Lithium ion battery capacity prediction method

Abstract

The invention discloses a method for predicting the capacity of a lithium ion battery. The method specifically comprises the following steps: S1, selecting and preparing a sample; s2, performing a complete charging and discharging test; s3, carrying out a discharge test under a specific cut-off voltage V; the invention relates to the technical field of lithium ion battery preparation. According to the method for predicting the capacity of the lithium ion battery, complete charging and discharging testing is not needed, the testing time is greatly shortened, and the production efficiency is improved; the step of complete discharging is reduced, so that the energy consumption in the testing process is reduced; battery loss is reduced; extra loss of the battery caused by complete discharge is avoided, and the service life of the battery is prolonged; the accuracy is high: a reliable mathematical model is established through correlation analysis of a large amount of sample data, and the prediction accuracy is ensured; the whole prediction process needs continuous monitoring and optimization to ensure that the model can adapt to the change of the battery manufacturing process and the difference of the battery performance.

Description

Technical Field

[0001] The invention relates to the technical field of lithium ion battery preparation, in particular to a method for predicting the capacity of a lithium ion battery. Background Art

[0002] Lithium-ion batteries, commonly known as rocking chair batteries, are a general term for batteries with lithium-ion embedded compounds as positive electrode materials. They are a type of secondary battery that mainly relies on the movement of lithium ions between the positive and negative electrodes to work. Lithium-ion batteries are composed of positive and negative electrodes, electrolytes and separators, plus positive and negative leads, battery shells, safety devices, etc. During the charging and discharging process of lithium-ion batteries, Li+ is embedded and deintercalated back and forth between the two electrodes: during charging, Li+ is deintercalated from the positive electrode and embedded into the negative electrode through the electrolyte, and the negative electrode is in a lithium-rich state; during discharging, the opposite is true. Mobile phones and laptops all use lithium-ion batteries, which are usually commonly referred to as lithium batteries. Petroleum coke and graphite are non-toxic and abundant in resources as negative electrode materials. Lithium ions are embedded in carbon, which overcomes the high activity of lithium and solves the safety problems of traditional lithium batteries. The positive electrode LixCoO2 can achieve a high level in charging and discharging performance and life, reducing costs. In short, the comprehensive performance of lithium-ion batteries has been improved.

[0003] Lithium-ion batteries are widely used in mobile electronic devices, electric vehicles, energy storage systems and other fields due to their high energy density, long cycle life and environmental protection characteristics. However, in the battery production process, the traditional capacity detection method requires full discharge to calculate the capacity. This process is time-consuming and has certain losses to the battery, while increasing production energy consumption and costs. Therefore, developing a fast and accurate battery capacity prediction method is of great significance to improving production efficiency and reducing costs. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for predicting the capacity of a lithium-ion battery, which solves the problems mentioned in the above background technology.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: A method for predicting the capacity of a lithium-ion battery, specifically comprising the following steps:

[0006] S1. Sample selection and preparation: First, select a certain number of lithium-ion battery samples, conduct a full charge and discharge test under standard conditions, and record the full discharge capacity of each sample;

[0007] S2. Full charge and discharge test: When charging, use a standard charger to charge each sample battery to the maximum voltage or full charge state recommended by the manufacturer. When discharging, under standard conditions, fully discharge the battery to the specified termination voltage and record the full discharge capacity C_total of each sample.

[0008] S3. Discharge test at a specific cut-off voltage V: When discharging again, the fully discharged battery is charged again to a fully charged state and discharged to V: Under the same conditions, each sample battery is discharged until a specific cut-off voltage V is reached, and the capacity C_V discharged during this process is recorded;

[0009] S4, Data Collection and Arrangement: Collect data: Arrange the C_total and C_V data of each sample battery; Clean data: Check and process abnormal values ​​or erroneous data to ensure data quality;

[0010] S5. Correlation analysis: Use statistical software to analyze the relationship between C_V and C_total. Based on the analysis results, select the most appropriate mathematical model to describe the relationship between C_V and C_total. By adjusting the model parameters, optimize the prediction performance of the model.

[0011] S6. Model validation and optimization: Use part of the sample data as a training set to train the model, and the rest as a validation set to evaluate the prediction performance of the model. Adjust the model parameters or select a more appropriate model type based on the validation results to improve the prediction accuracy.

[0012] S7, prediction process: discharge the new battery to be tested to a specific cut-off voltage V, and record its C_V value, input the C_V value of the battery to be tested into the established model, calculate and output its predicted capacity C_predicted when fully discharged;

[0013] S8. Result evaluation: Compare the predicted capacity C_predicted with the actual capacity C_actual obtained through the full discharge test to evaluate the accuracy of the prediction.

[0014] Preferably, in S1, it is ensured that all sample batteries are in the same or similar initial state before testing, such as the same charging state, temperature, etc.

[0015] Preferably, in S2, the standard discharge conditions include constant current discharge and specific temperature, and the termination voltage is 2.5V or lower.

[0016] Preferably, in S3, the cut-off voltage V, this V value should be selected based on previous research and experience to balance the test time and prediction accuracy.

[0017] Preferably, in S5, the statistical software uses one of a scatter plot, a correlation coefficient or a regression analysis, and the mathematical model uses one of a linear regression, a polynomial regression or a machine learning model.

[0018] Beneficial Effects

[0019] The present invention provides a method for predicting the capacity of a lithium-ion battery. Compared with the prior art, the method has the following beneficial effects: the method for predicting the capacity of a lithium-ion battery specifically includes the following steps: S1, sample selection and preparation; S2, full charge and discharge test; S3, discharge test under a specific cut-off voltage V; S4, data collection and collation; S5, correlation analysis; S6, model verification and optimization; S7, prediction process; S8, result evaluation; starting from collecting sample data, including performing a full charge and discharge test to obtain the full discharge capacity of each sample, and then charging the battery to a fully charged state, then discharging each sample battery to a specific cut-off voltage V and recording the discharge capacity in the process, after collecting the C_V data of all samples, performing a correlation analysis, establishing a mathematical model between C_V and C_total, and finally using this model to be tested The battery capacity is predicted, that is, after being discharged to a specific cut-off voltage V, the capacity at full discharge is predicted based on its C_V value, which improves production efficiency: there is no need to conduct a complete charge and discharge test, which greatly shortens the test time and improves production efficiency; reduces energy consumption: since the steps of full discharge are reduced, the energy consumption during the test is reduced; reduces battery loss: avoids additional battery loss caused by full discharge and extends the battery life; high accuracy: through correlation analysis of a large amount of sample data, a reliable mathematical model is established to ensure the accuracy of the prediction; the entire prediction process requires continuous monitoring and optimization to ensure that the model can adapt to changes in battery manufacturing processes and differences in battery performance. At the same time, with the advancement of technology and the accumulation of data, the prediction model can be continuously improved to improve the accuracy and reliability of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 The present invention provides a technical solution: a method for predicting the capacity of a lithium-ion battery, which specifically comprises the following steps:

[0023] S1. Sample selection and preparation: First, select a certain number of lithium-ion battery samples, conduct a full charge and discharge test under standard conditions, and record the full discharge capacity of each sample;

[0024] S2. Full charge and discharge test: When charging, use a standard charger to charge each sample battery to the maximum voltage or full charge state recommended by the manufacturer. When discharging, under standard conditions, fully discharge the battery to the specified termination voltage and record the full discharge capacity C_total of each sample.

[0025] S3. Discharge test at a specific cut-off voltage V: When discharging again, the fully discharged battery is charged again to a fully charged state and discharged to V: Under the same conditions, each sample battery is discharged until a specific cut-off voltage V is reached, and the capacity C_V discharged during this process is recorded;

[0026] S4, Data Collection and Arrangement: Collect data: Arrange the C_total and C_V data of each sample battery; Clean data: Check and process abnormal values ​​or erroneous data to ensure data quality;

[0027] S5. Correlation analysis: Use statistical software to analyze the relationship between C_V and C_total. Based on the analysis results, select the most appropriate mathematical model to describe the relationship between C_V and C_total. By adjusting the model parameters, optimize the prediction performance of the model.

[0028] S6. Model validation and optimization: Use part of the sample data as a training set to train the model, and the rest as a validation set to evaluate the prediction performance of the model. Adjust the model parameters or select a more appropriate model type based on the validation results to improve the prediction accuracy.

[0029] S7, prediction process: discharge the new battery to be tested to a specific cut-off voltage V, and record its C_V value, input the C_V value of the battery to be tested into the established model, calculate and output its predicted capacity C_predicted when fully discharged;

[0030] S8. Result evaluation: Compare the predicted capacity C_predicted with the actual capacity C_actual obtained through the full discharge test to evaluate the accuracy of the prediction.

[0031] In the present invention, in S1, it is ensured that all sample batteries are in the same or similar initial state before testing, such as the same charging state, temperature, etc.

[0032] In the present invention, in S2, the standard conditions for discharge include constant current discharge and specific temperature, and the termination voltage is 2.5V or lower.

[0033] In the present invention, in S3, the cut-off voltage V, this V value should be selected based on previous research and experience to balance the test time and prediction accuracy.

[0034] In the present invention, in S5, the statistical software uses one of a scatter plot, a correlation coefficient or a regression analysis, and the mathematical model uses one of a linear regression, a polynomial regression or a machine learning model.

[0035] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0036] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0037] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the capacity of a lithium-ion battery, characterized in that: The specific steps include: S1. Sample selection and preparation: First, select a certain number of lithium-ion battery samples, conduct a full charge and discharge test under standard conditions, and record the full discharge capacity of each sample; S2. Full charge and discharge test: When charging, use a standard charger to charge each sample battery to the maximum voltage or full charge state recommended by the manufacturer. When discharging, under standard conditions, fully discharge the battery to the specified termination voltage and record the full discharge capacity C_total of each sample. S3. Discharge test at a specific cut-off voltage V: When discharging again, the fully discharged battery is charged again to a fully charged state and discharged to V: Under the same conditions, each sample battery is discharged until a specific cut-off voltage V is reached, and the capacity C_V discharged during this process is recorded; S4, Data Collection and Arrangement: Collect data: Arrange the C_total and C_V data of each sample battery; Clean data: Check and process abnormal values ​​or erroneous data to ensure data quality; S5. Correlation analysis: Use statistical software to analyze the relationship between C_V and C_total. Based on the analysis results, select the most appropriate mathematical model to describe the relationship between C_V and C_total. By adjusting the model parameters, optimize the prediction performance of the model. S6. Model validation and optimization: Use part of the sample data as a training set to train the model, and the rest as a validation set to evaluate the prediction performance of the model. Adjust the model parameters or select a more appropriate model type based on the validation results to improve the prediction accuracy. S7, prediction process: discharge the new battery to be tested to a specific cut-off voltage V, and record its C_V value, input the C_V value of the battery to be tested into the established model, calculate and output its predicted capacity C_predicted when fully discharged; S8. Result evaluation: Compare the predicted capacity C_predicted with the actual capacity C_actual obtained through the full discharge test to evaluate the accuracy of the prediction.

2. The method for predicting the capacity of a lithium-ion battery according to claim 1, characterized in that: In S1, it is ensured that all sample batteries are in the same or similar initial state before testing, such as the same charging state, temperature, etc.

3. The method for predicting the capacity of a lithium-ion battery according to claim 1, characterized in that: In S2, the standard discharge conditions include constant current discharge and a specific temperature, and the termination voltage is 2.5V or lower.

4. The method for predicting the capacity of a lithium-ion battery according to claim 1, characterized in that: In the S3, the cut-off voltage V, this V value should be selected based on previous research and experience to balance test time and prediction accuracy.

5. The method for predicting the capacity of a lithium-ion battery according to claim 1, characterized in that: In S5, the statistical software uses one of a scatter plot, a correlation coefficient or a regression analysis, and the mathematical model uses one of a linear regression, a polynomial regression or a machine learning model.

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