Power battery capacity estimation method and device, electronic equipment, medium and product

Abstract

The invention relates to a power battery capacity estimation method and device, electronic equipment, a medium and a product, and the method comprises the steps: obtaining a temperature stress parameter and a discharge rate stress parameter of a power battery; establishing a capacity recession model of the power battery, obtaining a capacity recession model under the influence of the temperature stress parameters according to the temperature stress parameters of the power battery, and obtaining a capacity recession model under the influence of the discharge rate parameters according to the discharge rate stress parameters; and based on the capacity fading model under the influence of the temperature stress parameter and the capacity fading model under the influence of the discharge rate parameter, establishing a capacity fading model under the composite stress parameter, based on the stress dynamic change of the power battery, obtaining a capacity fading estimation model under the dynamic stress parameter, and generating a capacity estimation result of the power battery. The problem that the capacity estimation accuracy of the power battery is low due to the fact that the real-time change of the stress state is not considered in the prior art is solved, and the capacity estimation accuracy of the power battery is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power batteries, and in particular relates to a power battery capacity estimation method and device, electronic equipment, medium and product. BACKGROUND

[0002] The uncertainty of the maximum available capacity of the battery seriously affects the estimation accuracy of the state of charge (SOC) of the battery, and therefore an accurate available capacity prediction method is a prerequisite for accurate estimation of the SOC of the battery, and establishing a capacity degradation model is an effective solution.

[0003] In related technologies, the charging and discharging rate is constant at a certain temperature, and the accelerated test is mainly used. Based on the accelerated degradation data, a capacity degradation model of the power battery can be quickly established.

[0004] However, the stress state of the power battery is constantly changing, and in actual applications, it is often difficult to obtain the full process data of the degradation of the power battery, resulting in low capacity estimation accuracy of the power battery, which needs to be solved urgently. SUMMARY

[0005] The present application provides a power battery capacity estimation method, device, electronic equipment, medium and product to solve the problem of low capacity estimation accuracy of the power battery caused by not considering the real-time change of the stress state in related technologies, and improve the accuracy of the capacity estimation of the power battery.

[0006] The first aspect of the present application provides a power battery capacity estimation method, comprising the following steps: obtaining temperature stress parameters and discharge rate stress parameters of a power battery; establishing a capacity degradation model of the power battery, and based on the capacity degradation model, obtaining a capacity degradation model under the influence of temperature stress parameters according to the temperature stress parameters of the power battery, and obtaining a capacity degradation model under the influence of discharge rate parameters according to the discharge rate parameters; based on the capacity degradation model under the influence of the temperature stress parameters and the capacity degradation model under the influence of the discharge rate parameters, establishing a capacity degradation model under the influence of composite stress parameters, and based on the dynamic change of the stress of the power battery, obtaining a capacity degradation estimation model under the influence of dynamic stress parameters according to the capacity degradation model under the influence of the composite stress parameters, and generating a capacity estimation result of the power battery according to the capacity degradation estimation model under the influence of the dynamic stress parameters.

[0007] Further, in some embodiments, the establishing the capacity fade model of the power battery comprises: obtaining capacity fade data of the power battery; performing statistical calculation on the capacity fade data by using a preset statistical method to obtain characteristic values of the capacity fade model of the power battery; and fitting the capacity fade data and the characteristic values of the capacity fade model of the power battery to obtain the capacity fade model of the power battery.

[0008] Further, in some embodiments, the obtaining the capacity fade model under the temperature stress parameter and the capacity fade model under the discharge rate stress parameter based on the capacity fade model comprises: obtaining coefficients of the capacity fade model under the temperature stress parameter and coefficients of the capacity fade model under the discharge rate stress parameter; obtaining the coefficients of the capacity fade model under the temperature stress parameter based on an Arrhenius model, and rewriting the capacity fade model of the power battery to obtain the capacity fade model under the temperature stress parameter; and obtaining the coefficients of the capacity fade model under the discharge rate stress parameter based on an inverse power law model, and rewriting the capacity fade model of the power battery to obtain the capacity fade model under the discharge rate stress parameter.

[0009] Further, in some embodiments, the establishing the capacity fade model under the composite stress parameter based on the capacity fade model under the temperature stress parameter and the capacity fade model under the discharge rate stress parameter comprises: calculating an acceleration factor of the temperature stress parameter; and establishing the capacity fade model of the power battery under the composite stress parameter based on the acceleration factor, the capacity fade model under the temperature stress parameter and the capacity fade model under the discharge rate stress parameter.

[0010] Further, in some embodiments, the obtaining the capacity fade estimation model under the dynamic stress parameter based on the capacity fade model under the composite stress parameter comprises: obtaining a capacity fade coefficient of the power battery based on the capacity fade model of the power battery under the composite stress parameter; establishing a dynamic recursive model of the power battery based on a historical fade amount, a current stress state and the capacity fade coefficient of the power battery to obtain a dynamic fade amount; and establishing the capacity fade estimation model under the dynamic stress parameter based on the dynamic fade amount.

[0011] The power battery capacity estimation method provided by the embodiment of the present application solves the problem of low capacity estimation accuracy of the power battery caused by not considering real-time changes of stress states in the related art, and improves the accuracy of capacity estimation of the power battery.

[0012] The second aspect embodiment of the present application provides a power battery capacity estimation device, the device comprises: an acquisition module for acquiring temperature stress parameters and discharge rate stress parameters of a power battery; a mathematical modeling module for establishing a capacity degradation model of the power battery, and based on the capacity degradation model, obtaining a capacity degradation model under the influence of temperature stress parameters according to the temperature stress parameters of the power battery, and obtaining a capacity degradation model under the influence of discharge rate stress parameters according to the discharge rate stress parameters; an estimation module for establishing a capacity degradation model under composite stress parameters based on the capacity degradation model under the influence of temperature stress parameters and the capacity degradation model under the influence of discharge rate stress parameters, and obtaining a capacity degradation estimation model under dynamic stress parameters according to the capacity degradation model under composite stress parameters based on dynamic changes of stress of the power battery, and generating a capacity estimation result of the power battery according to the capacity degradation estimation model under dynamic stress parameters.

[0013] Further, in some embodiments, the mathematical modeling module is specifically configured to: acquire capacity degradation data of the power battery; perform statistical calculation on the capacity degradation data by using a preset statistical method to obtain characteristic values of the capacity degradation model of the power battery; and fit the capacity degradation data and the characteristic values of the capacity degradation model of the power battery to obtain the capacity degradation model of the power battery.

[0014] Further, in some embodiments, the mathematical modeling module is further configured to: acquire coefficients of the degradation model under the influence of temperature stress parameters and coefficients of the degradation model under the influence of discharge rate stress parameters; acquire the coefficients of the degradation model under the influence of temperature stress parameters based on an Arrhenius model, and rewrite the capacity degradation model of the power battery to obtain the capacity degradation model under the influence of temperature stress parameters; acquire the coefficients of the degradation model under the influence of discharge rate stress parameters based on an inverse power law model, and rewrite the capacity degradation model of the power battery to obtain the capacity degradation model under the influence of discharge rate stress parameters.

[0015] Further, in some embodiments, the estimation module is specifically configured to: calculate an accelerated degradation factor of the temperature stress parameter; and establish a capacity degradation model of the power battery under the influence of the composite stress parameter based on the accelerated degradation factor, the capacity degradation model under the influence of the temperature stress parameter, and the capacity degradation model under the influence of the discharge rate stress parameter.

[0016] Further, in some embodiments, the estimation module is further configured to: obtain a power battery capacity degradation coefficient based on the capacity degradation model of the power battery under the influence of the composite stress parameter; establish a power battery dynamic recursive model based on a historical degradation amount, a current stress state, and the power battery capacity degradation coefficient to obtain a dynamic degradation amount; and establish the capacity degradation estimation model under the influence of the dynamic stress parameter based on the dynamic degradation amount.

[0017] According to the power battery capacity estimation device provided by the embodiment of the present application, the temperature and the discharge rate stress parameter of the power battery are obtained, the capacity degradation model is established, the model under the influence of a single stress is derived, the composite stress model is constructed, the dynamic stress degradation estimation model is obtained from the composite model combined with the dynamic change of the stress, and finally the capacity estimation result is generated, so that the problem of low capacity estimation accuracy of the power battery caused by not considering the real-time change of the stress state in the related art is solved, and the accuracy of the capacity estimation of the power battery is improved.

[0018] The third aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the power battery capacity estimation method described in the above embodiments.

[0019] The fourth aspect of the present application provides a computer readable storage medium having a computer program stored thereon, and the program is executed by a processor to implement the power battery capacity estimation method described in the above embodiments.

[0020] The fifth aspect of the present application provides a computer program product comprising a computer program, and the computer program is executed to implement the power battery capacity estimation method described in the above embodiments.

[0021] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0023] Figure 1A flowchart of a power battery capacity estimation method according to an embodiment of the present application is provided.

[0024] Figure 2 A block diagram of a power battery capacity estimation device according to an embodiment of the present application is provided.

[0025] Figure 3 A structural schematic diagram of an electronic device according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0026] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0027] The power battery capacity estimation method, device, electronic device, medium and product of the embodiments of the present application are described below with reference to the accompanying drawings. In view of the problem of low capacity estimation accuracy of the power battery due to the real-time change of the stress state in the related art mentioned in the background art, the present application provides a power battery capacity estimation method, which obtains the temperature and discharge rate stress parameters of the power battery, establishes a capacity degradation model, and respectively derives the model under the influence of single stress, then constructs a composite stress model, and further obtains a dynamic stress degradation estimation model from the composite model combined with the dynamic change of stress, and finally generates a capacity estimation result, thereby solving the problem of low capacity estimation accuracy of the power battery due to the real-time change of the stress state in the related art, and improving the accuracy of the capacity estimation of the power battery.

[0028] Specifically, Figure 1 A flowchart of a power battery capacity estimation method according to an embodiment of the present application is provided.

[0029] As Figure 1 shown, the power battery capacity estimation method comprises the following steps:

[0030] In step S101, the temperature stress parameter and the discharge rate stress parameter of the power battery are obtained.

[0031] The temperature stress parameter refers to a physical quantity and model parameter for describing the influence of temperature on battery capacity degradation, and the discharge rate stress parameter refers to a physical quantity and model parameter for describing the influence of discharge rate on power battery capacity degradation.

[0032] In step S102, a capacity degradation model of the power battery is established, and based on the capacity degradation model, a capacity degradation model under temperature stress parameter is obtained according to the temperature stress parameter of the power battery, and a capacity degradation model under discharge rate parameter is obtained according to the discharge rate stress parameter.

[0033] The capacity degradation model of the power battery refers to a mathematical model for describing and predicting the gradual degradation law of the battery capacity with the increase of the use time or the cycle number. The capacity degradation model under the temperature stress parameter refers to a mathematical model constructed by quantifying the temperature related parameters based on the physical action mechanism of the temperature on the capacity degradation of the power battery. The capacity degradation model under the discharge rate parameter refers to a mathematical model for describing the influence of the discharge current size, i.e. the discharge rate, on the capacity degradation law of the power battery.

[0034] Further, in some embodiments, the capacity degradation model of the power battery is established by: obtaining the capacity degradation data of the power battery; performing statistical calculation on the capacity degradation data by using a preset statistical method to obtain the characteristic value of the capacity degradation model of the power battery; and fitting the capacity degradation data and the characteristic value of the capacity degradation model of the power battery to obtain the capacity degradation model of the power battery.

[0035] The capacity degradation data of the power battery refers to the change data of the battery capacity after each charge and discharge cycle of the power battery.

[0036] Specifically, the cumulative capacity degradation data of the battery after multiple charge and discharge cycles is obtained, the cumulative capacity degradation data of the battery is subjected to maximum likelihood estimation, and the characteristic value of the capacity degradation model of the power battery is obtained. The test data is subjected to curve fitting and regression analysis to obtain the capacity degradation model of the power battery.

[0037] For example, it is assumed that each charge and discharge cycle of the power battery causes a capacity loss ΔC of the battery. Since the environment and the internal state of the battery are complex and changeable during each cycle, ΔC is a random variable and is independently and identically distributed. Therefore, the cumulative capacity degradation x(s, t) of the battery after t cycles is approximately normally distributed according to the central limit theorem when t is large enough. That is, the capacity degradation process of the battery under the stress s is a Gaussian process. Therefore, the capacity degradation of the battery under the stress s is normally distributed:

[0038] x(s, t) ~ N(u(s, t), σ 2 (n))(1)

[0039] Wherein, x(s, t) is the capacity cumulative degradation amount of the battery after t cycles under stress level s (i.e. the sum of the degradation amount AC of the battery each cycle), and u(s, t) is the average capacity degradation amount of the battery after t cycles under stress level s; σ 2 (t) is the variance of the capacity degradation amount of the battery after t cycles.

[0040] Further, the degradation amount should be estimated according to the maximum likelihood estimation of the accelerated degradation test data, and the estimated value u^(s, t) of the degradation average is taken as the characteristic value of the prediction model, and the u^(s, t) of the test data is curve fitted and regression analyzed, and the capacity degradation function of the power battery is:

[0041]

[0042] Wherein, s is the stress type; h i (s) is a constant; when the stress s is respectively the temperature T and the discharge rate R d , i = 1, 2.

[0043] Further, in some embodiments, based on the capacity degradation model, the capacity degradation model under the influence of the temperature stress parameter is obtained according to the temperature stress parameter of the power battery, and the capacity degradation model under the influence of the discharge rate stress parameter is obtained according to the discharge rate stress parameter, including: obtaining the coefficient of the degradation model under the influence of the temperature stress parameter and the coefficient of the degradation model under the influence of the discharge rate stress parameter; based on the Arrhenius model, obtaining the coefficient of the degradation model under the influence of the temperature stress parameter, and rewriting the capacity degradation model of the power battery to obtain the capacity degradation model under the influence of the temperature stress parameter; based on the inverse power law model, obtaining the coefficient of the degradation model under the influence of the discharge rate stress parameter, and rewriting the capacity degradation model of the power battery to obtain the capacity degradation model under the influence of the discharge rate stress parameter.

[0044] Wherein, the coefficient of the degradation model under the influence of the temperature stress parameter refers to the parameter for quantifying the influence degree of the temperature on the capacity degradation of the power battery in the mathematical expression constructed based on the Arrhenius model, and the coefficient of the degradation model under the influence of the discharge rate stress parameter refers to the parameter for quantifying the influence degree of the discharge rate on the capacity degradation of the power battery in the mathematical expression constructed based on the inverse power law model.

[0045] Specifically, according to the Arrhenius model, the coefficient of the degradation equation under the influence of temperature is calculated, and the coefficient is brought into formula (2), so as to obtain the capacity degradation model under the influence of temperature stress parameters; meanwhile, in the traditional accelerated life theory, the relationship between electric stress and life characteristics of products conforms to the inverse power model, and since the degradation equation reflects the life information of products, therefore, the life characteristics and the degradation equation have a corresponding relationship, the average life of the power battery can be obtained from formula (2), and then the capacity degradation model under the influence of discharge rate stress parameters is obtained.

[0046] For example, according to the Arrhenius model, the coefficient of the degradation equation under the influence of temperature T is calculated as follows:

[0047]

[0048] Wherein, k, F are constants irrelevant to stress and time; T is absolute temperature; let G = ln k, then:

[0049]

[0050] Therefore, the capacity degradation model under the influence of temperature stress parameters is:

[0051]

[0052] Further, the inverse power model is:

[0053] ξ = Av -c (6)

[0054] Wherein, v is a generalized electric stress, including voltage and current; A is a normal number; c is a normal number related to activation energy; ξ is a life characteristic value, since the degradation equation reflects the life information of products, therefore, the life characteristic value ξ and the degradation equation have a certain relationship. As can be seen from formula (2), the average life L of the power battery is:

[0055]

[0056] Wherein, l is a failure threshold, which can represent that the actual capacity degradation is 80% of the initial capacity;

[0057] When ξ takes the average life of the battery,

[0058]

[0059]

[0060] Wherein, is a normal number; c' = ch i(v) is a normal number related to activation energy;When the discharge rate is taken as the acceleration stress, the coefficients of the degradation equation and the stress level follow the above relationship, and the formula (8) can be further extended to obtain:

[0061]

[0062] According to the formula (2) and the formula (9), the capacity degradation model under the influence of the discharge rate stress parameter can be obtained:

[0063]

[0064] Wherein, D, c', E, h2(R d ) are all constants, which can be obtained by fitting the capacity degradation data under the discharge rate parameter.

[0065] It should be noted that since the normally working battery will be subjected to high temperature stress and high discharge rate stress at the same time, when the temperature rises, the capacity will accelerate degradation, and the capacity degradation model under the single discharge rate stress will not accurately predict the degradation amount, therefore, the acceleration degradation factor of the temperature stress is used to reflect the acceleration effect of the temperature, and the capacity degradation model under the composite stress is established.

[0066] In step S103, based on the capacity degradation model under the influence of the temperature stress parameter and the capacity degradation model under the influence of the discharge rate parameter, the capacity degradation model under the composite stress parameter is established, and based on the dynamic change of the stress of the power battery, the capacity degradation estimation model under the dynamic stress parameter is obtained according to the capacity degradation model under the composite stress parameter, and the capacity estimation result of the power battery is generated according to the capacity degradation estimation model under the dynamic stress parameter.

[0067] Wherein, the capacity degradation model under the composite stress parameter refers to the comprehensive influence of temperature and discharge rate and other stress factors on the capacity degradation of the power battery, and the mathematical model is constructed by coupling the single stress model, and the capacity degradation estimation model under the dynamic stress parameter refers to the recursive mathematical model constructed by fusing the historical degradation state and the real-time stress parameter, aiming at the dynamic change characteristics of the stress state (such as temperature and discharge rate) of the power battery in actual use.

[0068] Specifically, the acceleration degradation factor of the temperature stress is used to reflect the acceleration effect of the temperature, the acceleration degradation factor of the temperature stress is integrated into the capacity degradation model to obtain the capacity degradation model under the composite stress parameter, meanwhile, the capacity degradation coefficients under the influence of the temperature stress parameter and the discharge rate parameter are defined, and the relationship between the degradation amount and the stress level is deduced, finally, the capacity degradation estimation model under the dynamic stress parameter is obtained, and the capacity estimation result of the power battery is generated.

[0069] In some embodiments, a capacity degradation model under combined stress parameters is established based on the capacity degradation model under the influence of temperature stress parameters and the capacity degradation model under the influence of discharge rate parameters. This includes: calculating the accelerated degradation factor of temperature stress parameters; and establishing a capacity degradation model of the power battery under combined stress parameters based on the accelerated degradation factor, the capacity degradation model under the influence of temperature stress parameters, and the capacity degradation model under the influence of discharge rate stress parameters.

[0070] Among them, the accelerated degradation factor of temperature stress parameter refers to the coefficient used in battery degradation research to quantify the impact of temperature changes on the rate of battery capacity degradation.

[0071] For example, the accelerated decay factor of the temperature stress parameter is:

[0072]

[0073] Taking the temperatures T1 = T0 and T2 = T in formula (11), we can obtain the following values ​​for (T, R). d Capacity decay model under combined stress parameters:

[0074]

[0075] in, This refers to the power battery at (T, d The rate of decay under the given state; when the state is determined, formula (12) is a fixed value.

[0076] It should be noted that since the usage state of a power battery is not constant, the prediction of battery degradation deviates significantly from the actual capacity degradation process during battery use. Therefore, it is necessary to establish a battery capacity degradation model under dynamic stress based on the current battery capacity state in order to more accurately describe the actual capacity degradation process of the battery.

[0077] Furthermore, in some embodiments, based on the dynamic stress changes of the power battery, a capacity degradation estimation model under dynamic stress parameters is obtained according to the capacity degradation model under composite stress parameters, including: obtaining the power battery capacity degradation coefficient based on the power battery capacity degradation model under the influence of composite stress parameters; establishing a dynamic recursive model of the power battery based on historical degradation amount, current stress state and power battery capacity degradation coefficient to obtain dynamic degradation amount; and establishing a capacity degradation estimation model under the influence of dynamic stress parameters based on dynamic degradation amount.

[0078] Among them, the power battery capacity degradation coefficient refers to the parameter used to quantify the capacity decay rate of the battery during use, and the power battery dynamic recursive model refers to the mathematical model based on recursive algorithm to describe the electrochemical reaction process and capacity degradation law of the battery under dynamic operating conditions.

[0079] For example, first define the power battery capacity degradation coefficient K(T, R d ) under the influence of temperature stress parameters and discharge rate parameters (T, R d ):

[0080]

[0081] Let u t (T, R d ) and u t+1 (T, R d ) be the capacity degradation amounts at t times and t+1 cycles respectively, then:

[0082]

[0083] where u t+1 (T, R d ) is the total degradation amount after one degradation under the stress level (T, R d ) based on the last degradation amount, so u t (T, R d ) is the degradation state of the battery that has occurred, which is related to the last degradation amount and the stress level of the last cycle, and is independent of the stress (T, R d ) of the current cycle, and T i , R d,i are the stresses at the i-th cycle, then the battery capacity degradation model under dynamic stress is:

[0084]

[0085]

[0086] where T i T i is the average value of the collected temperature in the cycle, and R d,i is the ratio of the average discharge current in the cycle to the rated capacity of the battery.

[0087] Further, according to the capacity degradation estimation model under the influence of dynamic stress parameters represented by formula (15), the capacity of the power battery can be estimated by formula: C t =C0-u t-1 (T t-1 ,R d,t-1 ), to generate the capacity estimation result of the power battery.

[0088] The power battery capacity estimation method provided by the embodiment of the present application solves the problem of low capacity estimation accuracy of the power battery caused by not considering real-time changes of stress states in the related art, and improves the accuracy of capacity estimation of the power battery.

[0089] Secondly, the power battery capacity estimation device provided by the embodiment of the present application is described with reference to the accompanying drawings.

[0090] Figure 2 The block diagram of the power battery capacity estimation device provided by the embodiment of the present application is shown.

[0091] As shown in the figure, the power battery capacity estimation device 10 comprises an acquisition module 100, a mathematical modeling module 200 and an estimation module 300. Figure 2

[0092] The acquisition module 100 is configured to acquire temperature stress parameters and discharge rate stress parameters of the power battery; the mathematical modeling module 200 is configured to establish a capacity degradation model of the power battery, and based on the capacity degradation model, obtain a capacity degradation model under the influence of the temperature stress parameters according to the temperature stress parameters of the power battery, and obtain a capacity degradation model under the influence of the discharge rate stress parameters according to the discharge rate stress parameters; and the estimation module 300 is configured to establish a capacity degradation model under the influence of composite stress parameters based on the capacity degradation model under the influence of the temperature stress parameters and the capacity degradation model under the influence of the discharge rate stress parameters, and obtain a capacity degradation estimation model under the influence of dynamic stress parameters according to the capacity degradation model under the influence of the composite stress parameters based on dynamic changes of the stress of the power battery, and generate a capacity estimation result of the power battery according to the capacity degradation estimation model under the influence of the dynamic stress parameters.

[0093] Further, in some embodiments, the mathematical modeling module 200 is specifically configured to: acquire capacity degradation data of the power battery; perform statistical calculation on the capacity degradation data by using a preset statistical method to obtain characteristic values of the capacity degradation model of the power battery; and perform fitting on the capacity degradation data and the characteristic values of the capacity degradation model of the power battery to obtain the capacity degradation model of the power battery.

[0094] ​Further, in some embodiments, the mathematical modeling module 200 is further configured to: obtain a coefficient of the degradation model under the temperature stress parameter and a coefficient of the degradation model under the discharge rate stress parameter; obtain the coefficient of the degradation model under the temperature stress parameter based on an Arrhenius model, and rewrite the capacity degradation model of the power battery to obtain a capacity degradation model under the temperature stress parameter; and obtain the coefficient of the degradation model under the discharge rate stress parameter based on an inverse power law model, and rewrite the capacity degradation model of the power battery to obtain a capacity degradation model under the discharge rate stress parameter.

[0095] Further, in some embodiments, the estimation module 300 is specifically configured to: calculate an accelerated degradation factor of the temperature stress parameter; and based on the accelerated degradation factor, the capacity degradation model under the temperature stress parameter, and the capacity degradation model under the discharge rate stress parameter, establish a capacity degradation model of the power battery under the composite stress parameter.

[0096] Further, in some embodiments, the estimation module 300 is further configured to: based on the capacity degradation model of the power battery under the composite stress parameter, obtain a power battery capacity degradation coefficient; based on a historical degradation amount, a current stress state, and the power battery capacity degradation coefficient, establish a dynamic recursive model of the power battery to obtain a dynamic degradation amount; and based on the dynamic degradation amount, establish a capacity degradation estimation model under a dynamic stress parameter.

[0097] It should be noted that the above explanation of the power battery capacity estimation method embodiment is also applicable to the power battery capacity estimation device of the embodiment, which will not be described here.

[0098] According to the power battery capacity estimation device provided by the embodiment of the present application, the temperature and the discharge rate stress parameter of the power battery are obtained, the capacity degradation model is established, the model under the single stress is derived respectively, the composite stress model is constructed, and then the dynamic stress degradation estimation model is obtained from the composite model combined with the dynamic change of the stress, and finally the capacity estimation result is generated, thereby solving the problem of low capacity estimation accuracy of the power battery caused by not considering the real-time change of the stress state in the related art, and improving the accuracy of the capacity estimation of the power battery.

[0099] Figure 3 The electronic device provided by the embodiment of the present application is shown in the structural schematic diagram. The electronic device can include:

[0100] The memory 301, the processor 302, and the computer program stored in the memory 301 and executable on the processor 302.

[0101] The processor 302 executes the program to implement the power battery capacity estimation method provided in the above embodiments.

[0102] Further, the electronic device further comprises:

[0103] The communication interface 303 is configured to communicate between the memory 301 and the processor 302.

[0104] The memory 301 is configured to store a computer program executable on the processor 302.

[0105] The memory 301 can include a high-speed RAM memory, and can further include a non-volatile memory, for example, at least one disk memory.

[0106] If the memory 301, the processor 302 and the communication interface 303 are implemented independently, the communication interface 303, the memory 301 and the processor 302 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0107] Optionally, in a specific implementation, if the memory 301, the processor 302 and the communication interface 303 are integrated on a chip, the memory 301, the processor 302 and the communication interface 303 can complete communication between each other through an internal interface.

[0108] The processor 302 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present application.

[0109] In addition, the embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the power battery capacity estimation method as above.

[0110] In addition, the embodiments of the present application also provide a computer program product, which includes a computer program, and the computer program is executed to implement the power battery capacity estimation method as above.

[0111] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or N embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0112] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0113] Any process or method descriptions in flow charts or described herein in other ways can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing the specified logical functions or processes, and the preferred embodiments of the present application also include additional implementation involving other processes or methods. It will be appreciated that the steps shown and described in the figures or elsewhere herein can be performed in an order other than the shown or discussed order, including substantially concurrently or in reverse order, as will be appreciated by those skilled in the art of the embodiments to which the present application pertains.

[0114] It should be understood that parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-described embodiments, N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. As in another embodiment, if implemented in hardware, any of the following technologies known in the art or their combinations can be used: discrete logic circuit with logic gate circuit for implementing logical functions on data signals, application specific integrated circuit with suitable combination of logic gate circuits, programmable gate array (PGA), field programmable gate array (FPGA) and the like.

[0115] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-described embodiment method can be completed by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium. The program, when executed, includes one or a combination of the steps of the method embodiment.

Claims

1. A method of estimating the capacity of a power battery, characterized in that, The method comprises the following steps: obtaining temperature stress parameters and discharge rate stress parameters of a power battery; establishing a capacity degradation model of the power battery, and obtaining a capacity degradation model under temperature stress parameters according to the temperature stress parameters of the power battery and a capacity degradation model under discharge rate stress parameters according to the discharge rate stress parameters based on the capacity degradation model; based on the capacity degradation model under temperature stress parameters and the capacity degradation model under discharge rate stress parameters, establishing a capacity degradation model under composite stress parameters, and obtaining a capacity degradation estimation model under dynamic stress parameters according to the capacity degradation model under composite stress parameters based on dynamic changes of stress of the power battery, and generating a capacity estimation result of the power battery according to the capacity degradation estimation model under dynamic stress parameters.

2. The method of claim 1, wherein, The method comprises the following steps: obtaining capacity degradation data of the power battery; performing statistical calculation on the capacity degradation data by using a preset statistical method to obtain characteristic values of the capacity degradation model of the power battery; fitting the capacity degradation data and the characteristic values of the capacity degradation model of the power battery to obtain the capacity degradation model of the power battery.

3. The method of claim 1, wherein, The method comprises the following steps: obtaining coefficients of the capacity degradation model under temperature stress parameters and coefficients of the capacity degradation model under discharge rate stress parameters; obtaining the coefficients of the capacity degradation model under temperature stress parameters based on an Arrhenius model, and rewriting the capacity degradation model of the power battery to obtain the capacity degradation model under temperature stress parameters; obtaining the coefficients of the capacity degradation model under discharge rate stress parameters based on an inverse power law model, and rewriting the capacity degradation model of the power battery to obtain the capacity degradation model under discharge rate stress parameters.

4. The method of claim 1, wherein, The method comprises the following steps: calculating an acceleration degradation factor of the temperature stress parameters; based on the acceleration degradation factor, the capacity degradation model under temperature stress parameters and the capacity degradation model under discharge rate stress parameters, establishing the capacity degradation model of the power battery under composite stress parameters.

5. The method of claim 1, wherein, The method comprises the following steps: based on the capacity degradation model of the power battery under composite stress parameters, obtaining a power battery capacity degradation coefficient; based on a historical degradation amount, a current stress state and the power battery capacity degradation coefficient, establishing a power battery dynamic recursion model to obtain a dynamic degradation amount; based on the dynamic degradation amount, establishing the capacity degradation estimation model under dynamic stress parameters.

6. A power cell capacity estimation device, characterized by, The device comprises: an acquisition module, configured to acquire a temperature stress parameter and a discharge rate stress parameter of a power battery; a mathematical modeling module, configured to establish a capacity degradation model of the power battery, and based on the capacity degradation model, to obtain a capacity degradation model under a temperature stress parameter according to the temperature stress parameter of the power battery, and to obtain a capacity degradation model under a discharge rate stress parameter according to the discharge rate stress parameter; an estimation module, configured to establish a capacity degradation model under a composite stress parameter based on the capacity degradation model under the temperature stress parameter and the capacity degradation model under the discharge rate stress parameter, to obtain a capacity degradation estimation model under a dynamic stress parameter according to the capacity degradation model under the composite stress parameter based on dynamic changes of the stress of the power battery, and to generate a capacity estimation result of the power battery according to the capacity degradation estimation model under the dynamic stress parameter.

7. The apparatus of claim 6, wherein The mathematical modeling module is specifically configured to: acquire capacity degradation data of the power battery; perform statistical calculation on the capacity degradation data by using a preset statistical method to obtain characteristic values of the capacity degradation model of the power battery; fit the capacity degradation data and the characteristic values of the capacity degradation model of the power battery to obtain the capacity degradation model of the power battery.

8. An electronic device, comprising: comprise: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the power battery capacity estimation method according to any one of claims 1-5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the power battery capacity estimation method according to any one of claims 1-5.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the power battery capacity estimation method according to any one of claims 1-5.

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