Method, device and equipment for predicting remaining service life of battery and readable storage medium

Abstract

A battery remaining service life prediction method, apparatus and device, and a readable storage medium relate to the field of batteries, and comprise the steps of obtaining a target energy retention rate and a target cycle test number corresponding to a last cycle test of a to-be-predicted battery at a target temperature level; the target drift rate is calculated through a target Ailin model used for describing the function relation between the drift rate and the temperature and the function relation between the drift rate and the cycle test turns, the target cycle test turns and the target temperature level; on the basis of the residual service life probability density function, the residual service life of the battery to be predicted is obtained by substituting the target cycle test circle number, the target drift rate, the target energy retention rate and the target diffusion coefficient value; the model parameter value and the target diffusion coefficient value of the target Ailin model are solved and determined on the basis of a parameter estimation method, wherein the target battery degradation model is composed of the initial Ailin model and the Wiener process model corresponding to the drift rate change. The method can effectively improve the prediction accuracy of the remaining service life of the battery.

Description

Technical Field

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

[0002] Lithium-ion batteries often experience complex and changeable operating environments during actual operation, especially different operating temperature conditions. Multi-temperature environments not only increase the workload of the battery, but also place higher demands on its materials and structure. Among them, under different operating temperature conditions, the internal chemical reaction rates of the battery often differ, resulting in significant differences in its performance, and the battery degradation process is generally nonlinear, which brings huge challenges to predicting battery life and evaluating battery reliability indicators.

[0003] In related technologies, the Wiener process is usually used to describe the degradation process of the battery, and the Arrhenius model is combined to construct the relationship between the drift rate (ie, the degradation rate) and the temperature level, and then a battery degradation model based on the nonlinear Wiener process is constructed to evaluate the remaining service life of the battery.

[0004] However, the battery degradation process is not only nonlinear, and the degradation rate is not only related to the ambient temperature, but also changes with the battery aging process. When the Arrhenius model is used to construct the relationship between the drift rate and the stress level, the factor that the drift rate will change with the battery aging process is ignored, resulting in the degradation model fitting the data poorly and with large errors, making it impossible to accurately predict the remaining service life of the battery. Summary of the invention

[0005] The present application provides a method, device, equipment and readable storage medium for predicting the remaining service life of a battery, which can effectively improve the prediction accuracy of the remaining service life of a battery.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting the remaining service life of a battery, the method comprising:

[0007] Obtaining a target energy retention rate corresponding to the last cycle test of the battery to be predicted at a target temperature level and a target number of cycle tests corresponding to the last cycle test;

[0008] The target drift rate is calculated by using a preset target Eyring model for describing the functional relationship between the drift rate, temperature and the number of cycle test cycles, the target number of cycle test cycles and the target temperature level;

[0009] Based on the preset remaining service life probability density function, the target number of cycle test laps, the target drift rate, the target energy retention rate and the preset target diffusion coefficient value, the life prediction is performed to obtain the target remaining service life of the battery to be predicted;

[0010] Among them, the model parameter values ​​and the target diffusion coefficient values ​​in the target Eyring model are determined by solving the preset target battery degradation model based on a preset parameter estimation method. The target battery degradation model consists of an initial Eyring model and a Wiener process model corresponding to the drift rate change.

[0011] In combination with the first aspect, in one implementation, the target Eyring model is:

[0012]

[0013] In the formula, μ1 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

[0014] In combination with the first aspect, in one implementation, the remaining useful life probability density function for:

[0015]

[0016] Where s represents the remaining service life, T i represents the target temperature level, μ1(k ′ ,T i ) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

[0017] In combination with the first aspect, in one implementation, the target battery degradation model is:

[0018]

[0019] In the formula, Y1(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y1(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, a and b are the model parameters to be solved by the parameter estimation method, σ1 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

[0020] In combination with the first aspect, in one implementation, the target Eyring model is:

[0021]

[0022] In the formula, μ2 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

[0023] In combination with the first aspect, in one implementation, the remaining useful life probability density function for:

[0024]

[0025] Where s represents the remaining service life, T i represents the target temperature level, μ2(k ′ ,T i ) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

[0026] In combination with the first aspect, in one implementation, the target battery degradation model is:

[0027]

[0028] Where, Y2(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y2(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, c, d and e are the model parameters to be solved by the parameter estimation method, σ2 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

[0029] In a second aspect, an embodiment of the present application provides a battery remaining service life prediction device, the battery remaining service life prediction device comprising:

[0030] A parameter acquisition module, which is used to obtain a target energy retention rate corresponding to the last cycle test of the battery to be predicted at a target temperature level and a target number of cycle tests corresponding to the last cycle test;

[0031] A parameter calculation module, which is used to calculate the target drift rate through a preset target Eyring model for describing the functional relationship between the drift rate, temperature, and the number of cycle test turns, a target number of cycle test turns, and a target temperature level;

[0032] A life prediction module, which is used to predict the life based on a preset remaining service life probability density function, a target number of cycle test laps, a target drift rate, a target energy retention rate and a preset target diffusion coefficient value, to obtain a target remaining service life of the battery to be predicted;

[0033] Among them, the model parameter values ​​and the target diffusion coefficient values ​​in the target Eyring model are determined by solving the preset target battery degradation model based on a preset parameter estimation method. The target battery degradation model consists of an initial Eyring model and a Wiener process model corresponding to the drift rate change.

[0034] In conjunction with the second aspect, in one implementation, the target Eyring model is:

[0035]

[0036] In the formula, μ1 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

[0037] In conjunction with the second aspect, in one implementation, the remaining useful life probability density function for:

[0038]

[0039] Where s represents the remaining service life, T i represents the target temperature level, μ1(k ′ ,T i ) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

[0040] In conjunction with the second aspect, in one implementation, the target battery degradation model is:

[0041]

[0042] In the formula, Y1(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y1(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, a and b are the model parameters to be solved by the parameter estimation method, σ1 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

[0043] In conjunction with the second aspect, in one implementation, the target Eyring model is:

[0044]

[0045] In the formula, μ2 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

[0046] In conjunction with the second aspect, in one implementation, the remaining useful life probability density function for:

[0047]

[0048] Where s represents the remaining service life, T i represents the target temperature level, μ2(k ′ ,T i ) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

[0049] In conjunction with the second aspect, in one implementation, the target battery degradation model is:

[0050]

[0051] Where, Y2(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y2(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, c, d and e are the model parameters to be solved by the parameter estimation method, σ2 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

[0052] In a third aspect, an embodiment of the present application provides a battery remaining service life prediction device, which includes a processor, a memory, and a battery remaining service life prediction program stored in the memory and executable by the processor, wherein when the battery remaining service life prediction program is executed by the processor, the steps of the battery remaining service life prediction method as described above are implemented.

[0053] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a battery remaining service life prediction program is stored, wherein when the battery remaining service life prediction program is executed by a processor, the steps of the battery remaining service life prediction method as described above are implemented.

[0054] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0055] Temperature and cycle test number are used as factors affecting the degradation process of lithium-ion batteries, that is, by introducing temperature and cycle test number to describe the change of drift rate with the battery aging process, so as to accurately predict the remaining service life of the battery; the target Ailin model is used to construct the functional relationship between drift rate, temperature and cycle test number, so as to substitute the target cycle test number and target temperature level corresponding to the last cycle test of the battery to be predicted at the target temperature level into the target Ailin model to determine the target drift rate of the battery to be predicted; wherein, the target battery degradation model composed of the initial Ailin model and the Wiener process model corresponding to the drift rate change is parameter estimated to determine the model parameter value and target diffusion coefficient value of the target Ailin model; then based on the remaining service life probability density function, and by substituting the target energy retention rate, target cycle test number, target drift rate and target diffusion coefficient value corresponding to the last cycle test of the battery to be predicted at the target temperature level for life prediction, the remaining service life of the battery to be predicted can be accurately predicted. It can be seen that the prediction accuracy of the remaining service life of the battery can be effectively improved through this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for predicting remaining battery life of the present application;

[0057] Figure 2 A curve diagram of the battery RUL probability density function at 25°C based on the parameterized target battery degradation model 1 involved in the embodiment of the present application;

[0058] Figure 3 A battery RUL probability density function curve at 35°C based on a parameterized target battery degradation model 1 involved in an embodiment of the present application;

[0059] Figure 4 A battery RUL probability density function curve at 45°C based on a parameterized target battery degradation model 1 involved in an embodiment of the present application;

[0060] Figure 5 A battery RUL probability density function curve at 25°C based on a parameterized target battery degradation model 2 involved in an embodiment of the present application;

[0061] Figure 6 A battery RUL probability density function curve at 35°C based on a parameterized target battery degradation model 2 involved in an embodiment of the present application;

[0062] Figure 7 A curve diagram of the battery RUL probability density function at 45°C based on the parameterized target battery degradation model 2 involved in the embodiment of the present application;

[0063] Figure 8 This is a schematic diagram of the hardware structure of a battery remaining service life prediction device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0065] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0066] In a first aspect, an embodiment of the present application provides a method for predicting the remaining useful life of a battery.

[0067] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of an embodiment of the method for predicting the remaining service life of a battery in this application. Figure 1 As shown, the remaining battery life prediction method includes:

[0068] Step S10: Obtain a target energy retention rate corresponding to the last cycle test of the battery to be predicted at a target temperature level and a target number of cycle tests corresponding to the last cycle test.

[0069] For example, in this embodiment, when predicting the remaining service life of the battery to be predicted, it is first necessary to control the battery to be predicted to perform a preset number of constant power charge and discharge accelerated degradation cycle tests at the target temperature level to determine the target energy retention rate of the battery to be predicted in the last cycle test; it should be noted that the target temperature level refers to the temperature level to be tested, and its specific value can be determined according to the test requirements of the battery to be tested. For example, if the user expects to predict the remaining service life of the battery to be predicted at 25°C, the target temperature level is 25°C; for another example, if the user expects to predict the remaining service life of the battery to be predicted at 35°C, the target temperature level is 35°C. In addition, the specific value setting of the preset number of laps can be determined according to actual needs and is not limited here. For example, if the preset number of laps is set to 100, the last lap is the 100th lap, and the target number of cyclic test laps is 100.

[0070] It should be understood that if the remaining service life of multiple batteries of the same model to be predicted is predicted at the same time, after obtaining the target energy retention rate of each battery to be predicted at the target temperature level, it is also necessary to take the average of all target energy retention rates to obtain the target average energy retention rate, and substitute the target average energy retention rate into the remaining service life probability density function to predict the remaining service life of the battery to be predicted, and the energy retention rate failure threshold in the remaining service life probability density function needs to be adaptively adjusted to the average energy retention rate failure threshold.

[0071] Step S20: The target drift rate is calculated by using a preset target Eyring model for describing the functional relationship between the drift rate and the temperature and the number of cycle test cycles, the target number of cycle test cycles and the target temperature level; wherein, the model parameter values ​​in the target Eyring model and the target diffusion coefficient value are determined by performing parameter estimation on a target battery degradation model composed of an initial Eyring model and a Wiener process model corresponding to the drift rate change.

[0072] It should be noted that the target battery degradation model is:

[0073]

[0074] In the formula, Y1(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y1(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, a and b are the model parameters to be solved by the parameter estimation method, σ1 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

[0075] Alternatively, the target battery degradation model is:

[0076]

[0077] Where, Y2(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y2(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, c, d and e are the model parameters to be solved by the parameter estimation method, σ2 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

[0078] Exemplarily, in this embodiment, before calculating the drift rate of the battery to be predicted, it is necessary to first construct an Eyring model (i.e., a target Eyring model, hereinafter referred to as the target Eyring model for the sake of uniformity of description) to describe the functional relationship between the drift rate and the temperature and the number of cycle tests.

[0079] Specifically, first, carry out constant power charge and discharge accelerated degradation cycle tests at different ambient temperatures (such as 25°C, 35°C, 45°C, etc.) on lithium-ion batteries, and stop the test until the performance characteristic quantity of all batteries reaches the failure threshold, and record and save the charge and discharge energy data of the battery under each cycle test, so as to calculate the performance characteristic quantity based on the charge and discharge energy data. Among them, the performance characteristic quantity is the energy retention rate, which can be obtained by the following formula:

[0080]

[0081] Among them, Energy retention refers to the energy retention rate, E i is the discharge energy of the i-th cycle test, and E0 is the initial discharge energy.

[0082] Secondly, this embodiment uses the ambient temperature as a factor affecting the battery degradation process, and simulates the change trend of the battery energy retention rate based on the Wiener process of drift rate change. The Wiener process model is expressed as follows:

[0083] Y(t0+Δt)=Y(t0)-μΔt+σB(Δt) (2)

[0084] Wherein, t0 represents the initial cycle test number, Δt represents the interval of the cycle test number, Y(t0+Δt) represents the energy retention rate of the battery at the (t0+Δt)th cycle test number; Y(t0) represents the energy retention rate of the battery at the t0th cycle test number; B(Δt) represents standard Brownian motion; μ represents drift rate; σ represents diffusion coefficient.

[0085] It should be noted that, in this embodiment, the diffusion coefficient σ of the Wiener process is regarded as a constant value, and the drift rate μ is set as a function with temperature and the number of cycle test cycles as variables; in addition, it should be understood that the increments Y(t4)-Y(t3) and Y(t2)-Y(t1) of the Wiener process (where, ) are independent of each other, and the increment ΔY=Y(t2)-Y(t1) obeys the Gaussian distribution ΔY~N(μΔt,σ 2 Δt), where Δt=t2-t1.

[0086] Then, in order to fit the relationship between the drift rate, temperature, and the number of cycle tests, this embodiment uses the generalized Eyring model shown in formula (3) to describe the above relationship:

[0087]

[0088] Where μ(k,T) represents the drift rate; k represents the number of cycle tests; T represents the temperature level; γ0,γ1,γ2,γ3,m,n are model parameters.

[0089] By selecting different parameter combinations and substituting them into formula (3) for fitting experimental calculations, the following two initial Eyring models can be obtained:

[0090] Initial Eyring Model 1:

[0091] Initial Eyring Model 2:

[0092] It should be understood that under the initial Eyring model 1, the temperature and the number of cycle test cycles have a synergistic effect on the drift rate, while under the initial Eyring model 2, the temperature and the number of cycle test cycles will have independent effects on the drift rate respectively; wherein, μ1(k, T) in formula (4) represents the drift rate of the initial Eyring model 1; μ2(k, T) in formula (5) represents the drift rate of the initial Eyring model 2; a, b, c, d, e are the parameters to be estimated of the model.

[0093] Next, according to the initial Eyring model 1 and the initial Eyring model 2 and combined with the Wiener process model corresponding to formula (2), the Wiener process expression of the drift rate change based on the Eyring model is obtained, that is, the improved battery degradation model corresponding to two different initial Eyring models is generated:

[0094] Target battery degradation model 1:

[0095] Target battery degradation model 2:

[0096] Where t0 is assigned to k, Δt is assigned to 1, a, b, c, d and e are model parameters to be solved, and σ1 and σ2 are diffusion coefficient values ​​to be solved.

[0097] It should be understood that after obtaining the accelerated test data of the battery, the present embodiment can obtain the estimated values ​​of the parameters to be solved in the target battery degradation model 1 and the target battery degradation model 2 by the parameter estimation method, wherein the parameter estimation method is a method for parameter estimation such as the maximum likelihood estimation method; for example, the maximum likelihood estimation method is preferably used to construct the likelihood functions corresponding to the target battery degradation model 1 and the target battery degradation model 2 respectively:

[0098]

[0099] Wherein, L1(a, b, σ1) is the likelihood function of the target battery degradation model 1; L2(c, d, e, σ2) is the likelihood function of the target battery degradation model 2; a, b, c, d, e are the parameters to be estimated of the model, σ1 is the diffusion coefficient to be estimated of the target battery degradation model 1, σ2 is the diffusion coefficient to be estimated of the target battery degradation model 2; T i represents the temperature corresponding to the i-th temperature level (for example, i=1, 2, 3 represent 25°C, 35°C, 45°C respectively); j represents the battery sample number corresponding to each temperature level (i.e., the j-th battery); k represents the number of cycle tests; k start Indicates that from the kth start Input data for the start of the loop test; k end Indicates that the kth end Input data for loop test termination; ΔY i,j,k It represents the energy retention rate increment of the jth battery in the kth cycle test at the i-th temperature level.

[0100] In this embodiment, the minimize function of the Python scipy library can be preferably used to solve equations (8) and (9) respectively, that is, the corresponding parameters when equations (8) and (9) take the maximum value are used as the parameter estimation values ​​of the target battery degradation model 1 and the target battery degradation model 2 It should be noted that the calculation methods and principles of parameter estimation methods such as maximum likelihood estimation are common knowledge in the art, and therefore will not be elaborated here for the sake of brevity.

[0101] Finally, Substituting the corresponding initial Eyring model, the corresponding target Eyring model can be generated, and They are respectively used as the target diffusion coefficient values ​​corresponding to different target Eyring models.

[0102] Therefore, in this embodiment, the target number of cycle tests and the target temperature level are substituted into the target Eyring model to calculate the target drift rate of the battery to be predicted. It can be understood that the target drift rate takes into account the changes that occur during the battery aging process.

[0103] Furthermore, in one embodiment, the target Eyring model is:

[0104]

[0105] In the formula, μ1 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

[0106] For example, in this embodiment, when realizing the prediction of the remaining service life of the battery to be predicted, if the synergistic effect of temperature and the number of cycle test cycles on the drift rate is to be considered, the value calculated by the parameter estimation method such as the maximum likelihood estimation method is and Substituting the model parameter values ​​into the initial Eyring model 1, the target Eyring model 1 can be generated:

[0107]

[0108] μ1(k,T) in formula (10) represents the drift rate corresponding to the synergistic effect of temperature and number of cycle test cycles.

[0109] Furthermore, in one embodiment, the target Eyring model is:

[0110]

[0111] In the formula, μ2 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

[0112] For example, in this embodiment, when predicting the remaining service life of the battery to be predicted, if you want to consider the independent effects of temperature and the number of cycle test cycles on the drift rate, the value calculated by the maximum likelihood estimation method or other parameter estimation method is Substituting the model parameter values ​​into the initial Eyring model 2, the target Eyring model 2 can be generated:

[0113]

[0114] μ2(k,T) in formula (11) represents the drift rate corresponding to the independent effects of temperature and number of cycle test cycles.

[0115] Step S30: Perform life prediction based on a preset remaining service life probability density function, a target number of cycle test laps, a target drift rate, a target energy retention rate and a preset target diffusion coefficient value to obtain a target remaining service life of the battery to be predicted.

[0116] Exemplarily, it can be understood that after a given energy retention rate failure threshold ω of a battery, the failure time S of the battery can be defined by the time when the energy retention rate failure threshold is first reached; wherein, according to the important characteristics of the Wiener process, the time S when the energy retention rate failure threshold is first reached obeys the inverse Gaussian distribution of the conversion:

[0117] S~tIG(((1-ω) / μ),((1-ω) 2 / σ 2 )) (12)

[0118] It can be seen that the remaining useful life s (i.e., Remaining useful life, RUL) of the battery will also obey the inverse Gaussian distribution. Based on this, the inverse Gaussian distribution expression obeyed by the remaining useful life s based on the target battery degradation model can be derived, that is, the probability density function obeyed by the remaining useful life s (i.e., the remaining useful life probability density function) can be obtained.

[0119] Therefore, when evaluating the remaining service life s of the battery to be predicted, it is only necessary to substitute the target number of cycle test laps, target drift rate, target energy retention rate and target diffusion coefficient value into the remaining service life probability density function for life prediction, and the time measurement index of battery reliability can be calculated; it should be noted that, as shown in Table 1, the commonly used time measurement indicators are: average RUL, mode RUL, median RUL and 95% confidence RUL, among which Table 1 also lists the calculation method of each indicator.

[0120] Table 1 Calculation method of reliability time measurement index

[0121]

[0122] Therefore, the remaining service life can be determined by the remaining service life probability density function according to the selected specific reliability time measurement index. For example, if the mode RUL is selected as the reliability time measurement index, the k corresponding to the maximum value of the remaining service life probability density function is selected as the target remaining service life of the battery to be predicted. It can be seen that the prediction accuracy of the remaining service life of the battery can be effectively improved through this embodiment.

[0123] Furthermore, in one embodiment, the remaining useful life probability density function for:

[0124]

[0125] Where s represents the remaining service life, T i represents the target temperature level, μ1(k ′ ,T i ) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

[0126] For example, in this embodiment, when evaluating the remaining service life of the battery to be predicted, if the synergistic effect of temperature and the number of cycle tests on the drift rate is considered, the energy retention rate Y of the battery to be predicted at the last cycle test at the target temperature level i is calculated. i (k end ), target Eyring model 1 and target diffusion coefficient value To construct the remaining useful life probability density function Therefore, the last cycle test k calculated by the target Eyring model 1 at the target temperature level i end The corresponding target cycle test number k ′ The corresponding target drift rate and energy retention rate Y under the condition of i (k end ) and the target diffusion coefficient value Substitute the remaining useful life probability density function In the example, the remaining service life of the battery to be predicted can be predicted.

[0127] Furthermore, in one embodiment, the remaining useful life probability density function for:

[0128]

[0129] Where s represents the remaining service life, T i represents the target temperature level, μ2(k ′ ,T i ) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

[0130] For example, in this embodiment, when evaluating the remaining service life of the battery to be predicted, if the separate effects of temperature and the number of cycle tests on the drift rate are considered, then the energy retention rate Y of the battery to be predicted at the last cycle test at the target temperature level i is calculated. i (k end ), target Eyring model 2 and target diffusion coefficient value To construct the remaining useful life probability density function

[0131] The target Eyring model 2 calculates the final cycle test k at the target temperature level i. end The corresponding target cycle test number k ′ The corresponding target drift rate and energy retention rate Y under the condition of i (k end ) and the target diffusion coefficient value Substitute the remaining useful life probability density function In the example, the remaining service life of the battery to be predicted can be predicted.

[0132] The following explains the principle of predicting the remaining service life of lithium-ion batteries at three ambient temperatures of 25°C, 35°C and 45°C.

[0133] First, at three ambient temperatures of 25°C, 35°C and 45°C, constant power charge and discharge accelerated degradation cycle tests were carried out on lithium-ion batteries respectively until the energy retention rate of all batteries decayed to 0.8 (that is, the energy retention rate threshold was 0.8), that is, the test was stopped when the battery energy retention rate degradation amount was 0.2, and the energy retention rate data of the battery in each cycle test was recorded; then, target battery degradation model 1 and target battery degradation model 2 based on the nonlinear Wiener process of drift rate change were constructed; and then the maximum likelihood estimation method was used to estimate and solve the parameters in target battery degradation model 1 and target battery degradation model 2 respectively, and the results are shown in the following table:

[0134] Table 2 Parameter estimates of target battery degradation model 1

[0135]

[0136] It should be understood that in order to verify the fitting effect of the target battery degradation model 1 and the target battery degradation model 2 and calculate the error, the present embodiment may preferably use the Monte Carlo method to fit and generate the degradation curve based on the Wiener process of the drift rate change, and perform error calculation based on the degradation curve. The parameter values ​​are substituted into equations (6) and (7) to obtain parameterized target battery degradation model 1 and target battery degradation model 2:

[0137] Parameterized target battery degradation model 1:

[0138] Parameterized target battery degradation model 2:

[0139] Then, the Monte Carlo method is used to simulate and generate several Wiener process curves of formula (13) and formula (14), and the average values ​​of these curves are calculated, and the average values ​​obtained are compared with the average values ​​of the energy retention rate data of each battery at each temperature to calculate the error; wherein, the error index can be selected as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE), and the calculation formulas are shown in formula (15), formula (16) and formula (17) respectively:

[0140]

[0141] Among them, Z i,krepresents the average value of the energy retention rate of several simulation curves in the kth cycle test at the i-th temperature level; Y i,k It represents the average value of the energy retention rate of each battery in the kth cycle test at the i-th temperature level; the error results corresponding to the two models are shown in the following table:

[0142] Table 4 Fitting error of parameterized target battery degradation model 1

[0143]

[0144] Table 5 Fitting error of parameterized target battery degradation model 2

[0145]

[0146] It can be seen from the fitting error calculation results that the fitting error values ​​of the parameterized target battery degradation model 1 and the parameterized target battery degradation model 2 are both at a low level, which means that the models have excellent fitting accuracy.

[0147] Then, substitute the energy retention rate data corresponding to the last cycle test of the battery whose remaining service life needs to be predicted and the target number of cycle tests corresponding to the last cycle test, and then draw the battery RUL probability density function curves at three temperature levels based on the parameterized target battery degradation model 1 and the parameterized target battery degradation model 2, respectively, as shown in Figure 2 to Figure 7 shown.

[0148] Finally, according to the battery RUL probability density function curve, the average RUL, median RUL, mode RUL and 95% confidence RUL of the battery at different temperatures can be calculated respectively. The results are shown in Tables 6 and 7.

[0149] Table 6 Predicted battery RUL values ​​under parameterized target battery degradation model 1

[0150]

[0151] Table 7 Predicted battery RUL values ​​under parameterized target battery degradation model 2

[0152]

[0153]

[0154] In summary, this embodiment takes temperature and the number of cycle test cycles as factors affecting the battery degradation process, and uses the Eyring model to construct the functional relationship between the drift rate and the temperature and the number of cycle test cycles, and then uses the maximum likelihood estimation method to estimate the parameters of the improved battery degradation model to construct a RUL probability density function that satisfies the inverse Gaussian distribution to achieve accurate prediction of the remaining service life of the battery at various operating temperatures; compared to the method using the Arrhenius model, this embodiment takes into account that the drift rate will change with the battery aging process, and then introduces two variables, temperature and the number of cycle test cycles, to construct the functional relationship of the drift rate in the Wiener process, which can greatly reduce the error of model fitting and significantly improve the accuracy of the predicted value.

[0155] In a second aspect, an embodiment of the present application also provides a device for predicting remaining battery life.

[0156] In one embodiment, the battery remaining service life prediction device includes:

[0157] A parameter acquisition module, which is used to obtain a target energy retention rate corresponding to the last cycle test of the battery to be predicted at a target temperature level and a target number of cycle tests corresponding to the last cycle test;

[0158] A parameter calculation module, which is used to calculate the target drift rate through a preset target Eyring model for describing the functional relationship between the drift rate, temperature, and the number of cycle test turns, a target number of cycle test turns, and a target temperature level;

[0159] A life prediction module, which is used to predict the life based on a preset remaining service life probability density function, a target number of cycle test laps, a target drift rate, a target energy retention rate and a preset target diffusion coefficient value, to obtain a target remaining service life of the battery to be predicted;

[0160] Among them, the model parameter values ​​and the target diffusion coefficient values ​​in the target Eyring model are determined by solving the preset target battery degradation model based on a preset parameter estimation method. The target battery degradation model consists of an initial Eyring model and a Wiener process model corresponding to the drift rate change.

[0161] Furthermore, in one embodiment, the target Eyring model is:

[0162]

[0163] In the formula, μ1 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

[0164] Furthermore, in one embodiment, the remaining useful life probability density function for:

[0165]

[0166] Where s represents the remaining service life, T i represents the target temperature level, μ1(k ′ ,T i ) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

[0167] Furthermore, in one embodiment, the target battery degradation model is:

[0168]

[0169] In the formula, Y1(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y1(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, a and b are the model parameters to be solved by the parameter estimation method, σ1 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

[0170] Furthermore, in one embodiment, the target Eyring model is:

[0171]

[0172] In the formula, μ2 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

[0173] Furthermore, in one embodiment, the remaining useful life probability density function for:

[0174]

[0175] Where s represents the remaining service life, T i represents the target temperature level, μ2(k ′ ,T i) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

[0176] Furthermore, in one embodiment, the target battery degradation model is:

[0177]

[0178] Where, Y2(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y2(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, c, d and e are the model parameters to be solved by the parameter estimation method, σ2 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

[0179] Among them, the functional implementation of each module in the above-mentioned battery remaining service life prediction device corresponds to the various steps in the above-mentioned battery remaining service life prediction method embodiment, and its functions and implementation processes are no longer repeated here one by one.

[0180] In a third aspect, an embodiment of the present application provides a battery remaining service life prediction device, which may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0181] Reference Figure 8 , Figure 8 Schematic diagram of the hardware structure of the battery remaining service life prediction device involved in the embodiment of the present application. In the embodiment of the present application, the battery remaining service life prediction device may include a processor, a memory, a communication interface and a communication bus.

[0182] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0183] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to interconnect the devices inside the battery remaining service life prediction device, and an interface used to interconnect the battery remaining service life prediction device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0184] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0185] The processor may be a general-purpose processor, and the general-purpose processor may call the battery remaining service life prediction program stored in the memory and execute the battery remaining service life prediction method provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the battery remaining service life prediction program is called may refer to the various embodiments of the battery remaining service life prediction method of the present application, which will not be repeated here.

[0186] Those skilled in the art will understand that Figure 8 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0187] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0188] The readable storage medium of the present application stores a battery remaining service life prediction program, wherein when the battery remaining service life prediction program is executed by a processor, the steps of the battery remaining service life prediction method as described above are implemented.

[0189] Among them, the method implemented when the battery remaining service life prediction program is executed can refer to the various embodiments of the battery remaining service life prediction method of the present application, and will not be repeated here.

[0190] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0191] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0192] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0193] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0194] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0195] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0196] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for predicting remaining battery life, characterized in that: The method for predicting the remaining service life of a battery comprises: Obtaining a target energy retention rate corresponding to the last cycle test of the battery to be predicted at a target temperature level and a target number of cycle tests corresponding to the last cycle test; The target drift rate is calculated by using a preset target Eyring model for describing the functional relationship between the drift rate, temperature and the number of cycle test cycles, the target number of cycle test cycles and the target temperature level; Based on the preset remaining service life probability density function, the target number of cycle test laps, the target drift rate, the target energy retention rate and the preset target diffusion coefficient value, the life prediction is performed to obtain the target remaining service life of the battery to be predicted; Among them, the model parameter values ​​and the target diffusion coefficient values ​​in the target Eyring model are determined by solving the preset target battery degradation model based on a preset parameter estimation method. The target battery degradation model consists of an initial Eyring model and a Wiener process model corresponding to the drift rate change.

2. The method for predicting the remaining service life of a battery according to claim 1, wherein: The target Eyring model is: In the formula, μ1 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

3. The method for predicting the remaining service life of a battery according to claim 2, wherein: The remaining useful life probability density function for: Where s represents the remaining service life, T i represents the target temperature level, μ1(k ′ ,T i ) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

4. The method for predicting the remaining service life of a battery according to claim 1, wherein: The target battery degradation model is: In the formula, Y1(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y1(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, a and b are the model parameters to be solved by the parameter estimation method, σ1 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

5. The method for predicting the remaining service life of a battery according to claim 1, wherein: The target Eyring model is: In the formula, μ2 represents the drift rate, k represents the number of cycle tests, and T represents the temperature level. and are the model parameter values ​​obtained by the parameter estimation method.

6. The method for predicting the remaining service life of a battery according to claim 5, characterized in that: The remaining useful life probability density function for: Where s represents the remaining service life, T i represents the target temperature level, μ2(k ′ ,T i ) indicates that at the target temperature level, the last cycle test k end The corresponding target cycle test number k ′ The corresponding target drift rate under the condition of i (k end ) represents the target energy retention rate of the battery to be predicted corresponding to the last cycle test at the target temperature level, ω represents the energy retention rate failure threshold, Represents the target diffusion coefficient value.

7. The method for predicting the remaining service life of a battery according to claim 1, wherein: The target battery degradation model is: Where, Y2(k+1) represents the energy retention rate corresponding to the k+1th cycle test, Y2(k) represents the energy retention rate corresponding to the kth cycle test, T represents the temperature level, k represents the number of cycle tests, represents the initial Eyring model, c, d and e are the model parameters to be solved by the parameter estimation method, σ2 represents the diffusion coefficient value to be solved by the parameter estimation method, and B(1) represents the standard Brownian motion.

8. A battery remaining service life prediction device, characterized in that: The battery remaining service life prediction device comprises: A parameter acquisition module, which is used to obtain a target energy retention rate corresponding to the last cycle test of the battery to be predicted at a target temperature level and a target number of cycle tests corresponding to the last cycle test; A parameter calculation module, which is used to calculate the target drift rate through a preset target Eyring model for describing the functional relationship between the drift rate, temperature, and the number of cycle test turns, a target number of cycle test turns, and a target temperature level; A life prediction module, which is used to predict the life based on a preset remaining service life probability density function, a target number of cycle test laps, a target drift rate, a target energy retention rate and a preset target diffusion coefficient value, to obtain a target remaining service life of the battery to be predicted; Among them, the model parameter values ​​and the target diffusion coefficient values ​​in the target Eyring model are determined by solving the preset target battery degradation model based on a preset parameter estimation method. The target battery degradation model consists of an initial Eyring model and a Wiener process model corresponding to the drift rate change.

9. A battery remaining service life prediction device, characterized in that: The battery remaining service life prediction device includes a processor, a memory, and a battery remaining service life prediction program stored in the memory and executable by the processor, wherein when the battery remaining service life prediction program is executed by the processor, the steps of the battery remaining service life prediction method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a battery remaining service life prediction program, wherein when the battery remaining service life prediction program is executed by a processor, the steps of the battery remaining service life prediction method according to any one of claims 1 to 7 are implemented.

Images