A battery evaluation method and screening method based on magnetic field timing change characteristics

Abstract

A battery evaluation and screening method based on the temporal variation characteristics of magnetic field is disclosed, relating to the field of battery testing technology. This method collects magnetic field distribution data of the battery under constant current conditions and calculates the temporal variation of the magnetic field relative to the initial state to map the dynamic evolution process of the non-uniform reaction inside the battery. Furthermore, a temporal response index for the dynamic change of the reaction is constructed, and derivative analysis is performed on its rate of change to extract inflection point features to evaluate the battery's stability and response regulation capability. By setting quality grading thresholds, battery performance evaluation and screening are achieved, classifying the batteries into four categories: A, B, C, and D. This method also supports response trend evaluation at different rate limits and is suitable for cell quality sorting, operational stability evaluation, and potential fault early warning. It possesses advantages such as non-invasiveness and high throughput, making it suitable for integration into production lines for intelligent screening.

Description

Technical Field

[0001] This invention belongs to the field of battery testing technology, specifically relating to a battery evaluation and screening method based on the temporal variation characteristics of magnetic fields. Background Technology

[0002] Currently, during the charging and discharging process of lithium-ion batteries, factors such as differences in electrode thickness, uneven distribution of electrode materials, asymmetry in tab structure, and differences in thermal management structure often result in a spatially uneven current density distribution, manifesting as a non-uniform reaction phenomenon. This non-uniformity is not a static, inherent property, but rather evolves dynamically under the continuous influence of the battery's internal feedback regulation mechanism during operation. The dynamic changes in the battery's reaction essentially reflect the cell's ability to regulate and maintain operational stability in response to structural deviations and load disturbances, thus possessing significant value for quality screening and performance prediction.

[0003] However, the detection and quantification of such non-uniform reaction dynamics still face many challenges. Traditional evaluation methods mainly rely on single-point or global parameters such as voltage, current, and temperature, which cannot reflect the evolution trend and regulation path of non-uniform reactions within the battery. Although spatial resolution methods such as infrared thermal imaging, X-ray tomography, or neutron imaging can reveal local differences, they suffer from high cost, long processing time, and significant structural interference, making them unsuitable for high-throughput screening and engineering applications. More importantly, these methods focus on the spatial distribution at a specific moment and cannot capture the overall response change trajectory of the battery during operation. Therefore, there is an urgent need for a technical method based on spatial sampling and constructing time evolution indicators through global response characteristics. This method can quantify the regulation rate of non-uniform reactions within the battery and, based on this, classify and evaluate the battery's stability and suitability, thereby serving the matching needs of industrial production and applications. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, this invention provides a battery evaluation and screening method based on the temporal variation characteristics of magnetic field. Under constant current operation, it can quantitatively evaluate and classify the internal non-uniform reaction regulation capability and operational stability of the battery by analyzing the overall dynamic variation characteristics of the battery's magnetic field response. This enables non-destructive identification and high-throughput screening of the cell state, overcoming the problems of poor spatial response capability, lack of dynamic characteristics, and inability to quantify regulation capability in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A battery evaluation method based on the temporal variation characteristics of magnetic field includes the following steps:

[0007] Step 1: Under constant current conditions, acquire the magnetic field distribution data of the battery during operation. Based on the initial reference state, the change in magnetic field distribution at each time step is calculated. This is used to map the dynamic changes in the non-uniform reaction distribution inside the battery;

[0008] Step 2: Test the entire battery testing area. Spatial summation is performed to construct a time-series response index that reflects dynamic changes: ;

[0009] Step 3, for The rate of change of the magnetic field distribution is obtained by taking the derivative of the curve over time. It is used to analyze the dynamic rate of change of non-uniform reactions inside the battery;

[0010] Step 4: Identify the inflection point in the derivative curve where it changes from a rapid decline to a gradual fluctuation. This serves as a marker point indicating that the internal reaction stage of the battery has transitioned from rapid redistribution to dynamic equilibrium. It reflects the battery's operational stability and its ability to regulate spatial differences in non-uniform reactions. A higher rate of change in magnetic field distribution indicates that there are greater spatial differences in non-uniform reactions that require internal equilibrium. The earlier the inflection point appears, the stronger the battery's ability to balance spatial differences in non-uniform reactions.

[0011] Repeat the above steps at different rates to obtain the rate response trend of the magnetic field distribution change rate and the inflection point time, evaluate the battery rate performance, and realize the battery rate quality classification.

[0012] Furthermore, in step one, the magnetic field distribution test plane is the battery. Length and width plane, The coordinates of the test site;

[0013] Furthermore, in step one, the magnetic field measuring device includes an array magnetic sensor and a scanning magnetic sensor.

[0014] Furthermore, in step two, the calculation method for the time-series response index reflecting dynamic changes is as follows:

[0015] Furthermore, in step four, the turning point of the non-uniform reaction evolution stage of the battery is determined by the time point when the rate of change of the magnetic field distribution changes from a rapid decrease to a dynamic and gradual fluctuation. The turning point of the derivative curve satisfies the condition that the continuous decrease of the first derivative stops and the rate of change of the second derivative reverses sign.

[0016] The above-mentioned cell evaluation method can compare the performance differences between different types of batteries;

[0017] The types of batteries include stacked batteries or wound batteries.

[0018] A battery screening method based on the temporal variation characteristics of magnetic fields includes the following steps: Based on the above battery evaluation method, a reference maximum rate of change is set. and reference turning point time The battery cells are classified into quality grades according to the following rules: Grade A: Maximum value of magnetic field distribution change rate. And the timing of the turning point Grade B: High-quality battery; Grade B: Maximum rate of change of magnetic field distribution And the timing of the turning point ,or And the timing of the turning point Standard mass battery; Grade C: Maximum rate of change of magnetic field distribution. And the timing of the turning point Grade A: Low-quality batteries; Grade D: The derivative curve has no obvious inflection point, indicating that the internal reaction distribution is continuously unstable, and the battery is at risk of degradation or failure; among them... , It is an adjustable proportionality coefficient used to control the sensitivity of the grading criterion.

[0019] Reference maximum rate of change and reference turning point time The selected standard battery test data can be used as a standard reference to evaluate the quality level of other battery optimization or design schemes. Alternatively, the average of multiple sample test data of the same type of battery can be used as a standard reference for rapid screening of batteries in the same system.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. This invention constructs an overall time-series response index by sampling and spatially integrating the magnetic field response of a battery under constant current conditions at multiple points, thereby extracting reaction evolution characteristics. By performing derivative analysis on the change curves, it can identify key transition behaviors during the reaction redistribution to dynamic equilibrium, thus achieving a quantitative characterization of the battery's non-uniform reaction regulation capability and operational stability. Compared to traditional static measurements or overall voltage and current judgments, this method is more sensitive to the internal dynamic evolution process and is suitable for the early identification of subtle anomalies.

[0022] 2. The evaluation indicators proposed in this invention can be embedded into the production line testing process, supporting automated grading and quality screening; they are applicable to the stability evaluation of cells with different structural types (such as stacked and wound types) and under various rate conditions; at the same time, they have the advantages of being non-invasive, high-throughput, and low-cost, and are expected to serve as a basic technical solution for next-generation battery state assessment and consistency screening, and can be widely used in cell factory screening, operational adaptability matching, and structural optimization feedback. Attached Figure Description

[0023] Figure 1 The flowchart shows the battery evaluation method and screening method based on the temporal variation characteristics of magnetic field described in this invention.

[0024] Figure 2 This is a schematic diagram showing the change in the battery's magnetic field distribution measured under constant current conditions.

[0025] Figure 3 To construct a time-series response index that reflects dynamic changes in the reaction Its derivative rate of change curve A typical schematic diagram;

[0026] Figure 4 A schematic diagram comparing the characteristics of inflection points in the rate of change curves of magnetic field distribution of different batteries;

[0027] Figure 5 A schematic diagram illustrating the construction of a grading criterion based on statistics from multiple sample cells;

[0028] Figure 6 This is a trend graph showing the rate of change of the battery magnetic field distribution versus the inflection point time at different rates, used to assist in evaluating rate stability. Detailed Implementation

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some embodiments of the invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0030] This embodiment provides a battery evaluation and screening method based on the temporal variation characteristics of magnetic fields, such as... Figure 1 As shown, the method includes the following specific steps:

[0031] Step 1: Under constant current conditions, acquire the magnetic field distribution data of the battery during operation. Based on the initial reference state, the change in magnetic field distribution at each time step is calculated. This is used to map the dynamic changes in the non-uniform reaction distribution inside the battery;

[0032] In this embodiment, the battery under test is a 5Ah wound soft-pack lithium-ion battery. According to the test method in step one, when the sample battery is under 1C constant current discharge, the magnetic field distribution is tested on a plane 5mm away from the battery surface. The test area size is 65mm×75mm, and the scanning resolution is 5mm interval. Based on the initial discharge state (0% DOD) as a reference, the change in magnetic field distribution at each time point is calculated. This reflects the dynamic changes in the internal reaction behavior of the battery.

[0033] Step 2: Test the entire battery testing area. Spatial summation is performed to construct a time-series response index that reflects dynamic changes: ;

[0034] In this embodiment, a fixed 16% depth of discharge (DOD) was used as the test interval to obtain the time-series response index of the dynamic changes in battery response. ,like Figure 3 As shown in (a), the non-uniform internal reaction of the battery exhibits a non-linear dynamic change as it discharges.

[0035] Step 3, for The rate of change of the magnetic field distribution is obtained by taking the derivative of the curve over time. It is used to analyze the dynamic rate of change of non-uniform reactions inside the battery;

[0036] In this embodiment, for The rate of change of magnetic field distribution is obtained by performing derivative calculations on the curve of change with discharge depth. ,like Figure 3 As shown in (b), the dynamic changes of the internal reaction of the battery have a large rate of change in the early stage of discharge, which gradually slows down as the reaction proceeds, indicating that there is a reaction redistribution and equilibrium regulation process inside the battery.

[0037] Step 4: Identify the turning point in the derivative curve where it changes from a rapid decline to a gradual fluctuation. This serves as a marker point for the battery's internal reaction stage to transition from rapid redistribution to dynamic equilibrium. It reflects the battery's operational stability and its ability to adjust for spatial differences in non-uniform reactions. A higher rate of change indicates that there are greater spatial differences in non-uniform reactions that require internal equilibrium. The earlier the turning point appears, the stronger the battery's ability to balance spatial differences in non-uniform reactions.

[0038] In this embodiment, derivative curves were calculated for three different battery structures, and the inflection point characteristics of their reaction dynamics were obtained, such as... Figure 4 As shown, the derivative curves and inflection points of the three different types of batteries show significant differences. Battery A has the smallest internal spatial reaction difference and reaches the dynamic equilibrium stage faster. Battery B has a relatively smaller internal reaction difference than battery C and a relatively slower equilibrium rate. Battery C has the largest internal reaction difference and is in an unstable state internally, with poor self-regulation ability. This reflects the identification role of this method in the regulation ability of internal reaction differences in batteries.

[0039] Step 5: Set the reference maximum rate of change and reference turning point time Battery cells are classified into quality grades according to the following rules: Grade A: Maximum rate of change of magnetic field distribution And the timing of the turning point Grade B: High-quality battery; Grade B: Maximum rate of change of magnetic field distribution And the timing of the turning point ,or And the timing of the turning point Standard mass battery; Grade C: Maximum rate of change of magnetic field distribution. And the timing of the turning point Grade A: Low-quality batteries; Grade D: The derivative curve has no obvious inflection point, indicating that the internal reaction distribution is continuously unstable, and the battery is at risk of degradation or failure; among them... , It is an adjustable proportional coefficient used to control the sensitivity of the grading criterion;

[0040] In this embodiment, the average of multiple sample battery test data of the 5Ah wound pouch battery in step one is used as the reference maximum rate of change. and reference turning point time , respectively , 71% DOD, classification ratio coefficient is =0.9, =0.9, and the other 10 items to be tested were evaluated and screened using the set reference threshold. The results are as follows: Figure 5 As shown, 2 batteries are classified as Grade A, 8 batteries as Grade B, and there are no Grade C or Grade D batteries, verifying the effectiveness of this screening method.

[0041] Step 6: Repeat the above steps at different rates to obtain the rate response trend of magnetic field change rate and inflection point time, evaluate battery rate performance, and realize battery rate quality classification.

[0042] In this embodiment, the dynamic response of the battery at different rate speeds was evaluated using constant current discharge conditions of 0.25C, 0.5C, and 1C. The results are as follows: Figure 6 As shown, the lower the rate, the smaller the difference in the internal reaction of the battery, and the earlier the inflection point appears, indicating that the reaction reaches dynamic equilibrium faster. Conversely, the higher the rate, the greater the difference in the internal reaction, and the later the inflection point appears.

[0043] This invention achieves non-destructive identification of the dynamic regulation behavior of non-uniform reactions by monitoring changes in the distribution of the external magnetic field during the operation of lithium-ion batteries. This method can effectively quantify the differences in electrochemical responses in different regions within the battery, identify key transition behaviors during the reaction redistribution to dynamic equilibrium, and thus achieve quantitative characterization of the battery's non-uniform reaction regulation capability and operational stability. Compared to traditional static measurements or overall voltage and current judgments, this method is more sensitive to the internal kinetic evolution process and performs rapid classification based on the overall reaction dynamic characteristics, which helps to achieve cell consistency screening and potential anomaly identification. This invention's method has the advantages of being non-contact, rapid, and high-throughput, and is particularly suitable for cell factory testing, degradation and fault analysis, battery module inconsistency analysis, and long-term battery state screening.

[0044] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A battery evaluation method based on the temporal variation characteristics of magnetic field, characterized in that, Includes the following steps: Step 1: Under constant current conditions, acquire the magnetic field distribution data of the battery during operation. Based on the initial reference state, the change in magnetic field distribution at each time step is calculated. This is used to map the dynamic changes in the non-uniform reaction distribution inside the battery; Step 2: Test the entire battery testing area. Spatial summation is performed to construct a time-series response index that reflects dynamic changes: ; Step 3, for The rate of change of the magnetic field distribution is obtained by taking the derivative of the curve over time. It is used to analyze the dynamic rate of change of non-uniform reactions inside the battery; Step 4: Identify the inflection point in the derivative curve where it changes from a rapid decline to a gradual fluctuation. This serves as a marker point indicating that the internal reaction stage of the battery has transitioned from rapid redistribution to dynamic equilibrium. It reflects the battery's operational stability and its ability to regulate spatial differences in non-uniform reactions. A higher rate of change in magnetic field distribution indicates that there are greater spatial differences in non-uniform reactions that require internal equilibrium. The earlier the inflection point appears, the stronger the battery's ability to balance spatial differences in non-uniform reactions.

2. The evaluation method according to claim 1, characterized in that: Repeat the steps of claim 1 at different rates to obtain the rate response trend of the magnetic field distribution change rate and the inflection point time, evaluate the battery rate performance, and realize the battery rate quality classification.

3. The evaluation method according to claim 1, characterized in that: In step one, the magnetic field distribution test plane is the battery. Length and width plane, These are the coordinates of the test site.

4. The evaluation method according to claim 1, characterized in that: In step two, the calculation method for the time-series response index that reflects dynamic changes is as follows: .

5. The evaluation method according to claim 1, characterized in that: In step four, the turning point of the non-uniform reaction evolution stage of the battery is determined by the time point when the rate of change of the magnetic field distribution changes from a rapid decrease to a dynamic and gradual fluctuation. The turning point of the derivative curve satisfies the condition that the continuous decrease of the first derivative stops and the rate of change of the second derivative reverses sign.

6. The evaluation method according to claim 1, characterized in that: In step one, the magnetic field measuring device is an array magnetic sensor or a scanning magnetic sensor.

7. The evaluation method according to claim 1, characterized in that: The battery evaluation method is used to compare the performance differences between different types of batteries.

8. The evaluation method according to claim 1, characterized in that: The battery type is either a stacked battery or a wound battery.

9. A battery screening method based on the temporal variation characteristics of magnetic fields, characterized in that: Based on the evaluation method described in any one of claims 1-8, a reference maximum rate of change is set. and reference turning point time Battery cells are classified into quality grades according to the following rules: Grade A: Maximum rate of change of magnetic field distribution And the timing of the turning point Grade B: High-quality battery; Grade B: Maximum rate of change of magnetic field distribution And the timing of the turning point ,or And the timing of the turning point Standard mass battery; Grade C: Maximum rate of change of magnetic field distribution. And the timing of the turning point This is a low-quality battery; Grade D: The derivative curve shows no obvious inflection point, indicating that the internal reaction distribution remains unstable, suggesting a risk of degradation or failure. , It is an adjustable proportionality coefficient used to control the sensitivity of the grading criterion.

10. The screening method according to claim 9, characterized in that: Reference maximum rate of change and reference turning point time The selected standard battery test data can be used as a standard reference to evaluate the quality level of other battery optimization or design schemes, or the average of multiple sample test data of the same type of battery can be used as a standard reference to quickly screen batteries in the same system.

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