Long-life lithium-ion battery life prediction evaluation method

Abstract

This invention belongs to the field of lithium-ion battery technology, specifically relating to a method for predicting and evaluating the lifespan of long-life lithium-ion batteries. Take 3n lithium-ion batteries manufactured under the same conditions and test their initial capacity, internal resistance, voltage, and electrochemical impedance spectroscopy (EIS) at 25°C. Cycle the lithium-ion batteries under temperature conditions T1 / T2 / T3 / ·· / Tn, and test their EIS impedance, capacity, and internal resistance after 100, 500, 1000, and 2000 cycles until the battery reaches 80% of its initial capacity. Take 3n batteries and leave them under temperature conditions T1 / T2 / T3 / ·· / Tn only, where n is greater than 3, for 0h, 500h, 1000h, 1500h, and 2000h. During this period, measure the battery's DC internal resistance (DCR), capacity, and internal resistance (ACR). Add a logic formula for data learning calculation to fit the data. Based on the usage conditions, confirm the degree of capacity decay and impedance to determine the lifespan termination point. The default battery lifespan termination is required when the capacity decays to 70% of the nominal capacity. The method of this invention provides a lithium-ion battery life prediction method. This method has the advantages of low cost, simple equipment requirements, and short preparation steps, and has the ability to accurately evaluate the life of long-life lithium-ion batteries.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery technology, specifically relating to a method for predicting and evaluating the lifespan of long-life lithium-ion batteries. Background Technology

[0002] Lithium-ion batteries are widely used as the primary energy storage device in military electronics, aerospace electronics, electric vehicles, and various portable electronic devices (such as laptops, digital cameras, tablets, and mobile phones) due to their long lifespan, fast charging, high energy density, small size, and pollution-free operation. However, in practical applications, the capacity of lithium-ion batteries decreases with the increase of charge-discharge cycles, and their performance gradually degrades, leading to battery life failure issues that may cause safety problems. Therefore, battery life prediction is particularly important.

[0003] Lithium-ion batteries experience lifespan degradation throughout their entire lifecycle. This degradation primarily refers to the gradual deterioration of factors affecting discharge capacity, such as the physicochemical properties of the positive and negative electrode active materials, the adhesion strength of the binder to the coating, and the quality of the separator, during cyclic charging and discharging. Unexpected battery lifespan termination often leads to system failure. Therefore, predicting and analyzing battery degradation can provide timely and effective maintenance measures and battery replacement decisions, which is crucial for improving system reliability and preventing catastrophic accidents. During long-term use, a series of internal physicochemical changes cause the discharge capacity of lithium iron phosphate batteries to gradually decline, i.e., the battery's state of health (state of charge) gradually decreases. While the internal resistance of a battery measured using impedance spectroscopy changes relatively little during degradation and its impact on the overall system is often negligible, it microscopically reveals the extent of irreversible reactions within the battery's internal structure.

[0004] Methods for predicting the remaining life of lithium-ion batteries can be categorized into three types: model-based methods, data-driven methods, and fusion methods. Model-based methods can better reflect the physical and electrochemical characteristics of the battery; however, they struggle to monitor the battery's internal state, and accurate physical models are often difficult to obtain. Model-based methods typically use prior knowledge of the product lifecycle to construct mathematical functions describing the system's physical characteristics and failure modes. Based on this, a mathematical model reflecting the physical laws of system performance degradation is established to delve into the essence of the system and obtain more accurate prediction results. Commonly used methods include Kalman filtering, extended Kalman filtering, and particle filtering. Data-driven methods are more popular due to their flexibility and ease of operation. However, data-driven methods are highly dependent on data; uncertainty or incompleteness in the data can significantly affect their performance. Data-driven methods typically extract typical parameters from sensor data (e.g., voltage, current, temperature, time).

[0005] Single methods are often insufficient to accurately describe the nonlinearity of battery degradation and adequately adapt to constantly changing battery operating conditions. Furthermore, foreign predictions of lithium-ion battery life mainly focus on modeling and algorithms, without comprehensive evaluation based on actual operating conditions, resulting in lower accuracy. A combination of human experience and algorithmic evaluation is needed to solve the life assessment of long-life lithium-ion batteries, especially ultra-long-life batteries. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides a method for predicting and evaluating the lifespan of long-life lithium-ion batteries. This method is based on measured data such as battery impedance, high-temperature aging, and calendar storage, as well as data learning methods to predict the lifespan of lithium-ion batteries. The prediction accuracy is improved by comparing the data with measured values.

[0007] The present invention provides a method for predicting and evaluating the lifespan of long-life lithium-ion batteries, the specific steps of which are as follows:

[0008] (1) Take out 3n lithium-ion batteries manufactured under the same conditions and test their initial capacity, internal resistance, voltage and electrochemical impedance spectroscopy (EIS) at 25°C;

[0009] (2) Cycle the lithium-ion battery under temperature conditions T1 / T2 / T3 / ··· / Tn, where n is greater than 3, and the cycle current is the nominal current of the battery. Test the EIS impedance, capacity and internal resistance when the battery is cycled 100 times, 500 times, 1000 times and 2000 times until it reaches 80% of its initial capacity.

[0010] (3) Take 3n batteries of the same model as (1) and place them under the temperature conditions of T1 / T2 / T3 / ·· / Tn, where n is greater than 3, and the placement time is 0h, 500h, 1000h, 1500h and 2000h. During this period, measure the DC internal resistance (DCR), capacity and internal resistance (ACR) of the batteries.

[0011] (4) Add logical formulas for fitting to the data in steps (2) and (3) respectively, and merge and fit according to the usage conditions of different batteries. According to the type and design of the battery, the R0 ~ R4 values ​​in the EIS four-circuit method are the most different with the degradation. The four-circuit method is used to represent R0: battery internal resistance (SEI resistance), R1: positive electrode internal resistance; R2: negative electrode internal resistance; R3: electrolyte diffusion resistance;

[0012] R0 / R1 / R2 / R3, Warburg coefficient (σ) represents the slope of the fitted line, ω represents the angular frequency in the low-frequency region, and Li + The diffusion coefficient is closely related to the Warburg coefficient (σ).

[191] The fitting calculation formula is as follows:

[0013] Z' = Re + Rct + σω −0.5 (1)

[0014] D Li + = R 2 T 2 / 2A 2 n 4 F 4 C 2 σ 2 (2)

[0015] In the formula, R is the ideal gas constant, T is the absolute temperature, A is the active area of ​​the electrode, n is the number of electrons lost or reduced in each molecule, F is the Faraday constant, C is the molar concentration of lithium ions intercalated in the LFP particles, and σ is the Warburg coefficient. This is determined by analyzing Z′ and ω in the low-frequency region. -0.5 The linear fit was obtained.

[0016] Before performing data learning calculations (Formula 1 and Formula 2), take the same type of battery and repeat the test according to steps (1) to (4);

[0017] Based on the operating conditions, confirm the degree of capacity degradation and the increase in impedance to determine the end-of-life point. The default battery life ends when the capacity degradation reaches 70% of the nominal capacity.

[0018] According to the long-life lithium-ion battery life prediction and evaluation method described in the article, the batteries with the same manufacturing conditions described in step (1) are products with the same structure manufactured using the same or similar equipment, and use the same positive electrode material, negative electrode material, electrolyte, foil and corresponding auxiliary materials.

[0019] According to the long-life lithium-ion battery life prediction and evaluation method described in the article, the selection of T1 / T2 / T3 / ··· / Tn in step (2) is based on the battery design temperature range, including the upper and lower limits of the battery design operating temperature and the operating conditions, and is the object of segmented test evaluation.

[0020] According to the long-life lithium-ion battery life prediction and evaluation method described in the article, the current selection in step (2) is based on the designed battery nominal current cycle and adjusted according to the actual current used by the battery.

[0021] According to the long-life lithium-ion battery life prediction and evaluation method described in the article, the test formula for DC internal resistance (DCR) in step (3) is DCR=(U1-U2) / (I2-I1), where U1 is the end voltage of step (2), U2 is the end voltage of step (3), I1 is the discharge current of step (2), and I2 is the discharge current of step (3).

[0022] According to the long-life lithium-ion battery life prediction and evaluation method described in the article, the fitting logic described in step (4) is used to calculate the formula of the attenuation logic using battery capacity and impedance data.

[0023] According to the long-life lithium-ion battery life prediction and evaluation method described in the article, the repeated operation of step (5) is to modify and learn the impedance data of the logic calculation in step (4), and summarize the changing trend of the battery system and fit the formula based on the growth law of the four resistors R0, R1, R2, and R3, the capacity decay rate, the charging and discharging time, the battery resting time and other parameters.

[0024] Compared with existing accelerated aging methods, the essential features and inventiveness of this invention are reflected in:

[0025] 1. The method of the present invention provides a lithium-ion battery life prediction method, which has the advantages of low cost, simple equipment requirements, short preparation process, and the ability to accurately evaluate the life of long-life lithium-ion batteries.

[0026] 2. This invention accelerates the actual lifespan evaluation of long-life lithium-ion batteries by creatively using different degradation rates of batteries under varying temperature conditions. It provides a practical application evaluation method and offers data support for maintenance cycles in end-use products. Temperature selection includes theoretically designed upper and lower temperature limits, set based on the electrochemical window size of the battery design through a combination of theoretical design and experimental measurements.

[0027] 3. In this invention, evaluation is conducted using two modes: continuous charge-discharge cycling and static aging. Weighted calculations can be performed based on actual battery usage conditions, including modes with different usage conditions, resulting in high compatibility.

[0028] 4. This invention employs repeated testing to modify and learn the current logic. Accuracy gradually converges and improves. Furthermore, it can predict real-world data with higher accuracy, unlike pure logical operations or data processing. Attached Figure Description

[0029] Figure 1 EIS curve of 18650 lithium iron phosphate battery in Example 1

[0030] Figure 2 Lifetime fitting curve of 18650 lithium iron phosphate battery Detailed Implementation

[0031] The present invention will be further described below with reference to specific embodiments, but the present invention is not limited to the following embodiments, and the methods described are conventional methods unless otherwise specified.

[0032] The specific implementation process of the long-life lithium-ion battery life prediction and evaluation method of the present invention includes the following steps:

[0033] First, 3n lithium-ion batteries manufactured under the same conditions were tested at 25°C for initial capacity, internal resistance, voltage, and electrochemical impedance spectroscopy (EIS). Batteries manufactured under the same conditions are products with the same structure manufactured using the same or similar equipment, and there are no significant differences in the positive electrode material, negative electrode material, electrolyte, foil, and corresponding auxiliary materials.

[0034] The lithium-ion battery was cycled at three or more temperature conditions (T1 / T2 / T3 / Tn) with the cycle current being the nominal current of the battery. The battery was tested for 100, 500, and 1000 cycles until it reached 80% of its initial capacity. Then, the EIS, capacity, and internal resistance of the same battery were measured at T1 / T2 / T3 / Tn and after cycling to 80% of its initial capacity (capacity retention rate SOH of 80%).

[0035] These are the objects to be evaluated through segmented testing based on the electrochemical window size of the battery design, including upper and lower limits and usage conditions. The current is selected according to the designed nominal battery current cycling and adjusted according to the actual current used by the battery.

[0036] In the same batch, 3n batteries were placed under temperature conditions of T1 / T2 / T3 / Tn for 0h, 500h, 1000h, 1500h, and 2000h. During this period, the DC internal resistance (DCR), capacity, and internal resistance (ACR) of the batteries were tested. The test data were then added to the logic for fitting, and finally combined and fitted according to the different operating conditions of the batteries.

[0037] Flowchart of the above steps

[0038] Logical formulas for fitting are added to the above data. The data is combined and fitted according to the usage conditions of different batteries. Based on the battery type and design, the R0 to R4 values ​​in the EIS four-circuit method vary the most with degradation. The four-circuit method is used to represent R0: battery internal resistance (SEI resistance), R1: positive electrode internal resistance; R2: negative electrode internal resistance; R3: electrolyte diffusion resistance.

[0039] R0 / R1 / R2 / R3, Warburg coefficient (σ) represents the slope of the fitted line, ω represents the angular frequency in the low-frequency region, and Li + The diffusion coefficient is closely related to the Warburg coefficient (σ).

[191] The fitting calculation formula is as follows:

[0040] Z' = Re + Rct + σω −0.5 (1)

[0041] D Li + = R 2 T 2 / 2A 2 n 4 F 4 C 2 σ 2 (2)

[0042] In the formula, R is the ideal gas constant, T is the absolute temperature, A is the active area of ​​the electrode, n is the number of electrons lost or reduced in each molecule, F is the Faraday constant, C is the molar concentration of lithium ions intercalated in the LFP particles, and σ is the Warburg coefficient. This is determined by analyzing Z′ and ω in the low-frequency region. -0.5 The linear fit was obtained.

[0043] Repeated testing of other batches of batteries was performed to correct the logic and learn the data, resulting in battery aging data. The battery life was then predicted in real time based on the battery's usage parameters.

[0044] The invention will be described in detail using a specific lithium battery as an example. Example

[0045] 1) Nine lithium-ion batteries manufactured under the same conditions were tested at 25°C for initial capacity, internal resistance, voltage and electrochemical impedance spectroscopy (EIS).

[0046] 2) Cycle the lithium-ion battery at three or more temperature conditions: 45℃ / 65℃ / 80℃, with the cycle current being the battery's nominal current;

[0047] 3) Test the EIS, capacity, and internal resistance at 100, 500, 1000, and 2000 cycles and at 80% of the battery's initial capacity (with a retention rate of 80% SOH);

[0048] 4) Another batch of 9 batteries were placed at three or more temperatures of 45℃ / 65℃ / 80℃ for 0h, 500h, 1000h, 1500h, and 2000h respectively.

[0049] 5) During the test, the battery’s DC internal resistance (DCR), capacity, and internal resistance (ACR) are tested. The test data are then logically fitted separately and combined according to different battery operating conditions.

[0050] 6) Confirm the degree of capacity decay and impedance increase according to the operating conditions to determine the end point of the battery life. Generally, the battery life ends when the capacity decays to 70% of the nominal capacity. Example

[0051] 1) Twelve lithium-ion batteries manufactured under the same conditions were tested at 25°C for initial capacity, internal resistance, voltage and electrochemical impedance spectroscopy (EIS).

[0052] 2) The lithium-ion battery is cycled at three or more temperature conditions of 5℃ / 45℃ / 60℃ / 85℃, and the cycle current is the nominal current of the battery.

[0053] 3) Test cycles of 100, 500, 1000, and 2000 times. Also, measure the EIS, capacity, and internal resistance when the battery reaches 80% of its initial capacity (sufficiency of charge retention (SOH) is 80%).

[0054] 4) Place 12 batteries from the same batch at three or more temperature conditions (5℃ / 45℃ / 65℃ / 85℃) for 0h, 500h, 1000h, 1500h, and 2000h respectively; during this period, test the battery’s DC internal resistance (DCR), capacity, and internal resistance (ACR).

[0055] 5) The above test data are logically fitted separately and then combined and fitted according to different battery usage conditions.

[0056] 6) Confirm the degree of capacity decay and impedance increase according to the operating conditions to determine the end point of the battery life. Generally, the battery life ends when the capacity decays to 70% of the nominal capacity.

[0057] This invention features high repeatability, low computational complexity, and high accuracy. Compared with existing accelerated aging methods, the essential characteristics and inventiveness of this invention are reflected in:

[0058] 1. The method of the present invention provides a lithium-ion battery life prediction method, which has the advantages of low cost, simple equipment requirements, short preparation process, and the ability to accurately evaluate the life of long-life lithium-ion batteries.

[0059] 2. This invention accelerates the actual lifespan evaluation of long-life lithium-ion batteries by creatively using different degradation rates of batteries under varying temperature conditions. It provides a practical application evaluation method and offers data support for maintenance cycles in end-use products. Temperature selection includes theoretically designed upper and lower temperature limits, set based on the electrochemical window size of the battery design through a combination of theoretical design and experimental measurements.

[0060] 3. In this invention, evaluation is conducted using two modes: continuous charge-discharge cycling and static aging. Weighted calculations can be performed based on actual battery usage conditions, including modes with different usage conditions, resulting in high compatibility.

[0061] 4. This invention employs repeated testing to modify and learn the current logic. Accuracy gradually converges and improves. Furthermore, it can predict real-world data with higher accuracy, unlike pure logical operations or data processing.

[0062] This invention has the advantages of simple process steps, short process, low equipment requirements for the whole route, good data stability and reduced energy consumption in the preparation of lithium iron phosphate cathode materials, and has important value for industrial promotion and application.

Claims

1. A method for predicting and evaluating the lifespan of long-life lithium-ion batteries, characterized in that, The specific steps are as follows: (1) Take out 3n lithium-ion batteries manufactured under the same conditions and test their initial capacity, internal resistance, voltage and electrochemical impedance spectroscopy (EIS) at 25°C; (2) Cycle the lithium-ion battery under temperature conditions T1 / T2 / T3 / ··· / Tn, where n is greater than 3, and the cycle current is the nominal current of the battery. Test the EIS impedance, capacity and internal resistance when the battery is cycled 100 times, 500 times, 1000 times and 2000 times until it reaches 80% of its initial capacity. (3) Take 3n batteries of the same model as (1) and place them under the temperature conditions of T1 / T2 / T3 / ·· / Tn, where n is greater than 3, and the placement time is 0h, 500h, 1000h, 1500h and 2000h. During this period, measure the DC internal resistance DCR, capacity and internal resistance ACR of the battery. (4) Add logical formulas for fitting to the data in steps (2) and (3) respectively, and merge and fit according to the usage conditions of different batteries. According to the type and design of the battery, the R0 ~ R4 values ​​in the EIS four-circuit method are the most different with the degradation. The four-circuit method is used to represent R0: battery internal resistance SEI resistance, R1: positive electrode internal resistance; R2: negative electrode internal resistance; R3: electrolyte diffusion resistance; R0 / R1 / R2 / R3, Warburg coefficients σ represent the slope of the fitted line, ω represents the angular frequency in the low-frequency region, and Li + The diffusion coefficient is closely related to the Warburg coefficient σ, and the fitting calculation formula is as follows: Z' = Re + Rct + σω -0.5 (1) D Li + =R 2 T 2 / 2A 2 n 4 F 4 C 2 σ 2 (2) In the formula, R is the ideal gas constant, T is the absolute temperature, A is the active area of ​​the electrode, n is the number of electrons lost or reduced in each molecule, F is the Faraday constant, C is the molar concentration of lithium ions intercalated in the LFP particles, and σ is the Warburg coefficient. This is determined by analyzing Z′ and ω in the low-frequency region. -0.5 The linear fit was obtained; Before performing data learning calculations (1) and (2), take the same type of battery and repeat the test according to steps (1) to (4); Based on the operating conditions, confirm the degree of capacity degradation and the increase in impedance to determine the end-of-life point. The default battery life ends when the capacity degradation reaches 70% of the nominal capacity.

2. The method for predicting and evaluating the lifespan of long-life lithium-ion batteries according to claim 1, characterized in that, The batteries manufactured under the same conditions described in step (1) are products with the same structure manufactured using the same or similar equipment, and use the same positive electrode material, negative electrode material, electrolyte, foil and corresponding auxiliary materials.

3. The method for predicting and evaluating the lifespan of long-life lithium-ion batteries according to claim 1, characterized in that, In step (2), the selection of T1 / T2 / T3 / ··· / Tn is based on the battery design temperature range, which includes the upper and lower limits of the battery design operating temperature and the operating conditions.

4. The method for predicting and evaluating the lifespan of long-life lithium-ion batteries according to claim 1, characterized in that, In step (2), the current is selected according to the designed nominal current of the battery and adjusted according to the actual current used by the battery.

5. The method for predicting and evaluating the lifespan of long-life lithium-ion batteries according to claim 1, characterized in that, in The test formula for DC internal resistance DCR in step (3) is DCR=(U1-U2) / (I2-I1), where U1 is the end voltage of step (2), U2 is the end voltage of step (3), I1 is the discharge current of step (2), and I2 is the discharge current of step (3).

6. The method for predicting and evaluating the lifespan of long-life lithium-ion batteries according to claim 1, characterized in that, The fitting logic described in step (4) is used to calculate the formula for the attenuation logic using the battery capacity and impedance data.

7. The method for predicting and evaluating the lifespan of long-life lithium-ion batteries according to claim 1, characterized in that, The repeated operation in step (5) is to modify and learn the impedance data of the logical calculation in step (4), and summarize the changing trend of the battery system and fit the formula based on the growth law of the four resistors R0, R1, R2, and R3, the capacity decay rate, the charging and discharging time, the battery resting time, and other parameters.

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