Battery evaluation method and screening method based on magnetic field time sequence change characteristics

Abstract

The invention discloses a battery evaluation method and screening method based on magnetic field time sequence change characteristics, and relates to the technical field of battery detection. According to the method, battery magnetic field distribution data is collected under a constant current working condition, and a magnetic field time sequence variation relative to an initial state is calculated, so that a dynamic evolution process of a non-uniform reaction in a battery is mapped. Furthermore, a time sequence response index reflecting dynamic changes is constructed, derivative analysis is carried out on the change rate of the time sequence response index, and turning point features are extracted to evaluate the stability and the reaction regulation capability of the battery. By setting a quality grading threshold value, battery performance evaluation and screening can be realized, and the battery performance can be divided into A / B / C / D. The method also supports response trend evaluation under different multiplying powers, and is suitable for cell quality sorting, operation stability evaluation and potential fault early warning. The method has the advantages of non-invasion, high flux and the like and is suitable for being embedded into a production line for intelligent screening.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery detection, and in particular relates to a battery evaluation method and a battery screening method based on the temporal variation characteristics of a magnetic field. Background Art

[0002] During the charge and discharge process of current lithium-ion batteries, spatially uneven current density distribution often occurs due to factors such as variations in electrode thickness, uneven distribution of electrode materials, asymmetric tab structures, and differences in thermal management structures, manifesting as a non-uniform reaction phenomenon. This non-uniformity is not a static, inherent property, but rather evolves dynamically during operation, continuously influenced by the battery's internal feedback regulation mechanisms. The dynamic characteristics of a battery's reaction essentially reflect the cell's ability to regulate and operate stably in response to structural deviations and load disturbances, and therefore have important value in quality screening and performance prediction.

[0003] However, the detection and quantification of the dynamic behavior of such non-uniform reactions still face many challenges. Traditional evaluation methods mainly rely on single-point or overall parameters such as voltage, current, and temperature, which cannot reflect the evolution trend and regulation path of non-uniform reactions inside the battery; although spatial resolution methods such as infrared thermal imaging, X-ray tomography or neutron imaging can be used to reveal local differences, they have problems such as high cost, long time, and large interference to the structure, making them difficult to apply to high-throughput screening and engineering applications. More importantly, these methods focus on the spatial distribution at a certain 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 a time evolution index through global response characteristics, which can not only quantify the regulation rate of non-uniform reactions inside the battery, but also conduct a graded evaluation of battery stability and applicability based on this, thereby serving the needs of industrial production and application matching. Summary of the Invention

[0004] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a battery evaluation method and screening method based on the time-series change characteristics of the magnetic field. Under constant current operating conditions, by analyzing the overall dynamic change characteristics of the battery magnetic field response, the internal non-uniform reaction regulation ability and operation stability can be quantitatively evaluated and quality graded, thereby realizing non-destructive discrimination and high-throughput screening of the battery 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] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A battery evaluation method based on magnetic field time-series variation characteristics includes the following steps:

[0007] Step 1: Under constant current conditions, obtain the magnetic field distribution data of the battery during operation , and based on the initial reference state, calculate the change in magnetic field distribution at each moment , used to map the dynamic changes of the non-uniform reaction distribution inside the battery;

[0008] Step 2: Test the entire battery area Perform spatial summation to construct a temporal response index that reflects dynamic changes: ;

[0009] Step 3: Perform derivative calculation on the curve changing with time to obtain the rate of change of magnetic field distribution , used to analyze the dynamic change rate of non-uniform reactions inside the battery;

[0010] Step 4. Identify the turning point in the derivative curve where the rapid decline turns into a gentle fluctuation. This point serves as a sign that the battery's internal reaction stage changes 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 the magnetic field distribution indicates that there are greater spatial differences in non-uniform reactions that require internal balance. The earlier the turning 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 turning point time, evaluate the battery rate performance, and achieve battery rate quality classification;

[0012] Furthermore, in step 1, the magnetic field distribution test plane is the battery Length and width plane, is the test site coordinate;

[0013] Furthermore, in step 1, the magnetic field measuring device includes an array magnetic sensor and a scanning magnetic sensor.

[0014] Furthermore, in step 2, the calculation method of the timing response index that reflects the dynamic change is

[0015] Furthermore, in step 4, the turning point of the battery's heterogeneous reaction evolution stage is determined by the time point at which the rate of change of the magnetic field distribution changes from a rapid decrease to a dynamic and gentle fluctuation, and the turning point of the derivative curve satisfies the conditions that the first-order derivative stops continuously decreasing and the rate of change of the second-order derivative reverses its sign;

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

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

[0018] A battery screening method based on the time series change characteristics of the magnetic field includes the following steps: on the basis of the above battery evaluation method, setting a reference maximum change rate and reference turning time The battery cells are classified into quality grades according to the following rules: Grade A: Maximum value of the magnetic field distribution change rate And the turning point occurs at , which is a high-quality battery; Grade B: Maximum value of magnetic field distribution change rate And the turning point occurs at ,or And the turning point occurs at , for standard quality batteries; Class C: Maximum value of magnetic field distribution change rate And the turning point occurs at , which is a low-quality battery; Grade D: The derivative curve has no obvious turning point, indicating that the internal reaction distribution is continuously unstable, and there is a risk of degradation or failure of the battery; Among them, , It is an adjustable proportional coefficient used to control the sensitivity of the classification criterion.

[0019] Reference maximum rate of change and reference turning time The selected standard battery test data can be used as a standard reference to evaluate the quality level of batteries for other battery optimization or design solutions. The average of multiple sample test data of the same type of battery can also be used as a standard reference to quickly screen batteries of the same system.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. This invention constructs a holistic temporal response index by performing multi-point sampling and spatial integration of the battery's magnetic field response under constant current conditions, extracting reaction evolution characteristics. Derivative analysis of the change curve identifies key transitions during the reaction redistribution to dynamic equilibrium, thereby quantitatively characterizing the battery's ability to regulate non-uniform reactions and operational stability. Compared to traditional static measurements or overall voltage and current assessments, this method is more sensitive to internal dynamic evolution processes and is suitable for early identification of subtle anomalies.

[0022] 2. The evaluation indicators proposed in this paper can be embedded in production line testing processes, supporting automated grading and quality screening. They are applicable to stability assessments of cells of varying structural types (e.g., laminated and wound) and under various rate conditions. Furthermore, they offer the advantages of being non-invasive, high-throughput, and low-cost, making them a promising foundational technology for next-generation battery state assessment and consistency screening, with widespread application in processes such as cell screening before shipment, operational compatibility matching, and structural optimization feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of the battery evaluation method and screening method based on the time-series variation characteristics of the magnetic field according to the present invention;

[0024] Figure 2 Schematic diagram of the change in the battery magnetic field distribution measured under constant current conditions;

[0025] Figure 3 To build a time series response index that reflects dynamic changes Its derivative rate of change curve Typical schematic diagram of

[0026] Figure 4 Schematic diagram comparing the turning point characteristics in the magnetic field distribution change rate curves of different batteries;

[0027] Figure 5 Construct a schematic diagram for the classification criteria based on statistics of multiple sample cells;

[0028] Figure 6 The response trend diagram of the battery magnetic field distribution change rate and turning point time at different rates is used to assist in evaluating rate stability. DETAILED DESCRIPTION

[0029] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

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

[0031] Step 1: Under constant current conditions, obtain the magnetic field distribution data of the battery during operation , and based on the initial reference state, calculate the change in magnetic field distribution at each moment , used to map the dynamic changes of 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 of step 1, when the sample battery is in a 1C constant current discharge, the magnetic field distribution test is performed on a plane 5mm away from the battery surface. The test area size is 65mm×75mm, the scanning resolution is 5mm interval, and the change in magnetic field distribution at each moment is calculated based on the initial discharge state (0% DOD) as a reference. , reflecting the dynamic change behavior of the internal reaction of the battery.

[0033] Step 2: Test the entire battery area Perform spatial summation to construct a temporal response index that reflects dynamic changes: ;

[0034] In this embodiment, a fixed 16% depth of discharge (DOD) is used as the test interval to obtain the timing response index of the dynamic change of the battery reaction. ,like Figure 3 As shown in (a), the internal non-uniform reactions of the battery show nonlinear dynamic changes as the discharge proceeds.

[0035] Step 3: Perform derivative calculation on the curve changing with time to obtain the rate of change of magnetic field distribution , used to analyze the dynamic change rate of non-uniform reactions inside the battery;

[0036] In this embodiment, Perform derivative calculation on the curve of discharge depth change to obtain the rate of change of magnetic field distribution ,like Figure 3 As shown in (b), the dynamic changes of the reaction inside the battery have a large change rate at the initial stage of discharge, and gradually slow down as the reaction proceeds, indicating that there is a reaction redistribution and balance adjustment process inside the battery.

[0037] Step 4: Identify the turning point in the derivative curve where the rapid decline turns into a gentle fluctuation. This point serves as a marker for the transition from rapid redistribution to dynamic equilibrium in the battery's internal reaction phase. It reflects the battery's operational stability and its ability to adjust 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 balance. 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 of three different structural types of batteries are calculated and the turning point characteristics of their reaction dynamic processes are obtained, such as Figure 4 As shown, the derivative curves and turning 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. The internal reaction difference of Battery B is smaller than that of Battery C, and the equilibrium rate is relatively slow. Battery C has the largest internal reaction difference and is continuously in an unstable state, with poor self-regulation ability. This reflects the recognition effect of this method on the regulation ability of battery internal reaction differences.

[0039] Step 5: Set the reference maximum change rate and reference turning time , the quality level of the battery cells is divided according to the following rules: Grade A: Maximum value of the magnetic field distribution change rate And the turning point occurs at , which is a high-quality battery; Grade B: Maximum value of magnetic field distribution change rate And the turning point occurs at ,or And the turning point occurs at , for standard quality batteries; Class C: Maximum value of magnetic field distribution change rate And the turning point occurs at , which is a low-quality battery; Grade D: The derivative curve has no obvious turning point, indicating that the internal reaction distribution is continuously unstable, and there is a risk of degradation or failure of the battery; 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 5Ah wound soft pack batteries in step 1 is used as the reference maximum change rate. and reference turning time , respectively , 71% DOD, the classification ratio coefficient is =0.9, =0.9, and the other 10 samples to be tested were evaluated and screened using the set reference threshold. The results are as follows Figure 5 As shown, 2 batteries are grade A batteries, 8 batteries are grade B batteries, and there are no grade C and grade D batteries, which verifies the effectiveness of the screening method;

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

[0042] In this embodiment, the 0.25C, 0.5C and 1C constant current discharge conditions are used to evaluate the dynamic performance of the battery at different rates. Figure 6 As shown, the lower the rate, the smaller the difference in the internal reaction of the battery, and the earlier the turning point appears, indicating that the reaction reaches dynamic equilibrium faster. On the contrary, the larger the rate, the greater the difference in the internal reaction, and the later the turning point appears.

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

[0044] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A battery evaluation method based on the temporal variation characteristics of a magnetic field, characterized in that: The following steps are involved: Step 1: Under constant current conditions, obtain the magnetic field distribution data of the battery during operation , and based on the initial reference state, calculate the change in magnetic field distribution at each moment , used to map the dynamic changes of the non-uniform reaction distribution inside the battery; Step 2: Test the entire battery area Perform spatial summation to construct a temporal response index that reflects dynamic changes: ; Step 3: Perform derivative calculation on the curve changing with time to obtain the rate of change of magnetic field distribution , used to analyze the dynamic change rate of non-uniform reactions inside the battery; Step 4. Identify the turning point in the derivative curve where the rapid decline turns into a gentle fluctuation. This point serves as a sign that the battery's internal reaction stage changes 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 the magnetic field distribution indicates that there are greater spatial differences in non-uniform reactions that require internal balance. The earlier the turning point appears, the stronger the battery's ability to balance spatial differences in non-uniform reactions.

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

3. The evaluation method according to claim 1, wherein: In step 1, the magnetic field distribution test plane is the battery Length and width plane, The coordinates of the test site.

4. The evaluation method according to claim 1, wherein: In step 2, the calculation method of the timing response index that reflects the dynamic changes is .

5. The evaluation method according to claim 1, wherein: In step 4, the turning point of the battery's non-uniform reaction evolution stage is determined by the time point when the rate of change of the magnetic field distribution changes from a rapid decline to a dynamic and gentle fluctuation. The turning point of the derivative curve meets the conditions that the first-order derivative stops continuously declining and the rate of change of the second-order derivative reverses its sign.

6. The evaluation method according to claim 1, wherein: In step 1, the magnetic field measuring device includes an array magnetic sensor and a scanning magnetic sensor.

7. The evaluation method according to claim 1, wherein: The battery evaluation method can compare the performance differences between different types of batteries.

8. The evaluation method according to claim 1, wherein: The types of batteries include stacked cells or wound cells.

9. A battery screening method based on the temporal variation characteristics of a magnetic field, characterized by: Based on the evaluation method described in any one of claims 1 to 8, a reference maximum change rate is set. and reference turning time , the quality level of the battery cells is divided according to the following rules: Grade A: Maximum value of the magnetic field distribution change rate And the turning point occurs at , which is a high-quality battery; Grade B: Maximum value of magnetic field distribution change rate And the turning point occurs at ,or And the turning point occurs at , for standard quality batteries; Class C: Maximum value of magnetic field distribution change rate And the turning point occurs at , which is a low-quality battery; Grade D: There is no obvious turning point in the derivative curve, indicating that the internal reaction distribution is continuously unstable and there is a risk of degradation or failure of the battery; , It is an adjustable proportional coefficient used to control the sensitivity of the classification criterion.

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

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