Cascade utilization method of waste power battery of new energy automobile

Abstract

The invention discloses an echelon utilization method for waste power batteries of new energy vehicles, which comprises the following steps: firstly, collecting and preliminarily classifying and screening retired batteries according to health states, and quickly eliminating potential safety hazards by using infrared thermal imaging and voltage consistency detection; comprehensive performance detection including complementary energy detection, discharge capacity test at different temperatures, direct current internal resistance measurement and charge and discharge temperature rise test is carried out; key parameters are extracted from the detection data, comprehensive performance analysis is carried out in combination with application scene requirements, and a comprehensive evaluation score is generated; selecting similar batteries to form a pre-combination unit according to the score sequence; a grouping result is optimized through a clustering algorithm in the three-dimensional performance coordinate system, and it is ensured that the deviation of core parameters of the same group of batteries is smaller than a set threshold value; the stability and the operation efficiency of the echelon utilization system are improved, the adaptive capacity of the echelon utilization system in different application scenes is enhanced, and a guarantee is provided for stable operation of the echelon utilization battery system.

Description

Technical Field

[0001] This invention relates to the field of waste power battery utilization technology, and in particular to a method for the tiered utilization of waste power batteries from new energy vehicles. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the disposal of retired power batteries, as one of the core components of electric vehicles, has attracted increasing attention. Although retired power batteries are no longer suitable for original vehicle use standards, they still have a high remaining capacity (usually above 70%), making them ideal for secondary use. By applying these batteries to areas with lower performance requirements, such as energy storage systems and low-speed electric vehicles, not only can the battery life be extended, but resource waste and environmental pollution can also be reduced.

[0003] However, current technologies for the cascade utilization of retired power batteries face a series of challenges and shortcomings. First, due to differences in manufacturing processes, usage environments, and individual aging levels, retired batteries often exhibit significant inconsistencies in key performance parameters such as capacity, internal resistance, and temperature rise rate. If these performance differences are not scientifically assessed and properly matched, they will severely impact the stability and operational efficiency of the cascade utilization system. Second, current preliminary screening of retired batteries relies primarily on manual inspection or basic testing methods, lacking systematic and intelligent health status assessment methods. This makes it difficult to comprehensively and accurately identify battery safety hazards and actual usability, potentially leading to the misuse of batteries with internal short circuits or potential thermal runaway risks, thus posing safety risks. Furthermore, current technologies generally neglect the sensitivity of batteries to environmental conditions, especially in extreme temperature environments (such as -20°C or 55°C). Their capacity retention, internal resistance changes, and temperature rise behavior may fluctuate significantly, resulting in insufficient adaptability and reliability of the cascade utilization system in practical applications. Finally, even after preliminary screening and classification, retired batteries may still experience unpredictable and random performance changes during subsequent use, such as sudden capacity drops or sudden increases in internal resistance.

[0004] Therefore, it is necessary to provide a method for the tiered utilization of spent power batteries from new energy vehicles to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention overcomes the shortcomings of the prior art and provides a method for the tiered utilization of waste power batteries from new energy vehicles.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for the tiered utilization of spent power batteries from new energy vehicles, comprising the following steps:

[0007] S1. Collect retired new energy vehicle power batteries and conduct preliminary classification and screening based on their health status;

[0008] S2. Conduct comprehensive performance testing on the initially classified batteries;

[0009] S3. Extract key parameters from the detection data and conduct comprehensive performance analysis in conjunction with application scenario requirements to generate a comprehensive evaluation score;

[0010] S4. Sort the comprehensive evaluation scores and select those with similar comprehensive evaluation scores as pre-groups;

[0011] S5. When the distance between the pre-combined components in three-dimensional space is ≤δ, divide them into the same gradient.

[0012] In a preferred embodiment of the present invention, in step S1, the initial health status classification is based on the battery's remaining capacity, health status indicators, and appearance integrity.

[0013] In a preferred embodiment of the present invention, in step S1, the rapid screening tool completes the preliminary classification and eliminates batteries that pose safety hazards.

[0014] In a preferred embodiment of the present invention, the rapid screening tool includes, but is not limited to, infrared thermal imaging and voltage consistency detection.

[0015] In a preferred embodiment of the present invention, in step S2, the comprehensive performance test includes: residual energy detection, discharge capacity test at temperature, discharge ohmic internal resistance test, and charge / discharge temperature rise test.

[0016] In a preferred embodiment of the present invention, the residual capacity detection determines the actual remaining capacity of the battery through a constant current discharge test, the discharge capacity test at the temperature is performed at three temperature conditions: 25°C, -20°C, and 55°C, the discharge ohmic internal resistance test measures the DC internal resistance of the battery using the AC impedance method, and the charge-discharge temperature rise test records the temperature rise rate of the battery during a 1C charge-discharge cycle.

[0017] In a preferred embodiment of the present invention, in step S3, the detection data is collected through an automated testing platform to predict its potential failure risk in secondary use. The key parameters include capacity, DC internal resistance, and 1C temperature rise rate.

[0018] In a preferred embodiment of the present invention, in step S4, the ranking of the comprehensive evaluation scores is based on a base score calculated from three parameters: battery capacity, internal resistance, and temperature rise rate. The parameter weights are adjusted according to the application scenario requirements to generate a weighted total score. The batteries are then sorted from high to low according to the weighted total score, and batteries with similar scores are selected as pre-combinations.

[0019] In a preferred embodiment of the present invention, in step S5, the X-axis of the three-dimensional performance coordinate system represents capacity, the Y-axis represents DC internal resistance, and the Z-axis represents the 1°C temperature rise rate.

[0020] In a preferred embodiment of the present invention, in step S5, the three-dimensional performance coordinate system is used to quantify the differences in battery performance and optimize the grouping results through a clustering algorithm to ensure that the deviations in capacity, internal resistance and temperature rise rate of batteries in the same group are all less than a set threshold.

[0021] This invention addresses the shortcomings of the prior art and has the following beneficial effects:

[0022] (1) This invention provides a method for the cascade utilization of spent power batteries of new energy vehicles. By introducing a weighted scoring mechanism to conduct a comprehensive performance evaluation of retired power batteries, and dynamically adjusting the weights of key parameters such as capacity, internal resistance and temperature rise rate according to the application scenario requirements, a scientific and reasonable scoring ranking is achieved. On this basis, batteries with similar scores are selected first to form pre-combined units, which effectively improves the consistency of subsequent battery packs in electrochemical characteristics and thermal behavior, reduces the risk of system imbalance caused by individual differences, and provides a guarantee for the stable operation of the cascade utilization battery system.

[0023] (2) This invention provides a method for the cascade utilization of waste power batteries of new energy vehicles. A three-dimensional performance coordinate system is constructed with capacity as the X-axis, DC internal resistance as the Y-axis, and 1C temperature rise rate as the Z-axis. Each battery is mapped to a point in this space, thereby intuitively quantifying its comprehensive performance status. By performing spatial distance analysis on these points, the performance differences between batteries can be accurately measured, providing a visual and quantifiable basis for subsequent clustering and grouping, and significantly improving the efficiency and accuracy of battery screening and grouping.

[0024] (3) This invention provides a method for the tiered utilization of spent power batteries from new energy vehicles. Based on a three-dimensional spatial model, a clustering algorithm is introduced to automatically classify and divide the batteries into gradients, ensuring high consistency in capacity retention, internal resistance variation trends, and thermal stability within each gradient. By setting a deviation threshold to control intra-group differences, not only is the safety and cycle life of the tiered battery modules improved, but their adaptability to different application scenarios is also enhanced. This provides technical support for the efficient reuse of large-scale retired batteries and has promising prospects for industrial application. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1This is a schematic flowchart of a preferred embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein. Therefore, the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0029] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the scope of protection of this application. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0030] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this application based on the specific circumstances.

[0031] like Figure 1 As shown, this invention provides a method for the cascade utilization of spent power batteries from new energy vehicles, comprising the following steps:

[0032] S1. Collect retired new energy vehicle power batteries and conduct preliminary classification and screening based on their health status. The preliminary classification of health status is based on the battery's remaining capacity, health status indicators, and appearance integrity. The preliminary classification is completed through infrared thermal imaging and voltage consistency detection, and batteries with potential safety hazards are eliminated.

[0033] It should be noted that infrared thermal imaging and voltage consistency detection are used to quickly eliminate batteries with potential safety hazards such as thermal runaway or abnormal voltage, ensuring the physical and chemical stability of batteries in subsequent processes. Simultaneously, preliminary grading is performed based on remaining capacity, health status, and appearance integrity, providing standardized samples for subsequent performance testing and reducing the cost of ineffective testing.

[0034] S2. Conduct comprehensive performance testing on the initially classified batteries. The comprehensive performance testing includes: residual energy testing, discharge capacity testing at temperature, discharge ohmic internal resistance testing, and charge / discharge temperature rise testing. Residual energy testing determines the actual remaining capacity of the battery through constant current discharge testing. Discharge capacity testing at temperature is conducted at three temperature conditions: 25℃, -20℃, and 55℃. Discharge ohmic internal resistance testing measures the DC internal resistance of the battery using the AC impedance method. Charge / discharge temperature rise testing records the temperature rise rate of the battery during a 1C charge / discharge cycle.

[0035] It should be noted that: residual energy detection clarifies the actual remaining capacity of the battery, directly reflecting its availability; combined with discharge capacity tests at 25℃, -20℃, and 55℃, the performance degradation patterns under different environmental conditions are simulated, providing data support for the environmental adaptability of energy storage systems and other scenarios; simultaneously, the AC impedance method is used to measure DC internal resistance, quantifying internal battery losses and predicting efficiency decline trends during long-term use, while the temperature rise rate test in 1C charge-discharge cycles is used to assess thermal management risks, ensuring the safety of the cascade utilization system; based on an automated testing platform and standardized testing procedures, all data are collected with high consistency, providing reliable, traceable, and high-quality input for subsequent big data analysis, supporting accurate modeling of battery performance and prediction of failure risks.

[0036] S3. Extract key parameters from the test data. The test data is collected through an automated testing platform to predict the potential failure risk in the secondary use. Key parameters include capacity, DC internal resistance and 1C temperature rise rate. Combined with the application scenario requirements, a comprehensive performance analysis is conducted to generate a comprehensive evaluation score.

[0037] It should be noted that by dynamically adjusting the weights of capacity, internal resistance, and temperature rise rate, a weighted total score is generated, making the scoring system more closely aligned with the actual needs of the target application scenario. The comprehensive evaluation score transforms complex performance indicators into a quantifiable weighted total score, providing a scientific basis for subsequent pre-combination and grouping strategies, and ensuring performance consistency and safety during the tiered utilization process.

[0038] S4. Sort the comprehensive evaluation scores and select those with similar comprehensive evaluation scores as pre-combinations. The ranking of comprehensive evaluation scores is based on the calculation of the basic score according to the three parameters of battery capacity, internal resistance and temperature rise rate. The parameter weights are adjusted according to the application scenario requirements to generate a weighted total score. Sort the batteries from high to low according to the weighted total score and prioritize the selection of batteries with similar scores as pre-combinations.

[0039] It should be noted that by sorting batteries according to their weighted total scores and prioritizing batteries with similar scores as pre-combination targets, the system ensures that batteries within the same group are highly similar in core parameters such as capacity, internal resistance, and temperature rise rate. This reduces efficiency losses or safety hazards caused by performance differences within the system. Furthermore, by dynamically adjusting parameter weights—for example, prioritizing high-capacity batteries for energy storage systems and low-internal-resistance batteries for low-speed electric vehicles—the pre-combination strategy better aligns with the actual needs of the target application scenario, improving the adaptability of the cascade utilization system. In addition, this method effectively reduces the need for redundant cooling systems due to significant performance differences, significantly lowering the development and maintenance costs of the cascade utilization system, achieving both economic and technical optimization.

[0040] S5. When the distance between the pre-grouped cells is ≤δ in three-dimensional space, they are divided into the same gradient. In the three-dimensional performance coordinate system, the X-axis is the capacity, the Y-axis is the DC internal resistance, and the Z-axis is the 1C temperature rise rate. The three-dimensional performance coordinate system is used to quantify the differences in battery performance and optimize the grouping results through clustering algorithms to ensure that the deviations of the same group of batteries in terms of capacity, internal resistance, and temperature rise rate are all less than the set threshold.

[0041] It should be noted that by constructing a three-dimensional performance coordinate system with capacity (X-axis), DC internal resistance (Y-axis), and 1C temperature rise rate (Z-axis) as dimensions, the performance differences of batteries in core parameters can be intuitively quantified, avoiding the one-sidedness of evaluation based on a single parameter. Based on this coordinate system, a clustering algorithm is used to optimize the grouping results, ensuring that the deviations of batteries in the same group in terms of capacity, internal resistance, and temperature rise rate are all controlled within set thresholds (e.g., capacity deviation <5%, internal resistance deviation <10%, temperature rise rate deviation <15%), thereby improving the operational stability and service life of the cascade utilization system. In addition, this method can adapt to the differentiated needs of different application scenarios by adding a fourth dimension parameter or adjusting the clustering algorithm strategy. For example, energy storage systems focus on capacity and temperature rise control, while low-speed electric vehicle scenarios need to consider both internal resistance and lightweight characteristics, achieving dynamic optimization and adaptation of the grouping strategy.

[0042] Example 1:

[0043] A batch of retired ternary lithium batteries (NCM) and lithium iron phosphate batteries (LFP) will be reused in a tiered manner. Target scenarios include: energy storage systems (requiring high capacity and low-temperature stability) and low-speed electric vehicles (requiring low internal resistance and lightweight characteristics).

[0044] S1: Use an infrared thermal imager to scan the battery surface to identify batteries with localized overheating (>50℃) or hot spots, and remove batteries at risk of thermal runaway. Use a voltage consistency tester to measure the voltage difference of individual cells in the battery pack and remove battery packs with voltage deviation >5%. Remaining capacity screening: Determine the remaining capacity using a constant current discharge test (0.5C) and retain batteries with a capacity ≥70%. Health status classification: Based on the capacity decay curve (capacity decay <20% is grade A, 20%-50% is grade B). Appearance integrity inspection: Remove batteries with damaged casings, swelling, or leakage.

[0045] Results: A total of 1000 battery groups were selected, including 600 Class A batteries, 400 Class B batteries, 300 NCM batteries, and 700 LFP batteries.

[0046] S2: Comprehensive Performance Testing

[0047] Residual capacity detection: Constant current discharge test (0.5C) to 3.0V, record the actual remaining capacity (SOC);

[0048] Discharge capacity test at temperature: Under conditions of 25℃, -20℃, and 55℃, discharge at 1C to the cutoff voltage and record the discharge capacity. The capacity at 25℃ is 85%, at -20℃ it is 70%, and at 55℃ it is 80%.

[0049] Discharge ohmic internal resistance test: The DC internal resistance (DCIR) is measured using the AC impedance method (1kHz frequency), with a typical range of 5-15mΩ;

[0050] Charge and discharge temperature rise test: Record the temperature rise rate during 1C charge and discharge cycles (e.g., charging temperature rise ≤ 5℃ / min, discharging temperature rise ≤ 8℃ / min);

[0051] Results: The average internal resistance of Class A batteries was 8mΩ, and the average internal resistance of Class B batteries was 12mΩ; LFP batteries had a higher capacity retention rate than NCM batteries at -20℃ (LFP: 75% vs NCM: 65%).

[0052] S3: Key parameter extraction: capacity (Ah), DC internal resistance (mΩ), 1C temperature rise rate (°C / min), predicting the remaining life of the battery in energy storage scenarios (e.g., the number of cycles when the capacity decays to 60%).

[0053] Weighted score calculation:

[0054] Energy storage scenario weighting: capacity (50%), internal resistance (30%), temperature rise rate (20%);

[0055] Weighting for low-speed electric vehicle scenarios: internal resistance (40%), temperature rise rate (30%), capacity (30%);

[0056] The formula for weighted total score is:

[0057] Score = w1·C + w2·R + w3·T

[0058] Where C is the capacity, R is the reciprocal of the internal resistance, and T is the reciprocal of the temperature rise rate.

[0059] Results: For energy storage scenarios, high-capacity, low-internal-resistance batteries are preferred, with LFP batteries achieving a weighted total score of 85 points; for low-speed electric vehicle scenarios, low-internal-resistance, lightweight batteries are preferred, with pouch NCM batteries achieving a weighted total score of 88 points.

[0060] S4: Sort batteries by weighted total score from highest to lowest, and prioritize batteries with similar scores (e.g., within ±2 points). For energy storage scenarios, manually increase the capacity weight to 60%; for low-speed electric vehicle scenarios, increase the internal resistance weight to 50%. Through consistency analysis, reduce the heat dissipation redundancy design caused by internal resistance differences, and handle batteries with temperature rise rate deviation >15% separately.

[0061] Results: Energy storage scenario combination: 20 LFP battery packs, capacity deviation <5%; Low-speed electric vehicle scenario combination: 15 pouch NCM battery packs, internal resistance deviation <10%.

[0062] S5: By constructing a three-dimensional performance coordinate system with capacity (X-axis), DC internal resistance (Y-axis), and 1C temperature rise rate (Z-axis) as dimensions, retired power batteries are quantitatively analyzed, and clustering algorithms are used to optimize the grouping results to ensure that batteries in the same group are highly consistent in core performance parameters, thereby improving the operational stability and service life of the cascade utilization system.

[0063] Meanwhile, this method has good scalability and can be adapted to the needs of different application scenarios such as energy storage systems and low-speed electric vehicles by adding a fourth dimension parameter (such as cycle life and capacity decay rate) or adjusting the clustering algorithm type (K-means, hierarchical clustering).

[0064] Construction of a three-dimensional performance coordinate system;

[0065] Each battery sample consists of a three-dimensional feature vector composed of the following three key performance indicators:

[0066] Capacity C i (Ah), DC internal resistance R i (mΩ), 1C temperature rise rate T i (℃ / min);

[0067] Let the i-th battery sample be represented as:

[0068] X i =[C i R i T i ]

[0069] Map all battery samples into a three-dimensional space to form a point set:

[0070] D = [X1, X2, ..., X...] n ]

[0071] Initial grouping is performed using the K-Means algorithm:

[0072] Set the number of clusters k=5, that is, try to divide the batteries into 5 groups.

[0073] The objective function (intra-cluster squared error and WCSS) is:

[0074]

[0075] Among them: G j Let μ represent the j-th cluster. j It is the center of the j-th cluster, and represents the Euclidean distance.

[0076] Performance consistency constraints:

[0077] To ensure the safety and efficiency of the tiered utilization system, the following deviation thresholds are set:

[0078] parameter Upper limit of deviation capacity ≤5％ Internal resistance ≤10％ Temperature rise rate ≤15％

[0079] That is: for any cluster G j It must meet the following requirements:

[0080] max(C i )-min(C i ≤5%

[0081] max(R i )-min(R i ≤10%

[0082] max(T i )-min(T i ≤15%

[0083] Enhanced scene adaptability: Introduction of a fourth dimension parameter

[0084] When more precise control is required or more diverse scenarios are needed, a fourth parameter can be added: cycle life and capacity decay slope.

[0085] At this point, the eigenvector becomes:

[0086] X i =[C i R i T i L i ]

[0087] Similarity is calculated using Euclidean distance:

[0088]

[0089] Then, bottom-up aggregation is performed based on this distance matrix, which is suitable for dynamically adjusting the clustering granularity.

[0090] After executing the above algorithm, the following grouping results are obtained:

[0091]

[0092]

[0093] The distribution of each group is displayed in three-dimensional space using visualization tools (Matplotlib, Plotly) to facilitate subsequent manual review and optimization.

[0094] In summary, this method for the cascade utilization of spent power batteries from new energy vehicles achieves comprehensive management of the entire process, from initial screening, performance testing, and comprehensive evaluation to final grouping optimization, through a systematic processing flow of five stages from S1 to S5. Overall, it significantly improves the safety, consistency, and adaptability of battery resources: in the initial stage, infrared thermal imaging and voltage detection are used to eliminate safety hazards; in the middle stage, big data analysis is combined to model performance degradation trends and generate weighted scores; finally, three-dimensional coordinate construction and clustering algorithms are used to achieve highly consistent grouping, ensuring a high degree of matching of batteries in the same group in key parameters such as capacity, internal resistance, and temperature rise rate. The entire process not only improves the stability and lifespan of the cascade utilization system but also effectively reduces development and maintenance costs and has good scalability, enabling flexible adaptation to various application scenarios such as energy storage systems and low-speed electric vehicles.

[0095] Based on the preferred embodiments of the present invention described above, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for the tiered utilization of spent power batteries from new energy vehicles, characterized in that, Includes the following steps: S1. Collect retired new energy vehicle power batteries and conduct preliminary classification and screening based on their health status; S2. Conduct comprehensive performance testing on the initially classified batteries; S3. Extract key parameters from the detection data and conduct comprehensive performance analysis in conjunction with application scenario requirements to generate a comprehensive evaluation score; S4. Sort the comprehensive evaluation scores and select those with similar comprehensive evaluation scores as pre-groups; S5. When the distance between the pre-combined components in three-dimensional space is ≤δ, divide them into the same gradient.

2. The method for the tiered utilization of spent power batteries from new energy vehicles according to claim 1, characterized in that: In S1, the initial health status classification is based on the battery's remaining capacity, health status indicators, and appearance integrity.

3. The method for the tiered utilization of spent power batteries from new energy vehicles according to claim 2, characterized in that: In S1, the rapid screening tool completes the initial classification and removes batteries that pose safety hazards.

4. The method for the tiered utilization of spent power batteries from new energy vehicles according to claim 1, characterized in that: The rapid screening tools include, but are not limited to: infrared thermal imaging and voltage consistency detection.

5. A method for the tiered utilization of spent power batteries from new energy vehicles according to claim 1, characterized in that: In S2, the comprehensive performance test includes: residual energy detection, discharge capacity test at temperature, discharge ohmic internal resistance test, and charge / discharge temperature rise test.

6. A method for the tiered utilization of spent power batteries from new energy vehicles according to claim 5, characterized in that: The residual capacity detection determines the actual remaining capacity of the battery through constant current discharge testing. The discharge capacity test at the specified temperature measures the discharge capacity at three temperature conditions: 25°C, -20°C, and 55°C. The discharge ohmic internal resistance test measures the DC internal resistance of the battery using the AC impedance method. The charge-discharge temperature rise test records the temperature rise rate of the battery during a 1C charge-discharge cycle.

7. A method for the tiered utilization of spent power batteries from new energy vehicles according to claim 1, characterized in that: In S3, the detection data is collected through an automated testing platform to predict its potential failure risk in secondary use. The key parameters include capacity, DC internal resistance, and 1C temperature rise rate.

8. A method for the tiered utilization of spent power batteries from new energy vehicles according to claim 1, characterized in that: In S4, the comprehensive evaluation scores are ranked based on a base score calculated from three parameters: battery capacity, internal resistance, and temperature rise rate. The parameter weights are adjusted according to the application scenario requirements to generate a weighted total score. The batteries are then sorted from high to low according to the weighted total score, and batteries with similar scores are selected as pre-combinations.

9. A method for the tiered utilization of spent power batteries from new energy vehicles according to claim 1, characterized in that: In the S5, the X-axis represents capacity, the Y-axis represents DC internal resistance, and the Z-axis represents the 1°C temperature rise rate.

10. A method for the tiered utilization of spent power batteries from new energy vehicles according to claim 1, characterized in that: In step S5, the three-dimensional performance coordinate system is used to quantify the differences in battery performance and optimize the grouping results through a clustering algorithm to ensure that the deviations in capacity, internal resistance, and temperature rise rate of batteries in the same group are all less than a set threshold.

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