Method for battery life prediction and corresponding system

Abstract

A method for battery life prediction and a corresponding system, medium and apparatus. The method comprises the following steps: acquiring battery capacity data of a battery after at least one charge-discharge cycle under ambient temperature stress and discharge rate stress; processing the battery capacity data under the environment temperature stress and the discharge rate stress at least by using an attenuation function to obtain cumulative attenuation of the single cycle capacity of the tth cycle under each stress and a battery capacity attenuation model; coupling the ambient temperature stress and the discharge rate stress to form a composite stress so as to obtain the cumulative attenuation of the average capacity of a single cycle of the tth cycle under the composite stress; determining the corresponding relation between the cumulative attenuation of the average capacity of the single cycle of the tth cycle under the combined stress and the corresponding loss capacity when the service life of the battery is ended; and establishing a model for predicting the service life of the battery under the combined stress according to the corresponding relationship. According to the invention, reliable prediction of the service life of the battery can be realized based on a composite stress coupling modeling mode.

Description

Technical Field

[0001] The present invention relates to the field of batteries, and in particular to a method for battery life prediction and a corresponding system, a computer-readable storage medium and a computer device. Background Art

[0002] In recent years, with the large-scale promotion and application of new energy vehicles, the research on capacity decay and aging and life prediction of lithium-ion batteries under external stress are of great significance to the vehicle battery industry.

[0003] Since the first use, lithium-ion batteries have begun to experience the aging process, which is manifested as capacity loss, power attenuation and increase in internal resistance. The aging factors of lithium-ion batteries under external stress can be both chemical and mechanical. The internal components of the battery are complex and diverse, and the various stresses that affect battery aging are interrelated and difficult to unify in time and space, making the study of aging mechanisms challenging.

[0004] Therefore, it is necessary to establish a modeling method for battery life prediction that includes common composite stress effects. Summary of the invention

[0005] The purpose of the present invention is to solve the above problems and to provide a method for battery life prediction and a corresponding system, computer-readable storage medium and computer device.

[0006] According to a first aspect of the present invention, a method for battery life prediction is provided, the method comprising: collecting battery capacity data after at least one charge and discharge cycle of the battery under ambient temperature stress and discharge rate stress respectively; processing the battery capacity data under the ambient temperature stress and the discharge rate stress respectively using at least a decay function to obtain the cumulative decay of the single cycle capacity of the t-th cycle under each of the ambient temperature stress and the discharge rate stress; obtaining a battery capacity decay model under each of the ambient temperature stress and the discharge rate stress based on the decay function; coupling the ambient temperature stress and the discharge rate stress to form a composite stress, and further obtaining the cumulative decay of the single cycle average capacity of the t-th cycle under the composite stress; determining the corresponding relationship between the cumulative decay of the single cycle average capacity of the t-th cycle under the composite stress and the corresponding loss capacity at the end of the battery life; and establishing a model for battery life prediction under the composite stress according to the corresponding relationship, so as to at least predict the life of the battery.

[0007] In one embodiment, the collecting of battery capacity data after at least one charge and discharge cycle under ambient temperature stress and discharge rate stress, respectively, comprises: under the same condition of the discharge rate stress, performing battery aging charge and discharge cycle experiments on the battery under different ambient temperature stresses, and recording the battery capacity of the battery after at least one charge and discharge cycle in the experiment; and under the same condition of the ambient temperature stress, performing battery aging charge and discharge cycle experiments on the battery under different discharge rate stresses, and recording the battery capacity of the battery after at least one charge and discharge cycle in the experiment.

[0008] In one embodiment, the at least using the attenuation function to process the battery capacity data under the ambient temperature stress and the discharge rate stress comprises: using a Gaussian attenuation equation to describe the battery capacity data under the ambient temperature stress and the discharge rate stress, respectively, wherein the Gaussian attenuation equation is: x(s, t) ~ N(u(s, t), σ 2 (t)), where s represents the ambient temperature stress or the discharge rate stress, x(s,t) represents the cumulative decay of the capacity of the tth cycle caused by the s stress; u(s,t) represents the cumulative decay of the single cycle capacity of the tth cycle caused by the s stress, σ 2 (t) represents the variance of the cumulative capacity decay in the tth cycle.

[0009] In one embodiment, the method further comprises: performing curve fitting and regression analysis on the battery capacity after the at least one charge and discharge cycle recorded in the experiment, and obtaining the cumulative decay of the single cycle capacity of the tth cycle caused by the ambient temperature stress or the discharge rate stress in the Gaussian decay equation as follows: Among them, f i (s) is the decay rate function, i represents the number of control groups in the experiment and h i (s) is the constant exponent.

[0010] In one embodiment, the battery capacity attenuation model under each of the ambient temperature stress and the discharge rate stress is obtained based on the attenuation function, including: when the stress is the ambient temperature stress, further based on the Arrhenius equation to obtain the battery capacity attenuation model under the ambient temperature stress; and when the stress is the discharge rate stress, further based on an exponential equation different from the Arrhenius equation to obtain the battery capacity attenuation model under the discharge rate stress.

[0011] In one embodiment, when under the ambient temperature stress, the decay rate function is f1(T)=exp(G+W / T), and the cumulative decay of the single cycle capacity of the tth cycle caused by the ambient temperature stress u(T,t) is expressed as: Wherein, T represents the ambient temperature stress; G=LnA, A represents the pre-exponential factor; W=-Ea / R, Ea represents the activation energy, R represents the gas constant, and / or, when under the discharge rate stress, the decay rate function is f1(R d )=(D d *R d cd +E d ), and the cumulative decay of the single cycle capacity of the tth cycle caused by the discharge rate stress u(R d ,t) is expressed as: Among them, R d Denotes the discharge rate stress; D d 、cd、E d is a constant.

[0012] In one embodiment, the cumulative decay of the single cycle average capacity of the tth cycle under the composite stress consisting of the ambient temperature stress and the discharge rate stress obtained after coupling is u(T, R d ,t),u(T,R d ,t)=exp(W(1 / T1-1 / T2))u(R d ,t), where T1 and T2 represent two ambient temperatures.

[0013] In one embodiment, the method further comprises: obtaining an initial battery capacity C(R d , 1), and the corresponding loss capacity at the end of the battery life can be expressed as 0.2C (R d , 1), and the corresponding relationship between the cumulative decay of the single cycle average capacity of the tth cycle under the composite stress and the corresponding loss capacity at the end of the battery life is expressed as: 0.2C(R d ,1)Ah=u(T,R d ,t)mAh.

[0014] In one embodiment, the model established for battery life prediction under the composite stress is: L = {[C (R d ,1)*0.2*1000] / [u(T,R d ,t)]} 1.27 , where L represents the predicted battery life.

[0015] According to a second aspect of the present invention, a system for battery life prediction is provided, characterized in that the system comprises:

[0016] A collection module, wherein the collection module is configured to collect battery capacity data after at least one charge and discharge cycle of the battery under ambient temperature stress and discharge rate stress;

[0017] a single stress battery capacity attenuation modeling module, the single stress battery capacity attenuation modeling module being configured to process the battery capacity data under the ambient temperature stress and the discharge rate stress respectively using at least a decay function to obtain the cumulative decay of the single cycle capacity of the t-th cycle under each of the ambient temperature stress and the discharge rate stress, and further configured to obtain a battery capacity attenuation model under each of the ambient temperature stress and the discharge rate stress respectively based on the decay function;

[0018] A composite stress battery capacity decay modeling module, wherein the composite stress battery capacity decay modeling module is configured to couple the ambient temperature stress and the discharge rate stress to form a composite stress, and further obtain a cumulative decay of a single cycle average capacity of the tth cycle under the composite stress; and

[0019] A battery life prediction modeling module, wherein the battery life prediction modeling module is configured to establish a corresponding relationship between the cumulative decay of the single-cycle average capacity of the tth cycle under the composite stress and the corresponding loss capacity at the end of the battery life; and is further configured to establish a model for battery life prediction under the composite stress according to the corresponding relationship, so as to at least predict the life of the battery.

[0020] In one embodiment, the acquisition module is further configured to: under the condition that the discharge rate stress is the same, the battery is subjected to a battery aging charge and discharge cycle experiment under different environmental temperature stresses, and the battery capacity of the battery after at least one charge and discharge cycle in the experiment is recorded; and under the condition that the environmental temperature stress is the same, the battery is subjected to a battery aging charge and discharge cycle experiment under different discharge rate stresses, and the battery capacity of the battery after at least one charge and discharge cycle in the experiment is recorded.

[0021] In one embodiment, the single stress battery capacity attenuation modeling module is configured to: use Gaussian attenuation equations to describe the battery capacity data under the ambient temperature stress and the discharge rate stress, respectively, wherein the Gaussian attenuation equation is: x(s, t) ~ N(u(s, t), σ 2(t)), where s represents the ambient temperature stress or the discharge rate stress, x(s,t) represents the cumulative decay of the capacity of the tth cycle caused by the s stress; u(s,t) represents the cumulative decay of the single cycle capacity of the tth cycle caused by the s stress, σ 2 (t) represents the variance of the cumulative capacity decay of the t-th cycle; and, curve fitting and regression analysis are performed on the battery capacity after the at least one charge and discharge cycle recorded in the experiment, and the cumulative decay of the single cycle capacity of the t-th cycle caused by the ambient temperature stress or the discharge rate stress in the Gaussian decay equation is obtained as follows: Among them, f i (s) is the decay rate function, i represents the number of control groups in the experiment and h i (s) is the constant exponent.

[0022] In one embodiment, the single stress battery capacity attenuation modeling module is also configured to: when the stress is the ambient temperature stress, further based on the Arrhenius equation to obtain the battery capacity attenuation model under the ambient temperature stress; and when the stress is the discharge rate stress, further based on an exponential equation different from the Arrhenius equation to obtain the battery capacity attenuation model under the discharge rate stress.

[0023] In one embodiment, the system also includes: a battery temperature control device, in which the battery is arranged, and the battery temperature control device is configured to control the temperature of the battery; a battery charging and discharging device, which is electrically connected to the battery temperature control device and is configured to perform charging and discharging operations on the battery; and / or a battery management device, which is electrically connected to the battery temperature control device and is configured to monitor the battery status of the battery in real time to prevent the battery from being overcharged or over-discharged.

[0024] In one embodiment, the system also includes: a control device, which is electrically connected to the battery charging and discharging device and / or the battery management device, and is configured to control the battery charging and discharging device to perform charging and discharging operations on the battery, and / or to monitor the battery charging and discharging process in real time through the battery management device; wherein the acquisition module at least acquires the battery capacity data of the battery after at least one charge and discharge cycle under ambient temperature stress and discharge rate stress respectively via the control device.

[0025] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described in any of the above embodiments when executed by a processor.

[0026] According to a fourth aspect of the present invention, there is provided a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in any one of the above embodiments when executing the computer program.

[0027] The advantages of the present invention are as follows: the method for predicting battery life and capacity attenuation modeling based on composite stress provided by the present invention obtains the capacity attenuation of the battery after recycling under different external stresses through aging cycle experiments, and records the battery capacity in the aging experiment, and respectively calculates the capacity attenuation of the battery under different external stresses and the ambient temperature T under different external stresses and the discharge rate R under different external stresses. d In the case of external stress, based on the attenuation function, the cumulative attenuation of the single cycle content of the t-th cycle under two single stresses is obtained, and on this basis, the above two stresses are coupled to establish a model for battery life prediction under composite stress (also known as "battery aging model"). This model can quantitatively analyze the change of battery capacity with the number of cycles under the action of both ambient temperature and discharge rate, and can provide an important basis for battery life prediction. Therefore, advantageously, the method of the present invention can achieve reliable prediction of battery life based on the modeling method of composite stress coupling. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Non-limiting and non-exhaustive embodiments of the present invention are described, by way of example, with reference to the following figures.

[0029] Figure 1 The flowchart of a method for battery life prediction according to an embodiment of the present invention is illustrated.

[0030] Figure 2 A more specific schematic diagram of a method for battery life prediction according to an exemplary embodiment of the present invention is shown.

[0031] Figure 3 A schematic diagram of comparing battery capacity attenuation curves under different discharge rate stresses according to an embodiment of the present invention is illustrated.

[0032] Figure 4 A schematic diagram of battery attenuation trend change under a composite stress consisting of ambient temperature stress and discharge rate stress according to an embodiment of the present invention is illustrated.

[0033] Figure 5 A schematic block diagram of a system for battery life prediction according to an embodiment of the present invention is illustrated.

[0034] Figure 6 A schematic block diagram of a system for battery life prediction according to another embodiment is illustrated. DETAILED DESCRIPTION

[0035] In order to make the above and other features and advantages of the present invention more clear, the present invention is further described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments given herein are for the purpose of explaining to those skilled in the art and are only exemplary and non-restrictive.

[0036] Figure 1 The flowchart of a method 100 for battery life prediction according to an embodiment of the present invention is illustrated. The method 100 comprises steps S102, S104, S106, S108, S110 and S112.

[0037] Figure 2 A more specific schematic diagram of a method for battery life prediction according to an exemplary embodiment of the present invention is shown.

[0038] In step S102, the battery capacity data after at least one charge and discharge cycle under the ambient temperature stress and the discharge rate stress is collected. The battery can be, for example, a lithium-ion battery, and its constituent materials can be common lithium-ion battery constituent materials. In one embodiment, Figure 2 As shown, the lithium-ion battery is specifically a LiMn2O4 battery, and the initial battery capacity calibrated by the manufacturer is 35Ah. In addition, in addition to the two external stresses of ambient temperature stress and discharge rate mentioned in the context, the external stresses of the commonly used battery aging method can also include charging rate, charge and discharge cycle start and end state of charge (SOC) intervals, etc. The method according to the present invention can also be appropriately extended to couple any two or more of the above-mentioned external stresses, and establish a corresponding battery life prediction model based on the coupled composite stress. In one embodiment, the battery capacity of the lithium-ion battery after different charge and discharge cycle periods can be collected, for example, the charge and discharge cycle can be selected to be 500 times, and the cycle period can be 50.

[0039] In step S104, at least the battery capacity data under the ambient temperature stress and the discharge rate stress are processed respectively using a decay function to obtain the cumulative decay of the single cycle capacity of the tth cycle under each of the ambient temperature stress and the discharge rate stress. The decay function includes but is not limited to linear, exponential, and Gaussian decay functions.

[0040] In step S106, a battery capacity attenuation model under each of the ambient temperature stress and the discharge rate stress is obtained based on the attenuation function.

[0041] In step S108, the ambient temperature stress and the discharge rate stress are coupled to form a composite stress, and the cumulative attenuation of the average capacity of a single cycle in the tth cycle under the composite stress is further obtained. In one embodiment, for the external stress of the commonly used battery aging method mentioned above, when establishing a battery aging model, a composite stress coupling method can be adopted to complete it, or it can also be understood that the battery capacity attenuation model under each stress that constitutes the composite stress is coupled. For example, coupling ambient temperature stress and discharge rate stress can be implemented by coupling the battery capacity attenuation model under each stress in the ambient temperature stress and the discharge rate stress.

[0042] In step S110, the corresponding relationship between the cumulative decay of the average capacity of the single cycle of the tth cycle under the composite stress and the corresponding loss capacity at the end of the battery life is determined. The end of the battery life can be defined as when the battery capacity loss reaches or exceeds a certain percentage value of the initial battery capacity. For example, in one embodiment, the end of the battery life can be defined as when its capacity loss reaches or exceeds 20% of the initial battery capacity. In other words, when the battery capacity loss reaches or exceeds 20% of the initial battery capacity, it can be understood that the battery life is terminated.

[0043] In step S112, a model for battery life prediction under the composite stress is established according to the corresponding relationship, so as to at least predict the life of the battery.

[0044] Through this embodiment, advantageously, those skilled in the art can establish a battery aging model to predict the service life of the battery based on the modeling concept of composite stress coupling, i.e., coupling different external stresses to obtain capacity attenuation under a composite stress composed of the different external stresses.

[0045] In one embodiment, the battery capacity data collected after at least one charge-discharge cycle under environmental temperature stress and discharge rate stress respectively includes: under the same discharge rate stress condition, the battery is subjected to different battery aging charge-discharge cycle experiments under the environmental temperature stress, and the battery capacity of the battery after at least one charge-discharge cycle in the experiment is recorded; and under the same environmental temperature stress condition, the battery is subjected to different battery aging charge-discharge cycle experiments under the discharge rate stress, and the battery capacity of the battery after at least one charge-discharge cycle in the experiment is recorded. In other words, the experiments under two different external stresses can be completed by the control variable method, wherein, for example, the battery aging life experiment under different discharge rate stresses is carried out in a temperature control room, and the ambient temperature is controlled to be absolutely consistent.

[0046] Specifically, Figure 2As shown in the embodiment, a LiMn2O4 battery is provided, and an accelerated stress battery aging life experiment is performed on the lithium ion battery, considering the ambient temperature T and the discharge rate R d Two factors. First, control the same discharge rate (such as 1C), when the ambient temperature is, for example, 20°C and 40°C, respectively, perform battery aging experiments on lithium-ion batteries under accelerated stress at different ambient temperatures, and record the battery capacity of the lithium-ion batteries after different cycle periods, such as C(1), C(50)... Second, control the same ambient temperature (such as 20°C), when the discharge rate is, for example, 1C, 2C and 3C, respectively, perform battery aging experiments on lithium-ion batteries under accelerated stress at different discharge rates, and record the battery capacity of the lithium-ion batteries after different cycle periods, such as C(1), C(50)... In one embodiment, the charge state (SOC) at the start of the charge and discharge cycle can be selected as 0.8, the charge state at the end of the charge and discharge cycle can be selected as 0.3, and the interval between each charge and discharge can be 15 minutes. It is conceivable that in other embodiments, different values ​​can be selected for the charge state at the start and end of the charge and discharge cycle and the interval between each charge and discharge according to actual conditions.

[0047] In addition, the battery aging life test method under accelerated stress at different ambient temperatures is as follows: the lithium ion battery is charged to SOC=0.8 at a charging rate of, for example, 0.5C, and then discharged to SOC=0.3 at an ambient temperature of 20°C and 40°C at a discharge rate of, for example, 1C, with an interval of 15 minutes between each charge and discharge, and the cycle is repeated 500 times. The battery capacity (Ah) of the lithium ion battery at the two groups of ambient temperatures of 20°C and 40°C in the above experiment is recorded respectively after 50 additional cycles, that is, C(1), C(50), C(100), C(150), C(200), C(250)...C(500) at T=20°C; C(1)', C(50)', C(100)', C(150)', C(200)', C(250)'...C(500)' at T=40°C.

[0048] Figure 3 The aging-capacity decay record curve of the battery under different discharge rate stress is shown. Figure 3 In the embodiment shown, the battery aging life test method with accelerated stress at different discharge rates is as follows: the lithium-ion battery is charged to SOC=0.8 at, for example, a 0.5C charge rate, and then discharged to, for example, SOC=0.3 at 1C, 2C, and 3C discharge rates at, for example, 20°C ambient temperature, with an interval of 15 minutes between each charge and discharge, and the cycle is repeated 500 times, and the battery capacity (Ah) at the three groups of discharge rates is recorded respectively, as described above.

[0049] like Figure 3As shown in the embodiment, the horizontal axis represents the number of charge and discharge cycles, and the vertical axis represents the attenuation. Within a certain range, the exponential function u(t) fitted according to the experimental data at a discharge rate of 1C is 38.57t 0.78 The curve connected by the solid squares shows that the exponential function u(t) fitted according to the experimental data at a discharge rate of 2C is 22.46t. 0.80 The curve connected by the solid circle shows that the exponential function u(t) fitted according to the experimental data at a discharge rate of 3C is 17.49t. 0.79 Shown are curved lines connected by solid triangles.

[0050] In one embodiment, the at least using the attenuation function to process the battery capacity data under the ambient temperature stress and the discharge rate stress includes: using Gaussian attenuation equations to describe the battery capacity data under the ambient temperature stress and the discharge rate stress. The Gaussian attenuation equation is: x(s, t) ~ N(u(s, t), σ 2 (t)), where s represents the ambient temperature stress or the discharge rate stress, x(s,t) represents the cumulative decay of the capacity of the tth cycle caused by the s stress; u(s,t) represents the cumulative decay of the single cycle capacity of the tth cycle caused by the s stress, σ 2 (t) represents the variance of the cumulative capacity decay in the tth cycle.

[0051] In one embodiment, the method further comprises: performing curve fitting and regression analysis on the battery capacity after the at least one charge and discharge cycle recorded in the experiment, and obtaining the cumulative decay of the single cycle capacity of the tth cycle caused by the ambient temperature stress or the discharge rate stress in the Gaussian decay equation as follows: Among them, f i (s) is the decay rate function, i represents the number of control groups in the experiment and h i (s) is a constant index. For example, if there are 5 groups of battery aging tests under external stress at different ambient temperatures, i can be 5. In addition, optionally, when s is the ambient temperature stress, i=1 and h1(T) is a constant; when s is the discharge rate stress, i=2 and h2(R d ) is a constant.

[0052] Specifically, the battery capacity decay under the two accelerated stresses of ambient temperature and discharge rate mentioned above is described by Gaussian decay equation, and the battery capacities C(1), C(50), C(100), C(150), C(200), C(250)...C(500) after t cycles recorded in the experiment are coupled with Gaussian functions and parameter matching is performed. The cumulative decay of the single cycle capacity of the tth cycle caused by single stress is subjected to curve fitting and regression analysis, and the cumulative decay of the single cycle capacity of the tth cycle under single stress is obtained.

[0053] In one embodiment, if Figure 2 As shown in, the battery capacity attenuation model under each of the ambient temperature stress and the discharge rate stress is obtained based on the attenuation function, including: when the stress is the ambient temperature stress, further based on the Arrhenius equation to obtain the battery capacity attenuation model under the ambient temperature stress; and when the stress is the discharge rate stress, further based on an exponential equation different from the Arrhenius equation to obtain the battery capacity attenuation model under the discharge rate stress. The Arrhenius equation can also be called the Arrhenius formula, which is an empirical formula for the relationship between the chemical reaction rate constant and the temperature, and is expressed as k=Ae -Ea / RT , where k is the rate constant, R is the molar gas constant, T is the thermodynamic temperature, Ea is the apparent activation energy and A is the pre-exponential factor (also called frequency factor). Because the series of changes inside the lithium-ion battery are essentially a series of complex chemical reactions between the positive and negative electrodes, when the external stress is the ambient temperature, the Arrhenius equation needs to be used to describe it. In addition, when the discharge rate is the external stress, the battery aging mechanism no longer follows the Arrhenius law, and an exponential function different from the Arrhenius equation needs to be used to fit the Arrhenius function instead of the Arrhenius function.

[0054] In one embodiment, when under the ambient temperature stress, the decay rate function is f1(T)=exp(G+W / T), and the cumulative decay of the single cycle capacity of the tth cycle caused by the ambient temperature stress u(T,t) is expressed as: Where T represents the ambient temperature stress; G = LnA, A represents the pre-exponential factor; W = -Ea / R, Ea represents the activation energy, and R represents the gas constant. For example, the R value can be selected as 8.3143 Jmol -1 K -1 .

[0055] In one embodiment, when under the ambient temperature stress, the decay rate function is f1(R d )=(Dd *R d cd +E d ), and the cumulative decay of the single cycle capacity of the tth cycle caused by the discharge rate stress u(R d ,t) is expressed as: Among them, R d Denotes the discharge rate stress; D d 、cd、E d is a constant.

[0056] Figure 4 A schematic diagram of battery attenuation trend changes under a composite stress consisting of ambient temperature stress and discharge rate stress according to an embodiment of the present invention is illustrated, which shows that the battery aging changes with ambient temperature, discharge rate and number of charge and discharge cycles.

[0057] In one embodiment, the cumulative decay of the single cycle average capacity of the tth cycle under the composite stress consisting of the ambient temperature stress and the discharge rate stress obtained after coupling is u(T, R d ,t), that is, u(T,R d ,t)=exp(W(1 / T1-1 / T2))u(R d ,t), where T1 and T2 represent two ambient temperatures. The test described in this article is carried out at two ambient temperatures respectively, and similarly it can be applied to the case of multiple temperatures.

[0058] As described above, based on the decay rate function, when the external stress ambient temperature T acts, according to the Arrhenius equation, the battery capacity decay model under the ambient temperature T stress is obtained; and when the external stress discharge rate acts, different exponential functions are used to replace the Arrhenius function for fitting, and the discharge rate R is obtained. d Battery capacity attenuation model under stress. Couple the above two external stresses (for example, the battery capacity attenuation model under the stress of ambient temperature T and the discharge rate R d The battery capacity attenuation model under stress is used to obtain the cumulative attenuation of the average capacity of a single cycle in the tth cycle under composite stress.

[0059] In one embodiment, if Figure 2 As shown in d , 1), and the corresponding loss capacity at the end of the battery life can be expressed as 0.2C (R d,1). For example, the actual capacity of a battery can be calculated using the Peukert equation. The Peukert function can also be called the Peukert equation. It is one of the capacity estimation methods in the field of electrochemistry. Its core is to define the relationship between the discharge time and discharge current of a battery. The Peukert equation is expressed as I n t = k, where I is the discharge current, t is the discharge time, n is the Peukert constant, and k is a constant, representing the theoretical capacity of the battery. However, the Peukert equation is not the only option, and other methods can also be used to simulate and estimate the battery capacity.

[0060] In one embodiment, the relationship between the cumulative decay of the single cycle average capacity of the tth cycle under the composite stress and the corresponding loss capacity at the end of the battery life is expressed as: 0.2C(R d ,1)Ah=u(T,R d ,t)mAh.

[0061] In one embodiment, the model established for battery life prediction under the composite stress is: L = {[C (R d ,1)*0.2*1000] / [u(T,R d ,t)]} 1.27 , where L represents the predicted battery life, that is, the usable life of the battery.

[0062] Figure 5 The schematic block diagram of a system for battery life prediction according to an embodiment of the present invention is illustrated, wherein the system comprises a collection module 502 , a single stress battery capacity decay modeling module 505 , a composite stress battery capacity decay modeling module 508 , and a battery life prediction modeling module 511 .

[0063] The acquisition module 502 is configured to acquire battery capacity data after at least one charge and discharge cycle of the battery under ambient temperature stress and discharge rate stress.

[0064] The single stress battery capacity attenuation modeling module 505 is configured to use at least a decay function to process the battery capacity data under the ambient temperature stress and the discharge rate stress respectively, so as to obtain the cumulative decay of the single cycle capacity of the tth cycle under each of the ambient temperature stress and the discharge rate stress; and is further configured to obtain the battery capacity attenuation model under each of the ambient temperature stress and the discharge rate stress respectively based on the decay function.

[0065] The composite stress battery capacity decay modeling module 508 is configured to couple the ambient temperature stress and the discharge rate stress to form a composite stress, and further obtain the cumulative decay of the single cycle average capacity of the tth cycle under the composite stress.

[0066] The battery life prediction modeling module 511 is configured to establish a corresponding relationship between the cumulative decay of the single-cycle average capacity of the tth cycle under the composite stress and the corresponding loss capacity at the end of the battery life; and is further configured to establish a model for battery life prediction under the composite stress based on the corresponding relationship, so as to at least predict the life of the battery.

[0067] In one embodiment, the acquisition module 502 is further configured to: under the condition that the discharge rate stress is the same, the battery undergoes a battery aging charge and discharge cycle experiment under different environmental temperature stresses, and records the battery capacity of the battery after at least one charge and discharge cycle in the experiment; and under the condition that the environmental temperature stress is the same, the battery undergoes a battery aging charge and discharge cycle experiment under different discharge rate stresses, and records the battery capacity of the battery after at least one charge and discharge cycle in the experiment.

[0068] In one embodiment, the single stress battery capacity attenuation modeling module 505 is configured to: use Gaussian attenuation equations to describe the battery capacity data under the ambient temperature stress and the discharge rate stress, respectively, wherein the Gaussian attenuation equation is: x(s, t) ~ N(u(s, t), σ 2 (t)), where s represents the ambient temperature stress or the discharge rate stress, x(s,t) represents the cumulative decay of the capacity of the tth cycle caused by the s stress; u(s,t) represents the cumulative decay of the single cycle capacity of the tth cycle caused by the s stress, σ 2 (t) represents the variance of the cumulative capacity decay of the t-th cycle; and, curve fitting and regression analysis are performed on the battery capacity after the at least one charge and discharge cycle recorded in the experiment, and the cumulative decay of the single cycle capacity of the t-th cycle caused by the ambient temperature stress or the discharge rate stress in the Gaussian decay equation is obtained as follows: Among them, f i (s) is the decay rate function, i represents the number of control groups in the experiment and h i (s) is the constant exponent.

[0069] In one embodiment, the single stress battery capacity attenuation modeling module 505 is also configured to: when the stress is the ambient temperature stress, further obtain the battery capacity attenuation model under the ambient temperature stress based on the Arrhenius equation; and when the stress is the discharge rate stress, further obtain the battery capacity attenuation model under the discharge rate stress based on an exponential equation different from the Arrhenius equation.

[0070] Figure 6 A schematic block diagram of a system for battery life prediction according to another embodiment is illustrated. Figure 6 The system includes Figure 5 In addition to the modules (not shown) described in the embodiment, the battery temperature control device 20, the battery charging and discharging device 30, the battery management device 40, and / or the control device 50 may also be included.

[0071] The battery 10 is disposed in the battery temperature control device 20, and the battery temperature control device 20 is configured to control the temperature of the battery 10. The battery charge and discharge device 30 is electrically connected to the battery temperature control device 20 and is configured to perform charge and discharge operations on the battery 10. The battery management device 40 is electrically connected to the battery temperature control device 20 and is configured to monitor the battery state of the battery 10 in real time to prevent overcharge / overdischarge.

[0072] The control device 50 is electrically connected to the battery charging and discharging device 30 and / or the battery management device 40, and is configured to control the battery charging and discharging device 30 to charge and discharge the battery 10, and / or to monitor the charging and discharging process of the battery 10 in real time through the battery management device 40. In addition, the acquisition module 502 acquires battery capacity data of the battery after at least one charge and discharge cycle under environmental temperature stress and discharge rate stress respectively through at least the control device 50.

[0073] In one embodiment, the battery temperature control device 20 may be a thermostat. The battery charging and discharging device 30 may be a battery charging and discharging device of Digatron EVT500-500. The battery management device 40 may be a battery management system (BMS). The control device 50 may be, for example, a host computer. The host computer is communicatively coupled with the battery charging and discharging device to be able to obtain battery capacity data after at least one charge and discharge cycle of the battery under different external stresses (such as ambient temperature stress and discharge rate stress, respectively).

[0074] In one embodiment, the system may further include a power supply device to provide power to the battery charging and discharging equipment.

[0075] The system for battery life prediction described herein can implement the steps of the method for battery life prediction discussed in any of the above implementation schemes or examples, and perform similar extensions, which will not be described in detail here.

[0076] Another aspect of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for battery life prediction described in any of the above embodiments.

[0077] Another aspect of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for battery life prediction described in any of the above embodiments are implemented.

[0078] Those of ordinary skill in the art can understand that all or part of the steps in the method according to the above-mentioned embodiments of the present invention can be implemented by instructing the relevant hardware to complete through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, the steps of the embodiments of the above-mentioned methods can be implemented. Among them, any reference to memory, storage, database or other media used in the various embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0079] The various technical features of the above implementation schemes can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above implementation schemes are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] Although the present invention has been described in conjunction with the embodiments, it will be appreciated by those skilled in the art that the above description and the accompanying drawings are exemplary and non-restrictive, and the present invention is not limited to the disclosed embodiments. Various modifications and variations are possible without departing from the spirit of the present invention.

Claims

1. A method for battery life prediction, characterized in that: The method comprises: Collecting battery capacity data after at least one charge and discharge cycle under ambient temperature stress and discharge rate stress; At least using a decay function to process the battery capacity data under the ambient temperature stress and the discharge rate stress respectively to obtain the cumulative decay of the single cycle capacity of the t-th cycle under each of the ambient temperature stress and the discharge rate stress; Based on the attenuation function, a battery capacity attenuation model under each of the ambient temperature stress and the discharge rate stress is obtained respectively; The ambient temperature stress and the discharge rate stress are coupled to form a composite stress, and the cumulative attenuation of the average capacity of a single cycle in the tth cycle under the composite stress is further obtained; Determine the corresponding relationship between the cumulative decay of the single cycle average capacity of the tth cycle under the composite stress and the corresponding loss capacity at the end of battery life; and According to the corresponding relationship, a model for predicting the battery life under the composite stress is established to at least predict the battery life.

2. The method according to claim 1, characterized in that The collecting of battery capacity data after at least one charge and discharge cycle under ambient temperature stress and discharge rate stress comprises: Under the condition of the same discharge rate stress, performing a battery aging charge-discharge cycle experiment on the battery under different environmental temperature stresses, and recording the battery capacity of the battery after at least one charge-discharge cycle in the experiment; and Under the condition of the same environmental temperature stress, a battery aging charge-discharge cycle experiment under different discharge rate stresses is performed on the battery, and the battery capacity of the battery after at least one charge-discharge cycle in the experiment is recorded.

3. The method according to claim 2, characterized in that The at least using the attenuation function to process the battery capacity data under the ambient temperature stress and the discharge rate stress respectively comprises: The battery capacity data under the ambient temperature stress and the discharge rate stress are described using a Gaussian attenuation equation, wherein the Gaussian attenuation equation is: x(s,t)~N(u(s,t),σ 2 (t)), Wherein, s represents the ambient temperature stress or the discharge rate stress, x(s,t) represents the cumulative decay of the capacity of the t-th cycle caused by the s stress; u(s,t) represents the cumulative decay of the single cycle capacity of the t-th cycle caused by the s stress, σ 2 (t) represents the variance of the cumulative capacity decay in the tth cycle.

4. The method according to claim 3, characterized in that The method further comprises: The battery capacity after at least one charge and discharge cycle recorded in the experiment is subjected to curve fitting and regression analysis, and the cumulative decay of the single cycle capacity of the tth cycle caused by the ambient temperature stress or the discharge rate stress in the Gaussian decay equation is obtained as follows: Among them, f i (s) is the decay rate function, i represents the number of control groups in the experiment and h i (s) is the constant exponent.

5. The method according to claim 4, characterized in that The battery capacity attenuation model under each of the ambient temperature stress and the discharge rate stress is obtained based on the attenuation function, including: When the stress is the ambient temperature stress, further obtaining a battery capacity attenuation model under the ambient temperature stress based on the Arrhenius equation; and When the stress is the discharge rate stress, a battery capacity attenuation model under the discharge rate stress is further obtained based on an exponential equation different from the Arrhenius equation.

6. The method according to claim 5, characterized in that When under the ambient temperature stress, the decay rate function is f1(T)=exp(G+W / T), and the cumulative decay of the single cycle capacity of the tth cycle caused by the ambient temperature stress u(T,t) is expressed as: Wherein, T represents the environmental temperature stress; G=LnA, A represents the pre-exponential factor; W=-Ea / R, Ea represents the activation energy, and R represents the gas constant. and / or, When under the discharge rate stress, the decay rate function is f1(R d )=(D d *R d cd +E d ), and the cumulative decay of the single cycle capacity of the tth cycle caused by the discharge rate stress u(R d ,t) is expressed as: Among them, R d Denotes the discharge rate stress; D d 、cd、E d is a constant.

7. The method according to claim 6, characterized in that The cumulative decay of the average capacity of a single cycle in the tth cycle under the composite stress consisting of the ambient temperature stress and the discharge rate stress obtained after coupling is u(T, R d ,t), u(T,R d ,t)=exp(W(1 / T1-1 / T2))u(R d ,t), Wherein T1 and T2 represent two ambient temperatures.

8. The method according to claim 7, characterized in that The method further comprises: Get the initial battery capacity C(R d , 1), and the corresponding loss capacity at the end of the battery life can be expressed as 0.2C (R d ,1), and, The corresponding relationship between the determined cumulative decay of the single cycle average capacity of the tth cycle under the composite stress and the corresponding loss capacity at the end of the battery life is expressed as: 0.2C(R d ,1)Ah=u(T,R d ,t)mAh。 9. The method according to claim 8, characterized in that The model established for battery life prediction under the composite stress is: L={[C(R d ,1)*0.2*1000] / [u(T,R d ,t)]} 1.27 , Where L represents the predicted battery life.

10. A system for battery life prediction, characterized in that: The system comprises: A collection module, wherein the collection module is configured to collect battery capacity data after at least one charge and discharge cycle of the battery under ambient temperature stress and discharge rate stress; a single stress battery capacity attenuation modeling module, the single stress battery capacity attenuation modeling module being configured to process the battery capacity data under the ambient temperature stress and the discharge rate stress respectively using at least a decay function to obtain the cumulative decay of the single cycle capacity of the t-th cycle under each of the ambient temperature stress and the discharge rate stress, and further configured to obtain a battery capacity attenuation model under each of the ambient temperature stress and the discharge rate stress respectively based on the decay function; A composite stress battery capacity decay modeling module, wherein the composite stress battery capacity decay modeling module is configured to couple the ambient temperature stress and the discharge rate stress to form a composite stress, and further obtain a cumulative decay of a single cycle average capacity of the tth cycle under the composite stress; and A battery life prediction modeling module, wherein the battery life prediction modeling module is configured to establish a corresponding relationship between the cumulative decay of the single-cycle average capacity of the tth cycle under the composite stress and the corresponding loss capacity at the end of the battery life, and is further configured to establish a model for battery life prediction under the composite stress based on the corresponding relationship, so as to at least predict the life of the battery.

11. The system according to claim 10, characterized in that The acquisition module is further configured as follows: Under the condition of the same discharge rate stress, the battery is subjected to a battery aging charge and discharge cycle experiment under different environmental temperature stresses, and the battery capacity of the battery after at least one charge and discharge cycle in the experiment is recorded; as well as Under the condition of the same environmental temperature stress, the battery is subjected to a battery aging charge-discharge cycle experiment under different discharge rate stresses, and the battery capacity of the battery after at least one charge-discharge cycle in the experiment is recorded.

12. The system according to claim 11, characterized in that The single stress battery capacity decay modeling module is configured as follows: The battery capacity data under the ambient temperature stress and the discharge rate stress are described using a Gaussian attenuation equation, wherein the Gaussian attenuation equation is: x(s,t)~N(u(s,t),σ 2 (t)), Wherein, s represents the ambient temperature stress or the discharge rate stress, x(s,t) represents the cumulative decay of the capacity of the t-th cycle caused by the s stress; u(s,t) represents the cumulative decay of the single cycle capacity of the t-th cycle caused by the s stress, σ 2 (t) represents the variance of the cumulative capacity decay in the tth cycle; and, The battery capacity after at least one charge and discharge cycle recorded in the experiment is subjected to curve fitting and regression analysis, and the cumulative decay of the single cycle capacity of the tth cycle caused by the ambient temperature stress or the discharge rate stress in the Gaussian decay equation is obtained as follows: Among them, f i (s) is the decay rate function, i represents the number of control groups in the experiment and h i (s) is the constant exponent.

13. The system according to claim 12, characterized in that The single stress battery capacity decay modeling module is further configured to: When the stress is the ambient temperature stress, further obtaining a battery capacity attenuation model under the ambient temperature stress based on the Arrhenius equation; as well as When the stress is the discharge rate stress, a battery capacity attenuation model under the discharge rate stress is further obtained based on an exponential equation different from the Arrhenius equation.

14. The system according to any one of claims 10 to 13, characterized in that: The system further comprises: a battery temperature control device, the battery being disposed in the battery temperature control device and configured to control the temperature of the battery; a battery charging and discharging device, the battery charging and discharging device being electrically connected to the battery temperature control device and configured to perform charging and discharging operations on the battery; and / or, A battery management device is electrically connected to the battery temperature control device and is configured to monitor the battery status of the battery in real time to prevent the battery from being overcharged or over-discharged.

15. The system according to claim 14, characterized in that The system further comprises: A control device, the control device is electrically connected to the battery charging and discharging device and / or the battery management device, and is configured to control the battery charging and discharging device to perform charging and discharging operations on the battery, and / or to monitor the battery charging and discharging process in real time through the battery management device; The acquisition module acquires battery capacity data of the battery after at least one charge and discharge cycle under environmental temperature stress and discharge rate stress respectively via at least the control device.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program implements the steps of the method according to any one of claims 1 to 9 when executed by a processor.

17. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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