Power battery aging and state-of-health estimation method and system based on expansion stress

Abstract

The invention relates to the technical field of power battery aging analysis, and particularly discloses a power battery aging and health state estimation method and system based on expansion stress, and the method comprises the steps: obtaining the charging and discharging voltage, stress expansion data and capacity data of a lithium battery in the whole life cycle; calculating an expansion increment by using the preprocessed charge-discharge voltage and stress expansion data, and drawing an expansion increment curve of each charge-discharge cycle; the peak value or the peak valley of each expansion increment curve and the corresponding SOH data form a data set; training the battery health state prediction model; acquiring current charging voltage and stress expansion data of the same type of lithium batteries to be tested, and drawing an expansion increment curve; and extracting peak value or peak valley data of the expansion increment curve, and obtaining a battery health state prediction result by using the trained battery health state prediction model. According to the method, the mechanical characteristics of the battery are fully considered in the prediction process, and the estimation accuracy and robustness are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power battery aging analysis, and in particular to a method and system for estimating power battery aging and health status based on expansion stress. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In recent years, lithium-ion batteries have been widely used in many fields due to their advantages such as high energy density, long life and low cost. However, as the number of charge and discharge cycles increases, the performance of lithium-ion batteries will continue to decline, which may lead to safety issues such as deformation and even explosion. Therefore, accurately analyzing the battery aging mechanism and estimating the battery's state of health (SOH) are crucial to ensure the safe and efficient operation of the battery management system.

[0004] The aging of lithium-ion batteries is closely related to a variety of internal degradation mechanisms. During the use of lithium batteries, lithium ions are constantly embedded and deintercalated in the positive and negative electrodes of the battery, causing the battery volume to expand and shrink. At the same time, as the number of charge and discharge cycles increases, lithium loss, SEI film thickening, electrolyte decomposition and gas generation will occur inside the battery, causing battery capacity decay and irreversible deformation. Therefore, it is necessary to conduct an in-depth analysis of the battery aging mechanism and its internal mechanical change mechanism.

[0005] Different scholars have conducted a lot of research on the analysis of the internal mechanism of batteries and the estimation of SOH, but the current estimation methods of SOH and the analysis of battery aging mechanisms are relatively simple, usually based on the electrochemical perspective. For example: The existing technology only extracts external parameters such as voltage and time to estimate the battery SOH, but does not deeply analyze the battery aging mechanism and consider the internal mechanical characteristics of the battery, resulting in low estimation accuracy.

[0006] The existing technology estimates the battery capacity based on the capacity increment curve and its corresponding voltage value input into the neural network model, but this method only considers the mechanism and characteristics of the change of the electrochemical characteristics of the battery, and it is difficult to further improve the accuracy of the battery pack capacity estimation; moreover, the drawing of the capacity increment curve requires obtaining the capacity value of the battery, which increases the difficulty of data acquisition to a certain extent.

[0007] The existing technology realizes battery aging analysis and life prediction by constructing a battery aging mathematical model. This method is highly dependent on the constructed model, is easily affected by the degree of battery aging and the external environment, has a large amount of calculation and poor applicability. Summary of the invention

[0008] In order to solve the above problems, the present invention proposes a method and system for estimating power battery aging and health status based on expansion stress. Based on the changing law of the battery expansion increment curve, the battery aging characteristics are analyzed; at the same time, the peak information is extracted from the expansion increment curve as a feature to estimate the battery SOH.

[0009] In some embodiments, the following technical solutions are adopted: A method for estimating aging and health status of a power battery based on expansion stress, comprising: Obtain the charge and discharge voltage, stress expansion data and capacity data of the lithium battery throughout its life cycle and perform preprocessing; Using the pre-processed charge and discharge voltage and stress expansion data, the expansion increment is calculated and the expansion increment curve of each charge and discharge cycle is plotted; the corresponding SOH is calculated based on the capacity data; Extracting peak or valley data of each expansion increment curve, wherein the peak or valley data of each expansion increment curve and the corresponding SOH data constitute a data set; using the data set to train a battery health state prediction model to obtain a trained battery health state prediction model; The current charging voltage and stress expansion data of the same type of lithium battery to be tested are obtained, and an expansion increment curve is drawn; the peak or peak-valley data of the expansion increment curve is extracted, and the peak or peak-valley data is used as input, and the battery health state prediction result is obtained using the trained battery health state prediction model.

[0010] As a further solution, the process of calculating the expansion increment is as follows: ; Among them, IE k is the expansion increment value at time k; V k With V k-1 are the battery voltage values ​​at time k and time k-1 respectively; E k With E k-1 are the battery expansion stress at time k and time k-1 respectively.

[0011] As a further solution, the battery expansion stress value is measured by setting a strain gauge on the battery surface.

[0012] As a further solution, the horizontal coordinate of the expansion increment curve is the voltage value, and the vertical coordinate is the expansion increment value corresponding to the voltage value.

[0013] As a further solution, the battery health status prediction model is a neural network model.

[0014] As a further solution, it also includes: the changes in the peak or valley positions of different expansion increment curves during the entire life cycle of the lithium battery are closely related to the aging state of the battery; establishing a corresponding relationship between the peak or valley position of the expansion increment curve and the aging state of the battery, and determining the aging state of the lithium battery based on the peak or valley position of the current expansion increment curve of the lithium battery to be tested.

[0015] In other embodiments, the following technical solutions are adopted: A power battery aging and health state estimation system based on expansion stress, comprising: The data acquisition module is used to obtain the charge and discharge voltage, stress expansion data and capacity data of the lithium battery throughout its life cycle and perform preprocessing; The curve drawing module is used to calculate the expansion increment using the pre-processed charge and discharge voltage and stress expansion data, and draw the expansion increment curve of each charge and discharge cycle; and calculate the corresponding SOH based on the capacity data; A model training module is used to extract peak or valley data of each expansion increment curve, wherein the peak or valley of each expansion increment curve and the corresponding SOH data constitute a data set; the battery health state prediction model is trained using the data set to obtain a trained battery health state prediction model; The state prediction module is used to obtain the current charging voltage and stress expansion data of the same type of lithium battery to be tested, and draw the expansion increment curve; extract the peak or peak-valley data of the expansion increment curve, use the peak or peak-valley data as input, and use the trained battery health state prediction model to obtain the battery health state prediction result.

[0016] In other embodiments, the following technical solutions are adopted: A terminal device includes a processor and a memory, wherein the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the above-mentioned power battery aging and health state estimation method based on expansion stress.

[0017] In other embodiments, the following technical solutions are adopted: A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the above-mentioned method for estimating aging and health status of a power battery based on expansion stress.

[0018] In other embodiments, the following technical solutions are adopted: A computer program product includes a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for estimating power battery aging and health status based on expansion stress.

[0019] Compared with the prior art, the present invention has the following beneficial effects: (1) Based on the strong correlation between the peaks or valleys on the expansion increment curve and the battery SOH, the present invention draws the battery expansion increment curve, extracts the peak or valley data as input, and uses a neural network model to predict the battery SOH. The prediction process fully considers the mechanical characteristics of the battery, thereby improving the estimation accuracy and robustness.

[0020] (2) The present invention analyzes the regularity and characteristic changes of different expansion increment curves of the battery, establishes the corresponding relationship between the change of the peak or valley position of the expansion increment curve and the aging state of the battery, and can help analyze the aging state of the battery; by drawing the battery expansion increment curve under different working conditions or aging conditions, according to the position information of its peak or valley, it helps analyze the internal mechanism of the battery without disassembling the battery.

[0021] (3) The present invention only needs to obtain the battery's expansion increment and voltage data to draw the expansion increment curve. These data are relatively easy to obtain, and they combine the mechanical and electrochemical characteristics of the battery. Using their peak or valley characteristics can help the model better capture the internal aging state of the battery and improve the estimation accuracy and robustness.

[0022] Other features and advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a method for estimating aging and health status of a power battery based on expansion stress in an embodiment of the present invention; Figure 2 It is a test schematic diagram of battery expansion stress; Figure 3 Schematic diagram of battery expansion change curve during charge and discharge cycle in an embodiment of the present invention; Figure 4 Schematic diagram of IE curves of different cycles in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0025] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0026] Embodiment 1 In one or more embodiments, a method for estimating the aging and health status of a power battery based on expansion stress is disclosed, combined with Figure 1 , specifically including the following process: S101: Acquire charging and discharging data of the entire life cycle of the lithium-ion battery and perform preprocessing.

[0027] In this embodiment, for a certain type of lithium-ion battery, a full life cycle charge and discharge experiment is performed on it to obtain full life cycle charge and discharge data, including data such as voltage V, expansion stress E and capacity Q, and the data is preprocessed; the data preprocessing process includes steps such as missing value processing, outlier processing, data smoothing processing and data normalization processing.

[0028] Among them, the capacity Q is used to calculate the SOH of the battery.

[0029] The expansion stress E can be obtained by sticking a strain gauge on the battery surface, implanting an optical fiber sensor inside the battery, or using a thin film pressure measurement system. The simplest way is to stick two small strain gauges evenly distributed on the battery surface for real-time measurement, such as Figure 2 As shown, the pasting method is simple and the measuring accuracy is high.

[0030] S102: Calculate the expansion increment using the pre-processed charge and discharge voltage and stress expansion data.

[0031] In this embodiment, the specific calculation formula for the expansion increment is: (1) Among them, IE k is the expansion increment value at time k; V k With V k-1 are the battery voltage values ​​at time k and time k-1 respectively; E k With E k-1 are the battery expansion stress at time k and time k-1 respectively.

[0032] S103: Draw an expansion increment curve for each charge and discharge cycle.

[0033] In this embodiment, an expansion increment curve is drawn based on the calculated expansion increment value and its corresponding voltage value; the abscissa of the expansion increment curve is the voltage value, and the ordinate is the expansion increment value corresponding to the voltage value.

[0034] S104: extracting peak or valley data of each expansion increment curve, wherein the peak or valley data of each expansion increment curve and the corresponding SOH data constitute a data set; and using the data set to train a battery health state prediction model to obtain a trained battery health state prediction model.

[0035] During the use of the battery, the lithium ions in the positive and negative electrodes are constantly inserted and removed, which will cause the battery volume to shrink and expand continuously, and then cause the battery to undergo irreversible deformation, capacity decay and performance degradation. Therefore, the deformation characteristics of the battery are closely related to the aging mechanism, and the measurement of the changes in the expansion and voltage characteristics is efficient and simple, which can be used to analyze the aging mechanism of the battery and predict SOH.

[0036] In this example, a soft-pack lithium-ion battery was subjected to 0.2C charge-discharge cycles at 25°C, and voltage, stress expansion, and capacity data were collected. The volume expansion changes of the battery during some continuous cycles are shown in Figure 2. Figure 3 As shown in the figure, it can be seen that the battery volume gradually expands during charging, while the battery volume shrinks during discharging, which has a certain regularity. Therefore, the battery expansion data can be used to help analyze and predict the battery aging status.

[0037] Based on formula (1), the expansion increment value is calculated and the expansion increment curve of the battery throughout its life cycle is plotted as follows: Figure 4 As shown, ①②③ show multiple peak positions in the expansion increment curve, different colors represent different cycles, and blue to red represent the IE curves during 0-350 charging processes respectively; Figure 4 It can be seen that each expansion increment curve contains multiple peaks, and the changes in the expansion increment curves of different cycles have certain regularity. As the battery ages, the expansion increment curve shifts to the right as a whole. This is due to the increase in the internal resistance and polarization effect of the battery. In order to achieve the same charge and discharge capacity, the battery needs to work in a higher voltage range. At the same time, it can also be seen from the expansion increment curve that each peak shows a decreasing trend as the battery ages. This is due to the reduction of active substances and the loss of lithium ions inside the battery during use, and the gradual thickening of the SEI film (solid electrolyte interface film), which slows down the electrochemical reaction rate of the battery. In addition, since the side reaction products will adhere to the electrode surface and accumulate continuously during the cycle, the rate of change of the battery volume will also decrease. Therefore, it is manifested as a gradual decrease in each peak. In summary, the changes in the positions of each peak are closely related to the aging of the battery and the changes in mechanical properties.

[0038] The position information of the three peaks in the different expansion increment curves is further extracted, including the peak value corresponding to the ordinate and the voltage value corresponding to the abscissa. The Pearson correlation coefficient is used to specifically calculate the correlation between the peak position and SOH. The Pearson correlation coefficient calculation formula is as follows: (2) In the formula, x i Represents the horizontal / vertical coordinate data of each peak, y i Represents battery SOH data, and Respectively represent x i and y i The correlation coefficients are shown in Table 1, where Pi and Pi x They represent the correlation between the peak value and voltage value corresponding to the peak and SOH respectively, and the negative sign represents a negative correlation between the two.

[0039] Table 1 Correlation between each peak information and SOH

[0040] It can be seen from Table 1 that the peak value of each expansion increment curve has a strong positive correlation with SOH, while its corresponding voltage value has a strong negative correlation with SOH. Therefore, each peak position has a good linear relationship with the battery SOH.

[0041] It should be noted that the above process uses peak data as an example for analysis and explanation, and the same requirements are also met for peak and valley data.

[0042] Therefore, in this embodiment, the peak or valley data of each expansion increment curve during the entire life cycle of the lithium-ion battery and the SOH data corresponding to each expansion increment curve are extracted to form a data set; the battery health state prediction model is trained using the data set, and the input during the training process is the peak or valley data of each expansion increment curve and the corresponding SOH, and the output is SOH; finally, a trained battery health state prediction model is obtained.

[0043] In this embodiment, the battery health status prediction model is a neural network model. As a specific example, a convolutional neural network model or a BP neural network model may be selected.

[0044] S105: Obtain the current charging voltage and stress expansion data of the same type of lithium battery to be tested, and draw an expansion increment curve; extract the peak or peak-valley data of the expansion increment curve, use the peak or peak-valley data as input, and use the trained battery health status prediction model to obtain a battery health status prediction result.

[0045] For the lithium-ion battery to be tested, the health status of the lithium-ion battery is predicted based on the battery health status prediction model trained with the data of the same type of lithium-ion batteries.

[0046] Specifically, a charging process is performed on the lithium-ion battery to be tested, the charging voltage and stress expansion data are obtained, the expansion increment is calculated using the aforementioned method, and an expansion increment curve is drawn; the peak or valley data in the expansion increment curve is extracted and used as input data to input into the trained battery health status prediction model, and the prediction result of the battery SOH can be output.

[0047] At the same time, according to the above analysis, the changes in the peak or valley positions of different expansion increment curves during the entire life cycle of lithium batteries are closely related to the aging state of the battery; the changes in the position information of each peak can help analyze the internal aging mechanism of the battery and reflect the aging state of the battery.

[0048] This embodiment establishes a corresponding relationship between the peak or valley position of the expansion increment curve and the battery aging state, and determines the aging state of the lithium battery based on the peak or valley position of the current expansion increment curve of the lithium battery to be tested.

[0049] By drawing the battery expansion increment curve under different working conditions or aging conditions, according to the position information of its peak or trough, without disassembling the battery, it can help analyze the internal mechanism of the battery's mechanical properties and aging characteristics. For example, the IE curve under different rate / temperature conditions can be drawn, and then the changes in the position information of the peak / trough can be compared to help analyze the internal mechanical properties of the battery; or according to the IE curve of the entire life cycle, it can be seen that as the number of battery cycles increases, the peak value gradually decreases. This is mainly due to the growth of the SEI film inside the battery, the loss of lithium ions, the decomposition of the electrolyte, etc., which leads to a decrease in the amount of lithium ions embedded and released in the positive and negative electrodes of the battery, thereby resulting in capacity loss and a slowdown in the change of the battery volume; these aging mechanisms are manifested as a decrease in the peak value of the IE curve. Therefore, the battery aging characteristics can also be analyzed based on the changes in its peak information.

[0050] This embodiment defines an expansion increment curve, and predicts the SOH of a lithium-ion battery based on the peak or valley data of the expansion increment curve, and performs a battery aging status assessment. The required data are battery voltage and battery expansion stress, and the data is easy to obtain. The data combines the mechanical and electrochemical properties of the battery, which can help the model better capture the internal aging state of the battery and improve the accuracy and robustness of SOH estimation.

[0051] Embodiment 2 In one or more embodiments, a power battery aging and health state estimation system based on expansion stress is disclosed, comprising: The data acquisition module is used to obtain the charge and discharge voltage, stress expansion data and capacity data of the lithium battery throughout its life cycle and perform preprocessing; The curve drawing module is used to calculate the expansion increment using the pre-processed charge and discharge voltage and stress expansion data, and draw the expansion increment curve of each charge and discharge cycle; and calculate the corresponding SOH based on the capacity data; A model training module is used to extract peak or valley data of each expansion increment curve, wherein the peak or valley of each expansion increment curve and the corresponding SOH data constitute a data set; the battery health state prediction model is trained using the data set to obtain a trained battery health state prediction model; The state prediction module is used to obtain the current charging voltage and stress expansion data of the same type of lithium battery to be tested, and draw the expansion increment curve; extract the peak or peak-valley data of the expansion increment curve, use the peak or peak-valley data as input, and use the trained battery health state prediction model to obtain the battery health state prediction result.

[0052] It should be noted that the specific implementation method of the above modules is the same as that in Example 1 and will not be described in detail.

[0053] Embodiment 3 In one or more embodiments, a terminal device is disclosed, which includes a processor and a memory, the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the power battery aging and health status estimation method based on expansion stress described in Example 1.

[0054] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0055] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0056] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.

[0057] Embodiment 4 In one or more embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the method for estimating aging and health status of a power battery based on expansion stress described in Example 1.

[0058] Embodiment 5 In one or more embodiments, a computer program product is disclosed, including a computer program / instruction, which, when executed by a processor, implements the method for estimating power battery aging and health status based on expansion stress described in Example 1.

[0059] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for estimating aging and health status of a power battery based on expansion stress, characterized in that: include: Obtain the charge and discharge voltage, stress expansion data and capacity data of the lithium battery throughout its life cycle and perform preprocessing; Using the pre-processed charge and discharge voltage and stress expansion data, the expansion increment is calculated and the expansion increment curve of each charge and discharge cycle is plotted; the corresponding SOH is calculated based on the capacity data; Extracting peak or valley data of each expansion increment curve, wherein the peak or valley data of each expansion increment curve and the corresponding SOH data constitute a data set; using the data set to train a battery health state prediction model to obtain a trained battery health state prediction model; The current charging voltage and stress expansion data of the same type of lithium battery to be tested are obtained, and an expansion increment curve is drawn; the peak or peak-valley data of the expansion increment curve is extracted, and the peak or peak-valley data is used as input, and the battery health state prediction result is obtained using the trained battery health state prediction model.

2. A method for estimating power battery aging and health status based on expansion stress according to claim 1, characterized in that: The process of calculating the expansion increment is as follows: ； Among them, IE k is the expansion increment value at time k; V k With V k-1 are the battery voltage values ​​at time k and time k-1 respectively; E k With E k-1 are the battery expansion stress at time k and time k-1 respectively.

3. A method for estimating power battery aging and health status based on expansion stress as claimed in claim 2, characterized in that: The battery expansion stress value is measured by setting a strain gauge on the battery surface.

4. The method for estimating power battery aging and health status based on expansion stress according to claim 1, characterized in that: The horizontal coordinate of the expansion increment curve is the voltage value, and the vertical coordinate is the expansion increment value corresponding to the voltage value.

5. The method for estimating power battery aging and health status based on expansion stress according to claim 1, characterized in that: The battery health status prediction model is a neural network model.

6. The method for estimating power battery aging and health status based on expansion stress according to claim 1, characterized in that: Also includes: The changes in the peak or valley positions of different expansion increment curves during the life cycle of lithium batteries are closely related to the aging state of the battery; A corresponding relationship between the peak or valley position of the expansion increment curve and the battery aging state is established, and the aging state of the lithium battery to be tested is determined based on the peak or valley position of the current expansion increment curve of the lithium battery to be tested.

7. A power battery aging and health status estimation system based on expansion stress, characterized in that: include: The data acquisition module is used to obtain the charge and discharge voltage, stress expansion data and capacity data of the lithium battery throughout its life cycle and perform preprocessing; The curve drawing module is used to calculate the expansion increment using the pre-processed charge and discharge voltage and stress expansion data, and draw the expansion increment curve of each charge and discharge cycle; and calculate the corresponding SOH based on the capacity data; A model training module is used to extract peak or valley data of each expansion increment curve, wherein the peak or valley of each expansion increment curve and the corresponding SOH data constitute a data set; the battery health state prediction model is trained using the data set to obtain a trained battery health state prediction model; The state prediction module is used to obtain the current charging voltage and stress expansion data of the same type of lithium battery to be tested, and draw the expansion increment curve; extract the peak or peak-valley data of the expansion increment curve, use the peak or peak-valley data as input, and use the trained battery health state prediction model to obtain the battery health state prediction result.

8. A terminal device, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the power battery aging and health state estimation method based on expansion stress as described in any one of claims 1-6.

9. A computer-readable storage medium storing a plurality of instructions, characterized in that: The instruction is suitable for being loaded by a processor of a terminal device and executing the method for estimating aging and health status of a power battery based on expansion stress as described in any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the method for estimating aging and health status of a power battery based on expansion stress as described in any one of claims 1 to 6 is implemented.

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