Battery current density distribution inversion method and system based on optical fiber sensing

Abstract

The present application relates to the technical field of battery current inversion, and provides a battery current density distribution inversion method and system based on optical fiber sensing, which can effectively invert the internal current distribution of a battery without disassembling the battery, to solve the problem that the existing battery current inversion technology relies on complex model parameters and calculation processes. The battery current density distribution inversion method based on optical fiber sensing includes mapping the coordinates of a U-turn optical fiber sensing path to a two-dimensional coordinate system of a battery surface, establishing a corresponding relationship between optical fiber measurement points and spatial positions, obtaining a discrete temperature measurement vector along the U-turn optical fiber sensing path and converting it into a discrete point state field, reconstructing it into a continuous two-dimensional grid continuous field, obtaining the in-plane anisotropic conductivity distribution, establishing a two-dimensional steady-state electric potential control equation, solving under the constraint of the tab boundary and adiabatic boundary conditions, obtaining the in-plane electric potential distribution of the battery, and calculating the current density vector field and its distribution accordingly.

Description

Technical Field

[0001] This invention relates to the field of battery current inversion technology, and in particular to a method and system for inverting battery current density distribution based on fiber optic sensing. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Non-uniformity in lithium-ion batteries can lead to problems such as uneven current distribution, enhanced local polarization, concentrated temperature rise, stress concentration, and accelerated aging. In severe cases, it can even induce local lithium plating, accelerated capacity decay, or thermal runaway. Therefore, obtaining information on the internal current distribution of the battery, especially identifying uneven regions within the battery without disassembling it, is a key issue in battery diagnostics and safety evaluation.

[0004] Existing technologies for analyzing the internal current distribution of batteries typically rely on complex model parameters and calculation processes, which are difficult to implement in engineering applications. This is especially true in large-size pouch or prismatic batteries, where there is usually a higher current density near the tabs due to the current convergence effect. Furthermore, winding deviations, uneven coating, differences in compaction density, local aging, and differences in liquid content can further cause spatial fluctuations in the conductivity of the central region, thus limiting the utilization of actual online monitoring data. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method and system for inverting battery current density distribution based on fiber optic sensing. This method can combine distributed sensing information to effectively invert the internal current distribution of a battery without disassembling the battery.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for inverting the current density distribution of a battery based on fiber optic sensing.

[0007] In one or more embodiments, a method for inverting battery current density distribution based on fiber optic sensing is provided, comprising: The coordinates along the path of the folded-back fiber optic sensing path pre-laid on the surface of the battery under test are mapped to the two-dimensional coordinate system of the battery surface to establish the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface. The discrete temperature measurements at each fiber optic measurement point are converted into equivalent temperature values. Then, based on the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface, the equivalent temperature values ​​at each fiber optic measurement point are converted into discrete point state fields. The discrete point state field is reconstructed into a continuous two-dimensional grid continuous field based on the Gaussian weighted nearest neighbor projection method. Based on the known coupling relationship between the continuous two-dimensional grid continuous field and the conductivity, the in-plane anisotropic conductivity distribution is obtained. Based on the in-plane anisotropic conductivity distribution, a two-dimensional steady-state potential control equation is established. Under the constraints of the tab boundary and adiabatic boundary conditions, the in-plane potential distribution of the battery is solved, and then the current density vector field and its distribution are calculated to realize the visualization and quantitative characterization of the non-uniformity of the current inside the battery.

[0008] As one implementation method, the process of converting discrete temperature measurements at each fiber optic measurement point into equivalent temperature values ​​is as follows: Calculate the ratio of the discrete temperature measurement value to the calibration coefficient at each fiber optic measurement point, and then sum it with the reference temperature value to calculate the equivalent temperature value.

[0009] As one implementation method, the process of mapping the coordinates along the foldback fiber optic sensing path pre-laid on the surface of the battery to the two-dimensional coordinate system of the battery surface is as follows: For any fiber optic measurement point, its coordinates along the foldback fiber optic sensing path are normalized. Multiply each normalized along-path coordinate by the total length of the foldback fiber path to obtain the corresponding actual position mapped onto the foldback fiber sensing path. By mapping the actual position onto the foldback fiber optic sensing path, the path segment to which each fiber optic measurement point belongs is determined, and its coordinates in the two-dimensional coordinate system on the battery surface are obtained through piecewise linear interpolation.

[0010] As one implementation method, the continuous field of a continuous two-dimensional mesh is characterized as follows: ; ; in, Any grid point on the battery surface Temperature value; For the first Spatial weights of each measurement point to a grid point (x, y); For the first The coordinates of each measurement point in a two-dimensional coordinate system on the battery surface; For the first The coordinates of each measurement point in the two-dimensional coordinate system on the battery surface The equivalent temperature corresponding to the discrete temperature measurement value; A coefficient used to control the spatial smoothing scale; This indicates that the summation is performed over all measurement points involved in the calculation.

[0011] As one implementation method, the known coupling relationship between the continuous field of a continuous two-dimensional grid and the conductivity is as follows: ; in: For any grid point on the battery surface ( x,y The in-plane conductivity of ). As a reference conductivity, Sensitivity coefficient; This is a reference temperature value; For any grid point on the battery surface ( x,y (temperature value)

[0012] As one implementation method, the two-dimensional steady-state potential control equation is: ; in, Indicates the electric potential distribution; Indicates the in-plane equivalent conductivity of the battery; This represents the potential gradient, and its direction reflects the direction of the fastest change in potential. It represents the current transport capability determined by both conductivity and potential gradient; This represents the divergence operator, used to describe whether a vector field has an inflow or outflow at a certain point.

[0013] As one implementation method, the current density vector field is set as : The current density vector field is set as : ; ; Among them, the current density vector field The direction indicates the direction of current flow, and the magnitude... Indicates local flow capacity; It represents the current transport capability determined by both conductivity and potential gradient; Let be the current density in the horizontal direction of the two-dimensional coordinate system on the battery surface; Let be the current density in the vertical direction of the two-dimensional coordinate system on the battery surface.

[0014] A second aspect of the present invention provides a battery current density distribution inversion system based on fiber optic sensing.

[0015] In one or more embodiments, a battery current density distribution inversion system based on fiber optic sensing includes: The measurement point coordinate transformation module is used to map the coordinates along the pre-laid folded fiber optic sensing path on the surface of the battery to be tested to the two-dimensional coordinate system of the battery surface, and establish the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface. The discrete point state field conversion module is used to convert the discrete temperature measurement values ​​of each fiber optic measurement point into equivalent temperature values, and then, based on the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface, convert the equivalent temperature values ​​of each fiber optic measurement point into a discrete point state field. The anisotropic conductivity distribution calculation module is used to reconstruct the discrete point state field into a continuous two-dimensional grid continuous field based on the Gaussian weighted nearest neighbor projection method. Based on the known coupling relationship between the continuous two-dimensional grid continuous field and conductivity, the in-plane anisotropic conductivity distribution is obtained. The current density and distribution calculation module is used to establish a two-dimensional steady-state potential control equation based on the in-plane anisotropic conductivity distribution, and solve for the in-plane potential distribution of the battery under the constraints of the tab boundary and adiabatic boundary conditions. Then, it calculates the current density vector field and its distribution to realize the visualization and quantitative characterization of the non-uniformity of the current inside the battery.

[0016] A third aspect of the present invention provides a computer-readable storage medium.

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method for inverting battery current density distribution based on fiber optic sensing.

[0018] A fourth aspect of the present invention provides an electronic device.

[0019] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the above-described method for inverting battery current density distribution based on fiber optic sensing.

[0020] Compared with the prior art, the beneficial effects of the present invention are: This invention establishes a correspondence between fiber optic measurement points and a two-dimensional space on the battery surface by deploying folded-back fiber optic sensing paths on the battery surface. The equivalent temperature values ​​of each fiber optic measurement point are converted into discrete point state fields, which are then reconstructed into a continuous two-dimensional grid field. Based on the known coupling relationship between the continuous two-dimensional grid field and conductivity, the in-plane anisotropic conductivity distribution is obtained. A two-dimensional steady-state potential control equation is established and solved under the constraints of tab boundaries and adiabatic boundary conditions. The current density vector field and its distribution are then calculated, realizing the visualization and quantitative characterization of the non-uniformity of the current inside the battery. This makes it more suitable for battery imbalance identification, operating condition comparison, and implementation effect evaluation. Attached Figure Description

[0021] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0022] Figure 1 This is a flowchart of the battery current density distribution inversion method based on fiber optic sensing according to an embodiment of the present invention; Figure 2 This is the inversion diagram of the first segment of current density in DC mode under 0.5C operating conditions according to an embodiment of the present invention; Figure 3 This is an inversion diagram of the mid-section current density in DC mode under 0.5C operating conditions, according to an embodiment of the present invention. Figure 4 This is an inversion diagram of the final current density in DC mode under 0.5C operating conditions, according to an embodiment of the present invention. Figure 5 This is the inversion diagram of the first segment of current density in CC-CV mode under 0.5C operating conditions according to an embodiment of the present invention; Figure 6 This is a mid-section current density inversion diagram under CC-CV mode at 0.5C operating conditions, according to an embodiment of the present invention. Figure 7 This is the inversion diagram of the final current density in CC-CV mode under 0.5C operating conditions according to an embodiment of the present invention; Figure 8 This is the inversion diagram of the first segment current density in DC mode under 1C operating condition according to an embodiment of the present invention; Figure 9 This is an inversion diagram of the mid-section current density in DC mode under 1C operating conditions, according to an embodiment of the present invention. Figure 10 This is an inversion diagram of the final current density in DC mode under 1C operating conditions, according to an embodiment of the present invention. Figure 11 This is the inversion diagram of the first segment of current density in CC-CV mode under 1C operating condition according to an embodiment of the present invention; Figure 12 This is a mid-section current density inversion diagram under CC-CV mode in 1C operating condition according to an embodiment of the present invention; Figure 13 This is the inversion diagram of the final current density in CC-CV mode under 1C operating condition according to an embodiment of the present invention; Figure 14 This is the inversion diagram of the first segment of current density in DC mode under 1.5C operating conditions according to an embodiment of the present invention; Figure 15 This is an inversion diagram of the mid-section current density in DC mode under 1.5C operating conditions, according to an embodiment of the present invention. Figure 16 This is an inversion diagram of the final current density in DC mode under 1.5C operating conditions, according to an embodiment of the present invention. Figure 17 This is the inversion diagram of the first current density in CC-CV mode under 1.5C operating conditions according to an embodiment of the present invention; Figure 18 This is the mid-section current density inversion diagram under CC-CV mode at 1.5C operating condition according to an embodiment of the present invention; Figure 19 This is the inversion diagram of the final current density in CC-CV mode under 1.5C operating conditions according to an embodiment of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] With the rapid development of lithium-ion batteries in large-scale applications such as electric vehicles, energy storage systems, and high-power equipment, their single-cell capacity and system scale are constantly increasing, and the internal state characterization of batteries during actual operation is receiving increasing attention. A battery is not a perfectly uniform conductor in space; its internal electrodes, electrolyte, separator, current collector, and tab connection areas develop varying degrees of non-uniformity during manufacturing, assembly, and cycling. This non-uniformity can lead to uneven current distribution, enhanced local polarization, concentrated temperature rise, stress concentration, and accelerated aging. In severe cases, it may even induce local lithium plating, accelerated capacity decay, or even thermal runaway.

[0027] To address the problems in the background technology, this invention proposes a battery current density distribution inversion method and system based on fiber optic sensing. This method involves deploying a foldback fiber optic path on the battery surface to acquire fiber optic response data along the path during charging and discharging, and mapping this data onto the battery's two-dimensional surface. Furthermore, it constructs an equivalent state field and conductivity field, solves for the potential distribution using simultaneous boundary conditions, and finally inverts the equivalent current density distribution map inside the battery. The inversion results are characterized by quantitative indicators such as uniformity coefficient, hotspot factor, effective conductive area ratio, and centroid shift.

[0028] Figure 1 A schematic diagram of the battery current density distribution inversion method based on fiber optic sensing, according to an embodiment of the present invention, is provided. Figure 1 The battery current density distribution inversion method based on fiber optic sensing in this embodiment may include the following steps S101 to S104.

[0029] The specific implementation process of steps S101 to S104 is as follows: Step S101: Map the coordinates along the folded-back fiber optic sensing path pre-laid on the surface of the battery to be tested to the two-dimensional coordinate system of the battery surface, and establish the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface.

[0030] In this embodiment, the process of mapping the coordinates along the foldback fiber optic sensing path pre-laid on the surface of the battery to the two-dimensional coordinate system of the battery surface is as follows: For any fiber optic measurement point, its coordinates along the foldback fiber optic sensing path are normalized. Multiply each normalized along-path coordinate by the total length of the foldback fiber path to obtain the corresponding actual position mapped onto the foldback fiber sensing path. By mapping the actual position onto the foldback fiber optic sensing path, the path segment to which each fiber optic measurement point belongs is determined, and its coordinates in the two-dimensional coordinate system on the battery surface are obtained through piecewise linear interpolation.

[0031] Specifically, the discrete measurement data acquired along the foldback fiber optic path first needs to be mapped onto a two-dimensional region on the battery surface. Assume the fiber optic path consists of several nodes connected in the laying sequence to form a polygonal line. The cumulative path length L can be obtained by calculating the distance between adjacent nodes. For any measurement point, its coordinates along the path are... Then it can be normalized to: (1); in, This represents the coordinates of a fiber optic measurement point along the path of a foldback fiber optic cable. Indicates the coordinates along the fiber optic path from the starting measurement point; Indicates the coordinates along the path of the measurement point where the fiber optic path terminates; This represents the normalized path location parameter, which typically ranges from 0 to 1 and is used to characterize the relative position of the measurement point within the entire fiber optic path.

[0032] The normalized coordinates along the path are further mapped to their actual positions on the polyline path: (2); in, Indicates the total length of the foldback fiber optic path; This represents the cumulative actual path length after normalization, i.e., the cumulative distance calculated from the measurement point along the return path starting from the fiber optic origin. This cumulative length allows us to further determine which segment of the broken path the measurement point is located on, and obtain its two-dimensional coordinates on the battery surface through piecewise linear interpolation. The purpose of this mapping is to transform the original one-dimensional sampling information along the fiber optic path into two-dimensional spatial coordinates, thus providing a foundation for subsequently constructing the in-plane physical field.

[0033] Step S102: Convert the discrete temperature measurement values ​​of each fiber optic measurement point into equivalent temperature values, and then, based on the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface, convert the equivalent temperature values ​​of each fiber optic measurement point into a discrete point state field.

[0034] Specifically, the process of converting the discrete temperature measurements at each fiber optic measurement point into equivalent temperature values ​​is as follows: Calculate the ratio of the discrete temperature measurement value to the calibration coefficient at each fiber optic measurement point, and then sum it with the reference temperature value to calculate the equivalent temperature value.

[0035] Let the first position in the two-dimensional coordinate system on the battery surface be... Discrete temperature measurement values ​​of the coordinates of each fiber optic measurement point m i The reference temperature value is The corresponding equivalent temperature can then be obtained through a linear relationship. : (3); in, This is the calibration coefficient. The physical significance of this relationship lies in transforming the temperature measurement values ​​into a continuous scalar field, so as to establish a mapping relationship with conductivity later.

[0036] Step S103: Based on the Gaussian weighted nearest neighbor projection method, the discrete point state field is reconstructed into a continuous two-dimensional grid continuous field. Based on the known coupling relationship between the continuous two-dimensional grid continuous field and the conductivity, the in-plane anisotropic conductivity distribution is obtained.

[0037] Since the measurement points are only distributed along the return path, it is necessary to further reconstruct the discrete point field into a two-dimensional continuous field. For any grid point on the battery surface... The corresponding temperature value can be calculated using the Gaussian weighted nearest neighbor projection method, and its expression is: (4); in, Any grid point on the battery surface Temperature value; For the first Each measurement point to grid point Spatial weights; For the first The coordinates of each measurement point in a two-dimensional coordinate system on the battery surface; This indicates that the summation is performed over all measurement points involved in the calculation.

[0038] The weighting function is defined as: (5); in, To control the coefficients of the spatial smoothing scale, when a grid point is far away from all measurement points, its weights approach zero. At this point, the point is backfilled as a reference value to avoid numerical instability or void areas.

[0039] In this embodiment of the invention, the known coupling relationship between the continuous field of the continuous two-dimensional grid and the conductivity is as follows: (6); in: For any grid point on the battery surface ( x,y The in-plane conductivity of ). As a reference conductivity, The sensitivity coefficient is used to characterize the degree of influence of state field changes on equivalent conductivity changes. It can be determined by fitting the terminal voltage, current and fiber temperature response data under different rates and different charge / discharge stages. The reference temperature value is used. This relationship reflects the nonlinear characteristic of the battery's internal conductivity changing with its state; that is, regions with higher states correspond to higher conductivity, thus affecting the spatial distribution of current.

[0040] Step S104: Based on the in-plane anisotropic conductivity distribution, a two-dimensional steady-state potential control equation is established. Under the constraints of the tab boundary and adiabatic boundary conditions, the in-plane potential distribution of the battery is solved, and the current density vector field and its distribution are calculated to achieve visualization and quantitative characterization of the current non-uniformity inside the battery. As one implementation method, the two-dimensional steady-state potential control equation is: (7); in, Indicates the electric potential distribution; Indicates the in-plane equivalent conductivity of the battery; This represents the potential gradient, and its direction reflects the direction of the fastest change in potential. It represents the current transport capability determined by both conductivity and potential gradient; This represents the divergence operator, used to describe whether a vector field flows into or out of a point. The equation is derived from the law of conservation of current, indicating that the current divergence is zero under passive conditions. By applying a fixed potential boundary condition at the battery tab and an adiabatic condition to the other boundaries, the potential distribution across the entire battery surface can be obtained.

[0041] Finally, based on the relationship between current density and potential gradient, the current density vector and magnitude can be calculated: (8); (9); Among them, the current density vector field The direction indicates the direction of current flow, and the magnitude... Indicates local flow capacity; Let be the current density in the horizontal direction of the two-dimensional coordinate system on the battery surface; This represents the current density in the vertical direction of a two-dimensional coordinate system on the battery surface. This result directly reflects the current distribution intensity at various locations on the battery surface. Through the above steps, a complete inversion process from surface measurement data to current distribution can be achieved.

[0042] To enhance the engineering applicability and judgment capability of this invention, the following evaluation indicators are defined: Average current density is defined as the average value of the current density magnitude at all grid points on the battery surface. ,Right now: (10); in, The total number of grid points. For the first k The current density modulus at each grid point; the units for both the average current density and the current density modulus are A / mm². This index is used to characterize the overall current carrying capacity; a higher value indicates a higher overall current transport level of the battery under that operating condition.

[0043] Uniformity coefficient Defined as the ratio of the standard deviation to the mean of the current density magnitude, i.e.: (11); in, A higher value indicates a more uneven current distribution; conversely, a lower value indicates a more uniform current distribution. This index can be used to characterize the dispersion of the current flow state inside the battery.

[0044] Hotspot Factors Defined as the ratio of the maximum current density to the average current density, i.e.: (12); in, ; To obtain the maximum value, this index is used to characterize the amplification factor of the local peak region relative to the overall average level. The larger the value, the more obvious the local current concentration phenomenon.

[0045] Total flux is defined as the area integral of the current density modulus over the entire surface of the battery, and its unit is Wb.

[0046] In a discrete grid, the total flux It can be represented as: (13); in, This represents the area of ​​a single grid cell. This index reflects the overall current-carrying capacity of the entire surface and can be used to compare the strength of the overall current field under different operating conditions.

[0047] To evaluate the consistency of current distribution patterns across different time periods, a segmented correlation index is further defined. Let the current density modulus fields corresponding to two adjacent time periods be respectively... and Then its correlation coefficient for: (14); in, and These represent the average current density for two time periods. The closer this index is to 1, the more similar the current distribution patterns are between adjacent time periods, and the more stable the model results are.

[0048] The aforementioned indicators collectively constitute an evaluation system for the current distribution inversion results. Average current density and total flux characterize the overall intensity, the uniformity coefficient characterizes the spatial dispersion, the hotspot factor characterizes the local concentration, and the piecewise correlation characterizes the repeatability and stability of the results. Through these indicators, this invention can not only output current distribution images but also generate quantitative results suitable for implementation examples and engineering evaluations.

[0049] This embodiment selects several sheet-like battery samples as the subjects. The battery geometry is set in the program to a width of 96mm and a height of 318mm. Experiments were conducted under 0.5C, 1.0C, and 1.5C conditions. During the test, a fold-back distributed optical fiber was laid on the battery surface, allowing the fiber to cover the battery height direction multiple times, forming a serpentine zigzag path. The fiber nodes were defined in millimeters as (16,0), (16,302), (32,318), (48,302), (48,16), (64,0), (80,16), and (80,318). By using the optical fiber to form multiple fold-back coverages on the battery surface, a high in-plane sampling density was obtained with a smaller number of channels. The test conditions included DC mode and CC-CV mode. In each mode, the program divided the raw data into three segments in sequence and extracted representative rows from the first, middle, and last segments for inversion to analyze the current distribution changes at different stages under the same operating condition. Under 0.5C operating conditions, the current density inversion diagrams for the first, middle, and last stages in DC mode are as follows: Figures 2-4 As shown; the current density inversion diagrams for the first, middle, and last stages under CC-CV mode at 0.5C are respectively as follows. Figures 5-7 As shown. Under 1C operating conditions, the current density inversion diagrams for the first, middle, and last stages in DC mode are respectively as follows. Figures 8-10 As shown; the current density inversion diagrams for the first, middle, and last stages under CC-CV mode in 1C operating condition are respectively as follows. Figures 11-13 As shown in the figure. Under 1.5C operating conditions, the current density inversion diagrams for the first, middle, and last stages in DC mode are respectively as follows. Figures 14-16 As shown; the current density inversion diagrams for the first, middle, and last stages under CC-CV mode at 1.5C are respectively as follows. Figures 17-19 As shown. In Figures 2-19 The current density values ​​in the images are all relative units. The larger the value (or the darker the color), the more severe the current density accumulation, and the higher the risk of local temperature rise or aging.

[0050] The following conclusions can be drawn from the current density inversion plots: 1) The current flow lines generally run from the top tab to the bottom tab, with the main direction extending along the height of the battery. This characteristic indicates that the direction of the obtained potential gradient is reasonable, and the model is self-consistent in its boundary condition settings.

[0051] 2) Significantly high-value areas appear near both the top and bottom tabs, with the thermal map color transitioning from blue-green or cyan-yellow in the middle to orange-red or even dark red near the tabs. This localized enhancement phenomenon can be explained as the "current congestion effect" of current convergence and diffusion, which is a normal physical phenomenon under a tab-dominated power supply structure.

[0052] 3) The central region generally exhibits a relatively flat distribution, but in some samples and stages, blocky or striped variations in high and low values ​​can be observed, especially in the later stages of 0.5C DC (Direct Current) mode and the final stages of 1C and 1.5C CC-CV (Constant Current-Constant Voltage) mode. These abnormal areas may reflect actual conductivity imbalances within the battery, such as coating differences, changes in compaction density, poor local contact, uneven liquid content, or impedance differences caused by aging.

[0053] 4) In DC mode, the three segments of the same sample image show a consistent mainstream line shape and high value region location, while the fluctuations in the middle section slightly increase as the stage progresses. This is consistent with the high segment correlation coefficient and moderate difference value in the subsequent quantification data, indicating that the current structure in this mode is repeatable.

[0054] 5) The first two stages of the CC-CV mode are highly similar to the DC mode, both exhibiting relatively uniform current in the middle and significant high values ​​at the upper and lower tabs. However, in the third stage of the CC-CV mode, the overall current amplitude decreases significantly, with a large area in the figure turning to low values, and only a weak conduction main path remaining in some local areas. In the quantized data, the terminal voltage at this point is approximately 4.08V to 4.18V, the average current density drops to approximately 3.14 to 3.31, and the total flux also decreases significantly. This result is consistent with the mechanism of current decay at the end of the constant voltage stage.

[0055] 6) Comparing the 1C and 1.5C operating conditions, it can be seen that at the end of the DC stage, the current distribution under the 1.5C condition is darker in color and the local concentration is more obvious, indicating that the degree of current imbalance is further enhanced. This phenomenon is consistent with the physical law that the internal polarization of the battery is intensified and the difference in transmission impedance is amplified under high rate conditions, indicating that the method of the present invention can effectively identify the difference in the degree of imbalance under different rates.

[0056] 7) In the final stage of CC-CV mode, although the overall temperature and current intensity decreased significantly, the current distribution still retained the non-uniform characteristics formed in the early stage. This indicates that after the battery enters the final stage of constant voltage, the overall response weakens, but the existing internal conductivity differences and bias current paths still have a certain degree of continuity, thus reflecting the "historical residue" characteristics of the current distribution.

[0057] The main physical trend revealed by this invention is reasonable: current is transmitted from the inlet tab to the outlet tab, the conductivity near the tab is significantly enhanced, the central region serves as the main transmission channel, and insulation constraints are maintained at the boundaries. These results demonstrate that this method can recover a two-dimensional current field with clear physical meaning from surface-folded fiber optic measurements, rather than merely providing a planar visualization of the original data.

[0058] Table 1 Summary of current distribution inversion results under different samples and operating conditions;

[0059] The data in Table 1 is from Figures 2-19 The inversion results are obtained from the data, in which the average current density and total flux are all in relative units (such as non-uniformity percentage or relative intensity index), and the uniformity coefficient, hotspot factor and piecewise correlation are all dimensionless values.

[0060] Table 1 shows that under DC conditions, the average current density of the three sample groups increases monotonically with increasing condition level. This result indicates that the method of the present invention has good distinguishing ability for different intensity conditions, and the inversion output can increase synchronously with the increase of condition. Under CC-CV conditions, the average current density in the first two stages also increases with increasing condition level; however, in the final stage, the average current density of the three sample groups significantly decreases to approximately 3.14, 3.18, and 3.31, while the terminal voltage remains in the range of approximately 4.08V to 4.18V. This indicates that at the end of the constant voltage period, the battery has entered a low current maintenance stage. Although the terminal voltage remains at a high level, the overall current significantly decreases, thus the overall intensity of the inverted current density field decreases synchronously. This result shows that the present invention can effectively distinguish the internal transport state between the constant current stage and the end of the constant voltage period.

[0061] From the perspective of distribution characteristics, the uniformity coefficient is generally concentrated between 0.543 and 0.568, while the hotspot factor is mainly distributed between 15.5 and 18.2. This indicates that under unified parameter conditions, the method of this invention has good consistency in results, and there is a significant local current concentration phenomenon near the tab, which is consistent with the physical law of current convergence in sheet batteries. The current centroid is basically stable in the lateral direction at 47.8 mm to 48.0 mm and in the longitudinal direction at around 159 mm, indicating that the overall current field remains symmetrical macroscopically, and the local imbalance is mainly reflected in the difference in strength in local areas rather than overall current bias. In addition, the correlation coefficient between adjacent stages is mostly maintained at around 0.99, indicating that the current distribution pattern obtained from different time periods under the same operating condition has high repeatability and stability. These results show that this invention can not only achieve visualized reconstruction of current distribution, but also stably identify the current distribution characteristics that persist under the same operating condition, and has good engineering application value.

[0062] As can be seen from this embodiment, the present invention does not require embedding sensors inside the battery. It can construct a two-dimensional current distribution inversion result simply by deploying folded-back optical fibers on the battery surface and acquiring the response along the path. Compared to traditional methods that only measure terminal voltage and total current, the present invention can provide spatial information on internal imbalances. Compared to pure simulation methods that rely entirely on prior parameters, the present invention introduces measured surface responses as a driving force, making the inversion results more data-supported. Compared to simple thermal imaging or single-point sensing methods, the present invention can obtain continuous spatial sampling information along the entire surface, thus being more suitable for identifying complex imbalance features such as banded, blocky, and locally concentrated patterns.

[0063] Compared with existing battery current model inversion methods, this invention maps one-dimensional distributed optical fiber data collected along the return path to a two-dimensional battery surface coordinate system, establishing a quantitative correspondence from the path coordinates to the battery surface position, enabling long-distance continuous sensing data from the surface to participate in two-dimensional field reconstruction. A two-dimensional anisotropic conductivity field is constructed using the functional relationship between conductivity and state variables. Under given tab and insulation boundary conditions, the two-dimensional partial differential equations of conductivity are solved to obtain the potential field and current density field, thus achieving non-invasive inversion of the internal current distribution. Furthermore, this invention calculates indicators such as average current density, maximum current density, and piecewise correlation, elevating the inversion results from visual display to quantifiable judgment, making it more suitable for battery imbalance identification, operating condition comparison, and implementation effect evaluation.

[0064] In one or more embodiments, a battery current density distribution inversion system based on fiber optic sensing is also provided, which can be implemented in software. The battery current density distribution inversion system based on fiber optic sensing includes the following software modules: The measurement point coordinate transformation module is used to map the coordinates along the pre-laid folded fiber optic sensing path on the surface of the battery to be tested to the two-dimensional coordinate system of the battery surface, and establish the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface. The discrete point state field conversion module is used to convert the discrete temperature measurement values ​​of each fiber optic measurement point into equivalent temperature values, and then, based on the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface, convert the equivalent temperature values ​​of each fiber optic measurement point into a discrete point state field. The anisotropic conductivity distribution calculation module is used to reconstruct the discrete point state field into a continuous two-dimensional grid continuous field based on the Gaussian weighted nearest neighbor projection method. Based on the known coupling relationship between the continuous two-dimensional grid continuous field and conductivity, the in-plane anisotropic conductivity distribution is obtained. The current density and distribution calculation module is used to establish a two-dimensional steady-state potential control equation based on the in-plane anisotropic conductivity distribution, and solve for the in-plane potential distribution of the battery under the constraints of the tab boundary and adiabatic boundary conditions. Then, it calculates the current density vector field and its distribution to realize the visualization and quantitative characterization of the non-uniformity of the current inside the battery.

[0065] It should be noted that each module in the battery current density distribution inversion system based on fiber optic sensing in this embodiment corresponds one-to-one with each step in the battery current density distribution inversion method based on fiber optic sensing in the above embodiment, and their specific implementation processes are the same, so they will not be repeated here.

[0066] The structure of the electronic device according to an embodiment of the present invention will be described in detail below. The electronic device provided in this embodiment includes: at least one processor, a memory, a user interface, and at least one network interface. The various components in the fiber optic sensing-based battery current density distribution inversion system are coupled together through a bus system. It can be understood that the bus system is used to realize the connection and communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus. The user interface may include a display, keyboard, mouse, trackball, click wheel, buttons, a touchpad, or a touch screen, etc.

[0067] It is understood that the memory can be volatile memory or non-volatile memory, or both. The memory in this embodiment of the invention is capable of storing data to support the operation of the terminal. Examples of this data include any computer programs used to operate on the terminal, such as operating systems and applications. The operating system includes various system programs, such as the framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0068] In some embodiments, the battery current density distribution inversion system based on fiber optic sensing provided in this invention can be implemented using a combination of hardware and software. For example, the battery current density distribution inversion system based on fiber optic sensing provided in this invention can be a processor in the form of a hardware decoding processor, programmed to execute the battery current density distribution inversion method based on fiber optic sensing provided in this invention. For instance, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0069] As an example, a processor can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where a general-purpose processor can be a microprocessor or any conventional processor, etc.

[0070] As an example of the hardware implementation of the battery current density distribution inversion system based on fiber optic sensing provided in this embodiment of the invention, the device provided in this embodiment of the invention can be directly executed by a processor in the form of a hardware decoding processor. For example, it can be executed by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the battery current density distribution inversion method based on fiber optic sensing provided in this embodiment of the invention.

[0071] The memory in this embodiment of the invention is used to store various types of data to support the operation of the fiber optic sensing-based battery current density distribution inversion system, or to store data for execution. Figure 1The program code for the method shown. Examples of this data include: any executable instructions for operation on a fiber optic sensing-based battery current density distribution inversion system, such as executable instructions that can be included in the executable instructions to implement the fiber optic sensing-based battery current density distribution inversion method of the embodiments of the present invention.

[0072] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by the central processing unit, it performs the various functions defined in the apparatus of this application.

[0073] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for inverting battery current density distribution based on fiber optic sensing, characterized in that, include: The coordinates along the path of the folded-back fiber optic sensing path pre-laid on the surface of the battery under test are mapped to the two-dimensional coordinate system of the battery surface to establish the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface. The discrete temperature measurements at each fiber optic measurement point are converted into equivalent temperature values. Then, based on the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface, the equivalent temperature values ​​at each fiber optic measurement point are converted into discrete point state fields. The discrete point state field is reconstructed into a continuous two-dimensional grid continuous field based on the Gaussian weighted nearest neighbor projection method. Based on the known coupling relationship between the continuous two-dimensional grid continuous field and the conductivity, the in-plane anisotropic conductivity distribution is obtained. Based on the in-plane anisotropic conductivity distribution, a two-dimensional steady-state potential control equation is established, and the in-plane potential distribution of the battery is solved under the constraints of the tab boundary and the adiabatic boundary conditions. Then, the current density vector field and its distribution are calculated to realize the visualization and quantitative characterization of the non-uniformity of the current inside the battery. The continuous field of a continuous two-dimensional grid is characterized as follows: ； ； in, Any grid point on the battery surface Temperature value; For the first Spatial weights of each measurement point to a grid point (x, y); For the first The coordinates of each measurement point in a two-dimensional coordinate system on the battery surface; For the first The coordinates of each measurement point in the two-dimensional coordinate system on the battery surface The equivalent temperature corresponding to the discrete temperature measurement value; A coefficient used to control the spatial smoothing scale; This indicates that the summation is performed over all measurement points involved in the calculation; The known coupling relationship between the continuous field of a continuous two-dimensional grid and the conductivity is as follows: ； in: For any grid point on the battery surface ( x,y The in-plane conductivity of ). As a reference conductivity, Sensitivity coefficient; This is a reference temperature value; For any grid point on the battery surface ( x,y Temperature value; The two-dimensional steady-state electromotive force control equation is: ; in, Indicates the electric potential distribution; Indicates the in-plane equivalent conductivity of the battery; This represents the potential gradient, and its direction reflects the direction of the fastest change in potential. It represents the current transport capability determined by both conductivity and potential gradient; This represents the divergence operator, used to describe whether a vector field has an inflow or outflow at a certain point.

2. The battery current density distribution inversion method based on fiber optic sensing as described in claim 1, characterized in that, The process of converting discrete temperature measurements at each fiber optic measurement point into equivalent temperature values ​​is as follows: Calculate the ratio of the discrete temperature measurement value to the calibration coefficient at each fiber optic measurement point, and then sum it with the reference temperature value to calculate the equivalent temperature value.

3. The battery current density distribution inversion method based on fiber optic sensing as described in claim 1, characterized in that, The process of mapping the coordinates along the foldback fiber optic sensing path pre-laid on the surface of the battery to the two-dimensional coordinate system of the battery surface is as follows: For any fiber optic measurement point, its coordinates along the foldback fiber optic sensing path are normalized. Multiply each normalized along-path coordinate by the total length of the foldback fiber path to obtain the corresponding actual position mapped onto the foldback fiber sensing path. By mapping the actual position onto the foldback fiber optic sensing path, the path segment to which each fiber optic measurement point belongs is determined, and its coordinates in the two-dimensional coordinate system on the battery surface are obtained through piecewise linear interpolation.

4. The battery current density distribution inversion method based on fiber optic sensing as described in claim 1, characterized in that, The current density vector field is set as : ； ； Among them, the current density vector field The direction indicates the direction of current flow, and the magnitude... Indicates local flow capacity; It represents the current transport capability determined by both conductivity and potential gradient; Let be the current density in the horizontal direction of the two-dimensional coordinate system on the battery surface; Let be the current density in the vertical direction of the two-dimensional coordinate system on the battery surface.

5. A battery current density distribution inversion system based on fiber optic sensing, characterized in that, The battery current density distribution inversion method based on fiber optic sensing as described in any one of claims 1-4 includes: The measurement point coordinate transformation module is used to map the coordinates along the pre-laid folded fiber optic sensing path on the surface of the battery to be tested to the two-dimensional coordinate system of the battery surface, and establish the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface. The discrete point state field conversion module is used to convert the discrete temperature measurement values ​​of each fiber optic measurement point into equivalent temperature values, and then, based on the correspondence between the fiber optic measurement points and the two-dimensional space of the battery surface, convert the equivalent temperature values ​​of each fiber optic measurement point into a discrete point state field. The anisotropic conductivity distribution calculation module is used to reconstruct the discrete point state field into a continuous two-dimensional grid continuous field based on the Gaussian weighted nearest neighbor projection method. Based on the known coupling relationship between the continuous two-dimensional grid continuous field and conductivity, the in-plane anisotropic conductivity distribution is obtained. The current density and distribution calculation module is used to establish a two-dimensional steady-state potential control equation based on the in-plane anisotropic conductivity distribution, and solve for the in-plane potential distribution of the battery under the constraints of the tab boundary and adiabatic boundary conditions. Then, it calculates the current density vector field and its distribution to realize the visualization and quantitative characterization of the non-uniformity of the current inside the battery.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the battery current density distribution inversion method based on fiber optic sensing as described in any one of claims 1-4.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the battery current density distribution inversion method based on fiber optic sensing as described in any one of claims 1-4.

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