Battery life prediction method, device and vehicle

Abstract

The present invention provides a method, device and vehicle for predicting the life of a battery cell. The method comprises: determining an overpotential database of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates; based on the overpotential database, selecting a preset number of sample data of the battery cells to be predicted as a training set; inputting the training set into a capacity loss model, and fitting the capacity loss model in combination with a fitting method to obtain optimal fitting parameters; bringing the optimal fitting parameters into the capacity loss model to obtain a capacity loss prediction model for the battery cell to be predicted; and determining the remaining life of the battery cell to be predicted based on the initial capacity, health status and capacity loss prediction model of the battery cell to be predicted, thereby simplifying the battery cell life prediction model and effectively improving the accuracy of the battery cell life prediction.

Description

Technical Field

[0001] The present invention relates to the field of battery cell technology, and in particular to a battery cell life prediction method, device and vehicle. Background Art

[0002] With the development of the new energy industry and the popularity of electric vehicles, consumers are paying more and more attention to the performance of battery cells, especially the battery life and charging time, which are directly related to consumers' purchasing intention. In battery research and development, cycle life is an important reference indicator of battery performance. The battery life is closely related to the battery's usage conditions. The battery cycle life is tested according to national standards, and data needs to be obtained through multiple working conditions and long-term testing, which will cause a great waste of time and cost. Therefore, battery life prediction is currently mostly carried out through physical and chemical models, statistical methods, or machine learning models.

[0003] However, most of the current life prediction methods will result in a large amount of data calculation if the data volume is large, and the small amount of data will lead to poor accuracy of life prediction results. Summary of the invention

[0004] The present invention provides a method, device and vehicle for predicting the life of a battery cell, so as to solve the defects of the prior art that the prediction of the life of a battery cell is highly complex or has poor accuracy.

[0005] The present invention provides a method for predicting the life of a battery cell, comprising:

[0006] Determine the overpotential database of the same type of cells to be predicted at different temperatures and different charge and discharge rates;

[0007] Based on the overpotential database, a preset number of sample data of the battery cells to be predicted are selected as a training set;

[0008] Inputting the training set into the capacity loss model, and fitting the capacity loss model in combination with the fitting method to obtain the optimal fitting parameters;

[0009] Substituting the optimal fitting parameters into the capacity loss model to obtain a capacity loss prediction model for the battery cell to be predicted;

[0010] Based on the initial capacity and health status of the battery cell to be predicted and the capacity loss prediction model, the remaining life of the battery cell to be predicted is determined.

[0011] According to a method for predicting the life of a battery cell provided by the present invention, determining an overpotential database of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates comprises:

[0012] Determine the charge and discharge curves and electromotive force curves of the same type of predicted battery cells at different temperatures and different charge and discharge rates;

[0013] Subtracting the voltage of the corresponding electromotive force curve from the voltage of the charge-discharge curve to obtain the overpotential of the same type of battery cell to be predicted at different charge-discharge rates;

[0014] Based on the overpotentials at different charge and discharge rates, a database of overpotentials of the same type of cells to be predicted at different temperatures and different charge and discharge rates is obtained by linear interpolation.

[0015] According to a method for predicting the life of a battery cell provided by the present invention, before determining the overpotential database of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates, the method further includes:

[0016] A primary capacity loss curve that determines the rate of capacity loss versus time;

[0017] Based on the temperature change and the Arrhenius formula, the primary capacity loss curve is adjusted to obtain a secondary capacity loss curve;

[0018] Based on the difference in SEI film growth rate during the charging process and the discharging process, the secondary capacity loss curve is split to obtain a capacity loss model.

[0019] According to a method for predicting the life of a battery cell provided by the present invention, the secondary capacity loss curve is split based on the difference in SEI film growth rate during the charging process and the discharging process to obtain a capacity loss model, including:

[0020] Based on the difference in SEI film growth rate during the charging process and the discharging process, the secondary capacity loss curve is split to obtain a primary capacity loss model;

[0021] Determining a charging SEI film growth weight factor during the charging process and a discharging SEI film growth weight factor during the discharging process respectively;

[0022] Based on the specific surface area of ​​graphite, the particle breakage loss capacity is determined;

[0023] The charge SEI film growth weight factor, the discharge SEI film growth weight factor and the rupture loss capacity are introduced into the primary capacity loss model to obtain a final capacity loss model.

[0024] According to a method for predicting the life of a battery cell provided by the present invention, the capacity loss model is:

[0025]

[0026] Among them, Q lossrepresents the capacity loss, t charge Indicates the charging time, t discharge represents the discharge time, Ah represents the accumulated ampere-hours, η represents the overpotential, R represents the constant 8.314, T represents the temperature, and A / B / C / D / E / F / G represent the fitting parameters.

[0027] According to a method for predicting the life of a battery cell provided by the present invention, the primary capacity loss curve of determining the rate of capacity loss changing with time includes:

[0028] When the specific surface area of ​​graphite remains unchanged, an electron tunneling model is obtained in which the rate of capacity loss changes with time.

[0029] The constants of the electron tunneling model are simplified to obtain a primary capacity loss curve.

[0030] According to a method for predicting the life of a battery cell provided by the present invention, the method of determining the remaining life of the battery cell to be predicted based on the initial capacity, health status and the capacity loss prediction model of the battery cell to be predicted includes:

[0031] Determining a capacity loss critical value by using the initial capacity and health status of the battery cell to be predicted;

[0032] The capacity loss critical value is input into the capacity loss prediction model to determine the remaining life of the battery to be predicted, and the parameters of the remaining life include the remaining discharge time and overpotential at different temperatures.

[0033] According to a method for predicting the life of a battery cell provided by the present invention, after determining the remaining life of the battery cell to be predicted, the method further includes:

[0034] updating the sample data according to the usage of the battery cell to be predicted;

[0035] Inputting the updated sample data into the capacity loss model to update the optimal fitting parameters;

[0036] The capacity loss prediction model is updated with the updated optimal fitting parameters, thereby updating the remaining life of the battery to be predicted.

[0037] The present invention also provides a battery cell life prediction device, comprising:

[0038] A determination module is used to determine the overpotential database of the same type of cells to be predicted at different temperatures and different charge and discharge rates;

[0039] A selection module, used for selecting a preset number of sample data of the battery cells to be predicted as a training set based on the overpotential database;

[0040] A fitting module, used for inputting the training set into the capacity loss model, and fitting the capacity loss model in combination with a fitting method to obtain optimal fitting parameters;

[0041] A prediction module is used to bring the optimal fitting parameters into the capacity loss model to obtain the capacity loss prediction model of the battery cell to be predicted; based on the initial capacity, health status and the capacity loss prediction model of the battery cell to be predicted, determine the remaining life of the battery cell to be predicted.

[0042] The present invention also provides a vehicle, which is used to execute the battery life prediction method as described in any one of the above items.

[0043] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for predicting the life of a battery cell as described above is implemented.

[0044] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the battery cell life prediction method described in any one of the above is implemented.

[0045] On the other hand, the present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the battery cell life prediction method described above is implemented.

[0046] The present invention provides a method, device and vehicle for predicting the life of a battery cell. The method includes: determining an overpotential database of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates; based on the overpotential database, selecting a preset number of sample data of the battery cells to be predicted as a training set; inputting the training set into a capacity loss model, and fitting the capacity loss model in combination with a fitting method to obtain optimal fitting parameters; bringing the optimal fitting parameters into the capacity loss model to obtain a capacity loss prediction model for the battery cell to be predicted; based on the initial capacity, health status and capacity loss prediction model of the battery cell to be predicted, determining the remaining life of the battery cell to be predicted. Since the capacity loss prediction model does not require a large number of training parameters, and does not require the input of parameters such as electrolyte conductivity and thickness of positive and negative electrode active materials, the amount of calculation is small, and since the remaining life is calculated during the cycle of the battery cell, the accuracy is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 is a flow chart of a method for predicting the life of a battery cell provided by an embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of the structure of a battery cell life prediction device provided by an embodiment of the present invention;

[0050] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0052] Combine the following Figure 1-Figure 3 A method, device and vehicle for predicting battery life of the present invention are described.

[0053] Figure 1 It is a flow chart of a method for predicting the life of a battery cell provided in an embodiment of the present invention.

[0054] like Figure 1 As shown, the battery life prediction method provided by the embodiment of the present invention can be executed by a battery management system, and the method mainly includes the following steps:

[0055] 101. Determine the overpotential database of the same type of predicted battery cells at different temperatures and different charge and discharge rates.

[0056] In a specific implementation process, the battery cells that need to be predicted for life are defined as the battery cells to be predicted, and the battery cells of the same type are selected and placed at different temperatures and different charge and discharge rates for testing. For example, the temperature may include: 0 degrees Celsius, 10 degrees Celsius, 25 degrees Celsius, 35 degrees Celsius and 45 degrees Celsius. Similarly, for example, the charge and discharge rates may include: 0.1C / 0.2C / 0.3C / 0.5C / 1C / 1.5C / 2C. The overpotential database refers to the change of overpotential at different temperatures and different charge and discharge rates. Therefore, through testing at different temperatures and different charge and discharge rates, the overpotential database can be quickly obtained.

[0057] 102. Based on the overpotential database, select a preset number of sample data of the battery cells to be predicted as a training set.

[0058] After constructing the overpotential database, a preset number of sample data of the cells to be predicted are selected from the overpotential database as a training set, that is, a preset number of sample data of the same type of cells are screened out based on the overpotential database, including: capacity loss, charging time, discharge time, temperature, cumulative ampere-hours and overpotential, and the sample data are used as a training set. The sample data includes data at different temperatures and different charge and discharge rates. The more temperature nodes and charge and discharge rate nodes, the more sample data in the training set, and the more accurate the final capacity loss model. The ampere-hour is an important indicator for measuring battery capacity, which indicates the length of time the battery can be discharged at a specified current. For example, a 1 ampere-hour battery can discharge for 1 hour at a current of 1 ampere. The larger the ampere-hour, the larger the capacity of the battery, which means it can store more electricity, provide longer battery life or a larger discharge current.

[0059] When selecting a preset number of sample data of battery cells to be predicted, obviously abnormal data can be automatically excluded, and then the data can be standardized to ensure that the final capacity loss model is more accurate.

[0060] 103. Input the training set into the capacity loss model, and fit the capacity loss model in combination with the fitting method to obtain the optimal fitting parameters.

[0061] Before life prediction, a capacity loss model was established, and the capacity loss model includes capacity loss and a series of unknown parameters. Since sample data was obtained before, the sample data, capacity loss, charging time, discharge time, temperature, cumulative ampere-hours and overpotential can be input into the capacity loss model to obtain a series of unknown parameters A / B / C / D / E / F / G. It can be understood as a linear equation with seven unknown quantities. Therefore, the parameters can be accurately obtained from seven sets of sample data. In order to make the results more accurate, multiple sample data can be input into the capacity loss model, and finally a more accurate series of unknown parameters can be obtained.

[0062] Then, in order to ensure the best parameters, it is necessary to fit them, and use the least squares method or support vector machine regression algorithm to fit the parameters to ensure that the parameters corresponding to the capacity loss model are the optimal fitting parameters. The capacity loss model can be understood as a curve, and the role of the optimal fitting parameters is to make it fit the curve better.

[0063] 104. Substitute the optimal fitting parameters into the capacity loss model to obtain a capacity loss prediction model for the battery cell to be predicted.

[0064] The role of sample data is to determine the parameters of the capacity loss model, which can be understood as a linear equation. The sample data is to determine the coefficients of the linear equation. After accurately obtaining the coefficients, that is, the optimal fitting parameters, they are substituted back into the capacity loss model to obtain the capacity loss prediction model of the battery cell to be predicted with independent variables and dependent variables.

[0065] The input of the capacity loss prediction model is the charging time, discharging time, temperature, cumulative ampere-hours and overpotential, and the output is the capacity loss. Therefore, the capacity loss corresponding to any charging time, discharging time, temperature, cumulative ampere-hours and overpotential can be predicted in real time.

[0066] 105. Determine the remaining life of the battery cell to be predicted based on the initial capacity, health status and capacity loss prediction model of the battery cell to be predicted.

[0067] After the battery cell is produced, the initial capacity is known, and the capacity loss prediction model can predict the current capacity loss in any time period in real time. The ratio of the current capacity loss to the initial capacity is the health status of the battery cell. For example, when the health status is set to 80%, it is determined that the battery cell is in a state of being scrapped. Therefore, the corresponding health status is a known quantity, the initial capacity is a known quantity, and the capacity loss prediction model is an unknown quantity. Then, the critical value of the capacity loss can be obtained based on the two known quantities, and then based on the critical value, the independent variable corresponding to the capacity loss model can be reversed to obtain the remaining life of the battery cell to be predicted. The remaining life can include different remaining lives at different temperatures and different charge and discharge rates.

[0068] The life prediction method provided in this embodiment has a small amount of calculation because the capacity loss prediction model does not require a large number of training parameters, and does not require the input of parameters such as electrolyte conductivity and thickness of positive and negative electrode active materials. In addition, since the remaining life is calculated during the cycle of the battery cell, the accuracy is higher. The parameters contained can be fitted according to fitting methods such as the least squares method, and the calculation time is short, so that the remaining life of the battery cell can be quickly predicted. For the development of new battery cells, the life of the battery cell can be known by a full cycle test for 2 months, which shortens the test time, speeds up the development progress, and saves test resources.

[0069] Furthermore, based on the above embodiments, the present embodiment determines an overpotential database of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates, including: determining the charge and discharge curves and electromotive force curves of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates; subtracting the voltage of the corresponding electromotive force curve from the voltage of the charge and discharge curve to obtain the overpotential of the same type of battery cells to be predicted at different charge and discharge rates; based on the overpotential at different charge and discharge rates, using linear interpolation to obtain an overpotential database of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates.

[0070] Specifically, the overpotential database is mainly determined by voltage. First, the charge and discharge curves of the same type of predicted battery cells at different temperatures and different charge and discharge rates are determined. The charge and discharge curve represents the relationship between the battery charge and discharge current and the battery voltage or the relationship between the electrode charge and discharge current and the electrode potential. Then determine the electromotive force curves at different temperatures and different charge and discharge rates. Both curves represent the relationship between voltage changes over time at different temperatures. Therefore, at any time at the same temperature, the voltage of the charge and discharge curve can be subtracted from the voltage of the electromotive force curve to obtain the overpotential at different charge and discharge rates. Then, the temperature is linearly interpolated to obtain the overpotential database at different temperatures and different rates. The overpotential database is used to characterize the voltage change curves of the battery cells at different temperatures and different charge and discharge rates.

[0071] Furthermore, based on the above-mentioned embodiment, before determining the overpotential database of the same type of predicted battery cells at different temperatures and different charge and discharge rates, this embodiment also includes: determining a primary capacity loss curve in which the rate of capacity loss changes with time; adjusting the primary capacity loss curve based on temperature changes and the Arrhenius formula to obtain a secondary capacity loss curve; and splitting the secondary capacity loss curve based on the difference in SEI film growth rates during the charging process and the discharging process to obtain a capacity loss model.

[0072] Specifically, the capacity loss model is derived based on the SEI (Solid Electrolyte Interface) film formation mechanism. This formula does not require the input of parameters such as the mass of active materials, electrode thickness, and electrolyte conductivity. During the activation stage of the battery, side reactions will occur on the surface of the negative electrode graphite, quickly forming a thin and dense SEI film. This SEI film can prevent the direct contact between the graphite negative electrode and the electrolyte, and prevent the side reactions on the graphite surface from continuing to occur violently. During the battery use stage (subsequent cycle stage), the SEI film will grow on the basis of the dense SEI film. The essence of growth is a series of chemical reactions: the electrolyte substance combines with the electrons that pass through the dense SEI film to form an SEI film. Ideally, these electrons should originally be combined with the lithium ions embedded in the graphite negative electrode in a 1:1 ratio in the graphite, and then be released from the graphite during the discharge stage for use, but the electrons used to grow the SEI film cannot be released and are lost. The amount of this part of the lost electrons has a linear relationship with the lost capacity, so the lost capacity can be obtained by calculating the amount of SEI film growth.

[0073] SEI growth requires electrons to pass through the existing SEI film. The electrons that pass through combine with substances in the electrolyte on the surface of the SEI film to generate new SEI film substances. When the specific surface area of ​​graphite remains unchanged, the electron tunneling model in which the rate of capacity loss changes with time is obtained, as shown in formula (1):

[0074]

[0075] Then the constants of the electron tunneling model are simplified to obtain the primary capacity loss curve, as shown in formula (2):

[0076] Q loss =A·n(1+B·t) (2)

[0077] Among them, Q loss represents capacity loss, A / B represents parameters, and t represents time.

[0078] After obtaining the primary capacity loss curve, the Arrhenius formula is added to formula (2) in combination with the effect of temperature change on the reaction rate, and formula (2) is deformed and adjusted to obtain the secondary capacity loss curve, such as formula (3):

[0079]

[0080] in, represents the reaction rate constant.

[0081] Since the reaction activation energy Ea has a linear relationship with the electrode overpotential, a = E·η+F, and formula (4) is obtained:

[0082]

[0083] Among them, η represents overpotential, R represents constant 8.314, T represents temperature, and E represents parameter.

[0084] Based on the difference in SEI film growth rate during the charging process and the discharging process, the secondary capacity loss curve is split to obtain the primary capacity loss model, as shown in formula (5):

[0085]

[0086] Among them, t charge Indicates the charging time, t discharge Indicates the discharge time.

[0087] Then, the SEI film growth weight factor during the charging process and the SEI film growth weight factor during the discharging process are determined respectively; let a·A=A, let c·C=C, and get formula (6):

[0088]

[0089] During the actual battery cycle, the graphite negative electrode continuously embeds and extracts lithium ions, and the negative electrode graphite particles will continue to expand or shrink. The particles will partially rupture due to uneven stress, exposing a fresh graphite surface. On this fresh graphite surface, SEI film will grow and capacity will be lost. For every 1Ah of charge or discharge, a small part of the graphite will be freshly exposed and a new SEI film will grow. Since the fresh surface exposed for every 1Ah of charge or discharge is very small, the capacity loss for every 1Ah of charge or discharge is determined to be G, and the capacity loss due to particle rupture is G·Ah. Substituting it into formula (6) can obtain the final capacity loss model, as shown in formula (7):

[0090]

[0091] Among them, Q loss represents the capacity loss, t charge Indicates the charging time, t discharge represents the discharge time, Ah represents the accumulated ampere-hours, η represents the overpotential, R represents the constant 8.314, T (unit K) represents the temperature, and A / B / C / D / E / F / G represent the fitting parameters.

[0092] Furthermore, based on the above-mentioned embodiments, the initial capacity, health status and capacity loss prediction model of the predicted battery cell in this embodiment determine the remaining life of the battery cell to be predicted, including: using the initial capacity and health status of the battery cell to be predicted to determine the capacity loss critical value; inputting the capacity loss critical value into the capacity loss prediction model to determine the remaining life of the battery to be predicted, and the parameters of the remaining life include the remaining discharge time and overpotential at different temperatures.

[0093] Specifically, SoH (state of health), the battery health status, reflects the battery life status, and is calculated as shown in formula (8):

[0094]

[0095] Among them, Q0 represents the initial capacity, Q loss Indicates the current capacity loss, and SoH indicates the state of health.

[0096] Using formula (8), after determining the scrap value of SoH, since Q0 is a known quantity, the capacity loss critical value corresponding to the health state of the battery cell when it needs to be scrapped can be determined. Then, the capacity loss critical value is used as the result of capacity loss and input into the capacity loss prediction model. The independent variable can be obtained by reverse deduction from the dependent variable, and the remaining life of the battery to be predicted can be obtained, including parameters such as discharge time at different temperatures and different charge and discharge rates.

[0097] Furthermore, based on the above-mentioned embodiments, after determining the remaining life of the battery cell to be predicted, the present embodiment also includes: updating sample data according to the usage of the battery cell to be predicted; inputting the capacity loss model with the updated sample data to update the optimal fit parameters; updating the capacity loss prediction model with the updated optimal fit parameters, and then updating the remaining life of the battery to be predicted.

[0098] Specifically, since the capacity loss prediction model always predicts the parameters of the battery cell in subsequent use, the sample data can be updated based on the usage time after each certain usage time. Due to the update of the sample data, the optimal fitting parameters change, and the capacity loss prediction model is updated in real time, thereby more accurately ensuring the life prediction of the battery cell to be predicted in the subsequent use process. By continuously updating the sample data, the capacity loss prediction model is also continuously updated, which better ensures the accuracy of the life prediction.

[0099] In the battery management system, each battery cell ages at different rates due to differences in production and usage. This model can continuously optimize the overall parameters of the model based on the data of the used batteries, that is, it is more accurate to predict the remaining life of the battery cell based on the battery cell usage.

[0100] The life prediction method of the present invention has a faster calculation speed and requires fewer resources for calculation. It can quickly predict the remaining life of the battery cell according to the capacity of the battery cell during the cycle process. Compared with the common finite element analysis method (the predicted remaining life of the battery cell is obtained after 12 hours of calculation), this method is faster (the predicted remaining life of the battery cell can be obtained in about 10 minutes). In addition, the fitting parameters can be adjusted in real time to optimize the capacity loss prediction model and improve the accuracy of life prediction.

[0101] Based on the same general inventive concept, the present invention also protects a battery cell life prediction device. The battery cell life prediction device provided by the present invention is described below. The battery cell life prediction device described below and the battery cell life prediction method described above can be referenced to each other.

[0102] Figure 2 It is a structural schematic diagram of a battery cell life prediction device provided by an embodiment of the present invention.

[0103] like Figure 2 As shown, this embodiment provides a battery cell life prediction device, comprising:

[0104] A determination module 201 is used to determine an overpotential database of the same type of cells to be predicted at different temperatures and different charge and discharge rates;

[0105] A selection module 202 is used to select a preset number of sample data of the battery cells to be predicted as a training set based on the overpotential database;

[0106] A fitting module 203 is used to bring the optimal fitting parameters into the capacity loss model to obtain a capacity loss prediction model for the battery cell to be predicted;

[0107] The prediction module 204 is used to bring the optimal fitting parameters into the capacity loss model to obtain the capacity loss prediction model of the battery cell to be predicted; based on the initial capacity, health status and capacity loss prediction model of the battery cell to be predicted, determine the remaining life of the battery cell to be predicted.

[0108] Furthermore, the determination module 201 in this embodiment is specifically used for:

[0109] Determine the charge and discharge curves and electromotive force curves of the same type of predicted battery cells at different temperatures and different charge and discharge rates;

[0110] Subtracting the voltage of the corresponding electromotive force curve from the voltage of the charge-discharge curve to obtain the overpotential of the same type of battery cell to be predicted at different charge-discharge rates;

[0111] Based on the overpotentials at different charge and discharge rates, a database of overpotentials of the same type of cells to be predicted at different temperatures and different charge and discharge rates is obtained by linear interpolation.

[0112] Further, this embodiment also includes a building block for:

[0113] A primary capacity loss curve that determines the rate of capacity loss versus time;

[0114] Based on the temperature change and the Arrhenius formula, the primary capacity loss curve is adjusted to obtain a secondary capacity loss curve;

[0115] Based on the difference in SEI film growth rate during the charging process and the discharging process, the secondary capacity loss curve is split to obtain a capacity loss model.

[0116] Further, this embodiment also includes a construction module, which is specifically used to:

[0117] Based on the difference in SEI film growth rate during the charging process and the discharging process, the secondary capacity loss curve is split to obtain a primary capacity loss model;

[0118] Determining a charging SEI film growth weight factor during the charging process and a discharging SEI film growth weight factor during the discharging process respectively;

[0119] Based on the specific surface area of ​​graphite, the particle breakage loss capacity is determined;

[0120] The charge SEI film growth weight factor, the discharge SEI film growth weight factor and the rupture loss capacity are introduced into the primary capacity loss model to obtain a final capacity loss model.

[0121] Further, the capacity loss model in this embodiment is:

[0122]

[0123] Among them, Q loss represents the capacity loss, t charge Indicates the charging time, t discharge represents the discharge time, Ah represents the accumulated ampere-hours, η represents the overpotential, R represents the constant 8.314, T represents the temperature, and A / B / C / D / E / F / G represent the fitting parameters.

[0124] Further, this embodiment also includes a construction module, which is specifically used to:

[0125] When the specific surface area of ​​graphite remains unchanged, an electron tunneling model is obtained in which the rate of capacity loss changes with time.

[0126] The constants of the electron tunneling model are simplified to obtain a primary capacity loss curve.

[0127] Furthermore, the prediction module 204 in this embodiment is specifically used for:

[0128] Determining a capacity loss critical value by using the initial capacity and health status of the battery cell to be predicted;

[0129] The capacity loss critical value is input into the capacity loss prediction model to determine the remaining life of the battery to be predicted, and the parameters of the remaining life include the remaining discharge time and overpotential at different temperatures.

[0130] Furthermore, the prediction module 204 in this embodiment is also specifically used for:

[0131] updating the sample data according to the usage of the battery cell to be predicted;

[0132] Inputting the updated sample data into the capacity loss model to update the optimal fitting parameters;

[0133] The capacity loss prediction model is updated with the updated optimal fitting parameters, thereby updating the remaining life of the battery to be predicted.

[0134] Based on the same general inventive concept, the present invention also protects a vehicle, which is used to execute the battery cell life prediction method as described in any of the above embodiments.

[0135] Figure 3It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0136] like Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the battery life prediction method, which includes: determining the overpotential database of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates; based on the overpotential database, selecting a preset number of sample data of the battery cells to be predicted as a training set; inputting the training set into the capacity loss model, and fitting the capacity loss model in combination with the fitting method to obtain the optimal fitting parameters; bringing the optimal fitting parameters into the capacity loss model to obtain the capacity loss prediction model of the battery cell to be predicted; determining the remaining life of the battery cell to be predicted based on the initial capacity, health status and the capacity loss prediction model of the battery cell to be predicted.

[0137] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0138] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the battery cell life prediction method provided by the above-mentioned methods, which includes: determining an overpotential database of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates; based on the overpotential database, selecting a preset number of sample data of the battery cells to be predicted as a training set; inputting the training set into a capacity loss model, and fitting the capacity loss model in combination with a fitting method to obtain the optimal fitting parameters; bringing the optimal fitting parameters into the capacity loss model to obtain the capacity loss prediction model of the battery cell to be predicted; and determining the remaining life of the battery cell to be predicted based on the initial capacity, health status and capacity loss prediction model of the battery cell to be predicted.

[0139] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the battery cell life prediction method provided by the above-mentioned methods, the method comprising: determining an overpotential database of the same type of battery cells to be predicted at different temperatures and different charge and discharge rates; based on the overpotential database, selecting a preset number of sample data of the battery cells to be predicted as a training set; inputting the training set into a capacity loss model, and fitting the capacity loss model in combination with a fitting method to obtain optimal fitting parameters; bringing the optimal fitting parameters into the capacity loss model to obtain a capacity loss prediction model for the battery cell to be predicted; and determining the remaining life of the battery cell to be predicted based on the initial capacity, health status and the capacity loss prediction model of the battery cell to be predicted.

[0140] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0141] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these 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.

Claims

1. A method for predicting the life of a battery cell, characterized in that: include: Determine the overpotential database of the same type of cells to be predicted at different temperatures and different charge and discharge rates; Based on the overpotential database, a preset number of sample data of the battery cells to be predicted are selected as a training set; The training set is input into the capacity loss model, and the capacity loss model is fitted in combination with the fitting method to obtain the optimal fitting parameters; the capacity loss model is: in, Indicates capacity loss, Indicates charging time. Indicates the discharge time, represents the accumulated ampere-hours, η represents the overpotential, R represents the constant 8.314, T represents the temperature, and A / B / C / D / E / F / G represent the fitting parameters; Substituting the optimal fitting parameters into the capacity loss model to obtain a capacity loss prediction model for the battery cell to be predicted; Based on the initial capacity, health status and the capacity loss prediction model of the battery cell to be predicted, the remaining life of the battery cell to be predicted is determined, including: using the initial capacity and health status of the battery cell to be predicted to determine a capacity loss critical value; inputting the capacity loss critical value into the capacity loss prediction model to determine the remaining life of the battery to be predicted, wherein the parameters of the remaining life include remaining discharge time and overpotential at different temperatures.

2. The method for predicting the life of a battery cell according to claim 1, characterized in that: The method of determining an overpotential database of the same type of cells to be predicted at different temperatures and different charge and discharge rates includes: Determine the charge and discharge curves and electromotive force curves of the same type of predicted battery cells at different temperatures and different charge and discharge rates; Subtracting the voltage of the corresponding electromotive force curve from the voltage of the charge-discharge curve to obtain the overpotential of the same type of battery cell to be predicted at different charge-discharge rates; Based on the overpotentials at different charge and discharge rates, a database of overpotentials of the same type of cells to be predicted at different temperatures and different charge and discharge rates is obtained by linear interpolation.

3. The method for predicting the life of a battery cell according to claim 1, characterized in that: Before determining the overpotential database of the same type of cells to be predicted at different temperatures and different charge and discharge rates, the method further includes: A primary capacity loss curve that determines the rate of capacity loss versus time; Based on the temperature change and the Arrhenius formula, the primary capacity loss curve is adjusted to obtain a secondary capacity loss curve; Based on the difference in SEI film growth rate during the charging process and the discharging process, the secondary capacity loss curve is split to obtain a capacity loss model.

4. The method for predicting the life of a battery cell according to claim 3, characterized in that: Based on the difference in SEI film growth rate during the charging process and the discharging process, the secondary capacity loss curve is split to obtain a capacity loss model, including: Based on the difference in SEI film growth rate during the charging process and the discharging process, the secondary capacity loss curve is split to obtain a primary capacity loss model; Determining a charging SEI film growth weight factor during the charging process and a discharging SEI film growth weight factor during the discharging process respectively; Based on the specific surface area of ​​graphite, the particle breakage loss capacity is determined; The charge SEI film growth weight factor, the discharge SEI film growth weight factor and the rupture loss capacity are introduced into the primary capacity loss model to obtain a final capacity loss model.

5. The method for predicting the battery life according to claim 3, characterized in that: The primary capacity loss curve for determining the rate of capacity loss over time comprises: When the specific surface area of ​​graphite remains unchanged, an electron tunneling model is obtained in which the rate of capacity loss changes with time. The constants of the electron tunneling model are simplified to obtain a primary capacity loss curve.

6. The method for predicting the life of a battery cell according to any one of claims 1 to 5, characterized in that: After determining the remaining life of the battery cell to be predicted, the method further includes: updating the sample data according to the usage of the battery cell to be predicted; Inputting the updated sample data into the capacity loss model to update the optimal fitting parameters; The capacity loss prediction model is updated with the updated optimal fitting parameters, thereby updating the remaining life of the battery to be predicted.

7. A battery cell life prediction device, characterized in that: include: A determination module is used to determine the overpotential database of the same type of cells to be predicted at different temperatures and different charge and discharge rates; A selection module, used for selecting a preset number of sample data of the battery cells to be predicted as a training set based on the overpotential database; The fitting module is used to input the training set into the capacity loss model, and fit the capacity loss model in combination with the fitting method to obtain the optimal fitting parameters; the capacity loss model is: in, Indicates capacity loss, Indicates charging time. Indicates the discharge time, represents the accumulated ampere-hours, η represents the overpotential, R represents the constant 8.314, T represents the temperature, and A / B / C / D / E / F / G represent the fitting parameters; A prediction module is used to bring the optimal fitting parameters into the capacity loss model to obtain the capacity loss prediction model of the battery cell to be predicted; based on the initial capacity, health status and the capacity loss prediction model of the battery cell to be predicted, determine the remaining life of the battery cell to be predicted, specifically used to: determine the capacity loss critical value using the initial capacity and health status of the battery cell to be predicted; input the capacity loss critical value into the capacity loss prediction model to determine the remaining life of the battery to be predicted, and the parameters of the remaining life include the remaining discharge time and overpotential at different temperatures.

8. A vehicle, characterized in that: The vehicle is used to execute the battery cell life prediction method as described in any one of claims 1-6.

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