New energy automobile battery recycling performance evaluation system

Abstract

The invention relates to the technical field of battery recycling, and discloses a new energy automobile battery recycling performance evaluation system which comprises the following steps: S1, preliminarily screening out dispute batteries; s2, acquiring a characteristic parameter set of the dispute battery in a plurality of temperature zones; s3, inputting the characteristic parameter set into a battery evaluation model, and respectively calculating a temperature sensitive factor, a heat capacity coupling coefficient and an impedance-temperature relation curve slope by constructing a plurality of coefficient evaluation functions in the model; s4, dynamically determining the weight of each coefficient evaluation function based on historical data, and constructing a standard evaluation function to calculate the health index of the dispute battery; and S5, performing echelon grading recycling on the dispute batteries according to the health indexes. According to the method, the performance difference between the batteries to be recycled is amplified by measuring the performance difference of the batteries to be tested at high temperature, normal temperature and low temperature, so that the evaluation accuracy of the batteries is improved, and the method has extremely high economic and safety benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of battery recycling, and in particular to a new energy vehicle battery recycling performance evaluation system. Background Art

[0002] Traditional battery recycling assessment methods mainly rely on the internal resistance method, which indirectly determines the battery aging state by measuring the battery's DC internal resistance or AC impedance. Typical implementation methods include: applying a single-frequency or narrow-band AC signal to the battery at a constant temperature, calculating the internal resistance value through the ohmic voltage drop, or using the DC pulse discharge method to obtain the instantaneous internal resistance change of the battery.

[0003] However, with the increasing complexity of new energy vehicle battery systems and the diversification of cascade utilization scenarios, the traditional internal resistance method has gradually exposed various shortcomings. It is unable to accurately and comprehensively indicate the battery's capacity attenuation, aging reasons, etc., and it is difficult to support the accurate grading and cascade recycling of waste batteries.

[0004] Therefore, in response to the above problems, a new technical solution needs to be proposed to support the accurate grading and tiered recycling of waste batteries. Summary of the invention

[0005] The present invention overcomes the deficiencies of the prior art and provides a new energy vehicle battery recycling performance evaluation system.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a new energy vehicle battery recycling performance evaluation method, comprising the following steps:

[0007] S1. Obtain the controversial batteries that have been preliminarily screened out through OCV measurement and appearance inspection;

[0008] S2. Obtaining characteristic parameter sets of the disputed battery in multiple temperature zones, wherein the characteristic parameter sets include battery internal resistance values, temperature change values, and battery capacity values ​​corresponding to each temperature zone;

[0009] S3, inputting the characteristic parameter set into the battery evaluation model, and constructing a multi-coefficient evaluation function in the model to calculate the temperature sensitivity factor, the heat capacity coupling coefficient, and the slope of the impedance-temperature relationship curve respectively; wherein the temperature sensitivity factor is used to quantify the low-temperature anti-attenuation capability of the disputed battery, the heat capacity coupling coefficient is used to characterize the correlation between the capacity loss and temperature rise of the disputed battery, and the slope of the impedance-temperature relationship curve is used to reflect the temperature sensitivity of the internal resistance;

[0010] S4. Dynamically determine the weight of each coefficient evaluation function based on historical data, and construct a standard evaluation function to calculate the health index of the disputed battery;

[0011] S5. Recycling the disputed batteries in a tiered manner according to the health index.

[0012] In a preferred embodiment of the present invention, in step S1, the OCV measurement adopts a multi-band AC impedance spectroscopy method, applies an AC excitation signal within a frequency range of 0.1 Hz-10 kHz, and measures the attenuation curve of the battery open circuit voltage as a function of frequency;

[0013] In a preferred embodiment of the present invention, in step S1, the appearance inspection is performed by dual-modality inspection using a 3D laser scanner and infrared thermal imaging to screen batteries by identifying surface deformation errors of the batteries.

[0014] In a preferred embodiment of the present invention, in step S2, the temperature zone includes:

[0015] Low temperature test area: -20℃±3℃;

[0016] Normal temperature test area: 25℃±3℃;

[0017] High temperature test area: 55℃±3℃;

[0018] The specific test method includes: applying 1C constant current charge and discharge in each temperature zone test, and recording various characteristic parameters when the battery reaches the set temperature stability value ±1℃.

[0019] In a preferred embodiment of the present invention, the multi-coefficient evaluation function in step S3 includes:

[0020] Anti-fading ability quantification function:

[0021]

[0022] Among them, TSF is the temperature sensitivity factor, R ct (-20℃) is the charge transfer impedance of the battery at -20℃, R ct (25℃) is the charge transfer impedance of the battery at -25℃, C nom is the nominal capacity of the battery,

[0023] C dis (-20℃) is the discharge capacity of the battery at -20℃;

[0024] Thermal response correlation function:

[0025]

[0026] Where TCC is the thermal capacitance coupling coefficient, ΔT / Δt| 55℃ The temperature rise rate per minute when the battery is charged at 55℃, C dis (25℃) is the battery capacity at 25℃;

[0027] Temperature rise response function:

[0028]

[0029] Where Slope is the slope of the impedance-temperature relationship curve, R Ω (55℃) is the internal resistance of the battery at 55℃, R Ω (-20℃) is the internal resistance of the battery at -20℃, and 75 is the temperature span.

[0030] In a preferred embodiment of the present invention, the weights of the multiple coefficient evaluation functions in step S4 are obtained by random forest algorithm processing, and the specific implementation steps include:

[0031] S41. Construct a cross-temperature zone training set containing at least 2,000 sets of historical battery samples, and annotate each sample with the actual cascade utilization results;

[0032] S42. Through feature importance analysis, the weight of the anti-attenuation capability quantification function ω1, the weight of the thermal response correlation function ω2, and the weight of the temperature rise degree reaction function ω3 are obtained respectively.

[0033] In a preferred embodiment of the present invention, in step S5, the standard evaluation function is:

[0034] CHI=ω1·TSF+ω2·TCC+ω3·Slope

[0035] Among them, CHI is the health index.

[0036] In a preferred embodiment of the present invention, the health index grading standard in step S5 is:

[0037] Health index ≥ 85%, first-level battery: used for grid-level energy storage system;

[0038] 60%≤Health index<85%, secondary battery: used for low-speed electric vehicles and base station backup power supply;

[0039] Health index <60% is a level 3 battery: disassemble and recycle the positive electrode material.

[0040] In a preferred embodiment of the present invention, after step S5, the following steps are further included:

[0041] S6. Continuously monitor the operating data of the battery in cascade utilization. When the actual capacity decay rate exceeds the predicted value by 15%, trigger the model retraining process and update the weight distribution ratio of the evaluation function.

[0042] On the other hand, the present invention provides a new energy vehicle battery recycling performance evaluation system, characterized in that it includes:

[0043] Preliminary screening test module: Integrates a multi-band AC impedance meter and a dual-mode scanning unit to perform OCV measurement and appearance defect detection;

[0044] Multi-temperature zone test chamber: Contains independent temperature control units for low temperature zone, normal temperature zone and high temperature zone, equipped with program-controlled charging and discharging devices;

[0045] Feature extraction module: used for real-time analysis of impedance spectrum data, capacity decay curve, and thermal response spectrum;

[0046] Evaluation model server: It is deployed with multiple coefficient evaluation function libraries and random forest algorithm engines for coefficient evaluation and health index evaluation;

[0047] Tier grading decision unit: Generates battery disassembly / reassembly / tiered utilization instructions based on the health index.

[0048] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0049] (1) The present invention sets multiple evaluation functions to test parameters such as the temperature rise rate, internal resistance, and battery capacity of batteries in different temperature zones, uses multiple evaluation functions to perform evaluations, determines the weights of various evaluation parameters based on the analysis of historical aging data sets, calculates the health index of the battery according to the standard evaluation function, and performs tiered recycling of the battery according to the health index of the battery. Compared with the prior art, the parameter values ​​calculated based on the test data and multiple functions can clearly and accurately reflect the health status of the battery. At the same time, the system of the present invention can analyze the cause of battery aging based on the data. The present invention utilizes the performance differences between aged batteries and healthy batteries in extreme temperature zones to amplify the subtle differences between different controversial batteries, thereby facilitating accurate tiered recycling of batteries.

[0050] (2) The present invention constructs an anti-fading capability quantification function that can quantify the anti-fading capability of the controversial battery at low temperatures, a thermal response correlation function that reflects the correlation between capacity loss and temperature rise rate, and a temperature rise degree reaction function that shows the degree to which the ohmic internal resistance changes with temperature. By evaluating the output TSF value, TCC value and Slope value, the performance of the battery at low temperature, room temperature and high temperature is comprehensively judged. The performance difference between aged batteries and healthy batteries in extreme temperature zones is used to amplify the subtle differences between different controversial batteries. Healthy batteries and aged batteries are accurately distinguished through data. Compared with the existing technology, the distinction accuracy is higher.

[0051] (3) Based on historical aging data and the performance of subsequent battery recycling, the present invention dynamically analyzes the weights of TSF value, TCC value and Slope value, and dynamically obtains the health index of the battery through a standard evaluation function. The evaluation method is more reasonable and accurate, and the system is self-learning. It provides a data basis for the recycling of waste batteries and is far superior to the traditional internal resistance method in distinguishing the degree of battery aging. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 is a flowchart of the steps of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0056] Exemplary methods:

[0057] like Figure 1 As shown, a new energy vehicle battery recycling performance evaluation method specifically includes the following steps:

[0058] S1. Obtain the controversial batteries that have been preliminarily screened out through OCV measurement and appearance inspection;

[0059] S2. Obtaining characteristic parameter sets of the disputed battery in multiple temperature zones, wherein the characteristic parameter sets include battery internal resistance values, temperature change values, and battery capacity values ​​corresponding to each temperature zone;

[0060] S3, inputting the characteristic parameter set into the battery evaluation model, and constructing a multi-coefficient evaluation function in the model to calculate the temperature sensitivity factor, the heat capacity coupling coefficient, and the slope of the impedance-temperature relationship curve respectively; wherein the temperature sensitivity factor is used to quantify the low-temperature anti-attenuation capability of the disputed battery, the heat capacity coupling coefficient is used to characterize the correlation between the capacity loss and temperature rise of the disputed battery, and the slope of the impedance-temperature relationship curve is used to reflect the temperature sensitivity of the internal resistance;

[0061] S4. Dynamically determine the weight of each coefficient evaluation function based on historical data, and construct a standard evaluation function to calculate the health index of the disputed battery;

[0062] S5. Recycling the disputed batteries in a tiered manner according to the health index.

[0063] The applicant found that the parameters of the disputed batteries were similar in routine tests, making it difficult to differentiate the health conditions of different disputed batteries based on a gradient.

[0064] Based on the above reasons, the method of the present invention introduces cross-temperature characteristic parameters to amplify the subtle differences between controversial batteries. The differences in parameters such as internal resistance and capacity between different controversial batteries at room temperature (such as 25°C) may be less than 5%, but at low temperatures (such as -20°C), the decrease in the migration rate of lithium ions inside the battery will expose the aging defects of the battery and aggravate the unevenness of lithium deposition. The test shows an abnormal increase in the internal resistance of the battery. At this time, the performance differences of batteries with different aging degrees will be significantly amplified; at high temperatures, the contact resistance between the electrode active material particles of the aged battery increases, the solid electrolyte interface (SEI film) thickens, and the electrolyte decomposes or dries up, resulting in an increase in the overall internal resistance (especially the charge transfer impedance). According to Joule's law, under the same charge and discharge current, the increase in internal resistance will directly lead to an increase in heat generation. Therefore, the high internal resistance of the aged battery makes it generate more heat per unit time when operating in a high temperature environment. The test shows that the temperature rise rate of the battery is accelerated; while the parameters of healthy batteries in extreme temperature zones tend to be stable;

[0065] Therefore, the present invention introduces a three-temperature zone test of -20°C / 25°C / 55°C to amplify the cross-temperature zone performance changes between different controversial batteries, and then amplify the subtle differences between different controversial batteries to facilitate subsequent gradient grading.

[0066] Specifically, in step S1, OCV measurement is to measure the open circuit voltage of waste batteries, screen out defective batteries, controversial batteries and healthy batteries. The system of the present invention specifically adopts multi-band AC impedance spectroscopy, applies AC excitation signals in the frequency range of 0.1Hz-10kHz, measures the attenuation curve of the battery open circuit voltage changing with frequency, and based on the low-frequency phase angle θ low , the semicircle diameter D of the intermediate frequency Nyquist plot, the high frequency intercept impedance Z and the correlation coefficient R of the attenuation curve2 The measured value reflects the degree of internal aging of the battery;

[0067] The battery screening mechanism based on OCV measurement is:

[0068] Defective battery: When θ low >-10° or D>1.3D0 or Z>Z0+10mΩ or R 2 <0.85; Z0 represents the high-frequency ohmic internal resistance of a new battery in its factory-delivered state, and D0 represents the semicircle diameter of the medium-frequency Nyquist diagram of a new battery in its factory-delivered state;

[0069] Controversial battery: When θ low ∈[-10°,-5°] or D∈[1.1D0,1.3D0] or R 2 ∈[0.85,0.95);

[0070] Healthy battery: When θ low ∈[-30°,-10°], D∈[0.85D0,1.1D0], Z≤Z0+5mΩ and R 2 ≥0.95;

[0071] Furthermore, in step S1, the appearance inspection is performed by dual-modality inspection of a 3D laser scanner and infrared thermal imaging, and based on the measured values ​​of the surface deformation error Δd and the local temperature difference ΔT, the degree of mechanical damage and thermal runaway risk of the battery is reflected;

[0072] The battery screening mechanism based on appearance inspection is:

[0073] Defective battery: Δd>2mm or ΔT>5℃;

[0074] Disputed battery: Δd∈(1mm,2mm] and ΔT∈(3℃,5℃];

[0075] Healthy battery: Δd≤1mm and ΔT≤3℃;

[0076] Combining OCV measurement and appearance inspection, it is possible to efficiently screen out defective batteries and healthy batteries that are easy to identify and have obvious characteristics, thereby reducing the number of subsequent measurement samples, speeding up battery evaluation and improving evaluation efficiency.

[0077] Furthermore, the multiple temperature conditions in step S2 include: low temperature test zone: -20℃±3℃; normal temperature test zone: 25℃±3℃; high temperature test zone: 55℃±3℃; the specific test method includes: applying 1C constant current charge and discharge during the test in each temperature zone, and recording the characteristic parameters of the battery when it reaches the set temperature stable value ±1℃, the characteristic parameters specifically include: battery internal resistance, actual battery capacity and temperature rise rate when the battery temperature reaches a stable value; this step provides data basis for subsequent multiple coefficient evaluation functions.

[0078] Furthermore, the multi-coefficient evaluation function in step S3 includes:

[0079] Anti-fading ability quantification function:

[0080]

[0081] Among them, TSF is the temperature sensitivity factor, R ct (-20℃) is the charge transfer impedance of the battery at -20℃, R ct (25℃) is the charge transfer impedance of the battery at -25℃, C nom is the nominal capacity of the battery, C dis (-20℃) is the discharge capacity of the battery at -20℃;

[0082] The temperature sensitivity factor (TSF) is used to quantify the battery's ability to resist attenuation at low temperatures. The charge transfer impedance of an aged battery will increase significantly at low temperatures, while the discharge capacity will drop significantly, resulting in a TSF value much higher than that of a healthy battery.

[0083] Specifically, when TSF≈0.8-1.2, it is a healthy battery; TSF>2.0 indicates that the low-temperature impedance of the battery surges and the capacity plummets, indicating an aged battery. The TSF value quantifies the battery's ability to resist attenuation at low temperatures, and the TSF value of the aged battery measured at low temperatures is significantly different from that of the healthy battery, thereby amplifying the difference in the measured values.

[0084] Thermal response correlation function:

[0085]

[0086] Where TCC is the thermal capacitance coupling coefficient, ΔT is the temperature change value, Δt is the test time value, ΔT / Δt| 55℃ The temperature rise rate per minute when the battery is charged at 55℃, C dis (25℃) is the battery capacity at 25℃;

[0087] The thermal capacitance coupling coefficient (TCC) is used to reflect the correlation between capacity loss and temperature rise rate. When an aged battery is charged at a high temperature, the temperature rise rate increases, while the capacity decreases, which is manifested as an abnormal increase or decrease in the TCC value. The cause of battery aging can also be distinguished based on the TCC value.

[0088] Specifically, when TCC>1.5, it means that the battery is aged mainly due to lithium deposition, and the test results show high heat generation and sudden drop in battery capacity; when TCC<0.8, it means that the battery is aged mainly due to SEI growth, and the test results show a gentle temperature rise but continuous capacity decay. The TCC value can reflect the correlation between capacity loss and temperature rise rate, and the TCC value can accurately determine the factors of battery aging.

[0089] Temperature rise response function:

[0090]

[0091] Where Slope is the slope of the impedance-temperature relationship curve, R Ω (35℃) is the internal resistance of the battery at 55℃, R Ω (-20℃) is the internal resistance of the battery under -20℃ environment, 75 is the temperature span, and the slope of the impedance-temperature curve of a healthy battery is used as the standard value. Compared with the measured value of a used battery, the internal resistance of an aged battery is more different at high and low temperatures, resulting in a significant deviation between the Slope value and the standard value of a healthy battery. If the measured Slope value is significantly different from the standard value, it means that the battery electrolyte may have dried up or the electrode may have fallen off, reflecting that the battery is aged to a large extent.

[0092] Furthermore, the weights of the multiple coefficient evaluation functions in step S4 are obtained by processing with a random forest algorithm, and the specific implementation steps include:

[0093] S41. Construct a cross-temperature zone training set containing at least 2,000 sets of historical battery samples, and annotate each sample with the actual cascade utilization results;

[0094] S42. Through feature importance analysis, the weight of the anti-attenuation capability quantification function ω1, the weight of the thermal response correlation function ω2, and the weight of the temperature rise degree reaction function ω3 are obtained respectively.

[0095] At least 2,000 sets of retired battery sample data are obtained from battery recycling centers, recycling companies, etc. The samples cover various mainstream battery types such as ternary lithium and lithium iron phosphate. The anti-attenuation capability quantification value TSF, thermal response correlation value TCC, temperature rise degree reaction value Slope and actual recycling results are input to build a random forest model. According to the feature importance evaluation of the random forest model, the contribution of each function is quantified, and finally the weight coefficient is obtained.

[0096] Furthermore, in step S5, the standard evaluation function is:

[0097] CHI=ω1·TSF+ω2·TCC+ω3·Slope

[0098] Among them, CHI is the health index.

[0099] Furthermore, in step S5, the health index grading standard is:

[0100] Health index CHI ≥ 85%, first-level battery: used for grid-level energy storage system;

[0101] 60%≤CHI<85%, secondary battery: used for low-speed electric vehicles and base station backup power supply;

[0102] CHI<60% is a level 3 battery: disassemble and recycle the positive electrode material.

[0103] Furthermore, after step S5, the method further comprises the following steps:

[0104] S6. Continuously monitor the operating data of the battery in cascade utilization. When the actual capacity decay rate exceeds the predicted value by 15%, trigger the model retraining process and update the weight distribution ratio of the evaluation function.

[0105] Step S6 ensures that the model can dynamically adjust the weights of multiple coefficients according to the system self-update, thereby improving the accuracy of CHI calculation.

[0106] The method of the present invention constructs a multi-coefficient evaluation function, and focuses on quantifying three key performances of the battery under the test conditions of low temperature, normal temperature, and high temperature zones: the ability to resist capacity decay in a low temperature environment, the correlation between the temperature rise rate and capacity loss, and the response sensitivity of the ohmic internal resistance to temperature fluctuations. Since aging batteries will significantly expose their performance shortcomings under extreme temperature conditions, manifested in characteristics such as increased internal resistance increase, abnormal temperature rise rate, and accelerated capacitance decay, while healthy batteries maintain relatively stable parameter performance, this difference in physical properties will form a gradient distribution of coefficient values ​​after calculation through the evaluation function, thereby effectively enhancing the distinction between batteries in different states.

[0107] In addition, to improve the evaluation accuracy, the system also introduces a dynamic optimization algorithm for historical aging data, adjusts the weight of each coefficient through machine learning, and finally generates a dynamically adaptable CHI health index. This index not only solves the technical pain point of the difficulty in accurately grading controversial batteries in traditional testing by amplifying the performance differences between batteries, but also provides a high-precision judgment basis for battery performance evaluation and cascade utilization scenarios, significantly improving the economy and resource utilization of battery recycling classification.

[0108] Example systems:

[0109] A new energy vehicle battery recycling performance evaluation system, comprising:

[0110] Preliminary screening test module: Integrates a multi-band AC impedance meter and a dual-mode scanning unit to perform OCV measurement and appearance defect detection;

[0111] Multi-temperature zone test chamber: Contains independent temperature control units for low temperature zone, normal temperature zone and high temperature zone, equipped with program-controlled charging and discharging devices;

[0112] Feature extraction module: used for real-time analysis of impedance spectrum data, capacity decay curve, and thermal response spectrum;

[0113] Evaluation model server: It is deployed with multiple coefficient evaluation function libraries and random forest algorithm engines for coefficient evaluation and health index evaluation;

[0114] Tier grading decision unit: Generates battery disassembly / reassembly / tiered utilization instructions based on the health index.

[0115] The above system integrates a preliminary screening test module to perform OCV measurement and appearance inspection on used batteries, conduct preliminary screening of used batteries, reduce the number of subsequent fine screening samples, and thus improve battery testing efficiency;

[0116] By building a multi-temperature zone test chamber, the battery can be charged and discharged directly through the program-controlled charging and discharging device, and the internal resistance, capacitance and temperature changes of the battery during charging and discharging can be recorded, which is convenient for obtaining the characteristic information required for subsequent function calculations;

[0117] By building a feature extraction module, the impedance spectrum data, capacity decay curve, and thermal response spectrum are analyzed, so that the battery can be graded and screened through feature extraction;

[0118] The simultaneous evaluation model server and the tiered grading decision unit can calculate and analyze the data output from the multi-temperature zone test chamber and the feature extraction module, and ultimately draw conclusions on the tiered recycling of used batteries through calculated data, ultimately improving the efficiency of battery screening and the accuracy of tiered utilization.

[0119] Example 1

[0120] An embodiment of the measurement steps based on the system of the present invention:

[0121] S1. Use 2000 historical aging data sets to train the model, and use random forest to determine the weights of the multi-coefficient evaluation function: ω1 = 0.36, ω2 = 0.48, ω3 = 0.16;

[0122] S2. Place the used batteries in a multi-temperature test chamber and test them at -20℃±3℃, 25℃±3℃ and 55℃±3℃. Apply 1C constant current charge and discharge to the battery pack and record the characteristic value of the battery when it reaches the temperature stability value ±1℃ in each temperature zone.

[0123] S3. Input the characteristic value into the system of the present invention and output the CHI health index.

[0124] Comparative Example 1

[0125] S1. Use a multimeter to measure the open-circuit voltage of the battery, and reject ternary lithium batteries with a voltage lower than 2.5 V or lithium iron phosphate batteries with a voltage lower than 1.5 V. At the same time, visually inspect the appearance of the battery manually to reject obviously defective batteries such as those with cracked casings or leakage.

[0126] S2. Let the battery stand in a constant-temperature oven at 25°C ± 3°C for more than 4 hours, and use the DC pulse method. Specifically, discharge the battery at a current of 1C for 20 s, and calculate the internal resistance value Rc.

[0127] S3. Classify the batteries according to the internal resistance value Rc. The classification criteria are: first-class batteries (Rc ≤ 1.2R0), second-class batteries (1.2R0 < Rc ≤ 1.5R0), and third-class batteries (R > 1.5R0), where R0 is the standard internal resistance value when the battery leaves the factory brand new.

[0128] Take 100 groups of batteries as the measurement samples, test them under the conditions of Example 1 and Comparative Example 1 respectively, and output the classification results. The test results of some batteries are shown in Table 1:

[0129] Table 1 Test results of battery numbers 1-10 in Example 1 and Comparative Example 1

[0130]

[0131]

[0132] Among them, the evaluation results are shown in Table 2:

[0133] Table 2 Evaluation result data

[0134] Evaluation Metrics Embodiment 1 (present invention) Comparative Example 1 (Traditional Internal Resistance Method) Classification accuracy 95％ 66％ Dangerous battery leakage detection rate 2％ 28％ Healthy battery mis-rejection rate 3％ 22％ Dispute battery recall rate 93％ Unpredictable Testing time 4.5 hours / group 4 hours / group Temperature zone coverage -20℃ / 25℃ / 55℃ 25℃

[0135] It can be concluded from Table 1 and Table 2 that compared with the traditional internal resistance method, although the test time of the method of the present invention is longer, the accuracy rate of battery classification is greatly improved, and the misjudgment rate is reduced by 29%, indicating that the system based on the present invention can accurately identify the true state of the battery; the missed detection rate of dangerous batteries of the method of the present invention is significantly lower than that of the traditional internal resistance method, improving the safety of battery evaluation; the false rejection rate of healthy batteries of the method of the present invention is also lower, avoiding the premature disassembly and recycling of recyclable batteries classified into the third class, resulting in waste of resources.

[0136] The present invention uses multi-dimensional parameter fusion, cross-temperature zone dynamic evaluation and intelligent weight allocation, and utilizes the performance changes of batteries when working in extreme temperature zones to amplify the test parameter differences between controversial batteries, thereby realizing the tiered recycling of waste batteries with higher accuracy. Multi-dimensional parameter fusion is used to improve the accuracy of grading, cross-temperature zone dynamic evaluation is used to amplify the gap between healthy batteries and aged batteries, and intelligent weight allocation realizes accurate judgment of battery health index, avoids the mistaken elimination of healthy batteries and the dangerous use of aged batteries, improves economic and safety benefits, and has technological foresight.

[0137] The above is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and the technical scope must be determined according to the scope of the claims.

Claims

1. A new energy vehicle battery recycling performance evaluation method, characterized in that: The following steps are involved: S1. Obtain the controversial batteries that have been preliminarily screened out through OCV measurement and appearance inspection; S2. Obtaining characteristic parameter sets of the disputed battery in multiple temperature zones, wherein the characteristic parameter sets include battery internal resistance values, temperature change values, and battery capacity values ​​corresponding to each temperature zone; S3, inputting the characteristic parameter set into the battery evaluation model, and constructing a multi-coefficient evaluation function in the model to calculate the temperature sensitivity factor, the heat capacity coupling coefficient, and the slope of the impedance-temperature relationship curve respectively; wherein the temperature sensitivity factor is used to quantify the low-temperature anti-attenuation capability of the disputed battery, the heat capacity coupling coefficient is used to characterize the correlation between the capacity loss and temperature rise of the disputed battery, and the slope of the impedance-temperature relationship curve is used to reflect the temperature sensitivity of the internal resistance; S4. Dynamically determine the weight of each coefficient evaluation function based on historical data, and construct a standard evaluation function to calculate the health index of the disputed battery; S5. Recycling the disputed batteries in a tiered manner according to the health index.

2. A new energy vehicle battery recycling performance evaluation method according to claim 1, characterized in that: In step S1, the OCV measurement adopts a multi-band AC impedance spectroscopy method, applies an AC excitation signal within a frequency range of 0.1 Hz-10 kHz, and measures the attenuation curve of the battery open circuit voltage varying with frequency.

3. A new energy vehicle battery recycling performance evaluation method according to claim 1, characterized in that: In step S1, the appearance inspection is performed through dual-modality inspection of 3D laser scanner and infrared thermal imaging to screen batteries by identifying battery surface deformation errors.

4. A new energy vehicle battery recycling performance evaluation method according to claim 1, characterized in that: In step S2, the temperature zone includes: Low temperature test area: -20℃±3℃; Normal temperature test area: 25℃±3℃; High temperature test area: 55℃±3℃; The specific test method includes: applying 1C constant current charge and discharge in each temperature zone test, and recording various characteristic parameters when the battery reaches the set temperature stability value ±1℃.

5. A new energy vehicle battery recycling performance evaluation method according to claim 1, characterized in that: The multi-coefficient evaluation function in step S3 includes: Anti-fading ability quantification function: Among them, TSF is the temperature sensitivity factor, R ct (-20℃) is the charge transfer impedance of the battery at -20℃, R ct (25℃) is the charge transfer impedance of the battery at -25℃, C nom is the nominal capacity of the battery, C dis (-20℃) is the discharge capacity of the battery at -20℃; Thermal response correlation function: Where TCC is the thermal capacitance coupling coefficient, ΔT / Δt| 55℃ The temperature rise rate per minute when the battery is charged at 55℃, C dis (25℃) is the battery capacity at 25℃; Temperature rise response function: Where Slope is the slope of the impedance-temperature relationship curve, R Ω (55℃) is the internal resistance of the battery at 55℃, R Ω (-20℃) is the internal resistance of the battery at -20℃, and 75 is the temperature span.

6. A new energy vehicle battery recycling performance evaluation method according to claim 1, characterized in that: The weights of the multiple coefficient evaluation functions in step S4 are obtained by processing with a random forest algorithm, and the specific implementation steps include: S41. Construct a cross-temperature zone training set containing at least 2,000 sets of historical battery samples, and annotate each sample with the actual cascade utilization results; S42. Through feature importance analysis, the weight of the anti-attenuation capability quantification function ω1, the weight of the thermal response correlation function ω2, and the weight of the temperature rise degree reaction function ω3 are obtained respectively.

7. A new energy vehicle battery recycling performance evaluation method according to claim 6, characterized in that: In step S5, the standard evaluation function is: CHI=ω1·TSF+ω2·TCC+ω3·Slope Among them, CHI is the health index.

8. A new energy vehicle battery recycling performance evaluation method according to claim 1, characterized in that: The health index grading standard in step S5 is: Health index ≥ 85%, first-level battery: used for grid-level energy storage system; 60%≤Health index<85%, secondary battery: used for low-speed electric vehicles and base station backup power supply; Health index <60% is a level 3 battery: disassemble and recycle the positive electrode material.

9. A new energy vehicle battery recycling performance evaluation method according to claim 1, characterized in that: After step S5, the method further comprises the following steps: S6. Continuously monitor the operating data of the battery in cascade utilization. When the actual capacity decay rate exceeds the predicted value by 15%, trigger the model retraining process and update the weight distribution ratio of the evaluation function.

10. A new energy vehicle battery recycling performance evaluation system, characterized in that: include: Preliminary screening test module: Integrates a multi-band AC impedance meter and a dual-mode scanning unit to perform OCV measurement and appearance defect detection; Multi-temperature zone test chamber: Contains independent temperature control units for low temperature zone, normal temperature zone and high temperature zone, equipped with program-controlled charging and discharging devices; Feature extraction module: used for real-time analysis of impedance spectrum data, capacity decay curve, and thermal response spectrum; Evaluation model server: It is deployed with multiple coefficient evaluation function libraries and random forest algorithm engines for coefficient evaluation and health index evaluation; Tier grading decision unit: Generates battery disassembly / reassembly / tiered utilization instructions based on the health index.

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