Lithium ion battery life prediction method based on impedance and acoustic emission

Abstract

The invention discloses a lithium ion battery life prediction method based on impedance and acoustic emission. The lithium ion battery life prediction method comprises the following steps: acquiring impedance and capacity evolution data of a lithium ion battery in a cycle process; collecting an acoustic emission signal released by the lithium ion battery; calculating a Pearson's correlation coefficient between the impedance parameter and the sound emission accumulated impact frequency; giving an exponential function relationship corresponding to the impedance parameter and the capacity; and establishing a life prediction model, and predicting the remaining service life of the battery by using the life prediction model. According to the method, the relation between impedance and electrode damage is analyzed by means of battery acoustic emission signal parameters, the prediction model is established by taking impedance parameters RCT and RSEI which can reflect a battery life attenuation mechanism and can be directly measured as indirect parameters, prediction of electrode life decline is realized, and the method is simple to operate, easy to realize and suitable for various types of lithium ion batteries.

Description

Technical Field

[0001] The present invention belongs to the field of lithium-ion batteries, and in particular relates to a lithium-ion battery life prediction method based on impedance and acoustic emission. Background Art

[0002] Lithium-ion batteries are widely used in electric vehicles, energy storage, and other fields, but their lifespan degradation has long constrained their development. This degradation is caused by damage to the electrodes caused by factors such as stress, which reduces their lithium storage capacity. Currently, commonly used battery life prediction methods are mainly divided into model-based and data-driven approaches. However, these methods have drawbacks such as difficulty in model establishment, high data requirements, and prediction accuracy affected by hyperparameters. Furthermore, they lack analysis of the mechanisms of battery life degradation.

[0003] Therefore, there is an urgent need for a battery life prediction method that can take into account the battery life degradation mechanism. Summary of the Invention

[0004] The present invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a lithium-ion battery life prediction method based on impedance and acoustic emission.

[0005] The technical solution of the present invention is: a lithium-ion battery life prediction method based on impedance and acoustic emission, comprising the following steps:

[0006] A. Obtain impedance and capacity evolution data of lithium-ion batteries during cycling;

[0007] B. Collect the acoustic emission signals released by lithium-ion batteries;

[0008] C. Calculate the Pearson correlation coefficient between the impedance parameter and the cumulative number of acoustic emission impacts;

[0009] D. Give the exponential function relationship between impedance parameters and capacity;

[0010] E. Establish a life prediction model and use it to predict the remaining service life of the battery.

[0011] Furthermore, step A obtains the impedance and capacity evolution data of the lithium-ion battery during the cycle process. The specific process is as follows:

[0012] The impedance and capacity evolution data of lithium-ion batteries during the cycle are obtained through the electrochemical workstation and blue-electric battery charging and discharging equipment.

[0013] Furthermore, the capacity is used as a criterion for judging the battery life.

[0014] Furthermore, the impedance is selected as the charge transfer impedance R CT and SEI film resistance R SEIAs an indirect parameter to characterize and predict battery capacity decay.

[0015] Furthermore, step B collects the acoustic emission signal released by the lithium-ion battery. The specific process is as follows:

[0016] First, the acoustic emission signals released by the lithium-ion battery are collected during the charge and discharge cycle;

[0017] Then, the cumulative number of impacts of the battery acoustic emission signal is used to quantify the degree of electrode damage.

[0018] Furthermore, step C calculates the Pearson correlation coefficient between the impedance parameter and the cumulative number of acoustic emission impacts. The specific process is as follows:

[0019] Firstly, the variation patterns of the capacity, impedance and acoustic emission parameters obtained through experiments with the number of cycles are analyzed;

[0020] Then, the Pearson correlation coefficient between the impedance parameters and the cumulative number of acoustic emission impacts was calculated to verify the relationship between the impedance parameters and the degree of electrode damage.

[0021] Furthermore, step E establishes a life prediction model and uses the life prediction model to predict the remaining service life of the battery. The specific process is as follows:

[0022] Firstly, based on the evolution law and correlation analysis of impedance parameters, a life prediction model is established using impedance parameters as indirect parameters for lithium-ion battery life prediction and Gaussian process regression as the life prediction method.

[0023] Then, the remaining service life of the battery is predicted using the life prediction model.

[0024] The beneficial effects of the present invention are as follows:

[0025] The present invention uses the battery acoustic emission signal parameters to analyze the relationship between impedance and electrode damage, and uses the impedance parameter R that can reflect the battery life attenuation mechanism and can be directly measured. CT and R SEI As an indirect parameter, a prediction model is established to predict the electrode life degradation. This method is simple to operate, easy to implement, and applicable to various types of lithium-ion batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The present invention Figure 1 Figure a is a schematic diagram of the electrochemical dynamic reaction process and its damage; b is a schematic diagram of the equivalent circuit and impedance spectrum corresponding to the electrochemical reaction process; c is a schematic diagram of an acoustic emission signal impact;

[0027] Figure 2 The present invention Figure 2a is the charge and discharge current-voltage curve; b is the SOH evolution; c is the impedance spectrum curve evolution; d is R0, R CT and R SEI evolution; e is the collected acoustic emission signal; f is the evolution of the cumulative number of impacts of the acoustic emission signal;

[0028] Figure 3 is the normalized evolution of impedance and acoustic emission cumulative impact times for different magnifications and materials in the present invention:

[0029] Among them, a is C rate-graphite electrode; b is C / 2 rate-graphite electrode; c is C rate-silicon carbon electrode; d is C / 2 rate-silicon carbon electrode;

[0030] Figure 4 is the Pearson correlation coefficient of the impedance and acoustic emission parameters of batteries with different charge and discharge rates and electrode materials in the present invention;

[0031] Figure 5 The present invention Figure 5 Where a is the predicted result; b is the error;

[0032] Figure 6 It is the prediction error of batteries with different charge and discharge rates and electrode materials in the present invention. DETAILED DESCRIPTION

[0033] Hereinafter, the present invention will be described in detail with reference to the accompanying drawings and embodiments:

[0034] like Figures 1 to 6 As shown, a lithium-ion battery life prediction method based on impedance and acoustic emission includes the following steps:

[0035] A. Obtain impedance and capacity evolution data of lithium-ion batteries during cycling;

[0036] B. Collect the acoustic emission signals released by lithium-ion batteries;

[0037] C. Calculate the Pearson correlation coefficient between the impedance parameter and the cumulative number of acoustic emission impacts;

[0038] D. Give the exponential function relationship between impedance parameters and capacity;

[0039] E. Establish a life prediction model and use it to predict the remaining service life of the battery.

[0040] Step A obtains the impedance and capacity evolution data of the lithium-ion battery during the cycle process. The specific process is as follows:

[0041] The impedance and capacity evolution data of lithium-ion batteries during the cycle are obtained through the electrochemical workstation and blue-electric battery charging and discharging equipment.

[0042] The capacity is used as a criterion for judging the battery life.

[0043] The impedance is selected as the charge transfer impedance R CT and SEI film resistance R SEI As an indirect parameter to characterize and predict battery capacity decay.

[0044] Step B collects the acoustic emission signal released by the lithium-ion battery. The specific process is as follows:

[0045] First, the acoustic emission signals released by the lithium-ion battery are collected during the charge and discharge cycle;

[0046] Then, the cumulative number of impacts of the battery acoustic emission signal is used to quantify the degree of electrode damage.

[0047] Step C calculates the Pearson correlation coefficient between the impedance parameter and the cumulative number of acoustic emission impacts. The specific process is as follows:

[0048] Firstly, the variation patterns of the capacity, impedance and acoustic emission parameters obtained through experiments with the number of cycles are analyzed;

[0049] Then, the Pearson correlation coefficient between the impedance parameters and the cumulative number of acoustic emission impacts was calculated to verify the relationship between the impedance parameters and the degree of electrode damage.

[0050] Step E: Establish a life prediction model and use it to predict the remaining service life of the battery. The specific process is as follows:

[0051] Firstly, based on the evolution law and correlation analysis of impedance parameters, a life prediction model is established using impedance parameters as indirect parameters for lithium-ion battery life prediction and Gaussian process regression as the life prediction method.

[0052] Then, the remaining service life of the battery is predicted using the life prediction model.

[0053] Example 1

[0054] A lithium-ion battery life prediction method based on impedance and acoustic emission comprises the following steps:

[0055] First, obtain experimental data on impedance, capacity, and acoustic emission

[0056] Graphite electrodes were selected for the experiment, and CR2032 lithium-ion batteries were assembled.

[0057] The LAND blue electric tester was used to conduct a C / 2 rate charge and discharge cycle experiment on the lithium-ion battery. After the cycle was completed, the electrochemical impedance spectroscopy of the battery was measured using a CHI604A electrochemical workstation. The electrochemical impedance spectroscopy was measured every 5 cycles. Figure 1The equivalent circuit shown is used to fit the electrochemical impedance spectroscopy to obtain the impedance evolution data.

[0058] During the cycling process, an acoustic emission sensor and an acquisition system are used to collect the acoustic emission signals of the battery.

[0059] The final evolution data of capacity, impedance and acoustic emission parameters are as follows: Figure 2 shown.

[0060] Then, correlation analysis was performed

[0061] The evolution relationship between impedance and acoustic emission parameters was analyzed, and the Pearson correlation coefficient between impedance parameters and the cumulative number of acoustic emission impacts was calculated, such as Figure 3 and 4 As shown, the analysis results of batteries with different charge and discharge rates and electrode materials are compared. The results show that the impedance parameter R CT and R SEI The correlation coefficients with the cumulative number of acoustic emission impacts are all higher than 0.9, indicating that the impedance parameters are closely related to the degree of electrode damage. The use of impedance as an indirect parameter for prediction takes into account the battery life attenuation mechanism; the correspondence between impedance and capacity is analyzed, and an exponential model is used to construct the phenomenological relationship between impedance R and capacity Q: Q = c + aexp(bR), where a, b and c are characteristic parameters.

[0062] Therefore, the purpose of indirectly predicting battery life degradation can be achieved by predicting the evolution of impedance parameters.

[0063] Finally, battery life prediction

[0064] The data before the battery capacity dropped to 80% was used as the training set, and the data when the battery capacity dropped from 80% to 50% was used as the test set. The Gaussian process regression prediction method was used for prediction to obtain the evolution data of impedance when the battery capacity dropped from 80% to 50%.

[0065] The predicted impedance evolution data was substituted into the exponential model to obtain the battery capacity evolution data and compared with the test set for error analysis. The capacity prediction results and errors are shown in Figure 2. Figure 5 The prediction errors of battery data using different experimental conditions are shown in Figure 6 shown.

[0066] from Figure 5 By comparison, we can see that using R CT and R SEI As an indirect parameter for prediction, the prediction results of the present invention are closer to the measured results, and the prediction error is basically below 5%, while the error of the traditional prediction using the number of cycles is very unstable, even exceeding 10%. Figure 6It can be seen that the prediction errors of the battery data under different experimental conditions using the prediction method of the present invention are all less than 10%, and most of them are around 5%, indicating that the prediction method of the present invention that takes into account the battery life attenuation mechanism shows excellent prediction ability and adaptability.

[0067] The present invention uses the battery acoustic emission signal parameters to analyze the relationship between impedance and electrode damage, and uses the impedance parameter R that can reflect the battery life attenuation mechanism and can be directly measured. CT and R SEI As an indirect parameter, a prediction model is established to predict the electrode life degradation. This method is simple to operate, easy to implement, and applicable to various types of lithium-ion batteries.

Claims

1. A lithium-ion battery life prediction method based on impedance and acoustic emission, characterized by: The following steps are involved: A. Obtain impedance and capacity evolution data of lithium-ion batteries during cycling; B. Collect the acoustic emission signals released by lithium-ion batteries; C. Calculate the Pearson correlation coefficient between the impedance parameter and the cumulative number of acoustic emission impacts; D. Give the exponential function relationship between impedance parameters and capacity; E. Establish a life prediction model and use it to predict the remaining service life of the battery.

2. The method for predicting lithium-ion battery life based on impedance and acoustic emission according to claim 1, characterized in that: Step A obtains the impedance and capacity evolution data of the lithium-ion battery during the cycle process. The specific process is as follows: The impedance and capacity evolution data of lithium-ion batteries during the cycle are obtained through the electrochemical workstation and blue battery charging and discharging equipment.

3. The method for predicting lithium-ion battery life based on impedance and acoustic emission according to claim 2, characterized in that: The capacity is used as a criterion for judging the battery life.

4. The method for predicting lithium-ion battery life based on impedance and acoustic emission according to claim 2, wherein: The impedance is selected as the charge transfer impedance R CT and SEI film resistance R SEI As an indirect parameter to characterize and predict battery capacity decay.

5. The method for predicting lithium-ion battery life based on impedance and acoustic emission according to claim 1, characterized in that: Step B collects the acoustic emission signal released by the lithium-ion battery. The specific process is as follows: First, the acoustic emission signals released by the lithium-ion battery are collected during the charge and discharge cycle; Then, the cumulative number of impacts of the battery acoustic emission signal is used to quantify the degree of electrode damage.

6. The method for predicting lithium-ion battery life based on impedance and acoustic emission according to claim 1, characterized in that: Step C calculates the Pearson correlation coefficient between the impedance parameter and the cumulative number of acoustic emission impacts. The specific process is as follows: Firstly, the variation patterns of the capacity, impedance and acoustic emission parameters obtained through experiments with the number of cycles are analyzed; Then, the Pearson correlation coefficient between the impedance parameters and the cumulative number of acoustic emission impacts was calculated to verify the relationship between the impedance parameters and the degree of electrode damage.

7. The method for predicting lithium-ion battery life based on impedance and acoustic emission according to claim 1, characterized in that: Step E: Establish a life prediction model and use it to predict the remaining service life of the battery. The specific process is as follows: Firstly, based on the evolution law and correlation analysis of impedance parameters, a life prediction model is established using impedance parameters as indirect parameters for lithium-ion battery life prediction and Gaussian process regression as the life prediction method. Then, the remaining service life of the battery is predicted using the life prediction model.

Images