Thermal coupling accelerated aging test system for new energy automobile PCB assembly

Abstract

The invention relates to a thermal-mechanical coupling accelerated aging test system for a new energy automobile PCB assembly. According to the system, through the integration test cabin, the thermal load module, the vibration loading module, the mechanical stress loading module, the data acquisition and monitoring module and the control and analysis system, complex environmental conditions faced by the new energy automobile in the operation process can be simulated. The system can apply temperature shock, vibration load and mechanical stress at the same time so as to realize an accelerated aging test on the PCB assembly in a multi-physics coupling environment. The test data is analyzed by applying an artificial intelligence algorithm, the test parameters are dynamically optimized, and the test efficiency and reliability are improved. The system is suitable for accelerated aging tests of a new energy automobile PCB assembly, a power battery management system (BMS), an electronic control unit (ECU) and the like.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicles, and particularly to a thermal-mechanical coupling accelerated aging test system for PCB components of new energy vehicles. This system is mainly used to accelerate the testing and evaluation of the reliability and durability of PCB components, battery management systems (BMS), and electronic control units (ECU) of new energy vehicles, especially to conduct accelerated aging tests on them under a multi-physical field coupling environment. Background Art

[0002] With the rapid development of new energy vehicle technology, the electronic control system and its related PCB components play a crucial role in the stability and safety of new energy vehicles. Most of the existing aging test equipment only simulates a single physical environment (such as temperature change or vibration, etc.), while ignoring that in actual use, PCB components are usually affected by multiple factors such as heat, force, and vibration. This makes the existing aging tests unable to comprehensively evaluate the reliability of these components under actual working conditions.

[0003] Therefore, there is an urgent need for an accelerated aging test system that can simulate a thermal-mechanical coupling environment and achieve the synergistic effect of multiple factors to improve the accuracy of reliability evaluation of key components of new energy vehicles. Summary of the Invention

[0004] The purpose of the present invention is to provide a thermal-mechanical coupling accelerated aging test system for PCB components of new energy vehicles. Through the simulation of a multi-physical field coupling environment, it can simultaneously apply temperature shock, vibration load, and mechanical stress, thereby conducting a more comprehensive accelerated aging test on PCB components and significantly improving the reliability and prediction accuracy of test results.

[0005] To achieve the above purpose, the present invention provides a thermal-mechanical coupling accelerated aging test system for PCB components of new energy vehicles, characterized in that the system includes:

[0006] A test chamber for providing a sealed environment and regulating temperature, humidity, and air flow to simulate the thermal stress influence in the operating environment of new energy vehicles;

[0007] A thermal load module connected to the test chamber, which applies temperature shock using programmable temperature control technology, including constant temperature aging, high and low temperature cycling, and temperature gradient change, to accelerate the material aging process of PCB components;

[0008] A vibration loading module configured to apply vibration loads with adjustable frequency and amplitude to the PCB components in multiple degrees of freedom directions (X, Y, Z) to simulate the dynamic loads under driving conditions and study the solder joint fatigue failure behavior;

[0009] A mechanical stress loading module, including a servo motor or a hydraulic device, is used to apply controllable mechanical stresses such as bending, twisting, or impact to the PCB assembly to verify the impact of mechanical stresses on the reliability of electrical connections;

[0010] A data acquisition and monitoring module, including temperature sensors, strain gauges, accelerometers, resistance monitoring devices, and optical deformation measurement devices, is used to collect the thermal response data of the PCB assembly in real time and monitor the aging process;

[0011] A control and analysis system comprehensively analyzes the test data based on artificial intelligence algorithms, dynamically adjusts the test parameters, and optimizes the test plan.

[0012] Among them, the system can simultaneously apply temperature shock, vibration load, and mechanical stress to implement an accelerated aging test of the PCB assembly in a multi-physical field coupling environment.

[0013] The thermal load module supports a temperature range of -40°C to 125°C, real-time monitors the temperature changes at key parts of the PCB through temperature sensors, and uses a PID temperature control algorithm to achieve high-precision temperature control.

[0014] The vibration loading module can apply multi-axis vibration with a frequency range of 10Hz to 2000Hz, combines strain gauges to measure the stress changes of the PCB and solder joints, and uses Fourier transform to analyze the vibration spectrum characteristics.

[0015] The data acquisition and monitoring module includes a non-contact optical measurement device, which detects the deformation of the PCB assembly through image processing technology and calculates the displacement change of key points:

[0016] Where P i and P ′ are the positions of the feature points before and after the test respectively, to quantify the thermo-mechanical coupling deformation of the PCB assembly.

[0017] The control and analysis system includes a test optimization function based on machine learning, which uses methods such as data preprocessing, failure mode analysis, and life prediction to improve the test efficiency and reliability.

[0018] Data preprocessing uses wavelet transform to denoise the collected temperature, stress, and resistance data, and reduces the data dimension through principal component analysis (PCA) to improve the calculation efficiency and the effectiveness of test data.

[0019] The remaining life prediction uses a Long Short-Term Memory (LSTM) network to analyze time series data. The inputs include test time, temperature, vibration, and stress data, and the output is the estimated remaining life value Yt. The calculation formula is: Yt = Woht + bo, where ht is the LSTM hidden state, and Wo and bo are the weights and biases of the output layer, to evaluate the reliability of the PCB components.

[0020] The test optimization function uses the Bayesian optimization algorithm to adjust the test parameters, including the temperature change rate, vibration intensity, and mechanical stress, to optimize the test efficiency and accuracy.

[0021] The system further includes an automatic adjustment function that optimizes the synergistic effects of thermal load, vibration, and mechanical stress through real-time data feedback to improve the stability and repeatability of the test.

[0022] The system is applicable to the accelerated aging tests of PCB components, battery management systems (BMS), and electronic control units (ECU) in new energy vehicles, and can improve the accuracy of reliability assessment through thermo-mechanical coupling environment simulation. Description of the Drawings

[0023] Figure 1 This is a schematic diagram of the overall structure of a thermo-mechanical coupling accelerated aging test system provided by the present invention, showing the test chamber and the PCB components, connecting the thermal load module, vibration loading module, mechanical stress loading module, data acquisition and monitoring module, and control and analysis system. The arrows indicate the relationships between the modules.

[0024] Figure 2 This is a cross-sectional view of the internal structure of the test chamber in a thermo-mechanical coupling accelerated aging test system provided by the present invention, showing the fixture fixing the PCB components in the test chamber, the vibration table and servo motor applying loads, and the optical measurement device at the top monitoring the deformation. The section is indicated by a dashed line.

[0025] Figure 3 This is a schematic diagram of the thermo-mechanical coupling test process of a thermo-mechanical coupling accelerated aging test system provided by the present invention, showing the process from parameter input to multi-field coupling loading, data acquisition, AI analysis, and parameter adjustment, and finally outputting the life prediction.

[0026] Figure 4 This is a schematic diagram of the working principle of the data acquisition and monitoring module in a thermo-mechanical coupling accelerated aging test system provided by the present invention, showing the temperature, stress, acceleration, resistance, and optical measurement devices around the PCB components. The data is transmitted to the control system through the collector, including the deformation calculation formula.

[0027] In the figure: 101, test chamber; 102, PCB assembly; 103, thermal load module; 104, vibration loading module; 105, mechanical stress loading module; 106, data acquisition and monitoring module; 107, control and analysis system; 108, fan; 109, fixture; 110, shaker; 111, servo motor; 112, optical measuring device; 113, temperature sensor; 114, stress sensor; 115, acceleration sensor; 116, resistance monitoring device; 117, data collector. Detailed implementation manners

[0028] To make the objectives, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below with reference to the accompanying drawings in this application. Apparently, the described embodiments are some but not all of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the protection scope of this application.

[0029] The terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features; in the description of this application, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0030] To achieve the above objectives, please refer to Figure 1 and Figure 2As shown, the present invention provides a thermo-mechanical coupling accelerated aging test system for a PCB component of a new energy vehicle, aiming to simulate complex operating environments and efficiently evaluate the reliability of the PCB component 102. The test chamber 101 is made of 304 stainless steel and is equipped with a temperature and humidity controller (Honeywell H7080B) and a fan 108 (power 500W) to achieve a temperature range of -40°C to 125°C, humidity control of 20% - 95%RH, and a maximum air flow velocity of 5m / s, truly reproducing high-temperature, high-humidity, and dynamic air flow conditions. The thermal load module 103 applies diverse temperature shocks through an electric heater (Nichrome, 2kW) and a refrigeration compressor (Danfoss, 1.5kW), such as constant temperature aging (85°C, 48 hours), high-low temperature cycling (-20°C to 100°C, cycle 30 minutes), or gradient change (5°C / min), significantly accelerating the material aging of the PCB component 102. Compared with 500 hours of traditional single-field tests, significant changes such as solder joint fatigue can be observed within 24 hours in this system, improving the test efficiency by more than 50%. The vibration loading module 104 is based on an electromagnetic vibration table 110 (LDS V850) and applies adjustable vibrations with frequencies of 10Hz - 2000Hz and amplitudes of 0.1g - 50g in the X, Y, and Z axes to simulate driving bumps. For example, under a load of 20Hz and 5g, the life of the solder joint stress concentration point is shortened to 1000 cycles, revealing the fatigue failure mechanism. The mechanical stress loading module 105 applies a 200N bend or instantaneous impact through a servo motor 111 (Panasonic A6, maximum force 500N) and a hydraulic device (impact force 1000N, duration 10ms) to verify the reliability of electrical connections. For example, the resistance change reaches 0.1Ω under bending stress. The data acquisition and monitoring module 106 integrates a temperature sensor 113 (K-type thermocouple), a stress sensor 114 (120Ω strain gauge), an acceleration sensor 115 (ADXL accelerometer), a resistance monitoring device 116 (Keithley 2000 ohmmeter), and an optical measurement device 112 (Basler acA2440 camera) to continuously monitor temperature (accuracy 0.1°C), stress (0.01MPa), and deformation (displacement resolution 2.83 pixels), ensuring high-precision acquisition of multi-dimensional data. The control and analysis system 107 runs on an Intel i7 industrial computer and develops AI software based on Python and TensorFlow. It coordinates multi-field loading through a time synchronization controller (NI cRIO). For example, simultaneously applying an 85°C high temperature, 20Hz vibration, and 100N bending force, dynamically adjusting parameters to reduce the temperature fluctuation from ±2°C to ±0.5°C, improving the test repeatability (standard deviation <5%).Aiming at the problems of the inability of traditional single physical fields to simulate coupling effects, insufficient data monitoring, and low efficiency, this system reproduces the operating environment of the PCB component 102 through the synergistic action of heat - vibration - stress, shortens the test cycle and optimizes parameters, and at the same time provides comprehensive aging analysis, reducing the prediction error to ±10%, providing efficient and stable technical support for the reliability assessment of the PCB component 102.

[0031] In some embodiments, the thermal load module 103 supports a wide temperature range from -40°C to 125°C, meeting the operating conditions of the PCB component 102 in new energy vehicles. Precise temperature shocks are applied through an electric heater (Nichrome, 2kW) and a refrigeration compressor (Danfoss, 1.5kW), such as constant temperature aging (85°C, 48 hours) or high - low temperature cycling (-20°C to 100°C, cycle 30 minutes), accelerating the material aging of the PCB component 102, shortening the traditional 500 - hour test to 24 hours, and increasing the efficiency by more than 50%. Temperature sensors 113 (K - type thermocouples) are arranged at key parts of the PCB component 102 (such as solder joints, chips) in the thermal load module 103, and the temperature changes are monitored in real - time through a NI - 9211 acquisition card with a resolution of 0.1°C, ensuring accurate capture of local temperature differences. For example, the dynamic process of the solder joint temperature rise from 25°C to 85°C is clearly visible. The PID temperature control algorithm (implemented by LabVIEW) is used, combined with a PLC to drive the heating / cooling unit, with a control accuracy of ±0.5°C, which is significantly optimized compared with the traditional ±2°C fluctuation, and the test repeatability is increased to a standard deviation <3%. Aiming at the problems of low temperature control accuracy of traditional systems and the inability to reflect the thermal response of the PCB component 102 in real - time, the thermal load module 103 provides a stable and reliable thermal stress environment through wide - temperature range support and high - precision closed - loop control, facilitating the analysis of the aging mechanism and reliability assessment of solder joints.

[0032] In some embodiments, the vibration loading module 104 uses a vibration table 110 (electromagnetic vibration table LDS V850), which supports multi-axis vibration from 10 Hz to 2000 Hz, simulates the dynamic load under driving conditions. For example, when applying 500 Hz, 5 g vibration for 12 hours, the peak stress of the solder joints of the PCB assembly 102 reaches 50 MPa, the fatigue life is shortened to 1000 cycles, and the accelerated aging efficiency is increased by 40% compared with the traditional single-frequency test. The vibration table 110 is driven by a DSPACE controller, the frequency step is accurate to 10 Hz, and the amplitude range is adjustable from 0.1 g to 50 g, which can flexibly adapt to different test requirements. The vibration loading module 104 combines a stress sensor 114 (120 Ω strain gauge, Vishay CEA series) and an HBM MGCplus strain gauge to measure the stress changes of the PCB assembly 102 and its solder joints in real time, with an accuracy of 0.01 MPa, and analyzes the vibration spectrum characteristics through Fourier transform (FFT) in MATLAB. For example, it can identify the stress resonance peak at the main frequency of 500 Hz, improving the scientific nature of failure analysis. Aiming at the problem that the traditional vibration test cannot comprehensively simulate multi-axis loads and spectral characteristics, the vibration loading module 104 provides a real working condition simulation through multi-degree-of-freedom loading and high-precision spectral analysis, significantly enhancing the evaluation ability of the fatigue behavior of solder joints.

[0033] In some embodiments, as Figure 4 shown, the data acquisition and monitoring module 106 is equipped with an optical measurement device 112, which uses a Basler acA2440 camera (resolution 2448x2048) to capture the surface feature points of the PCB assembly 102, and calculates the displacement of the positions P i and P i ′ before and after the test through OpenCV image processing technology. Specifically, P i represents the three-dimensional spatial position of the i-th feature point on the surface of the PCB assembly captured before the test through an optical measurement device (such as a high-resolution industrial camera combined with stereo vision technology), denoted as (x i , y i , z i ), where x i and y i represent the horizontal and longitudinal positions of the feature point on the PCB plane, and z i represents the height relative to the PCB reference plane; P i ′ represents the three-dimensional spatial position of the same feature point captured after the test through the same method, denoted as (x i ′, y i ′, z i ′); the feature points are identified and matched through image processing technology (such as the scale-invariant feature transform SIFT algorithm) to ensure the accuracy of the corresponding relationship before and after the test. For example, before the test, P i(10, 20), and the subsequent P i ′(12, 22). According to the formula in claim 4, the calculated displacement Δd is approximately 2.83 pixels, accurately quantifying the thermo-mechanical deformation with an error < 5%. The data acquisition and monitoring module 106 monitors the deformation process of the PCB assembly 102 in real time. For example, under 85°C and 20Hz vibration, the deformation amount reaches 3mm, revealing the characteristics of material aging, improving the efficiency by 30% compared to traditional contact measurement and avoiding contact interference. Aiming at the problem of the lack of non-contact deformation monitoring in traditional systems, the data acquisition and monitoring module 106 provides non-destructive and dynamic deformation analysis through a high-resolution optical measurement device 112, combined with multi-sensor data such as a temperature sensor 113 and a stress sensor 114, comprehensively improving the evaluation accuracy and reliability of the aging process of the PCB assembly 102.

[0034] In some embodiments, such as Figure 3 shown, the control and analysis system 107 is based on an Intel i7 industrial computer, running AI software developed in Python (integrating TensorFlow), optimizing the test process through data preprocessing, fault mode analysis, and life prediction. For example, inputting 24-hour test data (temperature, vibration, stress), predicting the solder joint life of the PCB assembly 102 to be 5000 hours, and reducing the error from ±20% to ±10%. The control and analysis system 107 analyzes the failure types of the PCB assembly 102 (such as solder joint cracking, with a classification accuracy > 90%), and dynamically adjusts parameters. For example, optimizing the temperature fluctuation from ±2°C to ±0.5°C, improving the test stability (standard deviation < 5%). Aiming at the problems of low efficiency of manual adjustment and insufficient intelligence in traditional systems, the control and analysis system 107 realizes the adaptive optimization of the test plan through machine learning. For example, shortening the test cycle by 20%, providing efficient and accurate support for the reliability evaluation of the PCB assembly 102.

[0035] In some embodiments, the data preprocessing in the data acquisition and monitoring module 106 uses wavelet transform (Daubechies D4, implemented by PyWavelets) to denoise the data of the temperature sensor 113, the stress sensor 114, and the resistance monitoring device 116. For example, removing high-frequency noise in 500Hz vibration, and improving the signal-to-noise ratio by 15dB to ensure data quality. Combining principal component analysis (PCA, implemented by Scikit-learn), extracting 95% variance features from 1000 groups of multi-dimensional data, reducing the dimension from 10 to 3, and shortening the calculation time from 10s to 3s, improving the efficiency by 70%. Aiming at the problems of large noise interference and computational redundancy in traditional data processing, the data acquisition and monitoring module 106 optimizes the effectiveness of test data through denoising and dimensionality reduction techniques. For example, improving the accuracy of solder joint stress analysis of the PCB assembly 102 to 0.01MPa, providing a reliable basis for subsequent life prediction and failure analysis.

[0036] In some embodiments, long short-term memory networks (LSTM, implemented in TensorFlow) are used for life prediction within the control and analysis system 107. By inputting time series data (test time, data from the temperature sensor 113, data from the vibration loading module 104, and data from the mechanical stress loading module 105), the remaining life is output through the formula Yt = Woht + bo (where ht is the hidden state, and Wo and bo are the weights and biases). For example, the predicted life of the PCB component 102 under 85°C, 20 Hz vibration, and 100 N stress is Yt = 4500 hours, with an error of ±5%. The model is trained based on 1000 sets of aging data. For example, it can identify the trend of the solder joint fatigue life decreasing from 5000 hours to 1000 hours, and the prediction accuracy is 20% higher than that of traditional linear models. To address the problem that traditional methods cannot accurately predict time-varying aging, the control and analysis system 107 analyzes the dynamic response through LSTM to provide high-precision life assessment, contributing to the reliability optimization of the PCB component 102.

[0037] In some embodiments, the test optimization function of the control and analysis system 107 uses the Bayesian optimization algorithm to adjust the temperature change rate (0.5 - 5°C / min) of the thermal load module 103, the vibration intensity (1 - 50 g) of the vibration loading module 104, and the stress (50 - 500 N) of the mechanical stress loading module 105. For example, after 50 iterations, the optimal parameters (2°C / min, 10 g, 200 N) are determined, and the test time is shortened from 30 hours to 24 hours, with a 20% increase in efficiency. The control and analysis system 107 constructs a surrogate model based on historical data. For example, after optimizing the vibration intensity, the aging characteristics of the solder joints of the PCB component 102 appear earlier, and the prediction error is reduced to ±8%. To address the problem that traditional test parameter adjustment relies on experience, the control and analysis system 107 realizes efficient and accurate test plan design through intelligent optimization, improving the scientific nature of the aging assessment of the PCB component 102.

[0038] In some embodiments, the control and analysis system 107 has an automatic adjustment function to optimize the collaborative effect of the thermal load module 103, the vibration loading module 104, and the mechanical stress loading module 105 through real-time data feedback. For example, when the data of the temperature sensor 113 exceeds the standard by ±2°C, the PLC reduces the heating power by 10% through the PID algorithm, and the stabilization time is shortened from 10 minutes to 5 minutes, with the fluctuation controlled within ±0.5°C. The vibration table 110 adjusts the amplitude according to the data of the stress sensor 114 (for example, from 5 g to 4.5 g) to ensure that the stress deviation < 5%, and the test repeatability is improved to a standard deviation < 3%. To address the problem of poor multi-field collaborative stability in traditional systems, the control and analysis system 107 provides a stable coupling environment through closed-loop regulation. For example, it runs for 24 hours under 85°C, 20 Hz, and 100 N loading without significant drift, enhancing the credibility of the aging test of the PCB component 102.

[0039] In some embodiments, the present invention provides a thermal-mechanical coupling accelerated aging test system applicable to a PCB assembly 102 of a new energy vehicle. The test chamber 101 is made of 304 stainless steel and is equipped with a temperature and humidity controller (Honeywell H7080B) and a fan 108 (with a power of 500W) to achieve temperature control in the range of -40°C to 125°C and humidity control in the range of 20% - 95%RH, truly simulating various operating environments. The fixture 109 supports the rapid installation of the PCB assembly 102 (PCB 50x50mm to 200x200mm, BMS with battery connectors, ECU with wire harness interfaces), with a switching time of less than 15 minutes, flexibly adapting to the testing requirements of multiple components. The thermal load module 103 applies customized temperature shocks through an electric heater (Nichrome, 2kW) and a refrigeration compressor (Danfoss, 1.5kW), such as high and low temperature cycling of the PCB assembly 102 (PCB) (-20°C to 100°C, cycle of 30 minutes), high temperature and high humidity aging of the PCB assembly 102 (BMS) (85°C, 85%RH, 48 hours), and extreme temperature change of the PCB assembly 102 (ECU) (-40°C to 125°C, cycle of 20 minutes), shortening the traditional 1000-hour aging to 48 hours and increasing the efficiency by more than 50%. The vibration loading module 104, based on a vibration table 110 (LDS V850), applies vibrations of 20Hz and 5g to the PCB assembly 102 (PCB) to simulate driving bumps, vibrations of 50Hz and 10g with large amplitudes to the PCB assembly 102 (BMS) to simulate battery pack resonance, and vibrations of 1000Hz and 20g with high frequencies to the PCB assembly 102 (ECU) to simulate the engine compartment conditions, with a frequency error <1Hz, accelerating the manifestation of solder joint fatigue. For example, the lifespan of the PCB assembly 102 (BMS) can be reduced from 5000 hours to within 48 hours and can be measured. The mechanical stress loading module 105 applies differential loads through a servo motor 111 (Panasonic A6, 500N) and a hydraulic device (impact force of 1000N, 10ms), such as a 200N bend for the PCB assembly 102 (PCB), a 150N twist for the PCB assembly 102 (BMS), and a 500N impact for the PCB assembly 102 (ECU), revealing the characteristics of electrical connection failures. The data acquisition and monitoring module 106 integrates a temperature sensor 113 (K-type thermocouple), a stress sensor 114 (120Ω strain gauge), an acceleration sensor 115 (ADXL accelerometer), a resistance monitoring device 116 (TI INA226 voltage / current sensor, dedicated for BMS), and a Vector VN1640 CAN signal collector (dedicated for ECU) to monitor temperature (accuracy 0.1°C), stress (0.01MPa), power consumption change (±0.1W), and communication interruption rate (<0.01%) in real time, providing multi-dimensional data support.The control and analysis system 107 runs on an Intel i7 industrial computer. The AI software based on TensorFlow combines physical and electrical data to predict the lifespan. For example, the remaining lifespan of the PCB component 102 (ECU) is 4,500 hours, and the error is reduced from ±20% to ±8%. Aiming at the problems of single-component testing, insufficient environmental simulation, and lack of electrical monitoring in traditional systems, this system provides accurate environmental simulation (humidity accuracy ±2%RH), efficient aging assessment (test cycle shortened by 50%), and comprehensive reliability improvement (covering physical deformation and electrical failure) through multi-field coupling and intelligent analysis, providing stable and efficient technical support for the research and development of key components of new energy vehicles.

[0040] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting it. The present application is not limited to the exact structures already described and illustrated in the drawings, and it cannot be determined that the specific implementation of the present application is only limited to these descriptions. For those of ordinary skill in the technical field to which the present application belongs, various changes and deformations made without departing from the concept of the present application should be regarded as belonging to the protection scope of the present application.

Claims

1. A thermal-mechanical coupling accelerated aging test system for a PCB component of a new energy vehicle, characterized in that, The system includes: A test chamber for providing a sealed environment and regulating temperature, humidity, and air flow to simulate the thermal stress effects in the operating environment of new energy vehicles; A thermal load module connected to the test chamber, which applies temperature shocks using programmable temperature control technology, including constant temperature aging, high and low temperature cycling, and temperature gradient changes, to accelerate the aging process of PCB component materials; A vibration loading module configured to apply vibration loads with adjustable frequencies and amplitudes to the PCB component in multiple degrees of freedom directions (X, Y, Z) to simulate dynamic loads under driving conditions and study the solder joint fatigue failure behavior; A mechanical stress loading module including a servo motor or a hydraulic device for applying controllable mechanical stresses such as bending, twisting, or impact to the PCB component to verify the effect of mechanical stress on the reliability of electrical connections; A data acquisition and monitoring module including temperature sensors, strain gauges, accelerometers, resistance monitoring devices, and optical deformation measurement devices for real-time collecting the thermal and mechanical response data of the PCB component and monitoring the aging process; A control and analysis system that comprehensively analyzes the test data based on artificial intelligence algorithms, dynamically adjusts the test parameters, and optimizes the test plan; Among them, the system can simultaneously apply temperature shocks, vibration loads, and mechanical stresses to achieve an accelerated aging test of the PCB component in a multi-physical field coupling environment.

2. The system according to claim 1, wherein The thermal load module supports a temperature range of -40°C to 125°C, and the temperature changes at key parts of the PCB are monitored in real time through temperature sensors, and high-precision temperature control is achieved using the PID temperature control algorithm.

3. The system according to claim 1, wherein The vibration loading module can apply multi-axis vibrations with a frequency range of 10Hz to 2000Hz, and the stress changes of the PCB and solder joints are measured in combination with strain gauges, and the vibration spectrum characteristics are analyzed using Fourier transform.

4. The system according to claim 1, wherein The data acquisition and monitoring module includes a non-contact optical measurement device, which detects the deformation of the PCB component through image processing technology and calculates the displacement change of key points: ; where P i and P i ' are the positions of the feature points before and after the test respectively, to quantify the thermo-mechanical deformation of the PCB assembly.

5. The system according to claim 1, characterized in that, The control and analysis system includes a test optimization function based on machine learning, which uses methods such as data preprocessing, failure mode analysis, and life prediction to improve the test efficiency and reliability.

6. The system according to claim 5, wherein The data preprocessing uses wavelet transform to denoise the collected temperature, stress, and resistance data, and reduces the data dimension through principal component analysis (PCA) to improve the calculation efficiency and the effectiveness of the test data.

7. The system according to claim 5, wherein The life prediction uses a long short-term memory network (LSTM) to analyze time series data. The inputs include test time, temperature, vibration, and stress data, and the output is the estimated remaining life value Yt. The calculation formula is: Yt = Woht + bo Where ht is the LSTM hidden state, and Wo and bo are the weights and biases of the output layer to evaluate the reliability of the PCB component.

8. The system according to claim 5, wherein The test optimization function uses the Bayesian optimization algorithm to adjust the test parameters, including the temperature change rate, vibration intensity, and mechanical stress, to optimize the test efficiency and accuracy.

9. The system according to any one of claims 1 to 8, characterized in that, The system further includes an automatic adjustment function to optimize the synergistic effects of the thermal load, vibration, and mechanical stress through real-time data feedback, improving the stability and repeatability of the test.

10. The system according to claim 1, wherein, The system is applicable to the accelerated aging test of PCB components, battery management systems (BMS), and electronic control units (ECU) of new energy vehicles, and can improve the accuracy of reliability assessment through thermal-mechanical coupling environment simulation.

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