Battery life estimation method and battery management system

Abstract

The present invention provides a battery life estimation method and a battery management system, the method comprising obtaining the cumulative number of cycles of a target battery; performing a scale conversion on the cumulative number of cycles of the target battery according to a pre-established time scale conversion model to obtain the cumulative number of cycles after scale conversion; determining the parameters of a pre-established battery life probability density function according to the cumulative number of cycles after scale conversion and a pre-established cumulative capacity degradation model; inputting the cumulative number of cycles after scale conversion and the parameters into the battery life probability density function to determine the battery life estimation value; performing a scale inverse conversion on the battery life estimation value to obtain a final life estimation value. By performing scale conversion, the degradation process of the battery is converted from a nonlinear Wiener process to a linear Wiener process, so as to accurately estimate the battery life.

Description

Technical Field

[0001] The present application belongs to the technical field of battery life prediction, and in particular, relates to a battery life estimation method and a battery management system. Background Art

[0002] In electric vehicles, the battery management system (BMS) monitors and manages the important parameters of lithium-ion batteries. State of charge (SOC) estimation and state of health (SOH) estimation are important components of BMS. SOH estimation has always been a hot topic and difficulty in BMS research. SOH is also called life state, which is used to describe the degree of battery degradation.

[0003] The existing battery reliability assessment and life estimation methods have some problems in measurement and calculation. For example, the estimated result may be greater than the actual life value of the battery, which may cause the operator to mistakenly believe that the failed battery is still in a normal state, resulting in the failure to timely discover the reduction of battery capacity. In addition, the cumulative degradation capacity curve of lithium-ion batteries is a nonlinear curve, which is not conducive to observation. Therefore, the accuracy of battery life estimation in the existing technology is low. Summary of the invention

[0004] In view of this, the present invention provides a battery life estimation method and a battery management system, aiming to solve the problem of low accuracy in battery life estimation.

[0005] A first aspect of an embodiment of the present invention provides a battery life estimation method, including:

[0006] Get the cumulative number of cycles of the target battery;

[0007] Performing a scale transformation on the cumulative number of cycles of the target battery according to a pre-established time scale transformation model to obtain a scale-transformed cumulative number of cycles;

[0008] Determine the parameters of the pre-established battery life probability density function according to the scale-transformed cumulative cycle number and the pre-established cumulative capacity degradation model;

[0009] Inputting the scale-transformed cumulative cycle number and the parameter into the battery life probability density function to determine the battery life estimation value;

[0010] Performing an inverse scale transformation on the battery life estimate to obtain a final life estimate;

[0011] Wherein, the time scale conversion model is:

[0012] m * =p1×m3 +p2×m 2 +p3×m

[0013] Among them, p i is the fitting coefficient, m is the cumulative number of cycles, and m * Cumulative number of cycles after scaling.

[0014] A second aspect of an embodiment of the present invention provides a battery life estimation device, characterized by comprising:

[0015] A data acquisition module, used to obtain the cumulative number of cycles of the target battery;

[0016] A scale conversion module, used for performing scale conversion on the cumulative cycle number of the target battery according to a pre-established time scale conversion model to obtain the cumulative cycle number after scale conversion;

[0017] A parameter determination module, used to determine the parameters of a pre-established battery life probability density function according to the scale-transformed cumulative cycle number and a pre-established cumulative capacity degradation model;

[0018] A life estimation module, used for inputting the cumulative number of cycles after scale transformation and the parameters into the battery life probability density function to determine the battery life estimation value;

[0019] The inverse scale transformation module is used to perform an inverse scale transformation on the battery life estimation value to obtain a final life estimation value; wherein the time scale conversion model is:

[0020] m * =p1×m 3 +p2×m 2 +p3×m

[0021] Among them, p i is the fitting coefficient, m is the cumulative number of cycles, and m * Cumulative number of cycles after scaling.

[0022] A third aspect of an embodiment of the present invention provides a battery management system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the battery life estimation method described in the first aspect above are implemented.

[0023] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the battery life estimation method described in the first aspect above are implemented.

[0024] The battery life estimation method and battery management system provided by the embodiment of the present invention include obtaining the cumulative number of cycles of the target battery; performing a scale transformation on the cumulative number of cycles of the target battery according to a pre-established time scale conversion model to obtain the cumulative number of cycles after scale transformation; determining the parameters of the pre-established battery life probability density function according to the cumulative number of cycles after scale transformation and the pre-established cumulative capacity degradation model; inputting the cumulative number of cycles after scale transformation and the parameters into the battery life probability density function to determine the battery life estimation value; performing a scale inverse transformation on the battery life estimation value to obtain the final life estimation value. By performing scale transformation, the degradation process of the battery is transformed from a nonlinear Wiener process to a linear Wiener process, so as to accurately estimate the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1 is an application environment diagram of the battery life estimation method provided by an embodiment of the present invention;

[0027] Figure 2 is a flow chart of an implementation of a battery life estimation method provided by an embodiment of the present invention;

[0028] Figure 3 is a flow chart of a battery life estimation method provided by an implementation example of the present invention;

[0029] Figure 4 is a capacity degradation curve diagram of a lithium-ion battery provided by an implementation example of the present invention;

[0030] Figure 5 is a single cumulative degradation amount curve diagram provided by an implementation example of the present invention;

[0031] Figure 6 is a schematic diagram of wavelet reconstruction of a battery capacity degradation curve in an embodiment of the present invention;

[0032] Figure 7 is a capacity degradation curve diagram of a lithium-ion battery after wavelet reconstruction in an embodiment of the present invention;

[0033] Figure 8 is a schematic diagram of the principle of battery life estimation provided by an implementation example of the present invention;

[0034] Fig. 9It is the cumulative capacity curve of lithium-ion battery after scale transformation under different cumulative amounts;

[0035] Fig.10 is a single cumulative capacity loss histogram provided by an implementation example of the present invention;

[0036] Fig.11 is a scatter distribution curve diagram provided by an implementation example of the present invention;

[0037] Fig.12 It is the probability density function curve of lithium-ion battery life under different accumulation amounts;

[0038] Fig.13 It is the function curve of lithium-ion battery life reliability under different accumulation amounts;

[0039] Fig.14 is a schematic diagram of the structure of a battery life estimation device provided by an embodiment of the present invention;

[0040] Fig.15 It is a schematic diagram of the structure of a battery management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0042] As global warming, air pollution and energy crisis become increasingly prominent, the development of new energy vehicles can effectively control the consumption of fossil energy. Benefiting from the continuous growth of new energy vehicles, the lithium-ion battery industry has ushered in a new round of development opportunities. Among commercial rechargeable batteries, lithium-ion batteries have the highest specific energy, especially polymer lithium-ion batteries, which can achieve thinner rechargeable batteries. Lithium-ion batteries are used in electric vehicles because of their high volumetric specific energy and mass specific energy and low pollution. Therefore, in order to ensure the reliable operation of electric vehicles, it is very necessary to evaluate the reliability of lithium-ion batteries.

[0043] In electric vehicles, the important parameters of lithium-ion batteries are monitored and managed through the Battery Management System (BMS). State of Charge (SOC) estimation and State of Health (SOH) estimation are important components of BMS. SOH estimation has always been a hot topic and difficulty in BMS research. SOH is also called the state of life, which is used to describe the degree of battery degradation. As the number of lithium-ion battery cycles increases, SOH gradually decreases. When SOH drops below 70% or 80%, the battery is considered to have failed. The remaining useful life (RUL) of the battery is usually used to characterize SOH.

[0044] There are generally three types of RUL estimation methods: RUL estimation methods based on physical failure models, RUL estimation methods based on data-driven models, and RUL estimation methods based on mathematical statistics theory. The RUL estimation method based on the physical failure model mainly analyzes the degradation mechanism of the battery according to factors such as the material properties and internal structure of the battery, and describes the degradation mechanism of the battery through the electrochemical model to achieve RUL estimation. Due to the complex structure of the electrochemical model and a large number of parameters, the model has a large computational cost, which limits its application. Estimation methods based on data-driven models include machine learning models and statistical regression models. Common artificial intelligence models include support vector machines (SVM), BP neural networks, relevance vector machines (RVM), etc. Common statistical regression models include moving average models (MA), autoregression models (AR), and automatic regression moving average (ARMA) models. The machine learning model has a high prediction accuracy, but the random parameters of the model have a greater impact on the results. The statistical regression model only considers the time factor, has poor generalization ability and is easily disturbed by the external environment. The RUL estimation method based on mathematical statistics uses mathematical statistics methods to analyze historical data, establish the probability density function of RUL, and realize RUL estimation. This method has small calculation amount, simple model, and is easy to apply to practical engineering.

[0045] In summary, the existing lithium-ion battery reliability assessment and life estimation methods have some problems in measurement and calculation. For example, the estimated result may be greater than the actual life value of the battery, which may cause the operator to mistakenly believe that the failed battery is still in a normal state, resulting in the failure to timely discover the reduction in battery capacity. In addition, the cumulative degradation capacity curve of the lithium-ion battery is a nonlinear curve, which is not conducive to observation.

[0046] The present invention converts the time scale of the accumulated cycle number of the battery so as to transform the degradation process of the battery from a nonlinear Wiener process to a linear Wiener process, so as to accurately estimate the battery life.

[0047] Figure 1 is an application environment diagram of the battery life estimation method provided by an embodiment of the present invention. The battery life estimation method provided by an embodiment of the present invention can be applied to, but is not limited to, this application environment. Figure 1 As shown, the system includes: a battery management system 11 and a battery 12. The battery management system 11 pre-stores a time scale conversion model, a cumulative capacity degradation model, a battery life probability density function, etc. corresponding to the lithium battery of this model. The battery management system 11 obtains the cumulative number of cycles of the battery 12 in real time, and determines the final life estimate of the battery 12 according to the pre-stored model and function.

[0048] The battery management system 11 may be a system composed of multiple modules such as an acquisition module, a storage module, and a computing module, and is used to manage and monitor the battery 12. The computing module may be an MCU, a single-chip microcomputer, a DSP, etc., which are not limited here. The battery 12 may be a lead-acid battery, a lithium battery, etc., which are not limited here.

[0049] Figure 2 FIG. 1 is a flow chart of the battery life estimation method provided by an embodiment of the present invention. Figure 2 As shown, in this embodiment, the battery life estimation method is applied to Figure 1 The battery management system 11 shown in FIG. 1 comprises:

[0050] S201, obtaining the cumulative number of cycles of the target battery.

[0051] In this embodiment, the cumulative number of cycles is the total number of times the battery completes a charge and discharge cycle. A charge and discharge cycle of the battery refers to a complete charging cycle, that is, a complete charge and discharge process, of the mobile phone battery.

[0052] S202, scaling the cumulative cycle number of the target battery according to a pre-established time scale conversion model to obtain a scale-transformed cumulative cycle number.

[0053] Since the accumulated capacity loss of lithium batteries does not meet the requirements of linear wiener, in this embodiment, a time scale conversion model is pre-established. The time scale conversion model can make the transformed accumulated number of cycles equal to the accumulated capacity degradation trajectory of the battery, so that the battery capacity degradation process conforms to the linear wiener process.

[0054] S203, determining parameters of a pre-established battery life probability density function according to the scale-transformed cumulative number of cycles and a pre-established cumulative capacity degradation model.

[0055] S204, inputting the scale-transformed cumulative cycle times and parameters into a battery life probability density function to determine a battery life estimation value.

[0056] S205, performing an inverse scale transformation on the battery life estimation value to obtain a final life estimation value.

[0057] Since a time scale transformation is performed before determining the battery life estimate, the determined battery life estimate is not the actual remaining battery life. Therefore, in this embodiment, it is necessary to perform an inverse scale transformation on the determined battery life estimate according to the time scale conversion model to determine the final life estimate.

[0058] Among them, the time scale conversion model is:

[0059] m * =p1×m 3 +p2×m 2 +p3×m (1)

[0060] Among them, p i is the fitting coefficient, m is the cumulative number of cycles, and m * Cumulative number of cycles after scaling.

[0061] In this embodiment, by performing scale conversion, the degradation process of the battery is transformed from a nonlinear Wiener process to a linear Wiener process, so as to accurately estimate the battery life.

[0062] In some embodiments, the battery life estimation method further includes:

[0063] A third-order polynomial model was established;

[0064] According to the degradation data of multiple reference batteries acquired in advance, a third-order polynomial model is fitted to determine the fitting coefficient corresponding to each cumulative number of cycles; wherein the reference battery is a battery of the same model as the target battery; wherein the degradation data includes multiple cumulative numbers of cycles and the cumulative capacity loss corresponding to each cumulative number of cycles;

[0065] Determine the battery cumulative capacity degradation trajectory of the battery model according to the fitting coefficient of the polynomial corresponding to each cumulative number of cycles;

[0066] The time scale transformation model of this type of battery is determined according to the battery cumulative capacity degradation trajectory.

[0067] In this embodiment, the third-order polynomial model is:

[0068] y3(x)=p1×x 3 +p2×x 2 +p3×x (2)

[0069] Among them, y3(x) is the dependent variable of the third-order polynomial, and x is the independent variable of the third-order polynomial.

[0070] In this embodiment, the cumulative capacity degradation trajectory β(m) of the battery of this model can be determined based on the degradation data of multiple reference batteries, so that each cumulative number of cycles satisfies m * =β(m), the fitting coefficients can be obtained by fitting. A set of fitting coefficients can be calculated for each cumulative number of cycles.

[0071] In some embodiments, the parameters of the pre-established battery life probability density function include a drift parameter, a diffusion parameter, and a failure threshold; the battery life probability density function is:

[0072]

[0073] Among them, f(m * ; L) is the battery life probability density function, μ * is the drift parameter, m * represents the number of cycles corresponding to the cumulative capacity loss after scale transformation, L is the failure threshold of the target battery, z0 is the cumulative capacity loss of the target battery at the initial moment, σ * is the diffusion parameter;

[0074] S204 may include:

[0075] The scale-transformed cumulative cycle number and parameters are input into the battery life probability density function to determine the expected value of the battery life probability density function, and the expected value is used as the battery life estimation value.

[0076] In this embodiment, since the linearizable Wiener process will eventually be converted into a univariate linear Wiener process, the remaining life probability density function and reliability function of the two are the same. For the linearizable Wiener process, the key is to solve the time scale model of the nonlinear degradation process. It has been proved that under the condition that the average performance degradation rate of the product is constant, the time distribution of the Wiener process reaching the failure threshold for the first time is an inverse Gaussian distribution.

[0077] In some embodiments, the battery life estimation method further includes:

[0078] Determine the battery life reliability function according to the battery life probability density function;

[0079] Calculate the reliability of the battery according to the battery life reliability function;

[0080] The battery life reliability function is:

[0081]

[0082] Among them, R(m * ; L) is the battery life reliability function, Ф(x) is the standard normal distribution function.

[0083] In some embodiments, S203 may include:

[0084] According to the pre-established cumulative capacity degradation model, the drift parameter, diffusion parameter and failure threshold are estimated by maximum likelihood to determine the parameter estimation expression;

[0085] The accumulated number of cycles after the scale transformation is input into the parameter estimation expression to obtain the estimated value of the drift parameter, the estimated value of the diffusion parameter, and the estimated value of the failure threshold;

[0086] The parameter estimation expression is:

[0087]

[0088] in, is the estimated value of the drift parameter, is the estimated value of the diffusion parameter, is the estimated value of the failure threshold, z i is the single capacity loss of the target battery during the i-th charge and discharge cycle.

[0089] In this embodiment, the single capacity loss can be determined according to the cumulative capacity degradation model, and then substituted into the parameter estimation expression to obtain the drift parameter, diffusion parameter, and failure threshold.

[0090] In some embodiments, the battery cumulative capacity degradation model is:

[0091]

[0092] Among them, z(m * ) represents the cumulative capacity loss, z0 is the cumulative capacity loss of the target battery at the initial moment, μ * is the drift parameter, m * represents the number of cycles corresponding to the cumulative capacity loss after scale transformation, σ * is the diffusion parameter, C(m * ) is a random process and obeys Brownian motion.

[0093] In some embodiments, after S201, the method further includes:

[0094] Determine the single capacity loss corresponding to multiple cumulative cycle times of the target battery according to a pre-established cumulative capacity degradation model;

[0095] A normal distribution test is performed based on each single capacity loss to verify whether the battery cumulative capacity degradation model conforms to the Wiener process;

[0096] If the battery cumulative capacity degradation model conforms to the Wiener process, it indicates that the cumulative capacity degradation model can be used;

[0097] If the battery cumulative capacity degradation model does not conform to the Wiener process, the battery cumulative capacity degradation model is deleted.

[0098] In this embodiment, a single cumulative capacity loss histogram and a scatter distribution curve can be drawn. By observing whether the histogram conforms to the normal distribution characteristics of left-right symmetry, the data distribution space of the single capacity loss of the lithium-ion battery can be obtained. The mean of the single cumulative capacity loss of the lithium-ion battery is calculated, and the confidence interval when the confidence level is 95% is obtained. Verify whether the single cumulative capacity loss of the lithium-ion battery satisfies the normal distribution, so as to determine whether the wiener process can be used to model the capacity degradation process of the lithium-ion battery.

[0099] The frequency histogram and scatter plot can be used to test the single cumulative capacity loss of the lithium-ion battery after pretreatment to see whether it conforms to the normal distribution. When the single cumulative capacity loss scatter points are distributed on a straight line, the scatter data conforms to the normal distribution.

[0100] In some embodiments, before fitting the third-order polynomial model according to the degradation data of a plurality of reference batteries acquired in advance, the method further includes:

[0101] Obtaining initial degradation information of multiple reference batteries;

[0102] The initial degradation information is reconstructed by wavelet transform to obtain degradation data of multiple reference batteries.

[0103] In this embodiment, Symlet wavelet can be used to scale decompose the lithium-ion battery capacity degradation data, and the vanishing moment and wavelet decomposition layer number are selected, and the soft threshold function is used to reconstruct the lithium-ion battery capacity data to obtain the reconstructed lithium-ion battery capacity degradation data.

[0104] The present invention improves the prior art mainly by using a time scale transformation model to improve the battery capacity degradation process. The details are as follows:

[0105] It is generally believed that when the capacity of a lithium battery decays to 70% to 80% of the rated capacity, the battery fails. In the lithium battery performance degradation model, capacity loss is used as a key performance parameter. The remaining life of the lithium battery is determined by analyzing the current capacity degradation of the lithium battery. The single capacity loss of lithium batteries follows an independent and identical distribution. By analyzing the distribution of single capacity loss, it is found that the single capacity loss follows a normal distribution.

[0106] The Wiener process is called Brownian motion with drift and is a typical random process. Brownian motion describes the process of particles moving irregularly under collision. The Wiener process is suitable for describing the non-stationary degradation process of product failure due to the continuous accumulation of large amounts of loss. If the random process {C(t), t≥0} satisfies the following three constraints, the motion is called Brownian motion.

[0107] (1) C(t) = 0;

[0108] (2)

[0109] (3) The random process C(t) has independent and stationary increments.

[0110] In Brownian motion, when σ 2 When is 1, {C(t), t≥0} is called standard Brownian motion. From constraint (2), we can see that the increment distribution of the Wiener process depends on the time difference, so the Wiener process is a homogeneous independent increment process and obeys the normal distribution. For t(t∈[1,n]), C(t k )as follows:

[0111]

[0112] The present invention adopts a univariate Wiener process with drift to describe the capacity degradation process of lithium batteries:

[0113] U(t)=u0+μ×t+σ×C(t) (8)

[0114] Where u0 is the initial value; μ is called the degradation rate, which is used to describe the degradation trend; when μ>0, the degradation amount shows an increasing trend; when μ<0, the degradation amount shows a decreasing trend; σ is the diffusion speed, which describes the influence of random factors on the degradation performance; C(t) is the standard Brownian motion; U(t) obeys the standard normal distribution N(u0+μt,σ 2 t).

[0115] When the degradation rate of the product is a constant, the degradation model can be established by using formula (8). However, in actual engineering, the degradation rate of the product is generally nonlinear. At this time, the use of the linear Wiener model to establish the degradation model cannot obtain an accurate life estimate. The nonlinear degradation process can be converted into a linear degradation process through scale transformation. Therefore, the present invention establishes a scale transformation model to transform the time scale t. Correspondingly, the quantity characterizing the time scale t in the present invention is the cumulative number of cycles m, so the cumulative number of cycles needs to be scaled.

[0116] The present invention can derive a time scale conversion model through the performance degradation curve of the product, thereby converting the time scale. Assume that there is a non-negative monotonically increasing function β(t) of t, and μt=μ * β(t), then we get the scale-transformed univariate Wiener degradation model.

[0117] U(β(t))=u0+μ * ×β(t)+σ×C(β(t)) (9)

[0118] Among them, μ * is the degradation rate after scale transformation.

[0119] Let α = β(t) and make G(α) = U(t), thereby transforming equation (3) into equation (4).

[0120] G(α)=u0+μ * ×α+σ×C(α) (10)

[0121] The nonlinear degradation process can be transformed into a linear degradation process through equation (10), and the degradation model can be established according to the univariate Wiener process after the time scale transformation. It can be seen from equation (10) that when α = t, the degradation model at this time is a linear Wiener degradation model.

[0122] An implementation example is provided below to illustrate the battery life estimation method of the present invention of this application, but it is not intended to be limiting. Figure 3 FIG. 1 is a flow chart of a battery life estimation method provided by an embodiment of the present invention. Figure 3 As shown, in this implementation example, the battery life estimation method is specifically as follows:

[0123] Step 1: Obtain complete lithium-ion battery capacity degradation data.

[0124] In this step, the lithium-ion battery is charged and discharged at room temperature. First, the battery is charged at a constant current of 1.5A. When the battery voltage reaches 4.2V, the battery is charged at a constant voltage. When the battery charging current drops to 0.02A, the battery is stopped from being charged. Secondly, the battery is discharged at a constant current of 2A. When the battery voltage drops to 2.5V, the battery is stopped from being discharged. Repeat the charge and discharge experiment on the lithium-ion battery until the battery reaches the end of life standard, and stop the experiment on the battery. The final lithium-ion battery capacity degradation curve and the calculated single cumulative degradation amount (i.e. the degradation data of the reference battery mentioned above) are obtained. Figure 4 : is a lithium-ion battery capacity degradation curve diagram provided by an embodiment of the present invention. Figure 4 As shown, the horizontal axis is the cumulative number of cycles and the vertical axis is the remaining capacity of the battery. Figure 5 is a single cumulative degradation curve provided by an implementation example of the present invention. Figure 5 As shown, the horizontal axis is the cumulative number of cycles, and the vertical axis is the capacity lost during a single battery charge and discharge cycle.

[0125] Step 2: preprocessing the lithium-ion battery capacity degradation data.

[0126] The specific implementation method of this step includes the following steps:

[0127] Step 2.1, reconstruct the lithium-ion battery capacity degradation data using wavelet transform.

[0128] Symlet wavelet is used to decompose the capacity degradation data of lithium-ion batteries. Symlet wavelet function is an approximately symmetrical wavelet function proposed by Ingrid Daubechies, which is an improvement on db function. Symlet wavelet system is usually expressed as symN (N = 2, 3, 5, 8). The support range of symN wavelet is 2N-1, the vanishing moment is N, and it also has good regularity. The appropriate vanishing moment and wavelet decomposition layer are selected, and the soft threshold function is used. Finally, the lithium-ion battery capacity data is reconstructed to obtain the reconstructed lithium-ion battery capacity degradation data. Compared with the original lithium-ion battery capacity degradation data, the reconstructed lithium-ion battery capacity degradation data will become smoother and can reflect the degradation characteristics of the original data. It is generally believed that the battery fails when the capacity of the lithium-ion battery decays to 70% to 80% of the rated capacity. The corresponding number of cycles and the corresponding failure threshold L when the lithium-ion battery fails can be obtained.

[0129] In this step, the sym5 wavelet is used to decompose the lithium-ion battery capacity degradation data into three layers, and a soft threshold function is used to reconstruct the lithium-ion battery capacity data.

[0130] Figure 6 It is a schematic diagram of wavelet reconstruction of a battery capacity degradation curve in an embodiment of the present invention. Figure 7 is a capacity degradation curve of a lithium-ion battery after wavelet reconstruction in an embodiment of the present invention. Figure 6 The horizontal axis is the cumulative number of cycles, and the vertical axis is the remaining capacity of the battery. Figure 7 The horizontal axis is the cumulative number of cycles, and the vertical axis is the cumulative capacity loss of the battery. Figure 6 and Figure 7 As shown, compared with the original lithium-ion battery capacity degradation data, the reconstructed lithium-ion battery capacity degradation data is smoother and can reflect the degradation characteristics of the original data.

[0131] Figure 8 FIG. 1 is a schematic diagram showing the principle of battery life estimation provided by an embodiment of the present invention. Figure 8 As shown, under the condition that the average performance degradation rate of the battery is constant, the battery life can be defined as the cumulative number of cycles when the cumulative capacity loss first reaches the failure threshold.

[0132] In actual applications, when a lithium-ion battery reaches the failure threshold for the first time, its performance may degrade but it has not completely failed. However, to prevent further aging of the battery, it is necessary to stop the battery from running. Figure 8 According to the lithium-ion battery life estimation principle, when the cumulative degradation amount of the lithium-ion battery {z(m), m≥0} reaches the threshold value L, the battery is considered to be failed. Formula (11) is used to define the life of the lithium-ion battery. This prevents the battery from entering a failed state, thereby improving the reliability and safety of the power supply system.

[0133] F=inf{m:z(m)≥L|z(0)≤L} (11)

[0134] Among them, F is the life span and inf{} is the lower bound function.

[0135] Depend on Figure 7 and Figure 8 It can be known that in the present invention, the battery is considered to fail when the battery decays to 70% (1.4 Ah) of the rated capacity. The corresponding number of cycles when the lithium-ion battery fails is 109 times, and the corresponding failure threshold is L=0.6029.

[0136] Step 2.2, establish a third-order scaling model to scale the number of cycles corresponding to the cumulative amount.

[0137] After multiple experimental analyses, the fitting errors of the third-order model and the fourth-order model are the smallest, and the fitting effects of the third-order and fourth-order models are similar. In order to reduce the computational cost, this study uses the third-order fitting model to perform a time scale transformation on the cumulative capacity loss curve of lithium-ion batteries. The fitting coefficients of the third-order polynomial model are used as the estimated values ​​of the polynomial scale transformation model parameters p1, p2, and p3; according to the number of cycles corresponding to the failure of the lithium-ion battery, the third-order polynomial model is used to fit the multiple corresponding sub-capacity cumulative values ​​of the lithium-ion battery, and the third-order fitting model is used to perform a time scale transformation on the cumulative capacity loss curve of the lithium-ion battery.

[0138] Assume that the cumulative capacity degradation trajectory of lithium-ion batteries is β(m). When the number of cycles corresponding to the failure of the lithium-ion battery is equal to β(m), the capacity degradation process of the lithium-ion battery conforms to the linear Wiener process. Therefore, the function β(m) can be used to scale the number of cycles of the cumulative capacity loss of lithium-ion batteries. According to the obtained third-order scaling model, the number of cycles under different cumulative amounts is scaled, and the cumulative number of capacity loss after scaling is recorded as m*(z(m)=z(m*)).

[0139] In this step, a third-order fitting model is used to perform a time scale transformation on the cumulative capacity loss curve of the lithium-ion battery. When t is 0, that is, the capacity loss of the lithium-ion battery is 0 in the initial stage, so the constant term of the scale transformation is 0. The fitting coefficients of the third-order polynomial model are used as the estimated values ​​of the polynomial scale transformation model parameters p1, p2 and p3. Under different accumulation amounts, the parameter estimates of the third-order scale transformation model are as follows:

[0140] Table 1. Three-order scaling model

[0141]

[0142] in, and are the estimated values ​​of parameters p1, p2 and p3 respectively.

[0143] Step 2.3, establish a cumulative capacity degradation model for lithium-ion batteries.

[0144] In this step, the number of cycles under different cumulative amounts is scaled according to the obtained third-order scale transformation model to obtain a lithium-ion battery cumulative capacity degradation model. Fig. 9 The cumulative capacity curve of lithium-ion batteries after scale transformation under different cumulative amounts. Fig. 9 As shown, the horizontal axis is the cumulative number of cycles, and the vertical axis is the cumulative capacity loss. Compared with the cumulative capacity loss curve of lithium-ion batteries before the scale transformation, the cumulative capacity loss curve after the scale transformation is more linear. The number of cycles after the scale transformation is shown in Table 2:

[0145] Table 2. Polynomial scaling transformation

[0146]

[0147] Step 3, perform a normal distribution test on the single capacity loss of the pre-treated lithium-ion battery to verify whether it conforms to the Wiener process.

[0148] In this step, a single cumulative capacity loss histogram and scatter distribution curve are drawn. By observing whether the histogram conforms to the normal distribution characteristics of left-right symmetry, the data distribution space of the single capacity loss of lithium-ion batteries is obtained. The mean of the single cumulative capacity loss of lithium-ion batteries is calculated, and the confidence interval when the confidence level is 95% is obtained. Verify whether the single cumulative capacity loss of lithium-ion batteries satisfies the normal distribution. In this way, it is determined whether the wiener process can be used to model the capacity degradation process of lithium-ion batteries.

[0149] In this step, the single cumulative capacity loss of the lithium-ion battery after pretreatment is tested using a frequency histogram and a scatter plot to see whether it conforms to the normal distribution. When the single cumulative capacity loss scatter points are distributed on a straight line, the scatter data conforms to the normal distribution.

[0150] Fig.10 It is a single cumulative capacity loss histogram provided by an implementation example of the present invention. Fig.11 It is a scatter distribution curve diagram provided by the implementation example of the present invention. Fig.10 and Fig.11 As shown, the horizontal axis represents the cumulative capacity loss and the vertical axis represents the reliability.

[0151] In this implementation example, it is observed that the histogram is symmetrical on the left and right and conforms to the normal distribution characteristics, and most of the data is distributed in the interval [0 0.01]. The mean of the single cumulative capacity loss is calculated to be 0.0051Ah, and the confidence interval at a confidence level of 95% is [0.0043, 0.0059]. Fig.11 The scatter points in the data are found to be distributed on a straight line, which once again verifies that the single cumulative capacity loss satisfies the normal distribution. Therefore, the Wiener process can be used to model the capacity degradation process of lithium-ion batteries.

[0152] Step 4, calculate the probability density function and reliability function of the lithium-ion battery life under different accumulation amounts and obtain the estimated value of the lithium-ion battery life under different accumulation amounts.

[0153] The specific implementation method of this step includes the following steps:

[0154] Step 4.1, drift parameters under different accumulation amounts and diffusion parameters Perform maximum likelihood estimation.

[0155] In this step, the drift parameters under different cumulative capacity losses are obtained by maximum likelihood estimation. and diffusion parameters

[0156] In this implementation example, the cumulative capacity loss after 60 times is Y60=0.3864, the cumulative capacity loss after 70 times is Y70=0.4468, the cumulative capacity loss after 80 times is Y80=0.4966, and the cumulative capacity loss after 90 times is Y90=0.5446. The failure threshold of lithium-ion battery is L=0.6029. According to formula (4), the drift parameters under different accumulation amounts are and diffusion parameters The maximum likelihood estimation is performed and the estimation results are shown in Table 3.

[0157] Table 3. Parameter estimates

[0158]

[0159] Step 4.2, calculate the probability density function of lithium-ion battery life under different accumulation amounts.

[0160] In this step, the cumulative capacity loss is used as a performance parameter to characterize the life of the lithium-ion battery, and the number of cycles corresponding to the failure of the lithium-ion battery is used as the remaining life value of the lithium-ion battery. The cumulative capacity loss corresponding to the failure is used as the failure threshold of the lithium-ion battery to obtain the failure threshold L of the lithium-ion battery. Under the failure threshold L, the probability density function of the lithium-ion battery life after scale transformation is obtained.

[0161] In this step, the drift parameters under different cumulative capacity losses are obtained by maximum likelihood estimation. and diffusion parameters The drift parameter and diffusion parameters By substituting it into the battery life probability density function, the probability density function of lithium-ion battery life under different accumulation amounts can be calculated, as shown in Table 4.

[0162] Table 4. Probability density functions calculated under different cumulants

[0163]

[0164] Fig.12 It is the probability density function curve of lithium-ion battery life under different accumulation amounts. Fig.12 As shown, in this implementation example, the obtained probability density function can be used to draw a probability density function curve of the lithium-ion battery life under different cumulative amounts, wherein the horizontal axis is the cumulative number of cycles and the vertical axis is the probability density.

[0165] Step 4.3, use the following formula to calculate the life reliability function of lithium-ion batteries under different accumulation amounts.

[0166] from Fig.12 The estimated life of lithium-ion batteries under different cumulative amounts can be obtained. It can be seen from the figure that the life of lithium-ion batteries obtained under different cumulative amounts are different. When the cumulative capacity loss is small, the estimated life of lithium-ion batteries has a large deviation from the actual value. This is because the small amount of data cannot well reflect the capacity degradation trend of lithium-ion batteries. With the increase of the cumulative amount, the estimated life of lithium-ion batteries is getting closer and closer to the actual value. When the cumulative amount increases, the historical degradation information obtained is more complete, and the parameter estimation in the scale transformation model is more accurate. Therefore, the life estimation value with a large cumulative amount is more accurate. When the cumulative amount of lithium-ion battery capacity loss is small, the battery life estimation is a long-term estimation. With the increase of the cumulative amount, the lithium-ion battery life estimation is a short-term estimation. Generally speaking, as the battery capacity degradation information becomes more complete, the accuracy of short-term estimation is higher than that of long-term estimation.

[0167] The drift parameter and diffusion parameters Substituting into the battery life reliability function shown in formula (4), the lithium-ion battery life reliability function under different accumulation amounts can be calculated, as shown in Table 5.

[0168] Table 5. Reliability functions calculated under different accumulations

[0169]

[0170]

[0171] Fig.13 The following is a graph showing the reliability function of lithium-ion battery life under different accumulation amounts. Fig.13 As shown, in this implementation example, the obtained reliability function can be used to draw a lithium-ion battery life reliability function curve under different accumulation amounts, wherein the horizontal axis is the cumulative number of cycles and the vertical axis is the reliability.

[0172] Step 4.4, obtain the estimated value of lithium-ion battery life under different accumulation amounts.

[0173] In this implementation example, the expected value of the probability density function can be used as the estimated value of the battery life, and the estimated value of the lithium-ion battery life under different accumulation amounts can be obtained from the probability density function curve. The reliability of the lithium-ion battery life under different accumulation amounts can also be obtained from the reliability curve.

[0174] The lithium-ion battery life value corresponding to the reliability of 0.5 is the expected value of the lithium-ion battery life, which is generally considered to be an estimated value. The estimated value of the lithium-ion battery life under different accumulation amounts can also be obtained from the reliability curve. Fig.13 The estimated lifetime of lithium-ion batteries obtained from Figure 7 The estimated value obtained in is consistent with that in . It can be seen from the figure that as the number of times the lithium-ion battery is used increases, the reliability of the lithium-ion battery is gradually decreasing. When the number of operations of the lithium-ion battery reaches 90 times, the reliability of the lithium-ion battery decreases significantly.

[0175] Step 4.5: Perform inverse scale transformation on the estimated life span of lithium batteries

[0176] The estimated life value obtained in the above steps has been scaled, so it is necessary to use inverse scale transformation to make the estimated life value more intuitive to observe the estimated remaining life of the battery, so as to judge the battery usage.

[0177] The estimated value of lithium-ion battery life can be obtained through the probability density function and reliability function of lithium-ion battery life, and then more intuitive observation data can be obtained through inverse scale transformation, as shown in Table 6.

[0178] Table 6. Prediction results of the improved Wiener model

[0179]

[0180] Table 6 shows that under different accumulations, there is a difference in the error between the estimated value and the true value of the improved Wiener process. The maximum prediction error at 60 and 65 accumulations is 16. This is because the capacity degradation data of lithium-ion batteries is incomplete in the early stage of prediction, and the less accumulation cannot more accurately reflect the overall degradation trend of lithium-ion batteries. Therefore, the multi-order scale transformation model obtained by fitting cannot accurately reflect the degradation trend of lithium-ion batteries. By comparison, it is found that with the increase of accumulation, the estimation error of the improved Wiener process gradually decreases. This is because with the increase of accumulation, the degradation information of lithium-ion batteries is more complete, and the multi-order scale transformation model can more accurately reflect the capacity attenuation trend of lithium-ion batteries. Therefore, the estimation result of the improved Wiener process is closer to the true value. The battery life estimation value of the improved Wiener process is less than the actual life value of the lithium-ion battery. Since the estimated value is less than the actual value, the aging battery can be replaced in time, which will not cause safety accidents due to excessive aging of the battery in actual engineering applications. By estimating the life value and reliability of lithium-ion batteries through the improved Wiener process, online monitoring and loss warning can be realized, so as to determine the maintenance plan and improve the system reliability through fault preprocessing. Compared with other processes, the estimated results of the improved Wiener process are all smaller than the actual life value of the battery, which can more efficiently replace the failed battery, thus preventing the battery from entering a failed state, thereby improving the reliability and safety of the power supply system.

[0181] The advantages and positive effects of the present invention are:

[0182] 1. Starting from the degradation mechanism of lithium-ion batteries, the present invention adopts wavelet transform to reconstruct the capacity degradation data of lithium-ion batteries, establishes a third-order scale transformation model to scale the number of cycles corresponding to the cumulative amount, thereby converting the nonlinear Wiener process into a linear Wiener process; the lithium-ion battery performance degradation model based on the improved Wiener process is used to estimate the remaining battery life RUL of the lithium-ion battery, and then accurately judge the health status of the lithium-ion battery, thereby ensuring the reliable operation of the electric vehicle.

[0183] 2. The RUL estimated values ​​obtained by the present invention are all smaller than the actual values, so that failed batteries can be discovered more timely and the safety of the electric vehicle power system can be ensured. Compared with the artificial intelligence model, the present invention can provide a reliability function under different accumulation amounts and analyze the reliability of lithium-ion batteries.

[0184] 3. The present invention is reasonably designed. The test results show that the estimation error of the proposed model is controlled within 16%, which can meet the needs of practical engineering. Compared with the prediction results of many existing estimation models, the estimation results of the proposed model are smaller than the actual value, which enables operators to replace batteries that are about to fail more promptly. It can improve the safety level of electric vehicle power systems and have a positive impact on promoting the development of the electric vehicle industry.

[0185] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0186] Fig.14 FIG. 1 is a schematic diagram of the structure of a battery life estimation device provided by an embodiment of the present invention. Fig.14 As shown, in some embodiments, the battery life estimation device 14 includes:

[0187] The data acquisition module 1410 is used to obtain the cumulative cycle number of the target battery;

[0188] A scale conversion module 1420 is used to scale the cumulative cycle number of the target battery according to a pre-established time scale conversion model to obtain the cumulative cycle number after scale conversion;

[0189] A parameter determination module 1430 is used to determine the parameters of a pre-established battery life probability density function according to the scale-transformed cumulative cycle number and the pre-established cumulative capacity degradation model;

[0190] The life estimation module 1440 is used to input the scaled cumulative cycle number and parameters into the battery life probability density function to determine the battery life estimation value;

[0191] The inverse scale transformation module 1450 is used to perform an inverse scale transformation on the battery life estimation value to obtain a final life estimation value; wherein the time scale conversion model is:

[0192] m * =p1×m 3 +p2×m 2 +p3×m

[0193] Among them, p i is the fitting coefficient, m is the cumulative number of cycles, and m * Cumulative number of cycles after scaling.

[0194] Optionally, the battery life estimation device 14 further includes: a scale transformation model fitting module 1460 .

[0195] The scale transformation model fitting module 1460 is used to establish a third-order polynomial model;

[0196] According to the degradation data of multiple reference batteries acquired in advance, a third-order polynomial model is fitted to determine the fitting coefficient corresponding to each cumulative number of cycles; wherein the reference battery is a battery of the same model as the target battery; wherein the degradation data includes multiple cumulative numbers of cycles and the cumulative capacity loss corresponding to each cumulative number of cycles;

[0197] Determine the battery cumulative capacity degradation trajectory of the battery model according to the fitting coefficient of the polynomial corresponding to each cumulative number of cycles;

[0198] The time scale transformation model of this type of battery is determined according to the battery cumulative capacity degradation trajectory.

[0199] Optionally, the parameters of the pre-established battery life probability density function include drift parameters, diffusion parameters, and failure thresholds. The battery life probability density function is:

[0200]

[0201] Among them, f(m * ; L) is the battery life probability density function, μ * is the drift parameter, m * represents the number of cycles corresponding to the cumulative capacity loss after scale transformation, L is the failure threshold of the target battery, z0 is the cumulative capacity loss of the target battery at the initial moment, σ * is the diffusion parameter.

[0202] The life estimation module 1440 is used to input the scale-transformed cumulative cycle times and parameters into the battery life probability density function to determine the expected value of the battery life probability density function and use it as the battery life estimation value.

[0203] Optionally, the battery life estimation device 14 further includes: a reliability calculation module 1470 .

[0204] A reliability calculation module 1470, configured to determine a battery life reliability function according to a battery life probability density function;

[0205] Calculate the reliability of the battery according to the battery life reliability function;

[0206] The battery life reliability function is:

[0207]

[0208] Among them, R(m * ; L) is the battery life reliability function, Ф(x) is the standard normal distribution function.

[0209] Optionally, a parameter determination module 1430 is used to perform maximum likelihood estimation on a drift parameter, a diffusion parameter, and a failure threshold according to a pre-established cumulative capacity degradation model, and determine a parameter estimation expression;

[0210] The accumulated number of cycles after scale transformation is input into the parameter estimation expression to obtain the estimated values ​​of drift parameter, diffusion parameter and failure threshold;

[0211] The parameter estimation expression is:

[0212]

[0213] in, is the estimated value of the drift parameter, is the estimated value of the diffusion parameter, is the estimated value of the failure threshold, z i is the single capacity loss of the target battery during the i-th charge and discharge cycle.

[0214] Optionally, the battery cumulative capacity degradation model is:

[0215]

[0216] Among them, z(m * ) represents the cumulative capacity loss, z0 is the cumulative capacity loss of the target battery at the initial moment, μ * is the drift parameter, m * represents the number of cycles corresponding to the cumulative capacity loss after scale transformation, σ * is the diffusion parameter, C(m * ) is a random process and obeys Brownian motion.

[0217] Optionally, the battery life estimation device 14 also includes: a model verification module 1480.

[0218] A model verification module 1480 is used to determine a single capacity loss corresponding to a plurality of cumulative cycle times of a target battery according to a pre-established cumulative capacity degradation model;

[0219] A normal distribution test is performed based on each single capacity loss to verify whether the battery cumulative capacity degradation model conforms to the Wiener process;

[0220] If the battery cumulative capacity degradation model conforms to the Wiener process, it indicates that the cumulative capacity degradation model can be used;

[0221] If the battery cumulative capacity degradation model does not conform to the Wiener process, the battery cumulative capacity degradation model is deleted.

[0222] Optionally, the battery life estimation device 14 also includes: a data reconstruction module 1490.

[0223] A data reconstruction module 1490 is used to obtain initial degradation information of multiple reference batteries;

[0224] The initial degradation information is reconstructed by wavelet transform to obtain degradation data of multiple reference batteries.

[0225] The vehicle bonding structure strength verification device provided in this embodiment can be used to execute the above-mentioned vehicle bonding structure strength verification method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0226] Fig.15 Schematic diagram of the structure of the battery management system provided by the embodiment of the present invention. Fig.15 As shown, the battery management system 15 of this embodiment includes: a processor 1500, a memory 1510, and a computer program 1520 stored in the memory 1510 and executable on the processor 1500. When the processor 1500 executes the computer program 1520, the steps in the above-mentioned various DC cross-talk detection method embodiments are implemented, such as Figure 2 Alternatively, when the processor 1500 executes the computer program 1520, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 7 Steps 701 to 704 are shown.

[0227] Exemplarily, the computer program 1520 may be divided into one or more modules / units, one or more modules / units are stored in the memory 1510, and are executed by the processor 1500 to implement the present invention. One or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 1520 in the battery management system 15.

[0228] The battery management system 15 can be a server, a terminal, etc., which is not limited here. The terminal can include but is not limited to a desktop computer, a laptop computer, a tablet computer, etc. The server can be an independent physical server, a cloud server, or a server cluster composed of multiple servers, which is not limited here. Those skilled in the art can understand that Fig.15 The battery management system 15 is merely an example and does not constitute a limitation on the battery management system 15 , which may include more or less components than those shown in the figure, or may combine certain components, or may have different components.

[0229] The processor 1500 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0230] The memory 1510 may be an internal storage unit of the battery management system 15, such as a hard disk or memory of the battery management system 15. The memory 1510 may also be an external storage device of the battery management system 15, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the battery management system 15. Further, the memory 1510 may also include both an internal storage unit of the battery management system 15 and an external storage device. The memory 1510 is used to store computer programs and other programs and data required by the terminal. The memory 1510 may also be used to temporarily store data that has been output or is to be output.

[0231] An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned DC mutual cross-talk detection method embodiment are implemented.

[0232] The computer readable storage medium stores a computer program 1520, which includes program instructions. When the program instructions are executed by the processor 1500, all or part of the processes in the above-mentioned embodiment method are implemented. The computer program 1520 can also be used to instruct related hardware to complete. The computer program 1520 can be stored in a computer readable storage medium. When the computer program 1520 is executed by the processor 1500, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program 1520 includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunications signals.

[0233] The computer-readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, such as a hard disk or memory of the terminal. The computer-readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0234] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0235] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0236] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0237] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0238] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0239] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0240] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0241] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0242] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A battery life estimation method, characterized in that: include: Get the cumulative number of cycles of the target battery; Performing a scale transformation on the cumulative number of cycles of the target battery according to a pre-established time scale transformation model to obtain a scale-transformed cumulative number of cycles; Determine the parameters of the pre-established battery life probability density function according to the scale-transformed cumulative cycle number and the pre-established cumulative capacity degradation model; Inputting the scale-transformed cumulative cycle number and the parameter into the battery life probability density function to determine the battery life estimation value; Performing an inverse scale transformation on the battery life estimate to obtain a final life estimate; Wherein, the time scale conversion model is: m * =p1×m 3 +p2×m 2 +p3×m Among them, p i is the fitting coefficient, m is the cumulative number of cycles, and m * represents the cumulative number of cycles after scale transformation; The parameters of the pre-established battery life probability density function include a drift parameter, a diffusion parameter, and a failure threshold; the battery life probability density function is: Among them, f(m * ; L) is the battery life probability density function, μ * is the drift parameter, L is the failure threshold of the target battery, z0 is the cumulative capacity loss of the target battery at the initial moment, σ * is the diffusion parameter; The step of inputting the scale-transformed cumulative cycle number and the parameter into the battery life probability density function to determine the battery life estimation value comprises: Inputting the scale-transformed cumulative cycle number and the parameter into the battery life probability density function to determine an expected value of the battery life probability density function, and using the expected value as the battery life estimation value; The cumulative capacity degradation model is: Among them, z(m * ) represents the cumulative capacity loss, z0 is the cumulative capacity loss of the target battery at the initial moment, μ * is the drift parameter, σ * is the diffusion parameter, C(m * ) is a random process and obeys Brownian motion.

2. The battery life estimation method according to claim 1, characterized in that: Also includes: A third-order polynomial model was established; According to the degradation data of a plurality of reference batteries acquired in advance, the third-order polynomial model is fitted to determine the fitting coefficient corresponding to each cumulative number of cycles; wherein the reference battery is a battery of the same model as the target battery; wherein the degradation data includes a plurality of cumulative numbers of cycles and the cumulative capacity loss corresponding to each cumulative number of cycles; Determine the battery cumulative capacity degradation trajectory of the battery model according to the fitting coefficient of the polynomial corresponding to each cumulative number of cycles; A time scale transformation model for the battery of this type is determined according to the battery cumulative capacity degradation trajectory.

3. The battery life estimation method according to claim 1, characterized in that: Also includes: Determining a battery life reliability function according to the battery life probability density function; Calculating the reliability of the battery according to a battery life reliability function; The battery life reliability function is: Among them, R(m * ; L) is the battery life reliability function, Ф(x) is the standard normal distribution function.

4. The battery life estimation method according to claim 1, characterized in that: The step of determining the parameters of the pre-established battery life probability density function according to the scale-converted cumulative cycle number and the pre-established cumulative capacity degradation model includes: According to a pre-established cumulative capacity degradation model, maximum likelihood estimation is performed on the drift parameter, the diffusion parameter, and the failure threshold to determine a parameter estimation expression; Inputting the scale-transformed cumulative cycle number into the parameter estimation expression to obtain an estimated value of the drift parameter, an estimated value of the diffusion parameter, and an estimated value of the failure threshold; The parameter estimation expression is: in, is the estimated value of the drift parameter, is the estimated value of the diffusion parameter, is the estimated value of the failure threshold, Δz i is the single capacity loss of the target battery during the i-th charge and discharge cycle.

5. The battery life estimation method according to claim 1, characterized in that: After obtaining the cumulative number of cycles after scaling, it also includes: Determining a single capacity loss corresponding to a plurality of cumulative cycle times of the target battery according to a pre-established cumulative capacity degradation model; Performing a normal distribution test based on each single capacity loss to verify whether the battery cumulative capacity degradation model conforms to the Wiener process; If the battery cumulative capacity degradation model conforms to the Wiener process, it indicates that the cumulative capacity degradation model can be used; If the battery cumulative capacity degradation model does not conform to the Wiener process, the battery cumulative capacity degradation model is deleted.

6. The battery life estimation method according to claim 2, characterized in that: Before fitting the third-order polynomial model according to the degradation data of a plurality of reference batteries acquired in advance, the method further includes: Obtaining initial degradation information of multiple reference batteries; The initial degradation information is reconstructed by wavelet transform to obtain degradation data of multiple reference batteries.

7. A battery management system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the battery life estimation method as claimed in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the battery life estimation method as described in any one of claims 1 to 6 are implemented.

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