New energy battery safety test system and method

Abstract

The invention relates to the technical field of new energy battery testing, and discloses a new energy battery safety testing system and method, and the system comprises a multi-physical field synchronous collection and analysis module which is used for synchronously collecting electric signals, temperature field distribution and stress-strain data in a battery testing process, and carrying out the multi-field coupling analysis; a self-adaptive test parameter regulation and control module; the thermal runaway risk prediction module is used for analyzing and predicting the risk probability and residual safety time of thermal runaway of the battery based on multi-parameter fusion; the microstructure-macroscopic performance correlation analysis module is used for acquiring microstructure information of the battery through nondestructive testing and establishing a correlation model with macroscopic safety performance, and the new energy battery safety test system realizes collaborative analysis of multiple parameters of electricity, heat and force through the multi-physics field synchronous acquisition and analysis module; the safety state of the battery can be reflected more comprehensively, and the defect that a traditional testing method is single in dimension is overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy battery testing, in particular to a new energy battery safety testing system and method. BACKGROUND

[0002] With the rapid development of new energy vehicles and energy storage industries, the safety of new energy batteries such as lithium ion batteries has attracted increasing attention. Batteries may face safety risks such as thermal runaway, short circuit, and explosion during use, so it is crucial to comprehensively and accurately test the safety of batteries.

[0003] In actual testing, the existing battery safety testing methods have the following shortcomings:

[0004] The test dimension is single, usually only focusing on electrical performance or thermal performance, lacking of synergistic analysis of electrical-thermal-mechanical multi-physical fields;

[0005] The test parameters are fixed, and the test strategy cannot be dynamically adjusted according to the battery state, resulting in low test efficiency or insufficient accuracy;

[0006] The microstructure and macroscopic safety performance are not related, and the safety failure mechanism cannot be revealed in essence.

[0007] To solve the above problems, the present application provides a new energy battery safety testing system and method. SUMMARY

[0008] The present application provides a new energy battery safety testing system and method to promote the solution to the problems mentioned in the background.

[0009] The present application provides the following technical solution: a new energy battery safety testing system, comprising:

[0010] A multi-physical field synchronous acquisition and analysis module is used to synchronously acquire electrical signals, temperature field distribution and stress-strain data during battery testing, and perform multi-field coupling analysis; An adaptive test parameter control module dynamically adjusts the test intensity, test step and safety boundary according to the real-time data output by the multi-physical field synchronous acquisition and analysis module; A thermal runaway risk prediction module predicts the risk probability and remaining safety time of battery thermal runaway based on multi-parameter fusion analysis; A microstructure-macroscopic performance correlation analysis module obtains battery microstructure information through non-destructive testing and establishes a correlation model with macroscopic safety performance; A full life cycle safety evaluation module is used to generate a full life cycle safety evaluation report through the safety performance changes of the battery at different life cycle stages; A central control and data fusion center is configured to coordinate the operation of the modules in the system and perform data fusion processing.

[0011] Further, the multi-physical field synchronous acquisition and analysis module includes an electrochemical parameter acquisition unit, an infrared thermal imaging unit, a distributed optical fiber sensing unit, a micro-strain sensing array, and a multi-field coupling analysis unit, and the output ends of the multi-physical field synchronous acquisition and analysis module are electrically connected to the input ends of the adaptive test parameter regulation module and the input ends of the central control and data fusion center, respectively.

[0012] Further, the input ends of the multi-field coupling analysis unit are electrically connected to the output ends of the electrochemical parameter acquisition unit, the infrared thermal imaging unit, the distributed optical fiber sensing unit, and the micro-strain sensing array, respectively, and the multi-field coupling analysis unit performs electro-thermal-force coupling analysis based on the acquired multi-physical field data, solves the multi-field coupling equation by using the finite element method, and the specific strategy of the multi-field coupling analysis unit (105) is as follows:

[0013] The electro-thermal coupling equation is: , wherein is the heat generation rate, is the current, is the open circuit voltage, is the working voltage, is the series resistance, is the battery density, is the specific heat capacity, is the temperature, is the time, is the thermal conductivity, is the partial derivative of temperature with respect to time, indicating the rate of change of temperature with respect to time.

[0014] The thermal-force coupling equation is: , wherein is the thermal stress, is the elastic modulus, is the thermal expansion coefficient, is the temperature change, is the strain, is the initial temperature.

[0015] Further, the dynamic adjustment of test intensity strategy in the adaptive test parameter regulation module is:

[0016] ,

[0017] wherein, is the test intensity at the moment, is the initial test intensity, and the value range of is 0.5C-2C, is the temperature influence coefficient, and the value range of is 0.01-0.1, is the stress influence coefficient, and the value range of is 0.5-2, is the real-time stress value, is the maximum allowable stress, and the value range of is 5-20MPa;

[0018] The strategy of test step in the adaptive test parameter regulation module is:

[0019] ,

[0020] wherein, is the test step, is the reference step, and the value range of is 1-10s, , are the weight coefficients of temperature change rate and stress change rate respectively, is the temperature change rate, is the stress change rate;

[0021] The safety boundary in the adaptive test parameter regulation module is corrected by self-adaption, and the safety boundary correction coefficient formula is: , is the measured parameter, is the parameter mean value, is the parameter standard deviation, is the correction coefficient.

[0022] Further, the input end of the thermal runaway risk prediction module is electrically connected with the output end of the adaptive test parameter regulation module, the thermal runaway risk prediction module adopts a BP neural network model, and the input layer of the BP neural network model includes 6 neurons corresponding to temperature, temperature change rate, voltage, voltage change rate, stress and stress change rate; the hidden layer includes 2 layers, each layer including 12 neurons; the output layer includes 2 neurons corresponding to thermal runaway risk probability and residual safety time,

[0023] thermal runaway risk probability​ The calculation formula is: wherein, is a bias parameter, is a feature weight, obtained by neural network training, is a normalized feature function, including temperature, voltage, gas concentration and stress feature parameters;

[0024] Remaining safety time The prediction formula is: wherein, is a temperature change rate function, is the current temperature, is a thermal runaway trigger temperature, and The thermal runaway trigger temperature formula due to aging factors is: wherein, is a new battery thermal runaway trigger temperature, is the cycle number, is an aging coefficient, represents the state of charge.

[0025] Further, the microstructure-macroscopic performance correlation analysis module includes an X-ray tomography unit, an ultrasonic detection unit, an electrochemical impedance spectroscopy analysis unit, and a micro-macro correlation modeling unit. The X-ray tomography unit is used to obtain the three-dimensional microstructure inside the battery. Specifically, a microfocus X-ray source is used to scan the entire battery monomer, and a three-dimensional reconstruction image is output and the porosity distribution is calculated. The ultrasonic detection unit uses a phased array probe to evaluate electrode interlayer peeling and crack propagation through reflection amplitude and phase changes. The electrochemical impedance spectroscopy analysis unit is used to analyze the battery interface characteristics. Specifically, a 10 mV sinusoidal perturbation signal is applied, and the SEI film impedance, charge transfer resistance, and diffusion coefficient are obtained through equivalent circuit fitting.

[0026] Further, the micro-macro correlation modeling unit is used to establish a quantitative relationship model of porosity, crack density and macroscopic safety performance. The quantitative relationship model includes a micro-porosity and thermal runaway correlation model and an electrode material crack density and mechanical strength relationship model. The formula of the micro-porosity and thermal runaway correlation model is: wherein, is the influence coefficient of micro-porosity on thermal runaway, is the measured porosity, is the initial porosity, is the correlation coefficient, and The value range of is 5-15. The formula of the electrode material crack density and mechanical strength relationship model is: , is the electrode material crack density The ultimate mechanical strength is only considered when is the ultimate strength without cracks, is the crack density, 、 is the material constant.

[0027] Furthermore, the safety index calculation formula in the full life cycle safety assessment module is:

[0028] ,

[0029] in, For battery life, for The safety probability at any moment, is the capacity retention rate, is the internal resistance change rate, 、 、 is the weight coefficient, and ;

[0030] The safety level in the full life cycle safety assessment module is divided into four levels: A, B, C, and D, which correspond to excellent, good, medium, and poor respectively. The specific strategies are as follows: For A-level, For B level, For C level, It is D-level;

[0031] The formula of the cyclic aging safety attenuation model in the full life cycle safety assessment module is:

[0032] ,

[0033] in, For initial safety performance, is the number of cycles, 、 is the attenuation coefficient, For ultimate safety performance;

[0034] The formula for the aging safety assessment model stored in the full life cycle safety assessment module is: ,

[0035] in, is the pre-exponential factor, is the activation energy, is the Boltzmann constant, is the storage temperature.

[0036] Further, the central control and data fusion center is respectively connected with a multi-physical field synchronous acquisition and analysis module, an adaptive test parameter regulation module, a thermal runaway risk prediction module, a microstructure-macroscopic performance correlation analysis module and a full life cycle safety evaluation module in bidirectional communication, and the central control and data fusion center is based on a LabVIEW developed control interface, and supports real-time data display.

[0037] Further, a new energy battery safety test method, the new energy battery safety test method comprises the following steps:

[0038] S1: Multi-dimensional reference parameter acquisition, obtaining basic electrochemical parameters, thermal parameters and mechanical parameters of the battery under different working conditions;

[0039] S2: Multi-physical field coupling excitation test, synchronously applying electric, thermal and force load, and obtaining multi-field coupling response data through a multi-physical field synchronous acquisition and analysis module;

[0040] S3: Based on the result of step S2, dynamically adjusting the test parameters through the adaptive test parameter regulation module to gradually approach the battery safety boundary;

[0041] S4: Real-time evaluation of battery thermal runaway risk by using a thermal runaway risk prediction module, predicting thermal runaway probability and remaining safety time;

[0042] S5: The mapping relationship between microstructure and macroscopic safety performance is established through the microstructure-macroscopic performance correlation analysis module;

[0043] S6: Combined with battery cycle aging and storage aging data, a full life cycle safety evaluation report is generated through a full life cycle safety evaluation module;

[0044] S7: According to the evaluation result, the test strategy is optimized, and a closed-loop test process of "test-evaluation-optimization" is formed;

[0045] S8: Based on the full life cycle safety evaluation result, battery safety use suggestions and retirement warning are generated.

[0046] The present application has the following beneficial effects:

[0047] The new energy battery safety test system realizes the collaborative analysis of electric-thermal-force multi-parameters through the multi-physical field synchronous acquisition and analysis module, can more comprehensively reflect the safety state of the battery, and overcomes the defect of single dimension of the traditional test method;

[0048] The new energy battery safety test system can dynamically adjust the test parameters according to the real-time state of the battery through the adaptive test parameter regulation module, which improves the test efficiency while ensuring the test accuracy and reduces unnecessary test loss;

[0049] The thermal runaway risk prediction module can predict the thermal runaway risk in advance, which helps to reduce the incidence of safety accidents, establishes the connection between microstructure and macro performance through the microstructure-macro performance correlation analysis module, and can reveal the mechanism of battery safety failure in essence, thereby providing a theoretical basis for battery design optimization.

[0050] The full life cycle safety evaluation module realizes the full life cycle safety evaluation of the battery from production to retirement, and can provide guidance for the safe use and retirement disposal of the battery. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 It is a system flowchart of the present application.

[0052] Figure 2 It is a flowchart of the multi-physical field synchronous acquisition and analysis module in the present application.

[0053] Figure 3 It is a flowchart of the microstructure-macro performance correlation analysis module in the present application.

[0054] Figure 4 It is a method flowchart of the present application.

[0055] In the figure: 1, multi-physical field synchronous acquisition and analysis module; 101, electrochemical parameter acquisition unit; 102, infrared thermal imaging unit; 103, distributed optical fiber sensing unit; 104, micro-strain sensing array; 105, multi-field coupling analysis unit; 2, adaptive test parameter regulation module; 3, thermal runaway risk prediction module; 4, microstructure-macro performance correlation analysis module; 401, X-ray tomography unit; 402, ultrasonic detection unit; 403, electrochemical impedance spectroscopy analysis unit; 404, micro-macro correlation modeling unit; 5, full life cycle safety evaluation module; 6, central control and data fusion center. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0057] Embodiment 1, refer to Figures 1-3 A new energy battery safety test system, comprising:

[0058] Multi-physics field synchronous acquisition and analysis module 1, used to synchronously acquire electrical signals, temperature field distribution, and stress-strain data during battery testing, and perform multi-field coupling analysis;

[0059] The multi-physical field synchronous acquisition and analysis module 1 includes an electrochemical parameter acquisition unit 101, an infrared thermal imaging unit 102, a distributed optical fiber sensing unit 103, a micro-strain sensing array 104 and a multi-field coupling analysis unit 105, and the output end of the multi-physical field synchronous acquisition and analysis module 1 is electrically connected to the input end of the adaptive test parameter control module 2 and the input end of the central control and data fusion center 6 respectively. The electrochemical parameter acquisition unit 101 adopts a voltage and current acquisition card to collect the voltage, current, internal resistance and SOC parameters of the battery. The infrared thermal imaging unit 102 is based on an infrared thermal imager to obtain the temperature field distribution on the battery surface. The distributed optical fiber sensing unit 103 adopts a distributed optical fiber sensor to measure the internal temperature distribution of the battery. The micro-strain sensing array 104 is based on multiple strain gauges mounted on the battery surface to collect stress and strain data on the battery surface.

[0060] The input end of the multi-field coupling analysis unit 105 is electrically connected to the output ends of the electrochemical parameter acquisition unit 101, the infrared thermal imaging unit 102, the distributed optical fiber sensing unit 103, and the microstrain sensing array 104, respectively. The multi-field coupling analysis unit 105 performs an electro-thermal-mechanical coupling analysis based on the collected multi-physical field data and solves the multi-field coupling equation using the finite element method. The specific strategy of the multi-field coupling analysis unit 105 is as follows:

[0061] The electrical-thermal coupling equation is: , ,in is the heat generation rate, is the current, is the open circuit voltage, is the operating voltage, is the series internal resistance, is the battery density, is the specific heat capacity, is the temperature, For time, is the thermal conductivity, The partial derivative of temperature with respect to time represents the rate of change of temperature with time;

[0062] The thermal-mechanical coupling equation is: , ,in For thermal stress, is the elastic modulus, is the coefficient of thermal expansion, is the temperature change, For strain, T0 is the initial temperature.

[0063] The adaptive test parameter regulation module 2 dynamically adjusts the test intensity, test step and safety boundary according to the real-time data output by the multi-physical field synchronous acquisition and analysis module 1;

[0064] The dynamic adjustment of the test intensity strategy in the adaptive test parameter regulation module 2 is:

[0065] ,

[0066] Among them, is the test intensity at the moment, is the initial test intensity, and The value range of is 0.5C-2C, is the temperature influence coefficient, and The value range of is 0.01-0.1, is the stress influence coefficient, and The value range of is 0.5-2, is the real-time stress value, is the maximum allowable stress, and The value range of is 5-20MPa; The strategy of the test step in the adaptive test parameter regulation module 2 is:

[0067]

[0068] ,

[0069] Among them, is the test step, is the reference step, and The value range of is 1-10s, , and are the weight coefficients of the temperature change rate and the stress change rate respectively, is the temperature change rate, is the stress change rate;

[0070] The safety boundary in the adaptive test parameter regulation module 2 is corrected adaptively, and the safety boundary correction coefficient formula is: , is the measured parameter, is the parameter mean value, is the parameter standard deviation, is the correction coefficient; The thermal runaway risk prediction module 3 predicts the risk probability and the remaining safety time of the battery from thermal runaway based on multi-parameter fusion analysis;

[0071] ​The input end of the thermal runaway risk prediction module 3 is electrically connected with the output end of the adaptive test parameter regulation module 2, the thermal runaway risk prediction module 3 adopts a BP neural network model, and the input layer of the BP neural network model includes 6 neurons corresponding to temperature, temperature change rate, voltage, voltage change rate, stress and stress change rate; the hidden layer includes 2 layers, each layer including 12 neurons; and the output layer includes 2 neurons corresponding to thermal runaway risk probability and residual safety time,

[0072] thermal runaway risk probability The calculation formula is: wherein, is a bias parameter, is a feature weight, obtained through neural network training, is a normalized feature function, including temperature, voltage, gas concentration and stress feature parameters;

[0073] residual safety time The prediction formula is: wherein, is a temperature change rate function, is a current temperature, is a thermal runaway trigger temperature, and The thermal runaway trigger temperature formula due to aging factors is: wherein, is a new battery thermal runaway trigger temperature, is a cycle number, is an aging coefficient, denotes a state of charge; The microstructure-macroscopic performance correlation analysis module 4 obtains battery microstructure information through non-destructive testing, and establishes a correlation model with macroscopic safety performance;

[0074] The microstructure-macroscopic performance correlation analysis module 4 includes an X-ray tomography unit 401, an ultrasonic detection unit 402, an electrochemical impedance spectroscopy analysis unit 403, and a micro-macro correlation modeling unit 404, wherein the X-ray tomography unit 401 is used to obtain the three-dimensional microstructure inside the battery, specifically: a micro-focus X-ray source is used to scan a range covering the entire battery monomer, and a three-dimensional reconstruction image is output and the porosity distribution is calculated; the ultrasonic detection unit 402 uses a phased array probe to evaluate electrode interlayer peeling and crack propagation through reflection amplitude and phase change; the electrochemical impedance spectroscopy analysis unit 403 is used to analyze the interface characteristics of the battery, specifically: a 10 mV sinusoidal disturbance signal is applied, and the SEI film impedance, charge transfer resistance and diffusion coefficient are obtained through equivalent circuit fitting.

[0075] The micro-macro correlation modeling unit 404 is configured to establish a quantitative relationship model of porosity, crack density and macroscopic safety performance, and the quantitative relationship model comprises a micro-porosity and thermal runaway correlation model and an electrode material crack density and mechanical strength relationship model, the formula of the micro-porosity and thermal runaway correlation model is: , is an influence coefficient of micro-porosity on thermal runaway, is a measured porosity, is an initial porosity, is a correlation coefficient, and the value range of is 5-15; the formula of the electrode material crack density and mechanical strength relationship model is: , is the limit mechanical strength when the crack density of the electrode material is , is the limit strength when there is no crack, is the crack density, , is a material constant; The full life cycle safety assessment module 5 is configured to generate a full life cycle safety assessment report by the safety performance changes of the battery in different life cycle stages;

[0076] The safety index calculation formula in the full life cycle safety assessment module 5 is:

[0077] ,

[0078] wherein, is the battery life, is the safety probability at moment, is the capacity retention rate, is the internal resistance change rate, , , is a weight coefficient, and ;

[0079] The safety level in the full life cycle safety assessment module 5 is divided into four levels A, B, C and D, and corresponds to excellent, good, medium and poor respectively, and the specific strategy is: A is A level, B is B level, C is C level, D is D level;

[0080] The formula of the cycle aging safety attenuation model in the full life cycle safety assessment module 5 is:

[0081] ,

[0082] wherein, is an initial safety performance, is a cycle number, 、 is a decay coefficient, is a limit safety performance;

[0083] The aging safety assessment model formula stored in the full life cycle safety assessment module 5 is: ,

[0084] wherein, is a pre-factor, is an activation energy, is a Boltzmann constant, is a storage temperature.

[0085] The central control and data fusion center 6 is used for coordinating the work of each module in the system and performing data fusion processing.

[0086] The central control and data fusion center 6 is respectively bidirectionally connected with the multi-physical field synchronous acquisition and analysis module 1, the adaptive test parameter regulation module 2, the thermal runaway risk prediction module 3, the microstructure-macroscopic performance correlation analysis module 4 and the full life cycle safety assessment module 5, and the control interface of the central control and data fusion center 6 is developed based on LabVIEW, supporting real-time display of data.

[0087] Example 2

[0088] Please refer to Figure 4 A new energy battery safety test method, the new energy battery safety test method comprises the following steps:

[0089] S1: Multi-dimensional reference parameter acquisition, obtaining basic electrochemical parameters, thermal parameters and mechanical parameters of the battery under different working conditions, specifically: at 25℃ environment temperature, 0.1C-2C different rate of charge and discharge test is carried out on the battery, and the capacity, internal resistance, open circuit voltage and other electrochemical parameters of the battery are obtained; the specific heat capacity and thermal conductivity of the battery are measured by a heat flow meter; the elastic modulus and Poisson's ratio of the battery are tested by a material testing machine;

[0090] S2: Multi-physical field coupling excitation test, synchronous application of electric, thermal and force load, multi-field coupling response data are obtained by the multi-physical field synchronous acquisition and analysis module 1, and the multi-physical field coupling excitation test specifically is: the battery is placed in a temperature controllable test cabin, and electric load and mechanical pressure are applied at the same time, the electric load range is-2C (discharge) to 1C (charge), and the mechanical pressure range is 0-5MPa;

[0091] S3: Based on the results of step S2, dynamically adjust the test parameters through the adaptive test parameter regulation module, gradually approach the battery safety boundary, specifically: adaptive test parameter regulation, according to the collected temperature change rate and stress change rate, dynamically adjust the charge-discharge rate and mechanical pressure through the adaptive test parameter regulation module 2, when the temperature change rate exceeds 5℃ / min or the stress change rate exceeds 1MPa / min, automatically reduce the test intensity; when the temperature and stress are stable, gradually increase the test intensity, approach the safety boundary;

[0092] S4: Real-time assessment of battery thermal runaway risk using thermal runaway risk prediction module 3, predicting thermal runaway probability and remaining safety time, specifically: input the collected multi-physical field data into the thermal runaway risk prediction module 3, real-time calculation of thermal runaway risk probability and remaining safety time, when the risk probability exceeds 80% or the remaining safety time is less than 5 minutes, send an early warning signal and automatically terminate the test;

[0093] S5: Establish the mapping relationship between microstructure and macroscopic safety performance through the microstructure-macroscopic performance correlation analysis module 4, specifically: X-ray tomography and ultrasonic detection are performed on the battery at different cycle stages (0 times, 500 times, 1000 times, 2000 times) to obtain porosity and crack density data; SEI membrane impedance changes are obtained through electrochemical impedance spectroscopy analysis; establish the correlation model of these microstructure parameters and macroscopic safety performance (such as thermal runaway temperature, maximum tolerance stress);

[0094] S6: Combine battery cycle aging and storage aging data, generate a full life cycle safety assessment report through the full life cycle safety assessment module 5, specifically: conduct cycle aging test (0-3000 cycles) and storage aging test (store at 25℃, 45℃, 60℃ for 0-12 months) on the battery, and measure the safety performance parameters of the battery regularly;

[0095] S7: According to the evaluation results, optimize the test strategy, form a "test-evaluation-optimization" closed-loop test process, specifically: according to the full life cycle safety assessment results, adjust the parameter range and weight coefficient of the multi-physical field coupling test, optimize the input features and weights of the thermal runaway risk prediction model;

[0096] S8: Based on the full life cycle safety assessment results, generate battery safety use suggestions and retirement warning, specifically: including the best use temperature range (recommended 15-35℃), charge-discharge rate limit (recommended 0.5C-1C) and expected safe service life; when the battery safety index is lower than the threshold value (recommended value 0.6), issue a retirement warning.

[0097] It is to be noted that, as used in this document, the term "indicia" is intended to encompass any type of data, information, or other content, whether in the form of text, graphics, images, video, audio, or otherwise. It is to be further noted that, as used in this document, the terms "coupled" and "connected," along with derivatives thereof, can be used to mean one or more of the following: in electrical communication with; physically contacting with; interacting with; included with; attached to one or more other elements; or a combination thereof.

[0098] The preferred embodiments herein disclosed merely by way of examples can be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the application, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims.

Claims

1. A new energy battery safety testing system, characterized in that: include: Multi-physics field synchronous acquisition and analysis module (1), used for synchronously acquiring electrical signals, temperature field distribution and stress-strain data during battery testing, and performing multi-field coupling analysis; An adaptive test parameter control module (2) dynamically adjusts the test intensity, test step length and safety margin according to the real-time data output by the multi-physics field synchronous acquisition and analysis module (1); Thermal runaway risk prediction module (3), which predicts the risk probability and remaining safety time of battery thermal runaway based on multi-parameter fusion analysis; Microstructure-macro performance correlation analysis module (4), which obtains battery microstructure information through non-destructive testing and establishes a correlation model with macro safety performance; A full life cycle safety assessment module (5) is used to generate a full life cycle safety assessment report based on the safety performance changes of the battery at different life cycle stages; The central control and data fusion center (6) is used to coordinate the work of each module in the system and perform data fusion processing.

2. The new energy battery safety testing system according to claim 1, characterized in that: The multi-physical field synchronous acquisition and analysis module (1) includes an electrochemical parameter acquisition unit (101), an infrared thermal imaging unit (102), a distributed optical fiber sensing unit (103), a micro-strain sensing array (104) and a multi-field coupling analysis unit (105), and the output end of the multi-physical field synchronous acquisition and analysis module (1) is electrically connected to the input end of the adaptive test parameter control module (2) and the input end of the central control and data fusion center (6), respectively. The electrochemical parameter acquisition unit (101) adopts a voltage and current acquisition card to acquire the voltage, current, internal resistance and SOC parameters of the battery. The infrared thermal imaging unit (102) is based on an infrared thermal imager to obtain the temperature field distribution of the battery surface. The distributed optical fiber sensing unit (103) adopts a distributed optical fiber sensor to measure the temperature distribution inside the battery. The micro-strain sensing array (104) is based on a plurality of strain gauges mounted on the battery surface to acquire stress and strain data of the battery surface.

3. The new energy battery safety testing system according to claim 2, characterized in that: The input end of the multi-field coupling analysis unit (105) is electrically connected to the output ends of the electrochemical parameter acquisition unit (101), the infrared thermal imaging unit (102), the distributed optical fiber sensing unit (103) and the micro-strain sensing array (104), respectively. The multi-field coupling analysis unit (105) performs an electric-thermal-mechanical coupling analysis based on the collected multi-physical field data and solves the multi-field coupling equation using a finite element method. The specific strategy of the multi-field coupling analysis unit (105) is as follows: The electrical-thermal coupling equation is: , ,in is the heat generation rate, is the current, is the open circuit voltage, is the operating voltage, is the series internal resistance, is the battery density, is the specific heat capacity, is the temperature, For time, is the thermal conductivity, The partial derivative of temperature with respect to time represents the rate of change of temperature with time; The thermal-mechanical coupling equation is: , ,in For thermal stress, is the elastic modulus, is the coefficient of thermal expansion, is the temperature change, For strain, is the initial temperature.

4. The new energy battery safety testing system according to claim 3, characterized in that: The strategy for dynamically adjusting the test intensity in the adaptive test parameter control module (2) is: ， in, for Always test your strength. is the initial test intensity, and The value range is 0.5C-2C, is the temperature influence coefficient, and The value range is 0.01-0.1, is the stress influence coefficient, and The value range is 0.5-2, is the real-time stress value, is the maximum allowable stress, and The value range is 5-20MPa; The test step strategy in the adaptive test parameter control module (2) is: ， in, is the test step length, is the base step length, and The value range is 1-10s. 、 are the weight coefficients of temperature change rate and stress change rate, is the temperature change rate, is the stress change rate; The safety margin in the adaptive test parameter control module (2) is corrected by adaptation, and the safety margin correction coefficient formula is: , is the measured parameter, is the parameter mean, is the parameter standard deviation, is the correction factor.

5. The new energy battery safety testing system according to claim 4, characterized in that: The input end of the thermal runaway risk prediction module (3) is electrically connected to the output end of the adaptive test parameter control module (2). The thermal runaway risk prediction module (3) adopts a BP neural network model, and the input layer of the BP neural network model includes 6 neurons, corresponding to temperature, temperature change rate, voltage, voltage change rate, stress and stress change rate respectively; the hidden layer includes 2 layers, each layer has 12 neurons; the output layer includes 2 neurons, corresponding to the thermal runaway risk probability and the remaining safety time respectively. Thermal runaway risk probability The calculation formula is: ,in, is the bias parameter, is the weight of each feature, obtained through neural network training, is the normalized characteristic function, including temperature, voltage, gas concentration and stress characteristic parameters; Remaining safety time The prediction formula is: ,in, is the temperature change rate function, is the current temperature, is the thermal runaway trigger temperature, and The thermal runaway trigger temperature formula due to aging factors is: ,in, The trigger temperature for thermal runaway of new batteries, is the number of cycles, is the aging coefficient, Indicates the state of charge.

6. The new energy battery safety testing system according to claim 5, characterized in that: The microstructure-macro performance correlation analysis module (4) includes an X-ray tomography unit (401), an ultrasonic detection unit (402), an electrochemical impedance spectroscopy analysis unit (403), and a micro-macro correlation modeling unit (404), wherein the X-ray tomography unit (401) is used to obtain the three-dimensional microstructure inside the battery, specifically by using a microfocus X-ray source to scan the entire battery cell, outputting a three-dimensional reconstructed image, and calculating the porosity distribution; the ultrasonic detection unit (402) uses a phased array probe to evaluate the interlayer delamination and crack propagation of the electrode by the change of the reflected wave amplitude and phase; the electrochemical impedance spectroscopy analysis unit (403) is used to analyze the battery interface characteristics, specifically by applying a 10mV sinusoidal perturbation signal and obtaining the SEI film impedance, charge transfer resistance, and diffusion coefficient by equivalent circuit fitting.

7. The new energy battery safety testing system according to claim 6, characterized in that: The micro-macro correlation modeling unit (404) is used to establish a quantitative relationship model between porosity, crack density and macro safety performance, and the quantitative relationship model includes a micro-porosity and thermal runaway correlation model and an electrode material crack density and mechanical strength relationship model. The formula of the micro-porosity and thermal runaway correlation model is: ,in, is the influence coefficient of micro porosity on thermal runaway, is the measured porosity, is the initial porosity, is the correlation coefficient, and The value range is 5-15; the relationship model formula between the crack density and mechanical strength of the electrode material is: , The crack density of the electrode material is The ultimate mechanical strength is only considered when is the ultimate strength without cracks, is the crack density, ''、 ' is the material constant.

8. The new energy battery safety testing system according to claim 7, characterized in that: The safety index calculation formula in the full life cycle safety assessment module (5) is: ， in, For battery life, for The safety probability at any moment, is the capacity retention rate, is the internal resistance change rate, 、 、 is the weight coefficient, and 1; The safety level in the life cycle safety assessment module (5) is divided into four levels: A, B, C, and D, which correspond to excellent, good, medium, and poor respectively. The specific strategies are as follows: For A-level, For B level, For C level, It is D-level; The formula of the cyclic aging safety attenuation model in the full life cycle safety assessment module (5) is: ， in, For initial safety performance, is the number of cycles, 、 is the attenuation coefficient, For ultimate safety performance; The aging safety assessment model formula stored in the full life cycle safety assessment module (5) is: , in, is the pre-exponential factor, is the activation energy, is the Boltzmann constant, is the storage temperature.

9. The new energy battery safety testing system according to claim 8, characterized in that: The central control and data fusion center (6) is bidirectionally connected to the multi-physics field synchronous acquisition and analysis module (1), the adaptive test parameter control module (2), the thermal runaway risk prediction module (3), the microstructure-macro performance correlation analysis module (4) and the full life cycle safety assessment module (5), and the central control and data fusion center (6) is based on a control interface developed by LabVIEW and supports real-time data display.

10. A new energy battery safety testing method, applied to the new energy battery safety testing system according to any one of claims 1 to 9, characterized in that: The new energy battery safety testing method comprises the following steps: S1: Multi-dimensional benchmark parameter acquisition to obtain the basic electrochemical parameters, thermal parameters and mechanical parameters of the battery under different working conditions; S2: Multi-physics field coupling excitation test, synchronously applying electrical, thermal, and mechanical loads, and obtaining multi-field coupling response data through the multi-physics field synchronous acquisition and analysis module (1); S3: Based on the result of step S2, the test parameters are dynamically adjusted (2) by the adaptive test parameter control module to gradually approach the battery safety boundary; S4: Use the thermal runaway risk prediction module (3) to conduct real-time assessment of the battery thermal runaway risk and predict the thermal runaway probability and remaining safety time; S5: Establish the mapping relationship between microstructure and macro safety performance through the microstructure-macro performance correlation analysis module (4); S6: Combine battery cycle aging and storage aging data to generate a full life cycle safety assessment report through the full life cycle safety assessment module (5); S7: Optimize the test strategy based on the evaluation results to form a closed-loop test process of "test-evaluate-optimize"; S8: Generate battery safety usage recommendations and retirement warnings based on the results of the full life cycle safety assessment.

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