the best fitting parameters. At this time, the best fitting parameters are the optimal cycle number decay exponent and the optimal cycle number baseline proportional coefficient.

[0106] It should be understood that statistical methods can be used to fit the formation depth test data. The statistical method can be the least squares method, and an appropriate method can be selected according to actual needs. This embodiment does not make specific limitations on this.

[0107] Step C13: Based on the optimal cycle number decay index, the optimal cycle number baseline ratio coefficient, and the second exponential function influence model, determine the third influence model between the cycle number and the formation depth.

[0108] It is understandable that by substituting the optimal fitting parameters—the optimal cycle number decay exponent and the optimal cycle number baseline proportional coefficient—into the second exponential function influence model, the fitted third influence model f3(n) can be obtained. cycles This allows the third influence model to be used to determine the depth model.

[0109] Step S20: Based on the formation depth model, charge and discharge tests are conducted on the power battery at different formation depths to obtain charge and discharge test data;

[0110] It should be noted that charge and discharge test data refers to quantitative information obtained through a series of tests and measurements. This data is used to evaluate various characteristics of the battery, such as performance, cycle life, and safety. It includes at least battery charge and discharge performance data, battery cycle stability data, battery internal resistance and impedance spectrum data, battery temperature characteristics data, battery aging characteristics data, battery safety performance data, battery material characteristics data, battery manufacturing process data, battery microstructure data, and battery thermal performance data.

[0111] Additionally, it should be noted that battery charge / discharge performance data should include at least the charging curves (voltage versus time or current curves) and discharging curves at different charging cutoff voltages, as well as charge / discharge efficiency, energy density, and power density; battery cycle stability data, obtained through multiple charge / discharge cycle tests, reflects the number of cycles, capacity retention, degradation rate, and cycle life within a certain period; battery internal resistance and impedance spectrum data, obtained through electrochemical impedance spectroscopy (EIS) testing, reflects the internal resistance and interface impedance of the battery under different charge / discharge states; battery temperature characteristic data, including battery performance data at different ambient and battery operating temperatures, including the impact of temperature on battery charge / discharge performance, internal resistance, and cycle life; and battery aging characteristic data. The data includes: battery performance data during long-term use, such as battery capacity, capacity decay rate, internal resistance, and electrolyte decomposition; battery safety performance data, including test data under extreme conditions such as overcharge, over-discharge, short circuit, and thermal shock; battery material characteristic data, including the physicochemical properties of key materials such as positive and negative electrode materials, electrolyte, and separator; battery manufacturing process data, including key parameters in the battery formation process, such as formation current, formation time, and formation temperature; battery microstructure data, including information on the microstructure of battery materials obtained through scanning electron microscopy (SEM), transmission electron microscopy (TEM); and battery thermal performance data, including information on the thermal behavior of the battery during charging and discharging, such as heat generation, heat diffusion, and thermal stability.

[0112] In one feasible implementation, step S20 may include: adjusting the charging cut-off voltage, charging rate, and number of cycles based on the formation depth model to obtain different formation depths, and performing charge-discharge tests on the power battery at different formation depths to obtain charge-discharge test data.

[0113] Understandably, by changing parameters such as charging cutoff voltage, charging rate, charging time, and number of cycles, the formation depth can be adjusted and controlled. Performance data such as battery capacity, battery capacity retention, battery internal resistance, battery voltage, cell expansion force, battery self-discharge rate, and cycle stability at different formation depths can be collected as charge-discharge test data.

[0114] Step S30: Fit the charge and discharge test data to determine the power battery cycle life assessment model with formation depth.

[0115] It should be noted that the power battery cycle life assessment model is a model for predicting / evaluating the cycle life of a power battery. After obtaining the charge-discharge test data, it is necessary to study the influence of formation depth on the cycle life of the power battery, perform curve fitting / regression analysis on the charge-discharge test data, determine the optimal fitting parameters, and thus determine the power battery cycle life assessment model. This model can then be used to predict the cycle life of the power battery at different formation depths.

[0116] Understandably, in order to find the optimal formation depth, it is necessary to test the battery performance at different formation depths when conducting charge and discharge tests on power batteries, and collect test data such as battery capacity, battery capacity retention / capacity decay rate, battery internal resistance, battery voltage, cell expansion force, battery self-discharge rate, and cycle stability at different formation depths.

[0117] In one feasible implementation, step S20 may include steps S301 to S303:

[0118] Step S301: Obtain the empirical model of the exponential function between the formation depth and the cycle life of the power battery. The base of the empirical model of the exponential function is the formation depth, the exponent of the empirical model of the exponential function is the influence coefficient of the formation depth, the coefficient of the empirical model of the exponential function is the negative of the baseline cycle life coefficient, and the constant term of the empirical model of the exponential function is the baseline cycle life coefficient.

[0119] It should be noted that the cycle life of a power battery typically refers to the number of cycles a battery can withstand during repeated charge and discharge cycles under a certain charge and discharge regime before its performance reaches a specified value. Therefore, charge and discharge cycles directly affect the cycle life of a power battery.

[0120] Additionally, it should be noted that the exponential function empirical model between formation depth and battery cycle life, i.e., the mathematical model corresponding to the influence law of formation depth on battery cycle life, can also be considered as an empirical formula between formation depth and battery cycle life. This embodiment uses an exponential function, with formation depth as the base, the formation depth influence coefficient as the exponent, and the negative of the baseline cycle life coefficient as the coefficient, as shown below:

[0121] CycleLife=a·(1-DOF b )

[0122] In the formula, DOF represents the formation depth, which is a dimensionless value between 0 and 1. 0 usually means no formation and 1 means complete formation. CycleLife is the cycle life of the power battery, which is the number of charge-discharge cycles the battery can withstand. a is the reference cycle life coefficient, which represents the reference value of the battery cycle life at the reference formation depth (usually the minimum or initial formation depth). b is the formation depth influence coefficient.

[0123] It is understood that, depending on the battery type, the materials used, the battery design, and the specific testing conditions, the empirical model of the exponential function between the formation depth and the cycle life of the power battery can be adjusted based on the actual situation. This embodiment does not impose specific limitations on this.

[0124] Step S302: Based on the charge and discharge test data corresponding to different formation depths, fit the empirical model of the exponential function to determine the optimal formation depth influence coefficient and the optimal reference cycle life coefficient.

[0125] It should be noted that the optimal formation depth influence coefficient is the most suitable formation depth influence coefficient obtained by fitting, and the optimal baseline cycle life coefficient is the most suitable baseline cycle life coefficient obtained by fitting.

[0126] It is understandable that by using test data corresponding to different formation depths, the above-mentioned exponential function empirical model can be fitted to find the best fitting parameters. At this time, the best fitting parameters are the optimal formation depth influence coefficient and the optimal baseline cycle life coefficient.

[0127] It should be understood that statistical methods can be used to fit the charge and discharge test data. The statistical method can be the least squares method, and an appropriate method can be selected according to actual needs. This embodiment does not make specific limitations on this.

[0128] Step S303: Based on the optimal formation depth influence coefficient, the optimal benchmark cycle life coefficient, and the exponential function empirical model, a power battery cycle life assessment model based on formation depth is obtained.

[0129] It is understandable that by substituting the optimal fitting parameters, namely the optimal formation depth influence coefficient and the optimal baseline cycle life coefficient, into the exponential function empirical model, the fitted formation depth-based power battery cycle life assessment model can be obtained. The formation depth-based power battery cycle life assessment model can be used to predict the cycle life of the power battery at different formation depths.

[0130] In this embodiment, battery charge-discharge tests are conducted at different formation depths to collect relevant test data. Statistical methods are used to perform curve fitting / regression analysis on the test data, fit empirical formulas, and determine the evaluation model to predict the cycle life of the battery at different formation depths.

[0131] Step S40: Determine the optimal formation depth based on the power battery cycle life assessment model;

[0132] In one feasible implementation, step S40 may include: predicting the cycle life of the power battery based on the power battery cycle life assessment model based on the formation depth, and obtaining the cycle life prediction data corresponding to the formation depth; determining the optimal cycle life data from the cycle life prediction data, and taking the formation depth corresponding to the optimal cycle life data as the optimal formation depth.

[0133] It should be noted that the optimal formation depth is the most suitable formation depth found in this embodiment. Cycle life prediction data refers to information related to battery cycle life predicted using a power battery cycle life assessment model, including performance parameters such as cycle count, battery capacity, battery capacity decay rate, battery internal resistance, battery voltage, battery self-discharge rate, and cell expansion force. Optimal cycle life data typically refers to the predicted power battery performance parameters that satisfy conditions such as high cycle count, high battery capacity, low battery capacity decay rate, low battery internal resistance, high battery voltage, low battery self-discharge rate, and low cell expansion force. The formation depth corresponding to the optimal cycle life data can be considered the most suitable formation depth. Therefore, this embodiment selects the formation depth from the cycle life prediction data that corresponds to the battery performance parameters satisfying conditions such as high battery capacity, low battery capacity decay rate, low battery internal resistance, high battery voltage, low battery self-discharge rate, and low cell expansion force as the optimal formation depth.

[0134] Step S50: Determine the charge and discharge regime of the power battery based on the optimal formation depth to improve the cycle life of the power battery.

[0135] Understandably, charging and discharging at the optimal formation depth can effectively improve the cycle life of power batteries.

[0136] This embodiment provides a method for improving the cycle life of power batteries based on formation depth. A formation depth model is determined based on the charging cutoff voltage, charging rate, and number of cycles. Based on the formation depth model, charge-discharge tests are conducted on the power battery at different formation depths to obtain charge-discharge test data. The charge-discharge test data is fitted to determine a power battery cycle life evaluation model based on formation depth. Based on the power battery cycle life evaluation model, the optimal formation depth is determined. Based on the optimal formation depth, the charge-discharge regime of the power battery is determined to improve its cycle life. Combining the principles and laws of battery performance changes during use, the influence of formation depth on the cycle life of power batteries is studied. An empirical formula is fitted to establish a power battery cycle life evaluation model, which can quickly and effectively predict the cycle life of power batteries, shorten the testing cycle, reduce costs, and determine the optimal formation depth based on the cycle life prediction data to optimize battery design and charge-discharge regime, thereby effectively improving the cycle life of power batteries.

[0137] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S50 may include step S501:

[0138] Step S501: Based on the optimal formation depth and optimal temperature range, determine the power battery charge and discharge regime to improve the cycle life of the power battery.

[0139] It is important to note that battery operating temperature is also a key factor affecting cycle life. Temperature significantly impacts battery capacity, internal resistance, power charging / discharging performance, safety, and cycle life. Power batteries have an optimal operating temperature range; within this range, as temperature increases, the activity of the internal active materials increases, accelerating the rate of internal chemical reactions. However, when the temperature exceeds a certain range, excessively high temperatures accelerate internal side reactions, speeding up battery aging and leading to performance degradation, thus shortening cycle life. Furthermore, excessively high temperatures can cause structural degradation of the positive electrode material and electrolyte decomposition, becoming a major factor affecting battery life. Excessively high temperatures can also lead to battery overheating and even thermal runaway, significantly impacting battery safety. Similarly, excessively low temperatures significantly reduce the activity of the internal active materials, increase internal resistance, and significantly reduce charging / discharging power and battery capacity, even causing irreversible capacity degradation and increasing safety risks. Therefore, determining the optimal operating temperature range for power batteries is crucial to ensuring optimal performance and cycle life.

[0140] Additionally, it should be noted that the optimal temperature range is the most suitable operating temperature range.

[0141] Understandably, an optimal temperature range can be set based on the optimal formation depth to further optimize the charging and discharging regime of the power battery, thereby further improving the cycle life of the power battery.

[0142] This embodiment provides a method for improving the cycle life of power batteries based on formation depth. A formation depth model is determined based on the charging cutoff voltage, charging rate, and number of cycles. Based on the formation depth model, charge-discharge tests are conducted on the power battery at different formation depths to obtain charge-discharge test data. The charge-discharge test data is fitted to determine a power battery cycle life evaluation model based on formation depth. Based on the power battery cycle life evaluation model, the optimal formation depth is determined. Based on the optimal formation depth and optimal temperature range, the power battery charge-discharge regime is determined to improve the cycle life of the power battery. Combining the principles and laws of battery performance changes during use, the influence of formation depth on the cycle life of power batteries is studied. An empirical formula is fitted to establish a power battery cycle life evaluation model, which can quickly and effectively predict the cycle life of power batteries, shorten the testing cycle, reduce costs, and determine the optimal formation depth based on the cycle life prediction data. According to the optimal formation depth and optimal temperature range, the battery design and charge-discharge regime can be optimized, thereby effectively improving the cycle life of the power battery.

[0143] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method for improving the cycle life of power batteries based on the formation depth of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0144] This application also provides a device for improving the cycle life of power batteries based on formation depth. Please refer to [reference needed]. Figure 3 The power battery cycle life improvement device based on formation depth includes:

[0145] Fitting module 10 is used to determine the deep model based on the charging cutoff voltage, charging rate and number of cycles.

[0146] Test module 20 is used to perform charge and discharge tests on power batteries at different formation depths based on the formation depth model, and obtain charge and discharge test data.

[0147] The fitting module 10 is also used to fit the charge and discharge test data to determine the cycle life assessment model of the formation depth.

[0148] The enhancement module 30 is used to determine the optimal formation depth based on the cycle life assessment model.

[0149] The enhancement module 30 is also used to determine the charge and discharge regime of the power battery based on the optimal formation depth, so as to improve the cycle life of the power battery.

[0150] In one feasible implementation, the fitting module 10 is further used to obtain a first influence model between the charging cutoff voltage and the formation depth, a second influence model between the charging rate and the formation depth, and a third influence model between the number of cycles and the formation depth.

[0151] Multiplying the first influence model, the second influence model, and the third influence model together yields the deep model.

[0152] In one feasible implementation, the fitting module 10 is further configured to determine a linear function influence model based on the influence of the charging cut-off voltage on the formation depth. The independent variable of the linear function influence model is the charging cut-off voltage, the coefficient of the linear function influence model is the charging cut-off voltage proportional coefficient, and the constant term of the linear function influence model is the charging cut-off voltage offset coefficient.

[0153] Based on the formation depth test data, the linear function influence model was fitted to determine the optimal charging cut-off voltage proportional coefficient and the optimal charging cut-off voltage offset coefficient.

[0154] Based on the optimal charging cutoff voltage proportional coefficient, the optimal charging cutoff voltage offset coefficient, and the linear function influence model, the first influence model between the charging cutoff voltage and the formation depth is determined.

[0155] In one feasible implementation, the fitting module 10 is further configured to determine a first exponential function influence model based on the influence of the charging rate on the formation depth. The base of the first exponential function influence model is the natural constant, and the exponent of the first exponential function influence model is the product of the square of the charging rate and the negative of the charging rate exponential decay coefficient.

[0156] Based on the formation depth test data, the first exponential function influence model was fitted to determine the optimal charging rate exponential decay coefficient.

[0157] Based on the optimal charging rate exponential decay coefficient and the first exponential function influence model, a second influence model between charging rate and formation depth is determined.

[0158] In one feasible implementation, the fitting module 10 is further configured to determine the second exponential function influence model based on the influence of the number of cycles on the formation depth, wherein the base of the second exponential function influence model is the number of cycles, the exponent of the second exponential function influence model is the cycle number decay exponent, and the coefficient of the second exponential function influence model is the cycle number baseline proportional coefficient.

[0159] Based on the formation depth test data, the influence model of the second exponential function was fitted to determine the optimal cycle number decay index and the optimal cycle number benchmark ratio coefficient.

[0160] Based on the optimal cycle number decay index, the optimal cycle number baseline ratio coefficient, and the second exponential function influence model, a third influence model between the cycle number and the formation depth is determined.

[0161] In one feasible implementation, the fitting module 10 is further used to obtain an exponential function empirical model between the formation depth and the cycle life of the power battery. The base of the exponential function empirical model is the formation depth, the exponent of the exponential function empirical model is the formation depth influence coefficient, the coefficient of the exponential function empirical model is the negative of the baseline cycle life coefficient, and the constant term of the exponential function empirical model is the baseline cycle life coefficient.

[0162] Based on charge-discharge test data corresponding to different formation depths, the empirical model of the exponential function is fitted to determine the optimal formation depth influence coefficient and the optimal baseline cycle life coefficient.

[0163] Based on the optimal formation depth influence coefficient, the optimal benchmark cycle life coefficient, and the exponential function empirical model, a cycle life assessment model for power batteries with formation depth is obtained.

[0164] In one feasible implementation, the enhancement module 30 is also used to predict the cycle life of the power battery based on the power battery cycle life assessment model based on the formation depth, and obtain the cycle life prediction data corresponding to the formation depth.

[0165] The optimal cycle lifetime data is determined from the cycle lifetime prediction data, and the formation depth corresponding to the optimal cycle lifetime data is taken as the optimal formation depth.

[0166] In one feasible implementation, the charge-discharge test data includes at least battery charge-discharge performance data, battery cycle stability data, battery internal resistance and impedance spectrum data, battery temperature characteristics data, battery aging characteristics data, battery safety performance data, battery material characteristics data, battery manufacturing process data, battery microstructure data, and battery thermal performance data.

[0167] The power battery cycle life improvement device based on formation depth provided in this application adopts the power battery cycle life improvement method based on formation depth in the above embodiments, which can solve the technical problems of long test cycle and high cost in traditional power battery cycle life testing. Compared with the prior art, the beneficial effects of the power battery cycle life improvement device based on formation depth provided in this application are the same as the beneficial effects of the power battery cycle life improvement method based on formation depth provided in the above embodiments, and other technical features in the power battery cycle life improvement device based on formation depth are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0168] This application provides a power battery cycle life improvement device based on formation depth. The power battery cycle life improvement device based on formation depth includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the power battery cycle life improvement method based on formation depth in the above embodiment 1.

[0169] The following is for reference. Figure 4 This document illustrates a structural schematic diagram of a power battery cycle life improvement device based on formation depth, suitable for implementing embodiments of this application. The power battery cycle life improvement device based on formation depth in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The illustrated device for improving the cycle life of power batteries based on formation depth is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0170] like Figure 4As shown, the battery cycle life enhancement device based on formation depth may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the battery cycle life enhancement device based on formation depth. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the formation depth-based battery cycle life enhancement device to exchange data with other devices wirelessly or via wired communication. Although the figure shows a formation depth-based battery cycle life enhancement device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0171] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0172] The power battery cycle life improvement device based on formation depth provided in this application adopts the power battery cycle life improvement method based on formation depth in the above embodiments, which can solve the technical problems of long test cycle and high cost in traditional power battery cycle life testing. Compared with the prior art, the beneficial effects of the power battery cycle life improvement device based on formation depth provided in this application are the same as the beneficial effects of the power battery cycle life improvement method based on formation depth provided in the above embodiments, and other technical features in the power battery cycle life improvement device based on formation depth are the same as the features disclosed in the method of the previous embodiment, and will not be repeated here.

[0173] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0174] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0175] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the method for improving the cycle life of a power battery based on formation depth in the above embodiments.

[0176] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0177] The aforementioned computer-readable storage medium may be included in a power battery cycle life improvement device based on formation depth; or it may exist independently and not assembled into a power battery cycle life improvement device based on formation depth.

[0178] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the power battery cycle life improvement device based on formation depth, the power battery cycle life improvement device based on formation depth performs the following actions: determines a formation depth model based on charging cutoff voltage, charging rate, and cycle number; performs charge-discharge tests on the power battery at different formation depths based on the formation depth model to obtain charge-discharge test data; fits the charge-discharge test data to determine a power battery cycle life evaluation model based on formation depth; determines the optimal formation depth based on the power battery cycle life evaluation model; and determines the charge-discharge regime of the power battery based on the optimal formation depth to improve the cycle life of the power battery.

[0179] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0180] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0181] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0182] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for improving the cycle life of power batteries based on formation depth. This solves the technical problems of long testing cycles and high costs associated with traditional power battery cycle life testing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the power battery cycle life improvement method based on formation depth provided in the above embodiments, and will not be repeated here.

[0183] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for improving the cycle life of a power battery based on formation depth.

[0184] The computer program product provided in this application can solve the technical problems of long testing cycles and high costs in traditional power battery cycle life testing. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the power battery cycle life improvement method based on formation depth provided in the above embodiments, and will not be repeated here.

[0185] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for improving the cycle life of power batteries based on formation depth, characterized in that, The method includes: Based on the charging cutoff voltage, charging rate, and number of cycles, a deep model is determined. Based on the formation depth model, charge and discharge tests were conducted on the power battery at different formation depths to obtain charge and discharge test data. The charge and discharge test data are fitted to determine the cycle life assessment model for the power battery at the formation depth. Based on the aforementioned power battery cycle life assessment model, the optimal formation depth is determined; The charge and discharge regime of the power battery is determined based on the optimized formation depth in order to improve the cycle life of the power battery.

2. The method as described in claim 1, characterized in that, The steps for determining the deep model based on the charging cutoff voltage, charging rate, and number of cycles include: Obtain the first influence model between charging cutoff voltage and formation depth, the second influence model between charging rate and formation depth, and the third influence model between cycle number and formation depth; Multiplying the first influence model, the second influence model, and the third influence model together yields the transformation depth model.

3. The method as described in claim 2, characterized in that, Before the steps of obtaining the first influence model between the charging cutoff voltage and the formation depth, the second influence model between the charging rate and the formation depth, and the third influence model between the number of cycles and the formation depth, the method further includes: Based on the influence of the charging cut-off voltage on the formation depth, a linear function influence model is determined. The independent variable of the linear function influence model is the charging cut-off voltage, the coefficient of the linear function influence model is the charging cut-off voltage proportional coefficient, and the constant term of the linear function influence model is the charging cut-off voltage offset coefficient. Based on the formation depth test data, the linear function influence model is fitted to determine the optimal charging cutoff voltage proportional coefficient and the optimal charging cutoff voltage offset coefficient. Based on the optimal charging cutoff voltage proportional coefficient, the optimal charging cutoff voltage offset coefficient, and the linear function influence model, a first influence model between the charging cutoff voltage and the formation depth is determined.

4. The method as described in claim 2, characterized in that, Before the steps of obtaining the first influence model between the charging cutoff voltage and the formation depth, the second influence model between the charging rate and the formation depth, and the third influence model between the number of cycles and the formation depth, the method further includes: Based on the influence of the charging rate on the formation depth, a first exponential function influence model is determined. The base of the first exponential function influence model is the natural constant, and the exponent of the first exponential function influence model is the product of the square of the charging rate and the negative of the charging rate exponential decay coefficient. Based on the formation depth test data, the first exponential function influence model is fitted to determine the optimal charging rate exponential decay coefficient. Based on the optimal charging rate exponential decay coefficient and the first exponential function influence model, a second influence model between charging rate and formation depth is determined.

5. The method as described in claim 2, characterized in that, Before the steps of obtaining the first influence model between the charging cutoff voltage and the formation depth, the second influence model between the charging rate and the formation depth, and the third influence model between the number of cycles and the formation depth, the method further includes: Based on the influence of the number of cycles on the formation depth, a second exponential function influence model is determined. The base of the second exponential function influence model is the number of cycles, the exponent of the second exponential function influence model is the cycle number decay exponent, and the coefficient of the second exponential function influence model is the cycle number baseline proportional coefficient. Based on the formation depth test data, the second exponential function influence model is fitted to determine the optimal cycle number decay index and the optimal cycle number benchmark ratio coefficient. Based on the optimal cycle number decay index, the optimal cycle number baseline ratio coefficient, and the second exponential function influence model, a third influence model between the cycle number and the formation depth is determined.

6. The method as described in claim 1, characterized in that, The step of fitting the charge-discharge test data to determine the cycle life assessment model of the power battery with the formation depth includes: An empirical model of an exponential function relating formation depth and cycle life of a power battery is obtained. The base of the empirical model is formation depth, the exponent is the formation depth influence coefficient, the coefficient is the negative of the baseline cycle life coefficient, and the constant term is the baseline cycle life coefficient. Based on charge-discharge test data corresponding to different formation depths, the empirical model of the exponential function is fitted to determine the optimal formation depth influence coefficient and the optimal baseline cycle life coefficient. Based on the optimal formation depth influence coefficient, the optimal benchmark cycle life coefficient, and the exponential function empirical model, a power battery cycle life assessment model for the formation depth is obtained.

7. The method as described in claim 1, characterized in that, The step of determining the optimal formation depth based on the cycle lifetime assessment model includes: Based on the power battery cycle life assessment model of the formation depth, the cycle life of the power battery is predicted, and the cycle life prediction data corresponding to the formation depth is obtained. The optimal cycle lifetime data is determined from the cycle lifetime prediction data, and the formation depth corresponding to the optimal cycle lifetime data is taken as the optimal formation depth.

8. The method according to any one of claims 1 to 7, characterized in that, The charge-discharge test data includes at least the following: battery charge-discharge performance data, battery cycle stability data, battery internal resistance and impedance spectrum data, battery temperature characteristics data, battery aging characteristics data, battery safety performance data, battery material characteristics data, battery manufacturing process data, battery microstructure data, and battery thermal performance data.

9. A device for improving the cycle life of a power battery based on formation depth, characterized in that, The power battery cycle life improvement device based on formation depth includes: The fitting module is used to determine the deep model based on the charging cutoff voltage, charging rate, and number of cycles. The testing module is used to perform charge and discharge tests on the power battery at different formation depths based on the formation depth model, and obtain charge and discharge test data. The fitting module is also used to fit the charge and discharge test data to determine the cycle life assessment model of the formation depth. An enhancement module is used to determine the optimal formation depth based on the cycle life assessment model. The enhancement module is also used to determine the charge and discharge regime of the power battery based on the optimized formation depth, so as to improve the cycle life of the power battery.

10. A device for improving the cycle life of power batteries based on formation depth, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for improving the cycle life of a power battery based on formation depth as described in any one of claims 1 to 8.

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