Lithium ion battery SOH rapid estimation method based on electrochemical impedance spectroscopy

Abstract

The invention discloses an electrochemical impedance spectroscopy-based lithium ion battery SOH (state of health) rapid estimation method, which comprises the following steps of: performing Pearson correlation analysis on a plurality of parameters through electrochemical impedance spectroscopy (EIS) detection under a plurality of aging levels, and extracting a minimum impedance amplitude and a curve real axis intercept as characteristic parameters; using curve fitting to obtain a function relationship among the minimum impedance amplitude, the curve real axis intercept and the aging level of the battery, and inputting the characteristic parameters into a function to estimate the health state of the battery. According to the lithium ion battery SOH estimation method provided by the invention, complex models and full-spectrum EIS data are not needed, the SOH estimation efficiency of the aged battery can be improved, a guarantee is provided for efficient sorting and echelon utilization of retired batteries, and the method is suitable for battery application occasions such as electric automobiles, energy storage systems, electric tools and the like.

Description

Technical Field

[0001] The present invention relates to a method for estimating the state of health of an aged lithium-ion battery, and in particular to a method for quickly estimating the state of health (SOH) of a lithium-ion battery based on electrochemical impedance spectroscopy. Background Art

[0002] Lithium-ion batteries gradually age during use, manifesting as capacity decay and increased internal resistance. Aged cells reduce the total available capacity of the battery pack, impacting performance. Furthermore, the increased internal resistance of aged cells increases heat generation, increasing the risk of battery safety incidents. Rapidly estimating the health status of aged batteries is crucial for battery reuse and safety management.

[0003] Current methods for estimating the state of health of aged batteries often use complex electrochemical models or time-consuming full-spectrum EIS data, making it difficult to achieve fast, effective, and highly practical SOH estimation and difficult to apply in actual scenarios. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for quickly estimating the state of health of lithium-ion batteries based on electrochemical impedance spectroscopy, so as to solve the problems of slow acquisition speed, low efficiency and poor practicality of the health status of aging batteries.

[0005] The technical solution for achieving the purpose of the present invention is: a method for quickly estimating the SOH of a lithium-ion battery based on electrochemical impedance spectroscopy, comprising the following steps:

[0006] Step 1: Perform charge and discharge cycle operations on the lithium-ion battery and record the EIS curves of the battery at different aging levels;

[0007] Step 2: Use the Pearson correlation coefficient to measure the correlation between the minimum impedance amplitude, the real axis intercept of the curve, the real and imaginary parts of the semicircle vertex, the real and imaginary parts of the inflection point of the curve and the battery health status in the EIS. The minimum impedance amplitude and the real axis intercept of the curve are selected as the characteristic parameters that significantly reflect the SOH change;

[0008] Step 3: Use curve fitting to establish the relationship between characteristic parameters and SOH;

[0009] Step 4: Input the characteristic parameters of the high-frequency EIS data of the battery to be tested into the fitted function to obtain the health status of the battery.

[0010] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the above method are implemented when the processor executes the program.

[0011] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0012] A computer program product comprises a computer program, which implements the steps of the above method when executed by a processor.

[0013] Compared with the prior art, the present invention has the following advantages: (1) The present invention provides a method for quickly estimating the state of health (SOH) of lithium-ion batteries based on electrochemical impedance spectroscopy. By measuring the EIS data of the high-frequency portion of the lithium-ion battery, the minimum impedance amplitude and the real-axis intercept of the curve are obtained. The SOH is then obtained by combining the functional relationship between the minimum impedance amplitude and the real-axis intercept of the curve and the battery aging level. (2) The proposed method for estimating the health status of lithium-ion batteries does not require complex models and full-spectrum EIS data, which can improve the efficiency of estimating the SOH of aged batteries and provide a guarantee for the efficient sorting and cascade utilization of retired batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Flowchart of the rapid estimation method for SOH of lithium-ion batteries.

[0015] Figure 2 This is the electrochemical impedance data of BAT001.

[0016] Figure 3 The Pearson correlation coefficient results of each parameter.

[0017] Figure 4 This is the electrochemical impedance data diagram of the high-frequency part of BAT002. DETAILED DESCRIPTION

[0018] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] like Figure 1 As shown, a method for quickly estimating SOH of a lithium-ion battery based on electrochemical impedance spectroscopy includes the following steps:

[0020] Step 1: Perform charge and discharge cycles on the lithium-ion battery and record the electrochemical impedance spectroscopy (EIS) curves of the battery at different aging levels;

[0021] Step 2: Use the Pearson correlation coefficient to measure the correlation between the minimum impedance amplitude, the real axis intercept of the curve, the real and imaginary parts of the semicircle vertex, the real and imaginary parts of the inflection point of the curve and the battery health state (SOH). The minimum impedance amplitude and the real axis intercept of the curve are selected as the characteristic parameters that significantly reflect the change of SOH.

[0022] Step 3: Use curve fitting to establish the relationship between characteristic parameters and SOH;

[0023] Step 4: Input the characteristic parameters of the high-frequency EIS data of the battery to be tested into the fitted function to obtain the health status of the battery.

[0024] This method uses EIS testing at multiple aging levels, and performs Pearson correlation analysis on the minimum impedance amplitude, the real axis intercept of the curve, the real and imaginary parts of the semicircle vertex, and the real and imaginary parts of the curve inflection point to determine that the characteristic parameters are the minimum impedance amplitude and the real axis intercept of the curve. Curve fitting software is used to obtain the functional relationship between the characteristic parameters and the battery aging level. The characteristic parameters extracted from the high-frequency EIS data of the battery to be tested are input into the function to calculate the health status of the battery.

[0025] Furthermore, in this embodiment, step one is specifically as follows:

[0026] Step 1-1: Place the lithium-ion battery at 25°C for 3 hours;

[0027] Step 1-2: Charge the battery at a constant current rate of 1 / 2C until the battery voltage reaches the upper cut-off voltage;

[0028] Step 1-3: Switch to constant voltage charging mode until the charging current drops to 1 / 20C;

[0029] Step 1-4: Discharge the battery at a 1 / 2C rate until the battery voltage drops to the lower cut-off voltage;

[0030] Step 1-5: After every 50 charge and discharge cycles, measure the amount of energy (Ah) provided by the battery when it is discharged from the upper cut-off voltage to the lower cut-off voltage to obtain health status information, where SOH is defined as where Q available is the available discharge capacity, Q nominal is the nominal discharge capacity.

[0031] Step 1-6: Charge the battery at a constant current of 1 / 2C until the remaining capacity reaches 50% SOC. After standing for 1 hour, measure the EIS data using an electrochemical workstation.

[0032] Furthermore, in this embodiment, step 2 is specifically as follows:

[0033] Step 2-1: Extract characteristic parameter groups from the EIS curve, including the minimum impedance amplitude, the real axis intercept of the curve, the real and imaginary part data of the semicircle vertex, and the real and imaginary part data of the curve inflection point;

[0034] Step 2-2: Use the Pearson correlation coefficient to measure the correlation between each parameter in the parameter group and the SOH. The Pearson correlation coefficient range is [-1, 1]. The closer the value is to 1 or -1, the stronger the correlation is; the closer the value is to 0, the weaker the correlation is. The calculation formula is: Among them, X and Y are feature variables and output variables respectively. and are their means respectively.

[0035] Step 2-3: Compare the Pearson correlation coefficients corresponding to the parameters, and select the minimum impedance amplitude and the real axis intercept of the curve that can most significantly reflect the SOH change as characteristic parameters.

[0036] Furthermore, step three in this embodiment is specifically as follows:

[0037] Step 3-1: Select multiple linear regression as the fitting model;

[0038] Step 3-2: Fit the values of the parameters in the fitting model and establish the functional relationship between the minimum impedance amplitude and the real axis intercept of the curve and the battery health status;

[0039] Furthermore, step four in this embodiment is specifically as follows:

[0040] Step 4-1: Measure the EIS curve of the high-frequency part of the battery and extract the minimum impedance amplitude and the real axis intercept of the curve;

[0041] Step 4-2: Input the extracted minimum impedance amplitude and the real axis intercept of the curve into the fitted function curve;

[0042] Step 4-3: The function outputs the calculated SOH value of the battery.

[0043] The present invention will be described in detail below using a lithium-ion battery as an example.

[0044] Example

[0045] This example uses an ISR18650-2.5Ah ternary lithium-ion battery as an experimental object, and performs step 1 of the method of the present invention at room temperature of 25°C. The specific process is as follows:

[0046] Step 1: Place the lithium-ion battery numbered BAT001 at 25°C for 3 hours.

[0047] Step 2: Charge the battery at a constant current rate of 1 / 2C until the battery voltage reaches the upper cut-off voltage;

[0048] Step 3: Switch to constant voltage charging mode until the charging current drops to 1 / 20C;

[0049] Step 4: Discharge the battery at a rate of 1 / 2C until the battery voltage drops to the lower cut-off voltage;

[0050] Step 5: After every 50 charge and discharge cycles, measure the amount of power provided by the battery when it is discharged from the upper cut-off voltage to the lower cut-off voltage to obtain health status information.

[0051] Step 6: Charge the battery at a constant current rate of 1 / 2C until the remaining capacity reaches 50% SOC. After standing for 1 hour, use an electrochemical workstation to measure the EIS data. The data is as follows: Figure 2 shown.

[0052] Then, the minimum impedance amplitude, the real axis intercept of the curve, the real and imaginary data of the semicircle vertex, and the real and imaginary data of the curve inflection point were selected as possible characteristic parameters in the EIS data, and the Pearson correlation coefficient was used to evaluate the correlation between each parameter and SOH. The results are shown in Figure 2. Figure 3 As shown. By comparing the correlation, the minimum impedance amplitude and the curve real axis intercept can be selected as the characteristic parameters reflecting the SOH change. The minimum impedance amplitude and curve real axis intercept data corresponding to each SOH are extracted, and multiple linear regression is selected for fitting. The fitting results of the y=A0+A1×x1+A2×x2 model are A0=1.75579, A1=-75.47309, A2=33.10226. The above steps 1 to 6 are performed on the lithium-ion battery numbered BAT002 to obtain the high-frequency EIS data, as shown Figure 4 As shown in the figure, the minimum impedance amplitude and real axis intercept of BAT002 at 0 cycles and 100 cycles were extracted and input into the fitting relationship function, and the calculated SOH values were 102.83% and 94.41%, respectively. Compared with the measured values of 101.29% and 93.83%, the SOH prediction errors were 1.52% and 0.62%, respectively.

[0053] In summary, the present invention proposes a method for rapid estimation of the SOH of lithium-ion batteries based on electrochemical impedance spectroscopy. By detecting electrochemical impedance spectroscopy (EIS) at multiple aging levels, the minimum impedance amplitude and the real axis intercept of the curve are selected as characteristic parameters through Pearson correlation analysis. The functional relationship between the minimum impedance amplitude and the real axis intercept of the curve and the battery aging level is obtained using multivariate linear regression fitting. The health status can be obtained by simply extracting the characteristic parameters from the high-frequency EIS data of the battery to be tested and inputting them into the function, thereby completing the SOH estimation of the aged battery. Compared with traditional SOH estimation methods for aged batteries, the rapid SOH estimation method for lithium-ion batteries proposed in the present invention uses only two features of the high-frequency part of the EIS to quickly estimate the SOH. It does not require complex models and full-spectrum EIS data with high time costs, thereby improving the efficiency of estimating the health status of aged batteries and providing guarantees for the efficient sorting and cascade utilization of retired batteries.

[0054] The above examples are intended only to illustrate the basic principles and effectiveness of the present invention and are not intended to limit the boundaries of its practical application. Relevant professionals are fully entitled to make appropriate adjustments and innovative practices without departing from the core concept and scope of application of the present invention. Therefore, any equivalent modifications or changes guided by the spirit and technical ideas of the present invention are within the scope of protection of the present invention.

Claims

1. A method for rapid estimation of SOH of lithium-ion batteries based on electrochemical impedance spectroscopy, characterized in that: The following steps are involved: Step 1: Perform charge and discharge cycle operations on the lithium-ion battery and record the EIS curves of the battery at different aging levels; Step 2: Use the Pearson correlation coefficient to measure the correlation between the minimum impedance amplitude, the real axis intercept of the curve, the real and imaginary parts of the semicircle vertex, the real and imaginary parts of the inflection point of the curve and the battery health status in the EIS. The minimum impedance amplitude and the real axis intercept of the curve are selected as the characteristic parameters that significantly reflect the SOH change; Step 3: Use curve fitting to establish the relationship between characteristic parameters and SOH; Step 4: Input the characteristic parameters of the high-frequency EIS data of the battery to be tested into the fitted function to obtain the health status of the battery.

2. The method for rapid estimation of SOH of lithium-ion batteries based on electrochemical impedance spectroscopy according to claim 1, characterized in that: The step 1 is specifically as follows: Step 1-1: Place the lithium-ion battery at 25°C for 3 hours; Step 1-2: Charge the battery at a constant current rate of 1 / 2C until the battery voltage reaches the upper cut-off voltage; Step 1-3: Switch to constant voltage charging mode until the charging current drops to 1 / 20C; Step 1-4: Discharge the battery at a 1 / 2C rate until the battery voltage drops to the lower cut-off voltage; Step 1-5: After every 50 charge and discharge cycles, measure the amount of power provided by the battery when it is discharged from the upper cut-off voltage to the lower cut-off voltage to obtain health status information, where SOH is defined as where Q available is the available discharge capacity, Q nominal is the nominal discharge capacity; Step 1-6: Charge the battery at a constant current of 1 / 2C until the remaining capacity reaches 50% SOC. After standing for 1 hour, measure the EIS data using an electrochemical workstation.

3. The method for rapid estimation of SOH of lithium-ion batteries based on electrochemical impedance spectroscopy according to claim 1, characterized in that: In step 2, the method for selecting characteristic parameters from the EIS curve is specifically as follows: Step 2-1: Extract characteristic parameter groups from the EIS curve, including the minimum impedance amplitude, the real axis intercept of the curve, the real and imaginary part data of the semicircle vertex, and the real and imaginary part data of the curve inflection point; Step 2-2: Use the Pearson correlation coefficient to measure the correlation between each parameter in the parameter group and the SOH. The Pearson correlation coefficient range is [-1, 1]. The closer the value is to 1 or -1, the stronger the correlation is; the closer the value is to 0, the weaker the correlation is. The calculation formula is: Among them, X and Y are feature variables and output variables respectively. and are their means respectively; Step 2-3: Compare the Pearson correlation coefficients corresponding to the parameters, and select the minimum impedance amplitude and the real axis intercept of the curve that can most significantly reflect the SOH change as characteristic parameters.

4. The method for rapid estimation of SOH of lithium-ion batteries based on electrochemical impedance spectroscopy according to claim 1, characterized in that: In step 3, the process of constructing the functional relationship between the characteristic parameters and the SOH is specifically as follows: Step 3-1: Select multiple linear regression as the fitting model; Step 3-2: Fit the values of the parameters in the fitting model and establish a functional relationship between the minimum impedance amplitude and the real axis intercept of the curve and the battery health status.

5. The method for rapid estimation of SOH of lithium-ion batteries based on electrochemical impedance spectroscopy according to claim 1, characterized in that: In step 4, the real-time SOH calculation is specifically as follows: Step 4-1: Measure the EIS curve of the high-frequency part of the battery and extract the minimum impedance amplitude and the real axis intercept of the curve; Step 4-2: Input the extracted minimum impedance amplitude and the real axis intercept of the curve into the fitted function curve; Step 4-3: The function outputs the calculated SOH value of the battery.

6. A computer 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, the steps of the method according to any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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