Vehicle-mounted boost inductance monitoring simulation test system and test method thereof

Abstract

The invention discloses a vehicle-mounted boost inductance monitoring simulation test system and a test method thereof. The vehicle-mounted boost inductance monitoring simulation test system comprises a vehicle-mounted power supply simulation module, an accelerated life test module, an environment test box, a fault early warning module and a data analysis and diagnosis module. The actual stress state of the inductor is comprehensively simulated by constructing a power supply simulation model matched with the actual working condition of the electric vehicle and combining a multi-environmental-factor coupled accelerated life test; real-time monitoring and fault early warning of inductive performance degradation are realized by adopting an acoustic emission technology; an inductance fault diagnosis knowledge base and an intelligent reasoning system are constructed by applying big data analysis and machine learning technologies. According to the system and the method provided by the invention, the inductance reliability test efficiency can be remarkably improved, the early warning of inductance degradation is realized, the accuracy and the intelligent level of fault diagnosis are improved, a solid basic guarantee is provided for safe operation of a new energy electric vehicle, and the system and the method have wide application prospects and popularization values.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy electric vehicle device testing, and in particular relates to a vehicle-mounted boost inductor monitoring simulation test system and a test method thereof. Background Art

[0002] In the power supply system of new energy electric vehicles, the on-board boost inductor is a key component of the DC / DC converter, and its performance directly affects the vehicle's range, power performance, and safety and reliability. However, in actual work, the on-board boost inductor faces complex stress environments such as high temperature, high humidity, vibration, and impact. In addition, the working conditions of electric vehicles are complex and changeable, and the input and output of the power supply system fluctuate frequently, causing the inductor to be in a state of repeated impact and transient overload for a long time, which is prone to parameter drift, insulation failure, core saturation and other degradation and failure problems, which seriously threaten the safe operation of electric vehicles.

[0003] At present, the methods for reliability research and fault diagnosis of vehicle-mounted boost inductors are mainly the following:

[0004] (1) Experience-based evaluation method. This method mainly relies on the experience and intuition of designers to make a qualitative evaluation of the reliability of the inductor based on factors such as the selection, process, and materials of the inductor. This method is highly subjective and lacks quantitative indicators, making it difficult to cope with the increasingly complex automotive application environment.

[0005] (2) Method based on accelerated life test. This method accelerates the aging process of the inductor and shortens the test cycle by increasing the stress levels such as temperature and humidity. However, traditional accelerated life tests mostly use a single stress or the superposition of two stresses, ignoring the multi-stress coupling effect, and the acceleration model is mostly based on empirical formulas and lacks the support of physical mechanisms, resulting in a large deviation between the test results and the actual life.

[0006] (3) Method based on failure physics analysis. This method studies the failure mechanism through anatomical analysis, material characterization, etc. after the inductor fails. However, this post-facto diagnosis cannot provide early warning of failure, and lacks data accumulation and knowledge precipitation, making it difficult to guide design improvements. Summary of the invention

[0007] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a vehicle-mounted boost inductor monitoring simulation test system and a test method thereof, which are mainly used to solve the problems in the prior art that there is a lack of accelerated life test methods that match the actual operating conditions of electric vehicles, and it is difficult to comprehensively examine the coupling effects of multiple environmental factors.

[0008] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0009] In a first aspect, the present invention provides a vehicle-mounted boost inductor monitoring simulation test system, comprising:

[0010] The vehicle power simulation module is used to simulate the power fluctuations under the actual working conditions of electric vehicles and provide power input for subsequent tests;

[0011] The accelerated life test module is used to formulate a test strategy for coupling multiple environmental factors. It defines at least four environmental stress factors: temperature, humidity, vibration, and shock. Several stress levels are set for each environmental stress factor. After selecting the factors and levels, the orthogonal experimental design method is used to construct an orthogonal table with multiple factors and levels to form a test strategy.

[0012] Environmental test chamber, used to place inductor samples and execute test strategies, with test fixtures to simulate the actual installation environment of the inductor;

[0013] Fault warning module, which is used to collect weak signals inside the inductor sample in real time, extract the time-frequency domain characteristics of the acoustic emission signal through signal processing, and identify the early signs of inductor degradation and failure;

[0014] The data analysis and diagnosis module is used to collect and analyze the test data of inductor samples under different test conditions, mine data features, and establish performance degradation models and fault diagnosis rule bases.

[0015] In some embodiments, the vehicle power supply simulation module includes a battery simulation unit, a motor simulation unit, an electronic control simulation unit, and a coupling unit;

[0016] The battery simulation unit adopts the Thevenin equivalent circuit model to simulate the open circuit voltage, polarization voltage, and ohmic internal resistance dynamic characteristics of the vehicle battery, and combines the battery SOC state of charge, temperature, and aging factors to predict its output voltage and power changes;

[0017] The motor simulation unit uses a dynamic equation in a dq coordinate system to establish a mathematical model for describing the stator current, torque, and speed characteristic parameters, and predicts its efficiency changes under different working conditions;

[0018] The electronic control simulation unit adopts a controller-switch tube-filter circuit cascade model to simulate its current and voltage conversion process based on control strategy, switching frequency, and dead zone compensation factors;

[0019] The coupling unit couples the models constructed by the battery simulation unit, the motor simulation unit, and the electronic control simulation unit, respectively, and constructs a vehicle-level power supply simulation model in the Simulink simulation platform.

[0020] In some embodiments, the accelerated life test module further includes defining a power supply fluctuation factor;

[0021] Using the vehicle power simulation module, the power fluctuation of electric vehicles is simulated for typical working conditions including constant speed, acceleration, braking, climbing, descending, and bumping, and the time domain waveforms of voltage and current are obtained. The time domain waveforms are feature extracted and statistically analyzed to obtain key parameters, including voltage spike value, voltage surge and drop slope, current root mean square value, and ripple coefficient. The key parameters are used as indicators to characterize power fluctuation factors.

[0022] In the orthogonal experimental design, the power supply fluctuation factor index is included in the factor level table to form an orthogonal matrix with other environmental stress factors;

[0023] Under each test scheme, the vehicle power simulation module is used to generate the corresponding voltage and current waveforms, which are then loaded onto the inductor sample under test through the power amplifier and signal generator. At the same time, the corresponding temperature, humidity, vibration and impact stress are applied in the environmental test chamber to perform accelerated life tests.

[0024] By monitoring the degradation of the electrical parameters and physical properties of the inductor and recording its failure time, life data can be obtained.

[0025] In some embodiments, based on the failure data of the orthogonal test, the maximum likelihood estimation and least squares method are used to fit the parameters in the generalized Eyring model to obtain the activation energy Ea, the Boltzmann constant kB, the humidity acceleration index m, the vibration acceleration index n, the impact acceleration index p, and the acceleration index q related to the power supply fluctuation, forming a complete accelerated life model:

[0026]

[0027] Wherein, AF represents the acceleration factor, Ea represents the activation energy, kB represents the Boltzmann constant, T represents the test temperature, T0 represents the reference temperature, RH represents the test humidity, RH0 represents the reference humidity, G represents the test vibration intensity, G0 represents the reference vibration intensity, S represents the test shock intensity, S0 represents the reference shock intensity, m represents the acceleration index of humidity, n represents the acceleration index of vibration, p represents the acceleration index of shock, V represents the power supply fluctuation factor index, V0 represents the reference power supply level, and q represents the acceleration index of power supply fluctuation;

[0028] Based on the accelerated life model, the failure time of the inductor under any combination of temperature, humidity, vibration, shock and power supply fluctuation is predicted, and its reliability level under actual electric vehicle operating conditions is evaluated.

[0029] In some embodiments, the fault warning module includes an acoustic emission sensor array, a feature extraction unit, and a post-processing unit;

[0030] The acoustic emission sensor array is configured to be arranged at a set key position of the inductor sample, sense the transient elastic stress wave inside the inductor sample in real time, and emit an acoustic emission signal;

[0031] The feature extraction unit is configured to preprocess and extract features of the collected acoustic emission signals, perform time-frequency decomposition of the acoustic emission signals through wavelet transform, extract energy, frequency, and attenuation features at different scales, and obtain instantaneous amplitude, instantaneous frequency, and instantaneous phase features of the acoustic emission signals through Hilbert-Huang transform;

[0032] The post-processing unit is configured to train a pattern recognition model based on the extracted acoustic emission features, learn the complex mapping relationship between the acoustic emission features and the fault modes by building a multi-layer neural network, establish a baseline model of the inductive acoustic emission features under normal working conditions, monitor the changes in the acoustic emission features through control charts, and identify abnormal points beyond the control limits.

[0033] In some embodiments, the fault warning module further includes a fault location unit;

[0034] The acoustic emission sensor array includes at least three sub-sensors that are not on the same straight line, forming a triangular or polygonal array area, and the distance between adjacent sub-sensors is a known preset distance;

[0035] The fault location unit is configured to record the arrival time of the acoustic emission signal emitted by each sub-sensor, construct a triangulated positioning equation group according to the time difference of the acoustic emission signals corresponding to any three sub-sensors in the sensor array, solve the equation group to obtain the estimated value of the fault source coordinates, map the estimated value of the fault source coordinates to the physical model of the inductor sample, and obtain the fault location.

[0036] In some embodiments, the data analysis and diagnosis module includes a big data processing unit, an analysis and identification unit, and a diagnosis unit;

[0037] The big data processing unit is configured to clean, fuse and annotate multi-source heterogeneous test data using a big data processing framework;

[0038] The analysis and identification unit is configured to construct a multi-dimensional feature extraction and data mining model, and use a machine learning algorithm to automatically identify inductor performance degradation patterns and abnormal data;

[0039] The diagnosis unit is configured to construct a fault diagnosis knowledge graph and an inference rule base to diagnose and locate the inductor fault mode.

[0040] In some embodiments, the big data processing unit is configured to obtain simulated driving condition data of the electric vehicle, including motor speed, battery voltage, current, temperature, brake energy recovery state, accelerator pedal opening, and charging state, to form a multi-dimensional time series reflecting the actual working state of the inductor;

[0041] Perform data cleaning on multidimensional time series, remove outliers, invalid values, duplicate values, smooth noise, and unify data formats and sampling frequencies;

[0042] Using data fusion technology, the driving condition data is associated with the inductance test data to construct a fusion data set that includes inductance performance indicators and operating condition characteristics;

[0043] Using data mining algorithms, the association rules and influence patterns between inductor performance indicators and driving condition characteristics in the fusion data set are analyzed to determine the corresponding relationship between inductor performance degradation and specific operating conditions, forming a degradation pattern library;

[0044] The fused data set is automatically annotated, and the degradation mode to which each record belongs is identified according to the association rules and threshold conditions in the degradation mode library, generating a training data set with degradation mode labels.

[0045] In a second aspect, the present invention provides a test method using a vehicle-mounted boost inductor monitoring simulation test system as described above, comprising the following steps:

[0046] Simulate power fluctuations under real working conditions of electric vehicles and provide power input for subsequent tests;

[0047] Formulate a test strategy for coupling multiple environmental factors, define at least four environmental stress factors: temperature, humidity, vibration, and shock, set several stress levels for each environmental stress factor, and after selecting the factors and levels, use the orthogonal experimental design method to construct an orthogonal table with multiple factors and levels to form a test strategy;

[0048] Place the inductor sample in the environmental test chamber, fix the inductor sample using a test fixture that simulates the actual installation environment of the inductor, and control the environmental test chamber to execute the test strategy;

[0049] Weak signals inside the inductor sample are collected in real time, and the time-frequency domain characteristics of the acoustic emission signal are extracted through signal processing to identify early signs of inductor degradation and failure;

[0050] Collect and analyze the test data of inductor samples under different test conditions, mine data features, and establish performance degradation models and fault diagnosis rule bases.

[0051] In some embodiments, the steps include:

[0052] Arranging an acoustic emission sensor array at a set key position of the inductor sample to sense the transient elastic stress wave inside the inductor sample in real time and emit an acoustic emission signal;

[0053] Preprocess and extract features of the collected acoustic emission signals. Perform time-frequency decomposition of the acoustic emission signals through wavelet transform, extract energy, frequency, and attenuation features at different scales, and obtain the instantaneous amplitude, instantaneous frequency, and instantaneous phase features of the acoustic emission signals through Hilbert-Huang transform.

[0054] Based on the extracted acoustic emission features, the pattern recognition model is trained. By building a multi-layer neural network, the complex mapping relationship between acoustic emission features and fault modes is learned, and a baseline model of the inductive acoustic emission features under normal working conditions is established. The changes in acoustic emission features are monitored through control charts, and abnormal points beyond the control limits are identified.

[0055] Compared with the prior art, the present invention has at least the following beneficial effects:

[0056] 1. Improved the efficiency and accuracy of the reliability test of the on-board boost inductor. The present invention constructs a power supply simulation model that matches the actual operating conditions of electric vehicles, and combines the accelerated life test with multiple environmental factors coupling to fully simulate the actual stress state of the inductor. The orthogonal experimental design method is adopted to optimize the test plan, reduce the number of tests, and shorten the test cycle. Through the analysis of experimental data, the performance degradation law of the inductor under multiple stress coupling is accurately characterized, and a high-confidence accelerated life model and failure prediction model are established, which can provide reliable and accurate data support for inductor design and improve the reliability level of the inductor under complex operating conditions of electric vehicles;

[0057] 2. Real-time monitoring and fault warning of vehicle-mounted boost inductor performance degradation are realized. The present invention innovatively introduces acoustic emission technology, arranges acoustic emission sensor arrays at key positions of the inductor, collects weak stress wave signals inside the inductor in real time, and constructs an online monitoring method for inductor performance degradation. Wavelet transform and Hilbert-Huang transform are used to extract the time-frequency domain characteristics of the acoustic emission signal, and an early warning model for inductor degradation and failure is established in combination with pattern recognition and anomaly detection algorithms. This method can sound an alarm when the inductor performance is slightly degraded, thereby buying precious time for status maintenance and fault diagnosis, avoiding serious accidents, and improving the safety and reliability of electric vehicles;

[0058] 3. A vehicle-mounted boost inductor fault diagnosis knowledge base and intelligent reasoning system have been constructed. The present invention uses big data processing technology to clean, fuse and annotate multi-source heterogeneous data such as accelerated life test data and acoustic emission monitoring data, and uses machine learning algorithms to automatically extract inductor degradation characteristics, identify typical degradation patterns and abnormal data, and form a degradation pattern library and abnormal data set. On this basis, the knowledge engineering method is used to construct an inductor fault mechanism knowledge graph and a diagnostic reasoning rule library, which realizes the automatic diagnosis and positioning of inductor fault patterns. This diagnostic method can significantly improve the efficiency and accuracy of fault diagnosis, reduce the time and cost of blind disassembly and inspection, and can continuously optimize and improve the diagnostic knowledge base through case accumulation and knowledge precipitation, thereby improving the intelligence level of the system.

[0059] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0061] Figure 1 The present invention is a system framework diagram of a vehicle-mounted boost inductor monitoring simulation test system under an embodiment.

[0062] Figure 2 The present invention is a schematic diagram of a framework of a vehicle power supply simulation module in an embodiment.

[0063] Figure 3 It is a schematic diagram of a framework of a fault warning module combined with an inductor sample in an embodiment.

[0064] Figure 4 The present invention is a schematic diagram of the framework of a data analysis and diagnosis module under an embodiment.

[0065] Figure 5 The present invention is a flow chart of a vehicle-mounted boost inductor monitoring simulation test method under an embodiment. DETAILED DESCRIPTION

[0066] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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.

[0067] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0068] The applicant found that:

[0069] At present, there are mainly the following methods for reliability research and fault diagnosis of vehicle-mounted boost inductors:

[0070] (1) Experience-based evaluation method. This method mainly relies on the experience and intuition of designers to make a qualitative evaluation of the reliability of the inductor based on factors such as the selection, process, and materials of the inductor. This method is highly subjective and lacks quantitative indicators, making it difficult to cope with the increasingly complex automotive application environment.

[0071] (2) Method based on accelerated life test. This method accelerates the aging process of the inductor and shortens the test cycle by increasing the stress levels such as temperature and humidity. However, traditional accelerated life tests mostly use a single stress or the superposition of two stresses, ignoring the multi-stress coupling effect, and the acceleration model is mostly based on empirical formulas and lacks the support of physical mechanisms, resulting in a large deviation between the test results and the actual life.

[0072] (3) Method based on failure physics analysis. This method studies the failure mechanism through anatomical analysis, material characterization, etc. after the inductor fails. However, this post-facto diagnosis cannot provide early warning of failure, and lacks data accumulation and knowledge precipitation, making it difficult to guide design improvements.

[0073] The existing methods have the following shortcomings: lack of accelerated life test methods that match the actual working conditions of electric vehicles, making it difficult to comprehensively examine the coupling effects of multiple environmental factors; lack of effective online monitoring methods for inductor performance degradation, making it impossible to achieve early warning of faults; lack of comprehensive and systematic test data accumulation, making it difficult to support data-driven modeling analysis and knowledge mining; lack of mechanism-based fault diagnosis reasoning methods, making it difficult to accurately locate fault modes and causes. These problems seriously restrict the improvement of the reliability test level of vehicle-mounted boost inductors.

[0074] In view of this, in the first aspect, this embodiment provides a vehicle-mounted boost inductor monitoring simulation test system, referring to Figure 1 , Figure 1 : is a system schematic diagram of this embodiment. Currently, the motor drive boost device for electric vehicles on the market involves a motor inverter. This embodiment is a system for building a simulation test of the boost inductor in the motor inverter, specifically including:

[0075] The vehicle power simulation module is used to simulate the power fluctuations under the actual working conditions of electric vehicles and provide power input for subsequent tests;

[0076] The accelerated life test module is used to formulate a test strategy for coupling multiple environmental factors. It defines at least four environmental stress factors: temperature, humidity, vibration, and shock. Several stress levels are set for each environmental stress factor. After selecting the factors and levels, the orthogonal experimental design method is used to construct an orthogonal table with multiple factors and levels to form a test strategy.

[0077] The environmental test chamber is used to place the inductor sample and execute the test strategy, and is equipped with a test tool for simulating the actual installation environment of the inductor; it should be noted that, optionally, there is one environmental test chamber, and the control of the four environmental stress factors of temperature, humidity, vibration and impact is completed in the environmental test chamber; optionally, there are multiple environmental test chambers, each of which applies one of the four environmental stress factors of temperature, humidity, vibration and impact separately. When conducting an orthogonal test, according to the test strategy, after completing the test of one environmental test chamber, enter another environmental test chamber for testing; in order to adapt to different environmental test chamber conditions, the number of environmental test chambers is not limited in this embodiment; preferably, when testing in different environmental test chambers, a power input simulating the power supply fluctuation under the actual working conditions of an electric vehicle is applied to the inductor sample;

[0078] Fault warning module, which is used to collect weak signals inside the inductor sample in real time, extract the time-frequency domain characteristics of the acoustic emission signal through signal processing, and identify the early signs of inductor degradation and failure;

[0079] The data analysis and diagnosis module is used to collect and analyze the test data of the inductor samples under different test conditions, mine the data features, and establish a performance degradation model and a fault diagnosis rule base; among which, the test data includes the electrical parameters of the inductor samples after being subjected to different environmental stress factors, such as voltage, current, inductance, quality factor, and temperature rise parameters.

[0080] It should be noted that in order to simulate the actual working conditions of electric vehicles, the vehicle power supply model is constructed based on the actual vehicle working condition data through the on-board power supply simulation module to simulate the voltage and current fluctuations of electric vehicles during driving; conventional steady-state power supplies usually provide constant voltage and current, while electric vehicles under actual working conditions, due to the influence of driving behavior, road conditions, load changes and other factors, the voltage and current of the power supply system present dynamic fluctuation characteristics. For example, under working conditions such as starting, acceleration, braking, and climbing, the motor drive current changes dramatically, and the battery output voltage also fluctuates, making the actual electrical stress of the on-board inductor far more complex than the steady-state working condition. During the driving of electric vehicles, they will face working conditions such as bumps, sudden braking, starting, stopping, and turning. When the working conditions are frequently switched, transient impact currents will be generated in the power supply system. These transient pulses will produce large impact stresses on the inductor, and conventional steady-state power supplies cannot simulate this transient effect. In addition, the electrical stress borne by the inductor is diverse and intermittent, which is significantly different from the single working condition of conventional steady-state power supplies.

[0081] Therefore, in this embodiment, the on-board power supply simulation module is used to simulate the power supply fluctuation under the actual working conditions of the electric vehicle, and the accelerated life test module is used to make decisions and control the environmental test chamber. In the environmental test chamber, environmental stresses such as temperature, humidity, vibration, and impact are applied to the inductor sample, and the power supply fluctuation stress is superimposed to carry out multi-stress coupling accelerated aging tests.

[0082] During the test, the fault warning module uses an acoustic emission sensor array to monitor the microscopic damage signals inside the inductor in real time, and uses intelligent signal processing and pattern recognition algorithms to detect signs of inductor performance degradation at an early stage.

[0083] After the test, the data analysis and diagnosis module comprehensively analyzes multi-source heterogeneous data, uses machine learning algorithms to build a degradation trend prediction model and fault diagnosis knowledge graph, and realizes automatic identification and location of inductor fault modes.

[0084] Through the test system in this embodiment, the reliability level of the vehicle-mounted boost inductor under complex working conditions can be comprehensively evaluated, the multi-stress coupling effect can be revealed, and the inductor design can be optimized. At the same time, early warning and rapid diagnosis of inductor failures can be achieved, thereby maximizing the safety and reliability of the electric vehicle power system.

[0085] Combination Figure 2In some possible embodiments, electric vehicles use batteries as the only power source and realize power transmission through motors. Their power consumption characteristics are significantly different from those of traditional fuel vehicles. On the one hand, the starting, acceleration, climbing and other processes of electric vehicles require the motor to provide high-power output, resulting in a sharp increase in battery discharge current and a sudden drop in power supply voltage; on the other hand, during braking and downhill, the motor of the electric vehicle can be used as a generator to convert the vehicle's kinetic energy into electrical energy to charge the battery, causing a sudden increase in the power supply voltage. The large fluctuations in the power supply pose a severe challenge to the working stability and reliability of the on-board inductor. Therefore, in this embodiment, in order to simulate the on-board power supply with high fidelity, the on-board power supply simulation module includes a battery simulation unit, a motor simulation unit, an electronic control simulation unit and a coupling unit. A physics-based modeling method is adopted to comprehensively consider the characteristics of key components such as batteries, motors, and electronic controls, among which:

[0086] The battery simulation unit uses the Thevenin equivalent circuit model to simulate the dynamic characteristics of the open circuit voltage, polarization voltage, and ohmic internal resistance of the vehicle battery, and combines the battery SOC state of charge, temperature, and aging factors to predict its output voltage and power changes; specifically, the Thevenin equivalent circuit model is used to describe the dynamic characteristics of the battery. The model consists of an ideal voltage source, a polarization resistor, a polarization capacitor, and an ohmic internal resistance, which can characterize the open circuit voltage, polarization voltage, and transient response of the battery. When modeling, firstly, the open circuit voltage, polarization resistor, polarization capacitor, and ohmic internal resistance of the battery at different SOC (state of charge) and temperature are obtained through experimental tests, and the mapping relationship between the parameters and SOC and temperature is established. Then, the SOC change of the battery is calculated in combination with the battery charge and discharge state equation. At the same time, considering the impact of battery aging on its capacity and internal resistance, the aging factor is introduced to correct the model parameters. Finally, the SOC, temperature, and aging factor are substituted into the Thevenin model to calculate the output voltage and power of the battery. This modeling method can accurately describe the output characteristics of the battery under dynamic conditions and provide boundary conditions for vehicle power simulation.

[0087] The motor simulation unit uses the dynamic equations in the dq coordinate system to establish a mathematical model for describing the characteristic parameters of the stator current, torque, and speed, and predict its efficiency changes under different working conditions; specifically, the dynamic equations in the dq coordinate system are used to establish the motor mathematical model. This model converts the three-phase AC motor into an equivalent two-phase DC motor, and describes the relationship between the stator current, torque, and speed through the d-axis and q-axis voltage balance equations. When modeling, first identify the motor parameters in the model, such as stator resistance, inductance, flux linkage, moment of inertia, etc., based on the motor nameplate parameters and experimental test data. Then, combined with the motor control strategy (such as vector control, direct torque control, etc.), establish a controller model and output dq-axis voltage instructions. At the same time, consider the modulation mode and switching characteristics of the inverter to simulate the voltage waveform actually applied to the motor. Finally, the motor model is coupled with the controller model to calculate the current, torque, and speed response of the motor under different working conditions, and combined with the efficiency mapping table to estimate the power loss of the motor. Through this modeling method, the operating characteristics of the motor under dynamic conditions can be accurately simulated to provide power requirements for vehicle power simulation;

[0088] The electronic control simulation unit adopts the controller-switch tube-filter circuit cascade model to simulate its current conversion and voltage transformation process based on the control strategy, switching frequency, and dead zone compensation factors; specifically, the controller-switch tube-filter circuit cascade model is used to simulate the current conversion and voltage transformation process of the electronic control system. The model consists of a control strategy module, an inverter module, and a filter circuit module, which can describe the modulation of the battery voltage and the control of the motor current by the electronic control system. When modeling, firstly, according to the vehicle control strategy, the control algorithm of the electronic control system is designed, such as constant current-constant voltage charging, vector control, energy recovery, etc., to form a controller model. Then, considering the topological structure and switching device characteristics of the inverter, the state space averaging method or the switching function method is used to establish the average model of the inverter to describe its voltage and current conversion relationship. At the same time, the conduction voltage drop and switching loss of switching devices such as IGBT and MOSFET are simulated to improve the simulation accuracy. Thirdly, according to the impedance characteristics of the motor and the power grid, the LC filter circuit is designed to suppress current ripple and harmonic interference. Finally, the controller, inverter and filter circuit models are cascaded to build an electronic control system simulation model, and factors such as control delay, measurement noise, and quantization error are considered to improve the authenticity of the simulation. Through this modeling method, the functions and performance of the electronic control system can be fully simulated, providing an actuator for the vehicle power simulation;

[0089] The coupling unit couples the models constructed by the battery simulation unit, the motor simulation unit, and the electronic control simulation unit, and constructs a vehicle-level power supply simulation model in the Simulink simulation platform; specifically, first, according to the system architecture of the electric vehicle, the topological structure of the power supply system is determined, and the energy flow and signal flow direction between the components are clarified. Then, in the Simulink simulation platform, the models of each component are integrated and connected, and a simulation block diagram of the vehicle power supply system is constructed. At the top level of Simulink, the battery, motor, and electronic control subsystem models are interconnected. In this process, the interface matching and data exchange between the components need to be handled properly to ensure energy balance and signal synchronization. Thirdly, typical working conditions are designed, such as constant speed, acceleration, braking, climbing, downhill, bumps, etc., and the speed and slope curves are planned to simulate the driver's operating instructions to form a closed-loop test sequence. Finally, the simulation model is run to obtain the dynamic response curves of the battery, motor, and electronic control system, analyze the fluctuation laws of key parameters such as voltage, current, power, speed, and torque, and evaluate the dynamic characteristics and energy efficiency of the power supply system.

[0090] It should be noted that the optimized on-board power simulation unit can highly restore the various power supply states encountered by electric vehicles on the actual road, including voltage spikes during startup, voltage drops during acceleration, and voltage surges during braking, etc., providing real and reliable input for the inductor. By combining it with the subsequent accelerated life test link, the working performance and failure mode of the inductor in a complex power supply environment can be fully evaluated.

[0091] In some possible embodiments, in the multi-stress accelerated life test, the introduction of vehicle-level power supply simulation factors can more realistically reflect the actual working state of the inductor used in electric vehicles and improve the reliability and applicability of the test results. Therefore, in this embodiment, the accelerated life test module also includes defining power supply fluctuation factors;

[0092] Using the vehicle power simulation module, the power fluctuation of electric vehicles is simulated for typical working conditions including constant speed, acceleration, braking, climbing, descending, and bumping, and the time domain waveforms of voltage and current are obtained. The time domain waveforms are feature extracted and statistically analyzed to obtain key parameters. The key parameters include voltage spike value, voltage surge and sag slope, current root mean square value, and ripple coefficient. The key parameters are used as indicators to characterize power fluctuation factors. In this step, the various working conditions of electric vehicles are represented by key parameters, and the various working conditions are quantified into power fluctuation factor indicators.

[0093] In the orthogonal test design, the power supply fluctuation factor index is included in the factor level table to form an orthogonal matrix with other environmental stress factors; for example, the temperature factor takes three levels (low temperature, normal temperature, high temperature), the humidity factor takes two levels (low humidity, high humidity), the vibration factor takes two levels (low vibration, high vibration), the impact factor takes two levels (low impact, high impact), and the power supply fluctuation factor takes three levels (constant, medium fluctuation, severe fluctuation), then an orthogonal table covering different combination test schemes of various factors can be formed;

[0094] Under each test scheme, the vehicle power simulation module is used to generate the corresponding voltage and current waveforms, which are then loaded onto the inductor sample under test through the power amplifier and signal generator. At the same time, the corresponding temperature, humidity, vibration and impact stress are applied in the environmental test chamber to perform accelerated life tests.

[0095] By monitoring the degradation of the electrical parameters and physical properties of the inductor and recording its failure time, life data can be obtained.

[0096] Furthermore, based on the failure data of the orthogonal test, the maximum likelihood estimation and least squares method are used to fit the parameters in the generalized Eyring model, and the activation energy Ea, Boltzmann constant kB, humidity acceleration index m, vibration acceleration index n, impact acceleration index p, and acceleration index q related to power supply fluctuations are obtained to form a complete accelerated life model:

[0097]

[0098] Among them, AF represents the acceleration factor, Ea represents the activation energy, kB represents the Boltzmann constant, T represents the test temperature, T0 represents the reference temperature, RH represents the test humidity, RH0 represents the reference humidity, G represents the test vibration intensity, G0 represents the reference vibration intensity, S represents the test shock intensity, S0 represents the reference shock intensity, m represents the acceleration index of humidity, n represents the acceleration index of vibration, p represents the acceleration index of shock, V represents the power supply fluctuation factor index, V0 represents the reference power supply level, and q represents the acceleration index of power supply fluctuation.

[0099] Based on the accelerated life model, the failure time of the inductor under any combination of temperature, humidity, vibration, shock and power supply fluctuation is predicted, and its reliability level under actual electric vehicle operating conditions is evaluated.

[0100] This embodiment introduces vehicle-level power supply simulation factors into the multi-stress accelerated life test. Through orthogonal experimental design, the interaction between power supply fluctuations and other environmental stresses is systematically evaluated, and an accelerated life model considering the influence of power supply fluctuations is established to predict the reliability level of the inductor under actual electric vehicle operating conditions. At the same time, the dynamic characteristics of power supply fluctuations are introduced to further improve the authenticity and reliability of the test results.

[0101] Preferably, for the four environmental stress factors of temperature, humidity, vibration and impact, the following specific test conditions can be adopted:

[0102] (1) For temperature cycling, perform 1000 cycles at high and low temperatures (-40℃ to +125℃), with a maximum dwell time of 30 minutes at each temperature and a maximum switching time of 1 minute. Relevant inspection items shall be tested within 24±4 hours after the test.

[0103] (2) For high temperature storage, conduct a 1000-hour test at the maximum operating temperature (e.g., 125°C for 1000 hours), with load; conduct relevant inspection items within 24±4 hours after the test.

[0104] (3) For high humidity, the test is conducted for 1000 hours at a temperature and humidity of 85°C / 85%RH. The relevant inspection items are tested within 24±4 hours after the test.

[0105] (4) For the working life, the rated load is applied at a maximum temperature of 125°C for 1000 hours; relevant inspection items are tested within 24±4 hours after the end of the test;

[0106] (5) For mechanical shock, the shock load was applied according to the following conditions: peak value = 1,500 g's, duration = 0.5 ms, half-sine waveform, velocity change = 15.4 ft / s;

[0107] (6) For vibration test, apply vibration load according to the following conditions: 5g's test for 12 cycles and 20 minutes in 3 directions from 10 to 2000HZ. Note: Use 8"X5"PCB, 0.31 thick, 7 safety points on the long side and 2 safety points at 2 ends of the corners. Parts are installed within 2 inches of the safety points.

[0108] The above test conditions of environmental stress factors are only examples and are not the only limitations. Those skilled in the art can make other examples within a reasonable range.

[0109] Combination Figure 3 In some possible embodiments, in electric vehicles, inductor faults may lead to serious consequences such as motor control failure, battery overcharge and over discharge, and high-voltage safety accidents. Therefore, in order to detect and warn of inductor faults as early as possible, the fault warning module in this embodiment includes an acoustic emission sensor array, a feature extraction unit, and a post-processing unit. The inductor fault early warning unit based on acoustic emission can effectively realize the early detection and positioning of inductor faults. Specifically:

[0110] The acoustic emission sensor array is configured to be arranged at a set key position of the inductor sample, sense the transient elastic stress wave inside the inductor sample in real time, and emit an acoustic emission signal; when microscopic damage, defect expansion, partial discharge and other degradation or failure processes occur inside the inductor, transient elastic stress waves will be released, and these stress waves will propagate in the inductor material in the form of sound waves and eventually reach the surface. The acoustic emission sensor array is coupled to the surface of the inductor to convert the sound waves into electrical signals to realize the collection of acoustic emission signals;

[0111] The feature extraction unit is configured to preprocess and extract features of the collected acoustic emission signals. Due to different degradation and failure mechanisms, acoustic emission signals with different characteristics will be generated, such as pulse type, continuous type, burst type, etc. Therefore, the acoustic emission signal is decomposed into time and frequency domains through wavelet transform, and the acoustic emission signal is simultaneously expanded into the time and frequency domains. A series of basis functions are generated by scaling and translating the mother wavelet, and the signal is projected onto these basis functions to obtain wavelet coefficients at different scales. The wavelet coefficients reflect the energy distribution of the signal at different time and frequency positions. According to the wavelet coefficients, the energy, frequency, and attenuation characteristics at different scales are extracted. Through wavelet transform, the time-frequency characteristics of the acoustic emission signal can be fully characterized, and multi-dimensional features such as energy, frequency, and attenuation can be extracted to provide effective feature vectors for pattern recognition and damage diagnosis of acoustic emission signals. Then, through the Hilbert-Huang transform, the instantaneous amplitude, instantaneous frequency, and instantaneous phase characteristics of the acoustic emission signal are obtained, and the time domain distribution and frequency modulation law of the signal energy are revealed. Specifically: the acoustic emission signal is subjected to the Hilbert transform to obtain its analytical signal, the modulus of the analytical signal is calculated, and the envelope of the acoustic emission signal, that is, the instantaneous amplitude curve, is obtained, the phase angle of the analytical signal is calculated, and the instantaneous phase curve of the acoustic emission signal is obtained. The instantaneous phase curve is differentiated to obtain the instantaneous frequency curve of the acoustic emission signal, and the characteristic parameters such as the instantaneous amplitude, instantaneous phase, and instantaneous frequency are combined to construct a multi-dimensional feature space of the acoustic emission signal, and the time-frequency energy distribution of the acoustic emission signal is comprehensively characterized;

[0112] The post-processing unit is configured to collect acoustic emission signals of the inductor under different fault modes based on the extracted acoustic emission features, extract various time-frequency features, construct a feature-label data set, use a multi-layer neural network as a pattern recognition model, train the pattern recognition model, learn the complex mapping relationship between the acoustic emission features and the fault mode by constructing a multi-layer neural network, and use a back propagation algorithm to minimize the error between the predicted output and the actual label by adjusting the network weights and thresholds, thereby optimizing the network parameters; further, establish a baseline model of the acoustic emission features of the inductor under normal working conditions, use the constructed baseline model to monitor the acoustic emission features of the inductor during operation in real time, monitor the changes in the acoustic emission features through a control chart, compare the current feature value with the control limit in the baseline model to determine whether it exceeds the normal range, and when the feature value at a certain moment exceeds the control limit, mark the moment as an abnormal point, indicating that the health state of the inductor may be offset, identify the abnormal points that exceed the control limit, and diagnose the current health state of the inductor based on the identified abnormal points in combination with the pattern recognition model to determine whether a fault occurs and infer possible fault modes.

[0113] By comprehensively utilizing pattern recognition, statistical control and other methods, an inductor fault diagnosis system based on acoustic emission characteristics was constructed. The system uses machine learning algorithms to establish a nonlinear mapping between acoustic emission characteristics and fault modes, and uses statistical modeling to characterize the changing laws of acoustic emission characteristics under normal conditions. Through control chart monitoring, online diagnosis and abnormal warning of the inductor health status are achieved. Compared with the traditional threshold alarm method, the system can adaptively track the changes in the inductor state, timely discover performance degradation trends, and improve the sensitivity and accuracy of fault diagnosis. At the same time, the system has a certain generalization ability and can adapt to inductors of different types and specifications, reducing dependence on expert experience. Integrating the diagnostic system into the fault monitoring platform of electric vehicles can achieve the health management of on-board inductors and ensure the reliable operation of the power system.

[0114] Combination Figure 3 ,Furthermore, the fault warning module also includes a fault locating unit;

[0115] The acoustic emission sensor array includes at least three sub-sensors that are not in the same straight line, forming a triangular or polygonal array area, and the distance between adjacent sub-sensors is a known preset distance. The acoustic emission sensor array covers the main area of ​​the inductor as much as possible, especially the high-prone area of ​​faults;

[0116] When degradation or failure occurs inside the inductor, an acoustic emission signal is excited at the fault source, and the signal propagates outward in the form of sound waves. Each sub-sensor in the sensor array receives the acoustic emission signal, and the fault location unit is configured to record the arrival time of the acoustic emission signal emitted by each sub-sensor. According to the time difference of the acoustic emission signals corresponding to any three sub-sensors in the sensor array, a triangulation positioning equation group is constructed, and the estimated value of the coordinates of the fault source is solved. The estimated value of the coordinates of the fault source is mapped to the physical model of the inductor sample to obtain the fault position.

[0117] In this embodiment, the acoustic emission sensor array is used to obtain the arrival time difference of the acoustic emission signal, and the coordinates of the fault source are estimated through the triangulation positioning principle to achieve accurate positioning of the internal fault of the inductor. The positioning accuracy and reliability are improved by optimizing the sensor array layout, improving the positioning algorithm, and fusing multiple positioning results. The positioning results are combined with the inductor physical model to provide an intuitive and quantitative basis for fault diagnosis and cause analysis, and guide subsequent maintenance and optimization work.

[0118] Combination Figure 4 ,In some possible embodiments, the data analysis and diagnosis module includes a big data processing unit, an analysis and identification unit, and a diagnosis unit;

[0119] The big data processing unit is configured to clean, fuse and annotate multi-source heterogeneous test data using a big data processing framework;

[0120] The analysis and identification unit is configured to construct a multi-dimensional feature extraction and data mining model, and use a machine learning algorithm to automatically identify inductor performance degradation patterns and abnormal data; specifically, extract test data features from multiple dimensions to characterize the static properties and dynamic behaviors of inductor performance, use a machine learning algorithm to automatically identify inductor performance degradation patterns from feature data, characterize the laws of inductor state evolution, and use an unsupervised learning algorithm to automatically identify abnormal data and fault precursors from test data to achieve early warning of inductor faults;

[0121] The diagnosis unit is configured to construct a fault diagnosis knowledge graph and an inference rule base to diagnose and locate the inductor fault mode; specifically, based on the identified degradation mode and abnormal data, the inductor fault type is located and diagnosed, and an ontology engineering method is used to construct an inductor fault diagnosis knowledge graph to formally represent the field knowledge of inductor fault mechanism, manifestation symptoms, and diagnosis methods. Based on the knowledge graph, the inference rules of inductor fault diagnosis are summarized to form a logical basis for diagnostic decisions. The identified degradation mode and abnormal data are input into the diagnosis knowledge graph and the inference rule base, and the location and diagnosis of the inductor fault type are achieved through semantic matching, logical reasoning and other technologies.

[0122] The data analysis and diagnosis module has built a complete inductor fault diagnosis method system. Through the big data processing unit, it realizes the multi-source fusion and quality improvement of test data. Through the analysis and recognition unit, it automatically extracts the inductor degradation mode and abnormal characteristics. Through the diagnosis unit, it integrates data-driven and knowledge-driven to intelligently diagnose the inductor fault type.

[0123] Specifically, the big data processing unit is configured to obtain simulated driving condition data of the electric vehicle, including motor speed, battery voltage, current, temperature, brake energy recovery state, accelerator pedal opening, and charging state, to form a multi-dimensional time series reflecting the actual working state of the inductor;

[0124] Perform data cleaning on multidimensional time series, remove outliers, invalid values, duplicate values, smooth noise, and unify data formats and sampling frequencies;

[0125] Using data fusion technology, the driving condition data is associated with the inductor test data to construct a fusion data set containing inductor performance indicators and operating condition characteristics. Each record in the fusion data set includes performance indicators such as timestamp, inductor temperature, inductor current, inductor voltage, harmonic components, impedance characteristics, and the driving condition characteristics at the corresponding time.

[0126] Using data mining algorithms, the association rules and influence patterns between inductor performance indicators and driving condition characteristics in the fusion data set are analyzed to determine the corresponding relationship between inductor performance degradation and specific operating conditions, forming a degradation pattern library;

[0127] The fused data set is automatically annotated, and the degradation mode to which each record belongs is identified according to the association rules and threshold conditions in the degradation mode library, generating a training data set with degradation mode labels.

[0128] In this embodiment, the inductance test data and driving condition data are deeply integrated to explore the intrinsic relationship between the inductance performance degradation and the actual working condition. Through data mining technology, the degradation pattern is automatically discovered, and the fused data set is intelligently labeled to generate a high-quality training data set.

[0129] Second, refer to Figure 5 This embodiment provides a test method for a vehicle-mounted boost inductor monitoring simulation test system using the above-mentioned embodiment, including the following steps:

[0130] Simulate the power supply fluctuations under the actual working conditions of electric vehicles to provide power input for subsequent tests; extract typical fluctuation patterns such as voltage sag, voltage spike, current pulse, etc. by analyzing the voltage and current waveforms of electric vehicles under different working conditions (such as starting, acceleration, climbing, braking, etc.). Then, use a programmable power supply or power supply simulator to generate a power supply waveform similar to the actual working conditions according to the extracted fluctuation pattern to provide simulated input for the inductor sample;

[0131] On the basis of power input simulation, it is also necessary to consider the complex environmental stresses faced by inductors in actual applications, formulate a test strategy for coupling multiple environmental factors, and define at least four environmental stress factors: temperature, humidity, vibration, and impact, to simulate the working state of the inductor under different climatic conditions and mechanical loads. According to the material properties and structural strength of the inductor, set several stress levels for each environmental stress factor. After selecting the factors and levels, use the orthogonal experimental design method to construct a multi-factor and multi-level orthogonal table to form a test strategy.

[0132] Place the inductor sample in an environmental test chamber, fix the inductor sample using a test fixture that simulates the actual installation environment of the inductor, and control the environmental test chamber to execute the test strategy; use a test fixture similar to the actual installation structure of the inductor to fix the inductor sample in the test chamber to ensure that its position and stress state are similar to actual applications. Then, according to the combination of factor levels specified in the test strategy, control the temperature, humidity, vibration, impact and other parameters of the environmental test chamber to apply corresponding environmental stress to the inductor; during the entire test process, use sensors and data acquisition cards to record changes in environmental parameters in real time;

[0133] While testing in the environmental chamber, the weak signals inside the inductor sample are collected in real time. After signal processing, the time-frequency domain characteristics of the acoustic emission signal are extracted to identify early signs of inductor degradation and failure. The acoustic emission sensor array is arranged at the key positions of the inductor sample, such as windings, magnetic cores, leads, etc., to sense the transient elastic stress waves inside the inductor in real time and convert them into electrical signals for output. Then, the collected acoustic emission signals are preprocessed, such as filtering, amplification, A / D conversion, etc., to improve the signal-to-noise ratio and resolution of the signal.

[0134] Collect and analyze the test data of inductor samples under different test conditions, mine data features, and establish performance degradation models and fault diagnosis rule bases; specifically, mine test data such as environmental parameters and electrical parameters, extract key features that reflect inductor performance degradation, and establish degradation models. Consider multi-source heterogeneous data such as acoustic emission, electricity, and environment, build a data fusion model, characterize the inherent mechanism and evolution law of inductor degradation, and achieve remaining life prediction. At the same time, summarize the typical characteristics of inductors under different fault modes, and form a fault diagnosis rule base to guide the analysis of test results and the location of fault causes.

[0135] Furthermore, the following steps are included:

[0136] Arrange an acoustic emission sensor array at set key positions of the inductor sample to sense the transient elastic stress wave inside the inductor sample in real time and emit an acoustic emission signal;

[0137] On this basis, the collected acoustic emission signals are preprocessed and feature extracted. The acoustic emission signals are decomposed into time and frequency by wavelet transform, and the energy, frequency, and attenuation characteristics at different scales are extracted. The instantaneous amplitude, instantaneous frequency, and instantaneous phase characteristics of the acoustic emission signals are obtained by Hilbert-Huang transform. The extraction of the time-frequency domain features of the acoustic emission signals specifically includes: on the one hand, the acoustic emission signals are decomposed into time and frequency by wavelet transform, and the energy, frequency, and attenuation characteristics at different scales are extracted to characterize the distribution law of the signal in the two dimensions of time and frequency; on the other hand, the instantaneous amplitude, instantaneous frequency, and instantaneous phase characteristics of the acoustic emission signals are obtained by Hilbert-Huang transform to characterize the dynamic changes of the signal energy and frequency over time. By extracting the multi-domain features of the acoustic emission signals, the microscopic process of the evolution of internal defects and damage accumulation of the inductor can be comprehensively evaluated, providing precursor information for fault warning and health management;

[0138] Based on the extracted acoustic emission features, the pattern recognition model is trained. By building a multi-layer neural network, the complex mapping relationship between acoustic emission features and fault modes is learned, and a baseline model of the inductive acoustic emission features under normal working conditions is established. The changes in acoustic emission features are monitored through control charts, and abnormal points beyond the control limits are identified.

[0139] In this embodiment, the acoustic emission signal features collected during the test are analyzed; based on the extracted acoustic emission features, a pattern recognition model is trained to establish a mapping relationship between the features and the fault mode; specifically, a multi-layer neural network is constructed, with the acoustic emission features as input and the known fault mode as output, and the network weights are learned through a back propagation algorithm to fit the complex nonlinear relationship between the features and the fault; at the same time, a baseline model is established with the acoustic emission features under normal conditions as a reference to characterize the statistical laws of the signals under the healthy state of the inductor; during the test, statistical tools such as control charts are used to monitor the changes in the acoustic emission features in real time, identify abnormal points beyond the normal range, and promptly discover the precursors of inductor degradation and failure.

[0140] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. A vehicle-mounted boost inductor monitoring simulation test system, characterized in that: include: The vehicle power simulation module is used to simulate the power fluctuations under the actual working conditions of electric vehicles and provide power input for subsequent tests; The accelerated life test module is used to formulate a test strategy for coupling multiple environmental factors. It defines at least four environmental stress factors: temperature, humidity, vibration, and shock. Several stress levels are set for each environmental stress factor. After selecting the factors and levels, the orthogonal experimental design method is used to construct an orthogonal table with multiple factors and levels to form a test strategy. Environmental test chamber, used to place inductor samples and execute test strategies, with test fixtures to simulate the actual installation environment of the inductor; Fault warning module, which is used to collect weak signals inside the inductor sample in real time, extract the time-frequency domain characteristics of the acoustic emission signal through signal processing, and identify the early signs of inductor degradation and failure; The data analysis and diagnosis module is used to collect and analyze the test data of inductor samples under different test conditions, mine data features, and establish performance degradation models and fault diagnosis rule bases.

2. The system according to claim 1, characterized in that The vehicle-mounted power supply simulation module includes a battery simulation unit, a motor simulation unit, an electronic control simulation unit and a coupling unit; The battery simulation unit adopts the Thevenin equivalent circuit model to simulate the open circuit voltage, polarization voltage, and ohmic internal resistance dynamic characteristics of the vehicle battery, and combines the battery SOC state of charge, temperature, and aging factors to predict its output voltage and power changes; The motor simulation unit uses a dynamic equation in a dq coordinate system to establish a mathematical model for describing the stator current, torque, and speed characteristic parameters, and predicts its efficiency changes under different working conditions; The electronic control simulation unit adopts a controller-switch tube-filter circuit cascade model to simulate its current and voltage conversion process based on control strategy, switching frequency, and dead zone compensation factors; The coupling unit couples the models constructed by the battery simulation unit, the motor simulation unit, and the electronic control simulation unit, respectively, and constructs a vehicle-level power supply simulation model in the Simulink simulation platform.

3. The system according to claim 2, characterized in that The accelerated life test module also includes defining power supply fluctuation factors; Using the vehicle power simulation module, the power fluctuation of electric vehicles is simulated for typical working conditions including constant speed, acceleration, braking, climbing, descending, and bumping, and the time domain waveforms of voltage and current are obtained. The time domain waveforms are feature extracted and statistically analyzed to obtain key parameters, including voltage spike value, voltage surge and drop slope, current root mean square value, and ripple coefficient. The key parameters are used as indicators to characterize power fluctuation factors. In the orthogonal experimental design, the power supply fluctuation factor index is included in the factor level table to form an orthogonal matrix with other environmental stress factors; Under each test scheme, the vehicle power simulation module is used to generate the corresponding voltage and current waveforms, which are then loaded onto the inductor sample under test through the power amplifier and signal generator. At the same time, the corresponding temperature, humidity, vibration and impact stress are applied in the environmental test chamber to perform accelerated life tests. By monitoring the degradation of the electrical parameters and physical properties of the inductor and recording its failure time, life data can be obtained.

4. The system according to claim 3, characterized in that Based on the failure data of orthogonal test, the maximum likelihood estimation and least square method are used to fit the parameters in the generalized Eyring model, and the activation energy Ea, Boltzmann constant kB, humidity acceleration index m, vibration acceleration index n, impact acceleration index p, and acceleration index q related to power supply fluctuation are obtained to form a complete accelerated life model: Wherein, AF represents the acceleration factor, Ea represents the activation energy, kB represents the Boltzmann constant, T represents the test temperature, T0 represents the reference temperature, RH represents the test humidity, RH0 represents the reference humidity, G represents the test vibration intensity, G0 represents the reference vibration intensity, S represents the test shock intensity, S0 represents the reference shock intensity, m represents the acceleration index of humidity, n represents the acceleration index of vibration, p represents the acceleration index of shock, V represents the power supply fluctuation factor index, V0 represents the reference power supply level, and q represents the acceleration index of power supply fluctuation; Based on the accelerated life model, the failure time of the inductor under any combination of temperature, humidity, vibration, shock and power supply fluctuation is predicted, and its reliability level under actual electric vehicle operating conditions is evaluated.

5. The system according to claim 4, characterized in that The fault warning module includes an acoustic emission sensor array, a feature extraction unit, and a post-processing unit; The acoustic emission sensor array is configured to be arranged at a set key position of the inductor sample, sense the transient elastic stress wave inside the inductor sample in real time, and emit an acoustic emission signal; The feature extraction unit is configured to preprocess and extract features of the collected acoustic emission signals, perform time-frequency decomposition of the acoustic emission signals through wavelet transform, extract energy, frequency, and attenuation features at different scales, and obtain instantaneous amplitude, instantaneous frequency, and instantaneous phase features of the acoustic emission signals through Hilbert-Huang transform; The post-processing unit is configured to train a pattern recognition model based on the extracted acoustic emission features, learn the complex mapping relationship between the acoustic emission features and the fault modes by building a multi-layer neural network, establish a baseline model of the inductive acoustic emission features under normal working conditions, monitor the changes in the acoustic emission features through control charts, and identify abnormal points beyond the control limits.

6. The system according to claim 5, characterized in that The fault warning module also includes a fault locating unit; The acoustic emission sensor array includes at least three sub-sensors that are not on the same straight line, forming a triangular or polygonal array area, and the distance between adjacent sub-sensors is a known preset distance; The fault location unit is configured to record the arrival time of the acoustic emission signal emitted by each sub-sensor, construct a triangulated positioning equation group according to the time difference of the acoustic emission signals corresponding to any three sub-sensors in the sensor array, solve the equation group to obtain the estimated value of the fault source coordinates, map the estimated value of the fault source coordinates to the physical model of the inductor sample, and obtain the fault location.

7. The system according to claim 6, characterized in that The data analysis and diagnosis module includes a big data processing unit, an analysis and identification unit, and a diagnosis unit; The big data processing unit is configured to use a big data processing framework to clean, fuse and annotate multi-source heterogeneous test data; The analysis and identification unit is configured to construct a multi-dimensional feature extraction and data mining model, and use a machine learning algorithm to automatically identify inductor performance degradation patterns and abnormal data; The diagnosis unit is configured to construct a fault diagnosis knowledge graph and an inference rule base to diagnose and locate the inductor fault mode.

8. The system according to claim 7, characterized in that The big data processing unit is configured to obtain simulated driving condition data of the electric vehicle, including motor speed, battery voltage, current, temperature, brake energy recovery state, accelerator pedal opening, and charging state, to form a multi-dimensional time series reflecting the actual working state of the inductor; Perform data cleaning on multidimensional time series, remove outliers, invalid values, duplicate values, smooth noise, and unify data formats and sampling frequencies; Using data fusion technology, the driving condition data is associated with the inductor test data to construct a fusion data set that includes inductor performance indicators and operating condition characteristics; Using data mining algorithms, the association rules and influence patterns between inductor performance indicators and driving condition characteristics in the fusion data set are analyzed to determine the corresponding relationship between inductor performance degradation and specific operating conditions, forming a degradation pattern library; The fused data set is automatically annotated, and the degradation mode to which each record belongs is identified according to the association rules and threshold conditions in the degradation mode library, generating a training data set with degradation mode labels.

9. A test method using a vehicle-mounted boost inductor monitoring simulation test system according to any one of claims 1 to 8, characterized in that: The following steps are involved: Simulate power fluctuations under real working conditions of electric vehicles and provide power input for subsequent tests; Formulate a test strategy for coupling multiple environmental factors, define at least four environmental stress factors: temperature, humidity, vibration, and shock, set several stress levels for each environmental stress factor, and after selecting the factors and levels, use the orthogonal experimental design method to construct an orthogonal table with multiple factors and levels to form a test strategy; Place the inductor sample in the environmental test chamber, fix the inductor sample using a test fixture that simulates the actual installation environment of the inductor, and control the environmental test chamber to execute the test strategy; Weak signals inside the inductor sample are collected in real time, and the time-frequency domain characteristics of the acoustic emission signal are extracted through signal processing to identify early signs of inductor degradation and failure; Collect and analyze the test data of inductor samples under different test conditions, mine data features, and establish performance degradation models and fault diagnosis rule bases.

10. The method according to claim 9, characterized in that The following steps are involved: Arranging an acoustic emission sensor array at a set key position of the inductor sample to sense the transient elastic stress wave inside the inductor sample in real time and emit an acoustic emission signal; Preprocess and extract features of the collected acoustic emission signals. Perform time-frequency decomposition of the acoustic emission signals through wavelet transform, extract energy, frequency, and attenuation features at different scales, and obtain the instantaneous amplitude, instantaneous frequency, and instantaneous phase features of the acoustic emission signals through Hilbert-Huang transform. Based on the extracted acoustic emission features, the pattern recognition model is trained. By building a multi-layer neural network, the complex mapping relationship between acoustic emission features and fault modes is learned, and a baseline model of the inductive acoustic emission features under normal working conditions is established. The changes in acoustic emission features are monitored through control charts, and abnormal points beyond the control limits are identified.

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