Lithium ion battery life prediction method based on life prediction combination model

Abstract

The invention discloses a lithium ion battery life prediction method based on a life prediction combination model, and the method specifically comprises the following steps: S1, carrying out the fitting analysis according to the data of a battery capacity recession experiment, and relates to the technical field of lithium ion battery life detection. According to the lithium ion battery life prediction method based on the life prediction combination model, the prediction precision of the battery life is improved, the life prediction model is established on the basis of a capacity attenuation curve fitting formula and an empirical model, and model parameters are updated on the basis of the improved empirical model; the established prediction model not only can accurately reflect the trend of battery capacity decline, but also can effectively combine the actual use state of the battery, so that the high-precision prediction of the residual service life of the lithium ion battery in different health states can be realized, and the prediction accuracy of the lithium ion battery in different health states is improved. And the development of the battery echelon use industry is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery life detection, and in particular to a lithium-ion battery life prediction method based on a life prediction combined model. Background Art

[0002] Lithium-ion batteries have the advantages of high energy density, long service life, high single-cell operating voltage, low self-discharge rate, and green environmental protection. With the rapid development of my country's mobile communications, new energy vehicles, advanced manufacturing and other industries, the demand for lithium batteries will continue to grow. Among them, the cycle life of lithium-ion batteries is a key issue of concern to manufacturers and users. Generally speaking, the actual life test of lithium-ion batteries takes a long time and cannot meet the development needs of products. If the battery life is to be tested, it will consume time and economic costs. Therefore, it is of great practical value to use the early partial cycle data characteristics to predict the service life of lithium-ion batteries.

[0003] At present, there are a certain number of theoretical foundations for the construction of lithium-ion battery aging models. Through research on existing literature, it is found that lithium-ion battery aging models are mainly divided into: mechanism models, semi-empirical models and empirical models. For different lithium-ion battery aging models, the model construction principles are different and the focus is also different. Therefore, each type of model has its unique advantages and corresponding defects.

[0004] An empirical model is an aging model that is extracted based on the common characteristics of a large number of data through a series of aging studies on the product. It involves a large number of parameters and complex calculations, so data-driven methods have gradually become a research hotspot in recent years. This method involves few battery parameters, does not consider the electrochemical reactions and failure mechanisms inside lithium-ion batteries, relies on a large amount of data, is affected by initial parameters, and cannot effectively predict the remaining life of the battery in actual use, and has little guiding significance for the actual use of the battery. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a lithium-ion battery life prediction method based on a life prediction combined model, which solves the problems in the prior art that the lithium-ion battery empirical degradation model has a low fit and cannot effectively predict the remaining service life of the battery.

[0006] To achieve the above objectives, the present invention is implemented by the following technical scheme: a lithium-ion battery life prediction method based on a life prediction combined model, specifically comprising the following steps:

[0007] S1. According to the battery capacity decay experimental data, a fitting formula for the battery capacity decay curve is established to characterize the relationship between the battery capacity retention rate and the number of charge and discharge cycles:

[0008] y=ax 2 +bx+c

[0009] In the formula, y represents the discharge capacity retention rate, x represents the number of cycles, and a, b, and c all represent constants;

[0010] S2. Analyze and establish a combined model based on the capacity decay curve fitting formula and empirical model, and use multiple sets of experimental data to verify the fit of the model;

[0011] S3. updating the prediction model parameters based on the improved empirical model;

[0012] S4. Calculate the remaining service life of the lithium-ion battery in different health states.

[0013] Preferably, in S1, the capacity decay curve fitting formula is a nonlinear fitting based on battery capacity decay experimental data.

[0014] Preferably, in S1, the battery capacity decay data is obtained through charge and discharge cycle experiments on lithium-ion batteries under different conditions.

[0015] Preferably, S2 further includes analyzing the capacity decay fitting formula and the empirical model, and combining them to establish a life prediction combined model, and the expression of the inverse power law model is:

[0016] ln ξ cycle (V) = B + Cln V

[0017] Where: ξ represents the battery life; B is a positive constant; V is the electrical stress, such as power, current, voltage, etc.; C is a positive constant related to the activation energy.

[0018] Preferably, S3 further includes analyzing the capacity decay fitting formula and the empirical model, and combining them to establish a life prediction combined model.

[0019] Preferably, in S4, the number of cycles obtained by inputting the battery in different health states and usage conditions into the prediction model is the remaining service life of the battery.

[0020] Beneficial Effects

[0021] The present invention provides a lithium-ion battery life prediction method based on a life prediction combined model. Compared with the prior art, the method has the following beneficial effects: the lithium-ion battery life prediction method based on the life prediction combined model specifically includes the following steps: S1, fitting analysis based on battery capacity decay experimental data; S2, establishing a combined model based on the capacity decay curve fitting formula and the empirical model, and verifying the fitting degree of the model using multiple groups of experimental data; S3, updating the prediction model parameters based on the improved empirical model; S4, calculating the remaining service life of the lithium-ion battery in different health states; in order to make the battery life prediction model better reflect the battery capacity decay characteristics and improve the prediction accuracy of the battery life, a life prediction model is established on the basis of the capacity decay curve fitting formula and the empirical model, and the model parameters are updated based on the improved empirical model, and then the remaining service life of the lithium-ion battery in different health states is calculated. The established prediction model can not only accurately reflect the trend of battery capacity decay, but also effectively combine the actual battery usage status. Therefore, it can achieve high-precision prediction of the remaining life of the lithium-ion battery in different health states, which is beneficial to the development of the battery cascade use industry;

[0022] At the same time, in the process of lithium-ion battery life prediction, it is only necessary to describe the correlation between the battery capacity and the number of charge and discharge cycles under different stresses. There is no need to analyze the complex electrochemical reactions inside the battery, which reduces the probability of introducing errors in the process of determining model parameters and improves the calculation speed, thereby achieving rapid prediction of the remaining life of lithium-ion batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart for predicting the remaining service life of a lithium-ion battery of the present invention;

[0024] Figure 2 The discharge curve diagram of the battery of the present invention at different cycle times;

[0025] Figure 3 The capacity decay fitting curve diagram of the relationship between the battery capacity retention rate and the number of charge and discharge cycles of the present invention;

[0026] Figure 4 It is a schematic diagram of the battery life prediction results under different health states of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1-4 The present invention provides a technical solution: a lithium-ion battery life prediction method based on a life prediction combined model, specifically comprising the following steps:

[0029] S1. According to the battery capacity decay experimental data, a fitting formula for the battery capacity decay curve is established to characterize the relationship between the battery capacity retention rate and the number of charge and discharge cycles:

[0030] y=ax 2 +bx+c

[0031] In the formula, y represents the discharge capacity retention rate, x represents the number of cycles, and a, b, and c all represent constants;

[0032] S2. Analyze and establish a combined model based on the capacity decay curve fitting formula and empirical model, and use multiple sets of experimental data to verify the fit of the model;

[0033] S3. updating the prediction model parameters based on the improved empirical model;

[0034] S4. Calculate the remaining service life of the lithium-ion battery in different health states.

[0035] In the present invention, in S1, the capacity decay curve fitting formula is a nonlinear fitting based on the battery capacity decay experimental data.

[0036] In the present invention, in S1, the battery capacity decay data is obtained through charge and discharge cycle experiments on lithium-ion batteries under different conditions.

[0037] In the present invention, S2 also includes analyzing the capacity decay fitting formula and the empirical model, and combining them to establish a life prediction combined model.

[0038] In the present invention, S3 also includes analyzing the capacity decay fitting formula and the empirical model, and combining them to establish a life prediction combined model.

[0039] In the present invention, in S4, the number of cycles obtained by inputting the battery in different health states and usage conditions into the prediction model is the remaining service life of the battery.

[0040] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0041] Example

[0042] The cylindrical lithium-ion battery with the model of 18650 and the rated capacity of 3.2A·h was used as the research object. Figure 1As shown, a lithium-ion battery life prediction method based on a life prediction combined model is established, as follows:

[0043] 1. Obtain lithium-ion battery capacity decay data and fit the decay curve:

[0044] The battery capacity decay data was obtained by using Wuhan Blue Electric CT3002K battery test system to charge and discharge 18650 cylindrical lithium-ion batteries under different conditions. The acquired data was fitted to establish a battery capacity decay curve fitting formula for characterizing the relationship between battery capacity retention rate and charge and discharge cycle number:

[0045] y=ax 2 +bx+c (1)

[0046] exist Figure 2 and Figure 3 In the figure, the nonlinear fitting of the relationship between capacity and voltage and the change of capacity retention rate during the cycle process is performed. The data of battery capacity retention rate and cycle number after cycling at different cut-off voltages are nonlinearly fitted to obtain the decay curve fitting formula. The constants of the fitting formula are shown in Table 1:

[0047] Table 1. Capacity decay fitting curve parameters after cycling under different conditions

[0048]

[0049] R 2 is the degree of linear fit, R 2 The closer the value is to 1, the higher the fitting accuracy. 2 At 0.9-1, it indicates a good fit;

[0050] 2. Select the empirical model of lithium-ion battery capacity decline:

[0051] The parameters of the empirical model are identified by the attenuation curve fitting formula and the fitting degree of the model is verified by using multiple sets of experimental data. The inverse power law model expression is shown in formula (2):

[0052] ln ξ cycle (V)=B+Cln V (2)

[0053] Where: ξ represents the battery life; B is a positive constant; V is the electrical stress, such as power, current, voltage, etc.; C is a positive constant related to the activation energy;

[0054] According to the characteristics of the model, the capacity decay experiment with cut-off voltage as stress was carried out, and the parameters of the model were fitted through the capacity decay fitting data.

[0055] 3. Analyze the capacity attenuation fitting formula and empirical model to establish a combined model:

[0056] Substitute the capacity decay fitting curve parameters into the nonlinear fitting formula, as shown in formulas (3) and (4):

[0057] y1=-0.00104x 2 1+0.00452x1+100.32,V=4.4V (3)

[0058] y2=-0.00075x 2 2+0.05987x2+99.29,V=4.3V (4)

[0059] In the formula, y represents the discharge capacity retention rate, and x represents the number of cycles;

[0060] According to the capacity attenuation fitting formula, the service life of the battery under different conditions in different health states can be calculated. By combining with the empirical model to establish a combined model, the corresponding relationship between the capacity retention rate and the service life under different use conditions can be obtained according to the combined model, as shown in formula (5):

[0061] ln(aSOH 2 + bSOH + c) = B + Cln V (5)

[0062] In the formula, SOH represents the health status of the battery, B is a positive constant; V is the electrical stress, such as power, current, voltage, etc.; C is a positive constant related to the activation energy, and a, b, and c are all constants;

[0063] 4. Update the prediction model parameters based on the improved empirical model:

[0064] In order to ensure the accuracy of the remaining battery life prediction, the model parameters need to be continuously updated as the battery health status changes. The parameter fitting formula is obtained by performing nonlinear fitting on the continuously updated parameters, and the empirical model is improved to solve such problems and improve the accuracy of the model prediction. The specific steps are as follows:

[0065] Step 1: Read the lithium-ion battery capacity decay data for prediction;

[0066] Step 2: Fit the acquired battery capacity decay data to obtain a fitting formula for the relationship between capacity retention rate and cycle number;

[0067] Step 3: Combine the fitting formula and the empirical model into a combined model, and fit the parameters of the combined model by analyzing the cycle data of the battery at different SOHs.

[0068] When SOH=70%

[0069] lnξ cycle (V C )=26.68-14.53ln V C (6)

[0070] Step 4: Analyze the parameters of the combined model of the battery at different SOH, take the model parameters as dependent variables, establish the observation equation of the parameters, and thus obtain the model parameter equation that changes with the SOH.

[0071] The observation equation is:

[0072] B(SOH)=115.5SOH 2 -153.1SOH+77.29 (7)

[0073] C(SOH)=-82.5SOH 2 +108.85SOH-50.3 (8)

[0074] Step 5: Use the observation equation to improve the method of empirical model parameter identification and update the prediction model parameters, thereby improving the prediction accuracy of the combined model. The improved combined model is shown in formula (9):

[0075] ln(aSOH 2 +bSOH+c)=B(SOH)+C(SOH)ln V C (9)

[0076] 5. Calculate the remaining service life of lithium-ion batteries:

[0077] Through the improved combination model, the service life of lithium-ion batteries under different usage environments as the battery SOH changes are calculated, and the accuracy of the model prediction data is verified by experimental data. The error results of the predicted value of the battery combination prediction model and the experimental data are shown in Table 2:

[0078] Table 2. Error analysis between the combined model prediction and experimental measurements

[0079]

[0080]

[0081] By analyzing the battery capacity decay data and fitting the decay curve, a lithium-ion battery remaining life prediction method based on a life prediction combined model is given by combining the empirical model and improving the model. It can not only improve the accuracy of the prediction results, but also predict the remaining service life of the battery in different health states.

[0082] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0083] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A lithium-ion battery life prediction method based on a life prediction combined model, characterized in that: The specific steps include: S1. According to the battery capacity decay experimental data, a fitting formula for the battery capacity decay curve is established to characterize the relationship between the battery capacity retention rate and the number of charge and discharge cycles: y=ax 2 +bx+c In the formula, y represents the discharge capacity retention rate, x represents the number of cycles, and a, b, and c all represent constants; S2. Analyze and establish a combined model based on the capacity decay curve fitting formula and empirical model, and use multiple sets of experimental data to verify the fit of the model; S3. updating the prediction model parameters based on the improved empirical model; S4. Calculate the remaining service life of the lithium-ion battery in different health states.

2. The method for predicting the life of a lithium-ion battery based on a life prediction combined model according to claim 1, characterized in that: In S1, the capacity decay curve fitting formula is a nonlinear fitting based on battery capacity decay experimental data.

3. The method for predicting the life of a lithium-ion battery based on a life prediction combined model according to claim 1, characterized in that: In S1, the battery capacity decay data is obtained through charge and discharge cycle experiments on lithium-ion batteries under different conditions.

4. The method for predicting the life of a lithium-ion battery based on a life prediction combined model according to claim 1, characterized in that: The S2 also includes analyzing the capacity decay fitting formula and the empirical model, and combining them to establish a life prediction combined model.

5. The method for predicting the life of a lithium-ion battery based on a life prediction combined model according to claim 1, characterized in that: In S3, the change of the combined model coefficient is predicted under different health states of the battery through the observation equation.

6. The method for predicting the life of a lithium-ion battery based on a life prediction combined model according to claim 1, characterized in that: In S4, the number of cycles obtained by inputting the battery in different health states and usage conditions into the prediction model is the remaining service life of the battery.

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