A method and system for predicting AC internal resistance based on the design parameters of wound battery cells.

Abstract

This invention relates to the field of lithium battery manufacturing technology, specifically to a method and system for predicting AC internal resistance based on the design parameters of a wound battery cell. The method includes: acquiring design parameters for multiple structural components of the battery cell; calculating the corresponding internal resistance components based on each design parameter; summing the internal resistance components to obtain the calculated AC internal resistance value of the battery cell; and substituting the calculated value into a pre-constructed correction model to output the final predicted AC internal resistance value. The correction model is obtained by linearly fitting the calculated AC internal resistance values ​​of multiple different battery cell models with measured values. This invention also provides a prediction system for executing the above method. This invention enables accurate prediction of AC internal resistance during the design stage before the finished battery cell is manufactured, effectively shortening the R&D cycle, reducing manufacturing costs, and providing reverse guidance for battery cell structure optimization.

Description

Technical Field

[0001] This invention relates to the field of lithium battery manufacturing technology, specifically to an AC internal resistance prediction method and system based on the design parameters of wound cells. Background Technology

[0002] Due to their compact structure, high production efficiency, and excellent energy density, wound lithium-ion cells have become the mainstream power solution in new energy vehicles, energy storage systems, and consumer electronics. Their internal structure involves the precise winding of positive electrode plates, negative electrode plates, and separators to form a multi-layered composite structure, which is then manufactured into a finished cell through processes such as tab welding, connector assembly, and casing. In this complex structure, the contact state, material properties, and geometric dimensions between the components directly determine the electrochemical performance and reliability of the cell.

[0003] Alternating Current Resistance (ACR) is one of the core indicators for evaluating battery cell performance, typically referring to the impedance value measured under a 1kHz AC signal. This frequency effectively avoids the effects of electrode polarization and electrochemical reactions. The measured impedance primarily reflects the ohmic internal resistance of the cell, including the bulk resistance of conductors such as terminals, connectors, and tabs, as well as the contact resistance between welding interfaces, coatings, and foils. The ACR value not only affects the cell's rate performance and energy efficiency but is also closely related to its heat generation characteristics, cycle life, and safety performance. Therefore, precise control of ACR is crucial in the cell design and manufacturing process.

[0004] In existing technologies, obtaining ACR (Acceptance Ratio) primarily relies on offline testing of finished battery cells. The specific method involves applying a sinusoidal current with a frequency of 1kHz to the battery, measuring the voltage response using lock-in amplification technology, and then calculating the impedance modulus. While this testing method is mature and reliable, it suffers from significant lag, as testing can only be performed after all manufacturing processes of the battery cell have been completed. If the test results do not meet design specifications, R&D personnel must retrospectively analyze possible causes, adjust design parameters, and re-produce samples. This design-prototyping-testing-modification cycle is time-consuming and costly, severely hindering the development efficiency of new lithium battery products.

[0005] More importantly, existing testing methods cannot quantitatively assess ACR during the design phase. Designers can only make rough estimates based on empirical formulas or by analogy with similar models, making it difficult to accurately predict the quantitative impact of changes in structural parameters (such as tab length, weld depth, foil thickness, etc.) on internal resistance. This black-box design model leads to a high reliance on trial and error in the R&D process, which not only prolongs the development cycle but also increases prototyping costs and wastes resources.

[0006] In summary, there is an urgent need to develop a method that can predict ACR (Acceleration Rate) during the cell design stage. This method would analyze the material properties and geometric parameters of the various structural components inside the cell, establish a theoretical calculation model, and then correct it using a small amount of measured data. This would achieve closed-loop optimization of design-prediction, significantly improving the R&D efficiency and design accuracy of lithium batteries. Summary of the Invention

[0007] The purpose of this invention is to provide an AC internal resistance prediction method and system based on the design parameters of wound battery cells, aiming to solve the core problem in the prior art that it is difficult to quantitatively predict the ACR value in the battery cell design stage, and to achieve accurate prediction of internal resistance in the early stage of design.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: In a first aspect, the present invention provides a method for predicting the AC internal resistance based on the design parameters of a wound battery cell, comprising the following steps: Obtain the design parameters of multiple structural components of the battery cell; Based on the design parameters of each structural component, calculate the internal resistance component corresponding to each component. The AC internal resistance of the battery cell is calculated by adding up all the internal resistance components. Based on the calculated AC internal resistance value, combined with the pre-constructed correction model, the predicted AC internal resistance value of the battery cell is output.

[0009] As a preferred embodiment of the present invention, the pre-built modified model is obtained through the following steps: Obtain the measured AC internal resistance values ​​of multiple different types of battery cells; The calculated AC internal resistance values ​​of each type of battery cell are fitted with the measured values ​​to obtain a corrected relationship.

[0010] As a preferred embodiment of the present invention, the expression of the modified relation is: Measured value = a × calculated value + b; Where a and b are the correction coefficients obtained from the fitting.

[0011] As a preferred embodiment of the present invention, the number of samples of different types of battery cells used for fitting is no less than 3 groups, the fitting method adopts univariate linear fitting, and the goodness of fit R of the fitted modified relation is... 2 Not less than 0.95.

[0012] As a preferred embodiment of the present invention, the internal resistance components corresponding to each component are: internal resistance of the pole post, internal resistance of the connecting piece, internal resistance of the tab, internal resistance of the foil, internal resistance of welding the connecting piece and the pole post, internal resistance of welding the tab and the connecting piece, and internal resistance of contact between the coating layer and the foil.

[0013] As a preferred embodiment of the present invention, the internal resistance components of each component are calculated using the following formula: R = ρ × L / S; Wherein, ρ is the resistivity corresponding to each structural component, and the structural components include poles, connecting pieces, tabs, foil, the welding interface between the connecting piece and the pole, the welding interface between the tab and the connecting piece, and the contact interface between the coating layer and the foil. L represents the design parameters corresponding to each structural component. The design parameters L include pole height, connecting piece current-carrying length, electrode tab length, foil width, welding depth between connecting piece and pole, welding depth between electrode tab and connecting piece, and electrode thickness. S represents the design parameters corresponding to each structural component. The design parameters S include the cross-sectional area of ​​the pole post, the cross-sectional area of ​​the connecting piece, the cross-sectional area of ​​the electrode lug, the average cross-sectional area of ​​the current-carrying path of the foil, the effective welding area between the connecting piece and the pole post, the effective welding area between the electrode lug and the connecting piece, and the planar area of ​​the electrode sheet.

[0014] As a preferred technical solution of the present invention, the predicted AC internal resistance value is used for performance evaluation or design optimization in the design stage before the manufacturing of the finished battery cell.

[0015] As a preferred embodiment of the present invention, the battery cell is a wound lithium-ion battery cell.

[0016] Secondly, the present invention provides a prediction system for performing the aforementioned ACR prediction method based on cell design parameters, comprising: The parameter acquisition module is used to acquire the design parameters of each structural component of the battery cell; The internal resistance calculation module is used to calculate the corresponding internal resistance components according to the design parameters of each structural component, and add the internal resistance components to obtain the AC internal resistance calculation value. The correction module is used to store the pre-built correction model and output the corrected AC internal resistance prediction value based on the AC internal resistance calculation value. The output module is used to output the predicted value of the AC internal resistance.

[0017] As a preferred technical solution of the present invention, it also includes an iterative optimization module, which is communicatively connected to the correction module and the output module respectively; the iterative optimization module is used to obtain the measured value of AC internal resistance after the mass production of the target cell, calculate the error between the predicted value and the measured value of AC internal resistance, and supplement the structural component design parameters, calculated value and measured value of AC internal resistance of the target cell as new sample data to the fitting sample set of the correction module, so as to iteratively optimize the correction relationship.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention analyzes the material properties and geometric parameters of various structural components within a battery cell (such as terminals, connecting pieces, tabs, foils, and welding interfaces), calculates each internal resistance component based on the physical formula for resistance (R=ρ×L / S), and sums them up. This allows for the calculation of the AC internal resistance value before the final battery cell is manufactured. This method changes the traditional R&D model of manufacturing first and then testing, moving the ACR evaluation node forward to the design stage, providing a feasible means for early prediction of battery cell performance. Based on theoretical internal resistance calculations, this invention constructs a univariate linear correction model based on measured data from multiple battery cell models. By performing univariate linear fitting between the calculated values ​​and measured values ​​of multiple different battery cell models, a corrected relationship is obtained. This correction model can effectively offset the systematic deviation between theoretical calculations and actual conditions, making the predicted values ​​closer to the actual test results. As shown in the example, after correction, the prediction error of the D-type battery cell decreased from 21.74% to 9.84%, verifying the accuracy and practicality of the method.

[0019] 2. This invention decomposes the AC internal resistance of the battery cell into seven components: the internal resistance of the terminal post, the internal resistance of the connecting piece, the internal resistance of the tab, the internal resistance of the foil, the internal resistance of the welding between the connecting piece and the terminal post, the internal resistance of the welding between the tab and the connecting piece, and the internal resistance of the contact between the coating layer and the foil. This covers the complete current conduction path from the terminal post to the electrode. This refined internal resistance decomposition not only improves the physical interpretability of the calculation model but also provides designers with a quantitative basis for identifying the main contributing factors of internal resistance and optimizing structural parameters accordingly.

[0020] 3. The prediction system provided by this invention includes an iterative optimization module, which can automatically supplement the fitted sample set with new sample data (including design parameters, calculated values, and measured values) after the battery cells are mass-produced and measured values ​​are obtained, and refit the corrected relationship. This closed-loop optimization mechanism enables the corrected model to continuously improve itself with the accumulation of samples, adapt to changes in different material systems, process conditions, and product specifications, and has strong generalization ability and engineering adaptability.

[0021] 4. By accurately predicting the ACR value during the design phase, R&D personnel can quickly assess the impact of different design schemes on internal resistance and select the optimal combination of structural parameters without conducting actual prototype manufacturing. This not only significantly reduces the number of rounds of sample manufacturing and testing, shortening the new product development cycle, but also effectively reduces material and labor costs. Simultaneously, precise internal resistance control helps improve the rate performance, energy efficiency, and cycle life of the battery cell, enhancing the product's market competitiveness. Attached Figure Description

[0022] Figure 1 This is a schematic diagram showing the fitting relationship between the calculated and measured ACR values ​​for different battery models. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the following detailed description of an AC internal resistance prediction method and system based on wound cell design parameters, in conjunction with the accompanying drawings and specific embodiments, will be provided. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Any simple modifications, equivalent substitutions, or improvements made by those skilled in the art based on the embodiments of this invention without inventive effort fall within the protection scope of this invention.

[0024] Example 1 This embodiment provides an AC internal resistance prediction method based on the design parameters of wound battery cells. It is applicable to wound lithium-ion battery cells and can be directly used for performance evaluation or design optimization in the design stage before the finished battery cell is manufactured. The method includes the following steps: S1. Obtain the design parameters of multiple structural components of the battery cell. First, obtain the design parameters for multiple structural components of the battery cell, including but not limited to: terminal height, terminal cross-sectional area, connecting piece current-carrying length, connecting piece cross-sectional area, tab length, tab cross-sectional area, foil width, average cross-sectional area of ​​the foil current-carrying path, welding depth, effective welding area, electrode thickness, electrode length, and electrode width. These parameters are derived from the battery cell's design drawings, raw material specifications, and welding process design documents, covering the core design indicators of all structural components and connection interfaces along the entire current path of the battery cell, while also matching the parameter requirements for subsequent internal resistance component calculations.

[0025] For each type of parameter, the corresponding values ​​on the positive and negative sides must be collected separately.

[0026] S2. Based on the design parameters of each structural component, calculate the internal resistance component corresponding to each component. Calculate the internal resistance of the positive and negative terminals: Terminal internal resistance R1 = resistivity × terminal height / terminal cross-sectional area; Calculate the internal resistance of the positive and negative terminals: Internal resistance R2 of the terminal = resistivity × current-carrying length of the terminal / cross-sectional area of ​​the terminal; Calculate the internal resistance of the positive and negative tabs: Tab internal resistance R3 = resistivity × tab length / tab cross-sectional area; Calculate the internal resistance of the positive and negative electrode foils: Foil internal resistance R4 = resistivity × foil width / average cross-sectional area of ​​the current carrying path; Calculate the internal resistance of the positive and negative electrode connecting pieces and the electrode post welding: Internal resistance of connecting pieces and electrode post welding R5 = resistivity × welding depth / effective welding area; Calculate the internal resistance of welding the positive and negative tabs and connecting pieces: Internal resistance of welding the tabs and connecting pieces R6 = resistivity × welding depth / effective welding area; Calculate the contact resistance between the coating layer and the foil: Contact resistance R7 between the coating layer and the foil = resistivity × electrode thickness / (electrode length × electrode width).

[0027] S3. Add up the internal resistances of each part to obtain the total internal resistance value. Calculated total internal resistance: R 计算值 =R1+R2+R3+R4+R5+R6+R7.

[0028] S4. Constructing the correction model To improve the accuracy of the calculated values, a correction model needs to be constructed. The specific steps are as follows: Select multiple battery cells of different models (with a sample size of no less than 3 groups) and obtain their measured ACR values; Calculate the ACR value for each type of battery cell according to steps S1 to S3: R 计算值1 R 计算值2 R 计算值3 ...; like Figure 1 As shown, a univariate linear fit is performed between the calculated and measured values ​​to obtain the corrected relation: Measured value = a × calculated value + b; Where a and b are the fitting coefficients, and the goodness of fit R0 is... 2 It should be no less than 0.95 to ensure the reliability of the model.

[0029] S5. For the new type of battery cell to be predicted, first calculate its ACR value according to S1~S3, and then substitute it into the correction formula to obtain the final ACR prediction value.

[0030] The predicted value can be directly used for performance evaluation and design optimization in the design stage before the finished battery cell is manufactured: when the predicted value is higher than the design target internal resistance, the parameters of the components with the highest internal resistance can be optimized in a targeted manner, such as increasing the number of parallel tabs, increasing the effective welding area, selecting a substrate with lower resistivity, and optimizing the electrode size design. The internal resistance of the battery cell can be optimized in advance without repeated prototyping and testing, which can significantly shorten the R&D cycle and reduce the R&D trial and error cost.

[0031] Example 2 Select three cell models, A, B, and C, and calculate the ACR value according to S1~S3. For example... Figure 1The calculated and measured values ​​of A, B, and C are fitted to obtain a linear relationship: measured value = 0.5458 × calculated value + 0.0866. For the D-type cell whose ACR needs to be predicted, the calculated ACR value is obtained according to S1~S3. Then, it is substituted into the formula measured value = 0.5458 × calculated value + 0.0866 to obtain the linear correction value. As shown in Table 1, the calculated ACR value of the D-type cell is 0.1033mΩ, with an error of 21.74% compared to the subsequent measured value.

[0032] The corrected value after linear correction is 0.1450mΩ, with an error of 9.84%, which significantly improves the prediction accuracy and fully verifies the effectiveness and reliability of the method.

[0033] Table 1 Calculation Data Example 3 This embodiment provides a prediction system for performing the above method, including the following modules: Parameter acquisition module: used to input or import design parameters of various structural components of the battery cell; Internal resistance calculation module: Calculates and sums the internal resistance components based on the design parameters to obtain the ACR calculation value; Correction module: Stores pre-built correction models and outputs corrected ACR prediction values ​​based on calculated values; Output module: Outputs the predicted values ​​in the form of charts or data for designers' reference.

[0034] To further enhance the model's adaptability, the system may also include an iterative optimization module, which communicates with the correction module and the output module. Once the battery cells are mass-produced and measured values ​​are obtained, the system automatically incorporates new sample data (design parameters, calculated values, and measured values) into the fitted sample set, refits the corrected relationship, and achieves continuous optimization of the model.

[0035] In summary, this invention provides an efficient, accurate, and iterative internal resistance prediction tool for the research and design of lithium-ion cells, which has strong theoretical value and engineering application prospects.

[0036] The above are merely preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be considered as limitations on the present invention, and the scope of protection of the present invention should be determined by the scope defined in the claims. For those skilled in the art, several improvements and modifications can be made without departing from the spirit and scope of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An ACR prediction method based on cell design parameters, characterized in that, Includes the following steps: Obtain the design parameters of multiple structural components of the battery cell; Based on the design parameters of each structural component, calculate the internal resistance component corresponding to each component. The AC internal resistance of the battery cell is calculated by adding up all the internal resistance components. Based on the calculated AC internal resistance value, combined with the pre-constructed correction model, the predicted AC internal resistance value of the battery cell is output.

2. The ACR prediction method based on cell design parameters according to claim 1, characterized in that, The pre-built modified model is obtained through the following steps: Obtain the measured AC internal resistance values ​​of multiple different types of battery cells; The calculated AC internal resistance values ​​of each type of battery cell are fitted with the measured values ​​to obtain a corrected relationship.

3. The ACR prediction method based on cell design parameters according to claim 2, characterized in that, The expression for the modified relation is: Measured value = a × calculated value + b; Where a and b are the correction coefficients obtained from the fitting.

4. The ACR prediction method based on cell design parameters according to claim 2 or 3, characterized in that, The number of samples from different battery cell models used for fitting should be no less than 3 groups. The fitting method should be univariate linear fitting, and the goodness of fit R of the fitted modified relationship should be determined. 2 Not less than 0.

95.

5. The ACR prediction method based on cell design parameters according to claim 1, characterized in that, The internal resistance components corresponding to each component are: pole internal resistance, connecting piece internal resistance, tab internal resistance, foil internal resistance, welding internal resistance between connecting piece and pole, welding internal resistance between tab and connecting piece, and contact internal resistance between coating layer and foil.

6. The ACR prediction method based on cell design parameters according to claim 5, characterized in that, The internal resistance components of each component are calculated using the following formula: R = ρ × L / S; Wherein, ρ is the resistivity corresponding to each structural component, and the structural components include poles, connecting pieces, tabs, foil, the welding interface between the connecting piece and the pole, the welding interface between the tab and the connecting piece, and the contact interface between the coating layer and the foil. L represents the design parameters corresponding to each structural component. The design parameters L include pole height, connecting piece current-carrying length, electrode tab length, foil width, welding depth between connecting piece and pole, welding depth between electrode tab and connecting piece, and electrode thickness. S represents the design parameters corresponding to each structural component. The design parameters S include the cross-sectional area of ​​the pole post, the cross-sectional area of ​​the connecting piece, the cross-sectional area of ​​the electrode lug, the average cross-sectional area of ​​the current-carrying path of the foil, the effective welding area between the connecting piece and the pole post, the effective welding area between the electrode lug and the connecting piece, and the planar area of ​​the electrode sheet.

7. The ACR prediction method based on cell design parameters according to claim 1, characterized in that, The predicted AC internal resistance value is used for performance evaluation or design optimization during the design phase before the finished battery cell is manufactured.

8. The ACR prediction method based on cell design parameters according to claim 1, characterized in that, The battery cell is a wound lithium-ion battery cell.

9. A prediction system for performing the ACR prediction method based on cell design parameters as described in any one of claims 1-8, characterized in that, include: The parameter acquisition module is used to acquire the design parameters of each structural component of the battery cell; The internal resistance calculation module is used to calculate the corresponding internal resistance components according to the design parameters of each structural component, and add the internal resistance components to obtain the AC internal resistance calculation value. The correction module is used to store the pre-built correction model and output the corrected AC internal resistance prediction value based on the AC internal resistance calculation value. The output module is used to output the predicted value of the AC internal resistance.

10. The prediction system according to claim 9, characterized in that, It also includes an iterative optimization module, which is communicatively connected to the correction module and the output module. The iterative optimization module is used to obtain the measured value of the AC internal resistance after the mass production of the target cell, calculate the error between the predicted value and the measured value of the AC internal resistance, and add the structural component design parameters, the calculated value and the measured value of the AC internal resistance of the target cell as new sample data to the fitting sample set of the correction module in order to iteratively optimize the correction relationship.

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