Lithium battery slurry dispersion state detection method and system based on dynamic thermal conductivity measurement

Abstract

The invention discloses a lithium battery slurry dispersion state detection method and system based on dynamic thermal conductivity measurement, and relates to the technical field of lithium battery production, and the method comprises the steps: employing a composite probe integrated with a miniature heating element and a temperature sensor to make thermal contact with to-be-detected slurry, and applying a 0.1-10-second short-time current pulse to generate transient temperature field disturbance; collecting a temperature relaxation curve at the frequency not lower than 10 Hz after the pulse is ended, and calculating the thermal conductivity and the thermal diffusivity of the slurry based on a transient plane heat source theoretical model; and quantitative or qualitative evaluation is carried out on the dispersion state of the slurry through an absolute threshold method, a time sequence stability analysis method or a spatial distribution diagram method. According to the method, the microcosmic interface state and the agglomeration degree of the slurry are directly reflected through the thermophysical parameters, the technical problem that the dispersion uniformity of the slurry is difficult to evaluate in a nondestructive, online and high-precision manner in the prior art is solved, and an effective means is provided for quality control of a lithium battery pole piece.

Description

Technical Field

[0001] This invention relates to the field of lithium battery production technology, and more specifically to a method and system for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement. Background Technology

[0002] Lithium-ion battery slurry is a high-solids-content liquid-solid suspension system composed of solid particles such as active materials, conductive agents, and binders, along with a solvent. Its dispersion uniformity directly affects the electrode coating quality and the consistency of the battery's electrochemical performance. Currently, the detection of slurry state mainly relies on rheological characterization methods. For example, patent publication number CN115791523A discloses a lithium-ion battery slurry and its preparation method, which comprehensively evaluates the slurry's dispersion stability by measuring the slurry's viscoelastic modulus, surface tension, and contact angle.

[0003] However, the above methods have the following shortcomings in practical applications: (1) Limited spatial resolution: Rheological parameters reflect the overall mechanical response of slurry at the macroscopic scale (above millimeter level), and it is still difficult to effectively distinguish the microstructural differences between 50nm primary particles and 5μm agglomerates. The ability to identify micro-defects such as micro-agglomerates and abnormal particle gradation is limited.

[0004] (2) The detection process involves multiple independent steps: usually modulus testing, homogenization, stratified sampling, surface tension testing, etc. need to be completed in sequence. Sample transfer and rebalancing are required between each step, resulting in a long overall detection cycle, which makes it difficult to meet the needs of rapid evaluation.

[0005] (3) Technical obstacles exist in online testing: Since the test relies on precision laboratory equipment such as rotational rheometer and contact angle meter, and has strict requirements for sample pretreatment conditions, it is not yet directly compatible with the continuous and automated process environment of the production line. Offline sampling and testing are required, which restricts its application in real-time quality control.

[0006] Therefore, there is an urgent need for a detection method that can directly, quickly, and non-destructively detect the microstructure of slurry. Summary of the Invention

[0007] The technical problem to be solved by this invention is how to solve the problem that existing detection methods cannot detect the micro-interface state and agglomeration degree of slurry online and non-destructively, which makes it difficult to accurately control the quality of battery electrodes.

[0008] This invention solves the above-mentioned technical problems through the following technical means: a method for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement, comprising: S1. The composite probe integrating a micro heating element and a temperature sensor is kept in thermal contact with the lithium battery slurry to be tested; a short current pulse is applied to the micro heating element to generate transient temperature field disturbance in a local area of ​​the slurry; after the pulse ends, the temperature sensor is used to record the temperature decay curve over time in real time to obtain the temperature relaxation curve. S2. Based on the heat transfer model, the temperature relaxation curve is processed to calculate the thermal conductivity and thermal diffusivity of the slurry. S3. Quantitatively or qualitatively evaluate the dispersion state of the slurry based on the thermal conductivity and thermal diffusivity.

[0009] Furthermore, the pulse width of the current pulse is 0.1 seconds to 10 seconds; the sampling frequency for recording the temperature relaxation curve is not less than 10 Hz.

[0010] Furthermore, the heat transfer model is a transient planar heat source theoretical model, which simplifies the probe as an ideal heat source, and the temperature rise response of the slurry satisfies the following relationship:

[0011] in, For time Temperature rise over time For heating power, For the thermal conductivity of the slurry, For the thermal diffusivity of the slurry, It is an exponential integral function. The characteristic radius of the probe; The experimentally measured temperature relaxation curves were fitted to the theoretical model described above. Using the nonlinear least squares method, the optimal values ​​of λ and α were solved to minimize the sum of squared residuals between the temperature curves calculated by the theoretical model and the experimentally measured curves, thereby retrieving the thermal conductivity and thermal diffusivity of the slurry.

[0012] Furthermore, the dispersed state evaluation method includes at least one of the following: absolute threshold method, time series stability analysis method, and spatial distribution map method.

[0013] Furthermore, the absolute threshold method involves comparing the calculated thermal conductivity λ with a preset qualified threshold range. When the λ value is lower than the lower limit of the threshold, it is determined that the slurry has agglomeration and poor dispersibility.

[0014] Furthermore, the time series stability analysis method involves continuously repeating measurements during slurry mixing or conveying, calculating the standard deviation of thermal conductivity λ within a set time window, and evaluating the slurry dispersion stability based on the fluctuation value of this standard deviation.

[0015] Furthermore, the spatial distribution map method involves taking multiple measurements at different depths or horizontal positions in the slurry container to generate a spatial distribution cloud map of the slurry's thermal conductivity. The uniformity of the cloud map is then analyzed to determine whether sedimentation, stratification, or regional agglomeration exists.

[0016] This invention also provides a lithium battery slurry dispersion state detection system based on dynamic thermal conductivity measurement, comprising: The thermal excitation-temperature probe maintains thermal contact with the lithium battery slurry under test. It integrates a miniature heating element and a temperature sensor to receive current pulses, generate transient temperature field disturbances, and detect the slurry temperature response signal. The control and acquisition module is connected to the thermal excitation-temperature probe signal and is used to apply a short current pulse to the micro heating element and acquire the temperature relaxation curve data output by the temperature sensor at a high sampling frequency after the pulse ends. The signal processing and analysis module, together with the control and acquisition module, incorporates a heat transfer model algorithm to calculate the thermal conductivity and thermal diffusivity of the slurry based on the temperature relaxation curve data, and to quantitatively or qualitatively evaluate the dispersion state of the slurry based on the thermal conductivity and thermal diffusivity.

[0017] The present invention also provides a processing device, including at least one processor and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the above-described method steps by calling the program instructions.

[0018] The present invention also provides a computer-readable storage medium storing computer instructions that cause the computer to perform the above-described method steps.

[0019] The advantages of this invention are: This invention utilizes a composite probe that integrates thermal excitation and temperature sensing to directly measure the temperature relaxation process of the slurry and calculate thermal conductivity and thermal diffusivity based on a heat transfer model. This enables online, non-destructive, and high-precision quantitative evaluation of the microscopic interface state and agglomeration degree of lithium battery slurry. It effectively solves the technical problem that existing technologies cannot directly detect the internal dispersion uniformity of the slurry and significantly improves the quality control capability of the battery electrode manufacturing process. Attached Figure Description

[0020] Figure 1 This is a flowchart of the lithium battery slurry dispersion state detection method based on dynamic thermal conductivity measurement according to Embodiment 1 of the present invention; Figure 2 This is a temperature relaxation curve diagram of Embodiment 1 of the present invention; Figure 3 This is a temperature relaxation curve diagram of Embodiment 2 of the present invention; Figure 4 This is a temperature relaxation curve diagram of Embodiment 3 of the present invention; Figure 5 This is a temperature relaxation curve diagram of Embodiment 4 of the present invention; Figure 6 This is a diagram showing the arrangement of measurement points inside the slurry storage tank in Embodiment 4 of the present invention; Figure 7 This is a spatial distribution cloud map of thermal conductivity in Embodiment 4 of the present invention; Figure 8 This is a system module diagram of Embodiment 5 of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1 like Figure 1 As shown, the method for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement includes: S1. The composite probe integrating a micro-heating element and a temperature sensor is kept in thermal contact with the lithium battery slurry to be tested; a short-duration current pulse is applied to the micro-heating element to generate a transient temperature field disturbance in a localized area of ​​the slurry; after the pulse ends, the temperature sensor is used to record the temperature decay curve over time in real time to obtain the temperature relaxation curve, such as... Figure 2 As shown, (a) uniformly dispersed slurry: the particles are evenly distributed, forming an efficient heat conduction network. The heat wave decays quickly, and the temperature relaxation curve drops rapidly. (b) slurry with agglomeration: agglomerates (gray clumps) disrupt the heat conduction path and increase thermal resistance. The heat wave decays slowly, and the tail of the temperature relaxation curve is elongated. Curve comparison: This visually demonstrates the difference in temperature relaxation curves between the two states. The slurry with excellent dispersibility exhibits a significantly faster temperature drop than the slurry with severe agglomeration.

[0023] Specifically, the pulse width of the current pulse is 0.1 seconds to 10 seconds; the sampling frequency for recording the temperature relaxation curve is not less than 10 Hz.

[0024] S2. Based on the heat transfer model, the temperature relaxation curve is processed to calculate the thermal conductivity and thermal diffusivity of the slurry.

[0025] Specifically, baseline correction is first performed, setting the temperature at the pulse end instant (t=0) as the starting point (ΔT=0). If ambient temperature drift exists, the drift component is subtracted through linear fitting. The signal is then smoothed and filtered to suppress high-frequency noise while preserving the main characteristics of temperature attenuation.

[0026] The heat transfer model is a transient planar heat source theoretical model, which simplifies the probe as an ideal heat source. The temperature rise response of the slurry satisfies the following relationship:

[0027] in, For time Temperature rise over time For heating power, For the thermal conductivity of the slurry, For the thermal diffusivity of the slurry, It is an exponential integral function. The characteristic radius of the probe is denoted as .

[0028] Using the nonlinear least squares method, with the Levenberg-Marquardt algorithm being preferred, the experimentally measured temperature relaxation curve is fully fitted to the above theoretical model to inversely derive a set of λ and α values ​​that minimize the sum of squared residuals between the theoretically calculated curve and the experimentally measured curve.

[0029] The algorithm implementation process is as follows: Initialization: Given initial values ​​for parameters T Set the damping factor and convergence conditions.

[0030] Iterative calculation: For the k-th iteration, use the current parameters Substitute into the theoretical formula and calculate at each time point. Corresponding theoretical temperature rise Thus, the theoretical curve was obtained.

[0031] Residual calculation: Calculating theoretical curves and experimental data The residual vector at each time point ,in .

[0032] Jacobian matrix calculation: Calculate the partial derivative matrix of the residuals with respect to the parameters. It describes how parameter variations affect the residuals.

[0033] Incremental parameter solving: Constructing and solving a system of linear equations ,in, Given the identity matrix, we obtain the parameter increments. .

[0034] Parameter Update and Evaluation: Update parameters Calculate the new residual sum of squares. If the new residual sum of squares decreases, accept the update and decrease it. If the value increases, proceed to the next iteration; if it increases... Value and recalculate .

[0035] Termination condition: When the parameter increment The iteration terminates when the change in the norm or the sum of squared residuals is less than a preset tolerance, or when the maximum number of iterations is reached. In This is the best result obtained from the inversion.

[0036] S3. Quantitatively or qualitatively evaluate the dispersion state of the slurry based on thermal conductivity and thermal diffusivity.

[0037] Specifically, based on the calculated thermal conductivity λ (the primary evaluation index) and thermal diffusivity α (an auxiliary reference index), at least one of the following quantitative or qualitative evaluations is performed: 1. Absolute threshold method The calculated thermal conductivity λ is compared with the acceptable threshold range established through a large number of standard samples. Specifically, an empirical database is built into the signal processing and analysis module, storing the acceptable thermal conductivity threshold ranges for slurries with different formulations and solid contents. The database construction method is as follows: a large number of slurry samples with known dispersion states (from best to worst) are prepared in the laboratory, and different dispersion grades are obtained by changing the dispersion process parameters; the λ of these standard samples is measured using this method, and the dispersion state is calibrated by traditional methods such as microscopic image analysis or particle size analysis; the slurry formulation / solid content is used as the primary key, and the corresponding qualified λ range and reference state label are stored in the database.

[0038] In actual testing, the operator first selects the current slurry formulation information in the software, and the software automatically calls the corresponding threshold range for comparison and judgment: For example, for this type of negative electrode slurry, it is known through numerous experiments that: like It was determined to be poorly dispersed.

[0039] like It was deemed qualified.

[0040] like If so, it indicates that there may be other process problems (such as incorrect formula or abnormal solid content).

[0041] 2. Time series stability analysis method During slurry mixing or conveying, perform continuous, repetitive periodic measurements (e.g., every 60 seconds). Calculate thermal conductivity. Standard deviation within a set time window (e.g., the most recent 20 measurement periods) : The formula for calculating the standard deviation is:

[0042] in, This represents the total number of data points (e.g., the number of consecutive measurements). For the first The value of each data point This is the arithmetic mean of all data points.

[0043] like This indicates that the slurry dispersion is extremely stable, and the system is marked with a green light.

[0044] like This indicates that the slurry condition is basically acceptable but has slight fluctuations. The system will light up a yellow light to alert the operator.

[0045] like This indicates that significant changes are occurring in the slurry (such as the start of sedimentation or agglomeration), and the system will illuminate a red light and issue an audible and visual alarm.

[0046] 3. Spatial distribution map method Using an automatic positioning device capable of moving along three dimensions, the composite probe is sequentially moved to multiple preset measurement points (M points, preferably 5-20, distributed at different depths or horizontal positions) within the slurry container. A complete measurement is performed at each point, and the thermal conductivity λ at that point is recorded. The position coordinates (x, y, z) of all measurement points and the corresponding λ values ​​are input into software (such as MATLAB). An interpolation algorithm (such as Kriging interpolation or radial basis function interpolation) is used to generate a spatial distribution cloud map of the thermal conductivity of the slurry storage tank.

[0047] The judgment is made by analyzing the uniformity of the cloud map: Acceptable condition: The cloud map shows uniform color, and the difference in thermal conductivity values ​​at each point is within ±5%, indicating that the slurry is uniformly dispersed and there is no sedimentation.

[0048] Unacceptable conditions: The cloud map displays a significant color gradient. For example, the thermal conductivity value in the bottom region is significantly higher than that in the middle and upper regions (because sedimentation increases the solid content at the bottom, leading to increased thermal conductivity). The software can calculate the standard deviation of the entire spatial data. If this value exceeds a preset threshold, it is determined that the slurry has settled and needs to be stirred again.

[0049] The formula for calculating the standard deviation is:

[0050] Where M is the total number of spatial measurement points. Let J be the thermal conductivity value at the j-th spatial measurement point. The average thermal conductivity of all M spatial measurement points, i.e. .this The value quantifies the degree of dispersion of thermal conductivity in the spatial dimension. The larger the value, the worse the spatial uniformity of the slurry and the more serious the sedimentation or stratification phenomenon.

[0051] The above method was verified through Examples 2, 3, and 4.

[0052] Example 2 This embodiment details how to detect the dispersion state of static slurry samples in a laboratory environment using an offline laboratory testing method.

[0053] The well-stirred lithium battery negative electrode slurry (graphite system, 50% solid content) was poured into a 100 ml insulated beaker. The composite probe was vertically inserted into the center of the slurry, ensuring that the sensitive section of the probe was completely submerged. A square wave pulse of 200 mA was applied for 2 seconds using a precision constant current source. After the pulse ended, the temperature relaxation curve was continuously recorded for 30 seconds at a sampling frequency of 50 Hz. Figure 3 As shown. After baseline correction and smoothing of the curve by the signal processing and analysis module, nonlinear least squares fitting is performed using the Levenberg-Marquardt algorithm based on the transient plane heat source model (initial values ​​λ0=0.70 W / (m·K), α0=5.0×10). -7 (m² / s, converged after 15 iterations), yielding λ = 0.52 W / (m·K) and α = 4.5 × 10⁻⁶. -7 m² / s. The software (such as MATLAB) calls the qualified threshold range [0.65, 0.80] W / (m·K) of the negative electrode slurry of this formulation from the built-in database. Since λ=0.52<0.65 is measured, the system automatically judges it as poor dispersion, with serious agglomeration and issues a red alarm.

[0054] Example 3 This embodiment demonstrates how to integrate the present invention into a slurry conveying pipeline to achieve real-time quality monitoring during the production process, enabling online real-time monitoring and stability assessment.

[0055] A flow cell with a diameter of 20 mm was designed. A composite probe was installed in the flow cell wall via a threaded seal, with its tip positioned in the center of the slurry flow. The flow cell was connected in series to the delivery pipe from the mixing tank to the coating head. The system was set to automatically perform a measurement cycle every 60 seconds: applying a 1-second, 350mA pulse, sampling at an 80 Hz frequency, and recording a 20-second temperature relaxation curve. Figure 4As shown in the diagram, the system automatically calculates and records the current thermal conductivity λ value of the slurry after each cycle. It continuously analyzes the changes in the thermal conductivity λ value over the most recent 20 data points (i.e., 20 minutes) and calculates the standard deviation σ of these 20 data points. In a continuous 8-hour production run, σ remained stable at 0.015 W / (m·K) for the first 4 hours (system green light); from the 5th hour onwards, σ gradually exceeded 0.05 W / (m·K) (red light alarm), while the average λ value slowly decreased. Timely inspection revealed a slight blockage in the dispersant supply pump, leading to a gradual deterioration in slurry dispersibility. Due to the timely warning, the entire batch of slurry was spared.

[0056] Example 4 This embodiment is used to evaluate the static stability of slurry in a storage tank and to detect whether sedimentation or stratification occurs.

[0057] The composite probe is fixed using an automatic positioning device that can move in three dimensions. In a 1 cubic meter slurry storage tank, three horizontal levels are set up: upper, middle, and lower (depths of 0.2m, 0.5m, and 0.8m respectively, assuming a tank height of approximately 1m). Five measurement points are selected for each level (the center point and four points are evenly distributed). Figure 5 As shown. The control probe is moved sequentially to 15 preset points, and a 2.0-second, 400 mA pulse is applied at each point. The sampling frequency is 60 Hz, and a 25-second temperature relaxation curve is recorded, as shown. Figure 6 As shown. Obtain the λ value at each point.

[0058] The three-dimensional coordinates and λ values ​​of all measurement points are input into the signal processing and analysis module. A spatial distribution cloud map of the thermal conductivity of the slurry storage tank is generated using a radial basis function interpolation algorithm and displayed visually using color mapping (blue represents low thermal conductivity, and red represents high thermal conductivity). For example... Figure 7 As shown in the comparative experiment, for the same batch of slurry: Before settling: mean λ = 0.72 W / (m·K), difference between layers <3%, spatial standard deviation =0.018, the cloud map color is uniform, and it is judged to be uniformly dispersed.

[0059] After standing for 24 hours: the average λ value of the bottom layer decreased to 0.85 W / (m·K), and that of the upper layer decreased to 0.60 W / (m·K). When the value is increased to 0.065, a distinct red high-value area appears at the bottom of the cloud map, indicating that sedimentation has occurred and re-stirring is required.

[0060] Example 5 Based on Example 1, Example 5 also provides a lithium battery slurry dispersion state detection system based on dynamic thermal conductivity measurement, such as... Figure 8 As shown, the system mainly includes: a thermal excitation-temperature probe, a control and acquisition module, and a signal processing and analysis module.

[0061] The thermal excitation-temperature probe is the core sensing component that maintains thermal contact with the lithium battery slurry under test. Its structure employs a platinum resistance temperature sensor (preferably PT1000) with a stainless steel sheath, internally integrating a miniature nickel-chromium alloy wire as a heating element. The probe diameter is preferably 3 mm, and the length is 50 mm to ensure sufficient surface area for heat exchange while maintaining structural strength. This probe simultaneously performs the dual functions of applying thermal excitation and accurate temperature measurement. Its characteristic radius r is the equivalent thermal conduction radius of the probe's geometry, typically determined within the range of 0.25 mm to 2.5 mm.

[0062] The control and acquisition module connects to the thermal excitation-temperature probe and includes a precision constant current source and a high-speed data acquisition card. The precision constant current source provides an adjustable constant current to the heating element from 50 mA to 500 mA, with a pulse width precisely adjustable from 0.1 seconds to 10 seconds. The high-speed data acquisition card has a sampling frequency of at least 100 Hz and a 24-bit resolution, used to accurately record the temperature relaxation curve output by the temperature sensor after the pulse ends.

[0063] The signal processing and analysis module is typically an industrial computer or embedded processor with built-in dedicated software programs (such as MATLAB or Python (using Matplotlib and SciPy libraries)) and built-in heat transfer model algorithms. It is used to calculate the thermal conductivity and thermal diffusivity of the slurry based on temperature relaxation curve data, and to quantitatively or qualitatively evaluate the dispersion state of the slurry based on these parameters. This includes: Signal preprocessing unit: Receives raw temperature relaxation curve data and performs digital filtering, baseline correction and noise suppression.

[0064] Parameter calculation unit: Built-in transient planar heat source theoretical model algorithm library and nonlinear least squares fitting algorithm library, used to calculate the thermal conductivity λ and thermal diffusivity α of slurry.

[0065] State evaluation unit: Built-in absolute threshold method, time series stability analysis method and spatial distribution map method algorithm library, used to quantitatively or qualitatively evaluate the dispersion state of slurry based on thermophysical parameters.

[0066] Results output unit: Outputs the evaluation results to the human-machine interface in the form of numerical values, charts, cloud maps, or audible and visual alarm signals.

[0067] The three modules work collaboratively as follows: The control and acquisition module, following instructions from the signal processing and analysis module, applies current pulses to the thermal excitation-temperature probe via a precision constant current source; the probe converts the detected temperature response signal into an electrical signal and returns it to the data acquisition card; the acquisition card transmits the digitized temperature relaxation curve data to the signal processing and analysis module, which then performs parameter calculations and status evaluation, feeding the results back to the control and acquisition module to adjust the measurement strategy and outputting the final diagnostic results. The entire system forms a closed-loop online detection and control system.

[0068] Example 6 Based on Embodiment 1, Embodiment 6 of the present invention also provides a processing device, including at least one processor and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the method steps of Embodiment 1 by calling the program instructions.

[0069] Example 7 Based on Embodiment 1, Embodiment 7 of the present invention also provides a computer-readable storage medium storing computer instructions that cause the computer to perform the steps of the method described in Embodiment 1.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement, characterized in that, include: S1. The composite probe integrating a micro heating element and a temperature sensor is kept in thermal contact with the lithium battery slurry to be tested; a short current pulse is applied to the micro heating element to generate transient temperature field disturbance in a local area of ​​the slurry; after the pulse ends, the temperature sensor is used to record the temperature decay curve over time in real time to obtain the temperature relaxation curve. S2. Based on the heat transfer model, the temperature relaxation curve is processed to calculate the thermal conductivity and thermal diffusivity of the slurry. S3. Quantitatively or qualitatively evaluate the dispersion state of the slurry based on the thermal conductivity and thermal diffusivity.

2. The method for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement according to claim 1, characterized in that, The pulse width of the current pulse is 0.1 seconds to 10 seconds; the sampling frequency for recording the temperature relaxation curve is not less than 10 Hz.

3. The method for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement according to claim 1, characterized in that, The heat transfer model is a transient planar heat source theoretical model, which simplifies the probe as an ideal heat source. The temperature rise response of the slurry satisfies the following relationship: in, For time Temperature rise over time For heating power, For the thermal conductivity of the slurry, For the thermal diffusivity of the slurry, It is an exponential integral function. The characteristic radius of the probe; The experimentally measured temperature relaxation curves were fitted to the theoretical model described above. Using the nonlinear least squares method, the optimal values ​​of λ and α were solved to minimize the sum of squared residuals between the temperature curves calculated by the theoretical model and the experimentally measured curves, thereby retrieving the thermal conductivity and thermal diffusivity of the slurry.

4. The method for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement according to claim 1, characterized in that, The distributed state evaluation method includes at least one of the following: absolute threshold method, time series stability analysis method, and spatial distribution map method.

5. The method for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement according to claim 4, characterized in that, The absolute threshold method is as follows: the calculated thermal conductivity λ is compared with the preset qualified threshold range. When the value of λ is lower than the lower limit of the threshold, it is determined that the slurry has agglomeration and poor dispersibility.

6. The method for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement according to claim 4, characterized in that, The time series stability analysis method is as follows: continuous and repeated measurements are performed during the slurry mixing or transportation process, the standard deviation of thermal conductivity λ within a set time window is calculated, and the fluctuation value of the standard deviation is used to evaluate the dispersion stability of the slurry.

7. The method for detecting the dispersion state of lithium battery slurry based on dynamic thermal conductivity measurement according to claim 4, characterized in that, The spatial distribution map method involves taking multiple measurements at different depths or horizontal positions in the slurry container to generate a spatial distribution cloud map of the slurry's thermal conductivity. The uniformity of the cloud map is then analyzed to determine whether sedimentation, stratification, or regional agglomeration exists.

8. A lithium battery slurry dispersion state detection system based on dynamic thermal conductivity measurement, characterized in that, include: The thermal excitation-temperature probe maintains thermal contact with the lithium battery slurry under test. It integrates a miniature heating element and a temperature sensor to receive current pulses, generate transient temperature field disturbances, and detect the slurry temperature response signal. The control and acquisition module is connected to the thermal excitation-temperature probe signal and is used to apply a short current pulse to the micro heating element and acquire the temperature relaxation curve data output by the temperature sensor at a high sampling frequency after the pulse ends. The signal processing and analysis module, together with the control and acquisition module, incorporates a heat transfer model algorithm to calculate the thermal conductivity and thermal diffusivity of the slurry based on the temperature relaxation curve data, and to quantitatively or qualitatively evaluate the dispersion state of the slurry based on the thermal conductivity and thermal diffusivity.

9. A processing device, characterized in that, The method includes at least one processor and at least one memory communicatively connected to the processor, wherein the memory stores program instructions executable by the processor, and the processor can execute the method as described in any one of claims 1 to 7 by invoking the program instructions.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause the computer to perform the method as described in any one of claims 1 to 7.

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