Electric vehicle charging interface electric traction magnetic interference detection method and system

Abstract

The invention relates to the technical field of electric vehicle charging safety detection, and discloses an electric vehicle charging interface electric traction magnetic interference detection method and system, and the method comprises the steps: obtaining the vibration speed data, internal temperature data and working current data of a charging interface housing; extracting double-source sound wave components of the engine and the motor by using a multi-channel band-pass filter bank and empirical mode decomposition; reconstructing a sound wave space propagation mode by using a beam forming algorithm to generate a double-source sound interference pattern matrix; calculating a magnetostriction constitutive parameter matrix of temperature correction based on the temperature data; inverting a double-source magnetic field based on the temperature correction parameter matrix, and separating contribution components of an engine magnetic source and a motor magnetic source; and outputting a double-source cooperative interference detection result. The technical problems that the dual-source in-phase resonance state cannot be recognized, the power switching transient strong magnetic pulse cannot be detected, and the magnetic field inversion precision is low under the condition of uneven temperature are solved.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging safety testing technology, and more specifically, to a method and system for detecting electric traction magnetic interference at an electric vehicle charging interface. Background Technology

[0002] When plug-in hybrid electric vehicles (PHEVs) are fast-charging at charging stations, the generator driven by the engine and the traction motor driven by the rear wheels operate simultaneously to maintain the high-power air conditioning and thermal management system, each generating a time-varying magnetic field. The relative phase relationship between the two magnetic sources determines the distribution of the interference field intensity at the charging interface. When the phases are synchronized, constructive interference occurs, increasing the interference to 1.8 times that of a single source. Simultaneously, the contact resistance inside the charging interface increases due to prolonged high-current operation, and the local temperature rise reaches 75°C when a large current passes through. The magnetostriction coefficient of the ferromagnetic material in the high-temperature region experiences temperature drift, resulting in spatial non-uniformity in the magnetic field-to-sound wave conversion efficiency.

[0003] Existing technology uses a magnetic field sensor to measure the magnetic field around the charging interface, and uses the magnetostrictive principle to convert the vibration signal of the charging interface shell into magnetic field strength information for indirect detection.

[0004] However, existing technologies have the following drawbacks: Traditional magnetic field sensors can only measure the superposition of dual-source magnetic fields, failing to distinguish the individual contributions of the engine and motor magnetic sources, thus making it impossible to determine whether the most dangerous in-phase interference state exists; at the moment of power source switching, the phase singularities of the two magnetic fields undergo topological annihilation and regeneration, leading to a drastic reconstruction of the magnetic field spatial distribution, and transient strong magnetic pulses may damage the communication module, but existing technologies cannot detect this transient process; the charging interface has a small internal space and uneven temperature distribution, and when using a fixed magnetostrictive coupling coefficient at room temperature for magnetic field inversion, the system error generated in the high-temperature region reaches 35%, making it impossible to accurately assess the true risk of dual-source cooperative interference. These drawbacks result in technical problems such as the inability to accurately identify the dual-source in-phase resonance state during hybrid vehicle charging, the inability to detect transient strong magnetic pulses during power switching, and low magnetic field inversion accuracy under uneven temperature conditions. Summary of the Invention

[0005] This invention provides a method and system for detecting electric traction magnetic interference at the charging interface of an electric vehicle, solving the technical problems in related technologies such as the inability to identify the dual-source in-phase resonance state of hybrid vehicles, the inability to detect transient strong magnetic pulses during power switching, and the low accuracy of magnetic field inversion under non-uniform temperature conditions.

[0006] This invention provides a method for detecting electric traction magnetic interference at an electric vehicle charging interface, comprising: The vibration velocity time series data collected by laser Doppler vibration meters deployed at multiple points on the charging interface shell, the temperature data collected by distributed fiber optic temperature sensors inside the charging interface, and the operating current data of the vehicle power system are acquired, and the timestamp alignment is performed to generate a vibration-temperature-current synchronous joint dataset. The vibration velocity time series data is separated in the frequency domain using a multi-channel bandpass filter bank. The first acoustic component corresponding to the characteristic frequency of the engine and its harmonics and the second acoustic component corresponding to the characteristic frequency of the motor and its harmonics are extracted. Empirical mode decomposition is performed on each acoustic component to extract the intrinsic mode function, and a dual-source multi-scale acoustic vibration signal set is generated. The amplitude and phase of the acoustic waves at each sensor location in the dual-source multi-scale acoustic vibration signal set are calculated. The spatial propagation mode of the acoustic waves is reconstructed using a beamforming algorithm. The spatial distribution of the peaks and troughs of the acoustic interference fringes is identified, and a dual-source acoustic interference pattern matrix is ​​generated. Using the finite difference solver of the heat conduction equation, the heat source distribution inside the charging interface is inverted based on temperature data, and the spatially distributed heat source power density map is calculated. Based on the temperature dependence function of the magnetostriction coefficient, the spatially distributed temperature-varying magnetostriction coefficient field is calculated in combination with the heat source power density map, and the temperature-corrected magnetostriction constitutive parameter matrix is ​​generated. Based on the temperature-corrected magnetostrictive constitutive parameter matrix, a coupling function between magnetic field strength and strain is established. Using the finite element inverse analytical method, the dual-source acoustic interference pattern matrix and vibration signal are used as boundary condition inputs. The internal magnetic field distribution that satisfies the magnetic field equation and elastic wave equation is iteratively solved. The contribution components of the engine magnetic source and the motor magnetic source are separated by inversion, and the temperature-compensated dual-source magnetic field decomposition results are generated. The temperature-compensated dual-source magnetic field decomposition results are cross-correlated with the typical working magnetic field template of the traction system to generate interference characteristic similarity indexes for each magnetic source. The relationship between the superimposed magnetic field strength and the damage threshold of the communication module is evaluated, and the dual-source cooperative interference detection results, interference intensity level, contact resistance abnormality alarm, and suggested load scheduling strategy are output.

[0007] Furthermore, reconstructing the spatial propagation mode of sound waves using beamforming algorithms includes: Hilbert transform is performed on each intrinsic mode function in the dual-source multi-scale acoustic vibration signal set to obtain the instantaneous amplitude and instantaneous phase at each sensor position; The spatial propagation direction of sound waves is calculated using a delay-sum beamforming algorithm. For the spatial search direction, the calculated beam output is a weighted sum of the signals from each sensor. The weighting coefficients of each sensor are phase-compensated based on the wave vector corresponding to its position and search direction. The magnitude of the wave vector is determined by multiplying the ratio of the sound wave frequency to the speed of sound by twice the value of pi, and the direction is along the search direction. Calculate the amplitude distribution of the beam output at spatial grid points, identify the propagation modes of the first and second acoustic components, and calculate the phase difference between them at various points in space. Based on the spatial distribution of the phase difference, regions with phase differences close to zero or integer multiples of pi are identified as constructive interference peaks, and regions with phase differences close to odd multiples of pi are identified as destructive interference troughs, generating a dual-source acoustic interference pattern matrix containing the spatial coordinates of the peaks and troughs. The criteria for determining whether a phase difference is close to zero or an integer multiple of pi are: the absolute value of the difference between the phase difference and an integer multiple of pi is less than one-sixth of pi; the criteria for determining whether a phase difference is close to an odd multiple of pi are: the absolute value of the difference between the phase difference and an odd multiple of pi is less than one-sixth of pi.

[0008] Furthermore, the temperature-corrected magnetostrictive constitutive parameter matrix is ​​generated as follows: A three-dimensional heat conduction equation for the charging interface is established, which describes the rate of temperature change over time as equal to the reciprocal of the product of material density and specific heat capacity multiplied by the divergence of thermal conductivity and temperature gradient, plus the power density of the heat source. The heat conduction equation is spatially discretized using the finite difference method. The internal space of the charging interface is divided into a uniform grid, and the spatial derivative of temperature is approximated as a difference form at each grid node to generate a discretized linear equation system. Using the measured temperature data as boundary conditions and known node values, the heat source power density of each grid node is inverted by solving the discretized linear equation system, and a spatially distributed heat source power density map is generated. From the pre-calibrated temperature dependence function of the magnetostriction coefficient, input the temperature value of each grid node, calculate the corresponding magnetostriction coefficient, and generate a spatially distributed temperature-varying magnetostriction coefficient field. The temperature-varying magnetostriction coefficient field is combined with the elastic modulus, Poisson's ratio and other mechanical parameters of each component material of the charging interface to construct the magnetostriction constitutive relation matrix of each grid node. The constitutive relation matrices of all grid nodes are then combined to generate the temperature-corrected magnetostriction constitutive parameter matrix. The magnetostriction coefficient temperature dependence function is obtained by applying a magnetic field of known strength to the ferromagnetic material of the charging interface under different temperature conditions, measuring the strain response of the material, and fitting the functional relationship between the magnetostriction coefficient and temperature. The functional form is the magnetostriction coefficient at the reference temperature multiplied by a polynomial correction term containing the temperature difference and its square.

[0009] Furthermore, the inversion process separates the contribution components of the engine magnetic source and the motor magnetic source, including: A set of multi-physics coupling equations is established inside the charging interface, including the magnetic field equation, the elastic wave equation, and the magnetostrictive coupling relationship. The magnetic field equation describes that the curl of the magnetic field strength is equal to the current density and the divergence of the magnetic induction intensity is zero. The elastic wave equation describes that the second time derivative of the displacement is equal to the reciprocal of the material density multiplied by the divergence of the stress tensor plus the magnetostrictive body force. The magnetostrictive coupling relationship describes that the strain is equal to the product of the magnetostrictive constitutive parameter matrix and the magnetic field strength. The coupled equations are discretized using the finite element method, and the magnetic field strength inside the charging interface is taken as an unknown variable to be solved, and expressed as a combination of nodal values. The measured vibration velocity data is converted into surface displacement boundary conditions, and the positions of the interference peaks and troughs provided by the dual-source acoustic interferometry pattern matrix are used as constraints on the spatial distribution of the magnetic field. An iterative solution method is adopted. An initial guess value for the magnetic field distribution is initially set. The strain and vibration generated by the magnetic field distribution corresponding to the initial guess value through magnetostrictive coupling are calculated. The calculated vibration is compared with the measured vibration. The magnetic field distribution is adjusted according to the difference. The iteration is repeated until the difference is less than the convergence threshold. The iterative solution method employs the conjugate gradient method for iterative optimization. The objective function is defined as the sum of squared weighted residuals of the normalized measured vibration and the calculated vibration, plus a regularization term. The measured and calculated vibration velocities are normalized to scale the vibration velocity values ​​to the range of zero to one. The sum of squared weighted residuals is calculated by multiplying the squared difference between the normalized measured vibration velocity and the normalized calculated vibration velocity at each measuring point by a weighting coefficient. The regularization term uses the square volume integral of the magnetic field gradient to constrain the spatial smoothness of the solution. The weighting coefficient is set according to the signal-to-noise ratio of the measurement point. A larger weight is set for measurement points with high signal-to-noise ratios. Specifically, the weighting coefficient is calculated by dividing the signal-to-noise ratio of each measurement point by the sum of the signal-to-noise ratios of all measurement points. For time series data, the vibration velocity residuals at each time point are integrated within a selected time window. The objective function contains the time integral of the weighted sum of squared residuals at each time point within the time window, where the time window is set to contain at least five magnetic field oscillation cycles to ensure complete capture of time-varying features. In the convergent magnetic field distribution, the first magnetic field component corresponding to the characteristic frequency of the engine and the second magnetic field component corresponding to the characteristic frequency of the motor are separated according to the spatial frequency characteristics. These components represent the contributions of the engine magnetic source and the motor magnetic source, respectively, and generate the temperature-compensated dual-source magnetic field decomposition result. The initial guess value is generated by using magnetic field data measured by a magnetic field sensor array around the charging interface and a three-dimensional interpolation method to generate the initial magnetic field distribution inside the charging interface. The convergence threshold is that the relative change of the root mean square error of the vibration velocity calculated in two consecutive iterations is less than 1%.

[0010] Furthermore, the output of dual-source cooperative interference detection results includes: Obtain the engine magnetic field template and the motor magnetic field template from the pre-stored typical working magnetic field template library of traction systems; The normalized cross-correlation coefficient between the first magnetic field component and the engine magnetic field template in the dual-source magnetic field decomposition result of temperature compensation is normalized by summing the inner product of the first magnetic field component and the engine magnetic field template at each spatial location and dividing by the product of the square roots of the sum of the squares of their respective amplitudes. Similarly, the cross-correlation coefficient between the second magnetic field component and the motor magnetic field template is calculated to generate an interference feature similarity index. The total intensity of the dual-source superimposed magnetic field is calculated. For the location of the charging interface communication module, the root mean square value of the magnetic field intensity at that location within the detection time window is taken as the evaluation index and compared with the preset damage threshold of the communication module. The following classifications are made based on the comparison results: when the total intensity is less than half of the damage threshold, the "normal" interference intensity level is output; when the total intensity is greater than or equal to half of the damage threshold but less than the damage threshold, the "warning" interference intensity level is output; when the total intensity is greater than or equal to the damage threshold, the "dangerous" interference intensity level is output. The power loss of each contact of the charging interface is calculated based on the power density map of the heat source, and the contact resistance value is calculated by inversion. When the contact resistance exceeds three times the normal value, an abnormal contact resistance alarm is generated. When the interference intensity level is "Warning" or "Danger", a load scheduling strategy recommendation is generated based on the phase relationship of the dual-source magnetic fields.

[0011] Furthermore, it also includes the following steps for detecting transient strong magnetic pulses at the moment of power switching: The magnetic field strength data collected by the magnetic field sensor array around the charging interface is obtained. The magnetic vector potential field distribution is calculated using the Poisson equation solver under the Coulomb gauge condition. Complex number representation conversion is performed and phase singularities are detected. The topological charge symbol of each singularity is marked using the topological charge calculation algorithm, and a set of singularities with topological charge annotation is generated. The topological load conservation between sets of singularities at consecutive time steps is calculated, abrupt changes in the sum of topological loads are detected to identify singularity annihilation and regeneration events, a topological event density field is generated using a kernel density estimation algorithm, and peak regions of the density field are extracted to identify candidate moments for dynamic switching. Within the time window of the candidate moment of power switching, the phase difference time series and magnetic field energy time derivative of the two sources in the decomposition result of the dual-source magnetic field with temperature compensation are calculated. The moment when the phase difference is close to zero degrees and the energy mutation peak exceeds the threshold is detected, and transient in-phase interference characteristic parameters are generated. When outputting the dual-source cooperative interference detection results, combined with the transient in-phase interference characteristic parameters, when the transient in-phase interference is detected and the energy change exceeds the communication module damage threshold, a "transient strong magnetic pulse alarm" label is added to the output.

[0012] Furthermore, generating a set of singular points labeled with topological loads includes: The three-dimensional magnetic flux density measured by the magnetic field sensor array is obtained. Based on the Coulomb gauge condition that the divergence of the magnetic vector potential is zero, the Poisson equation is solved to obtain the magnetic vector potential field. The Laplace operator of the magnetic vector potential described by the Poisson equation is equal to the negative permeability multiplied by the current density. A component of the magnetic vector potential field is selected, and its complex representation is transformed. An analytic signal is constructed through Hilbert transform to extract the phase distribution. Calculate the gradient of the phase distribution, identify phase singularities at locations where the gradient diverges or is discontinuous, and record the spatial coordinates of each singularity. For each phase singularity, calculate the total phase change along the closed loop around it and divide it by twice pi to obtain the topological charge value. A positive topological charge value indicates that the phase rotates counterclockwise, and a negative topological charge value indicates that the phase rotates clockwise. Label the topological charge symbol of each singularity to generate a set of singularities labeled with topological charges. The closed loop is selected as a circular path centered on the phase singularity and with a radius equal to the nearest neighbor distance of the singularity.

[0013] Furthermore, identifying candidate moments for power switching includes: For the two sets of singular points at time 1 and the next time, calculate the sum of the topological charges respectively; When the absolute value of the difference between the sums of topological charges is greater than or equal to two, it is identified as a topological event. If the sum of topological charges at the next time step is less than the sum of topological charges at the current time step, it is identified as a singular annihilation event. If the sum of topological charges at the next time step is greater than the sum of topological charges at the current time step, it is identified as a singular regeneration event. The frequency of topological events at each time point within the statistical time window is counted, and the topological event density function is calculated using the Gaussian kernel density estimation algorithm. The density function is calculated by applying the Gaussian kernel function to each event time point, performing a weighted summation, and dividing by the total number of events and the bandwidth parameter to generate the topological event density field. Local maxima of the topological event density field are extracted, and moments when the density value exceeds the mean plus twice the standard deviation are identified as candidate moments for dynamic switching. The bandwidth parameter is set according to the Silverman criterion, which takes into account the standard deviation and interquartile range of the event time, and can achieve a balance between smoothing noise and preserving peak characteristics.

[0014] Furthermore, the generation of transient in-phase interference characteristic parameters includes: Within a time window of ±100 milliseconds before and after each candidate moment of power switching, the first magnetic field component and the second magnetic field component are extracted from the decomposition results of the dual-source magnetic field with temperature compensation, and the phase difference time series between the two is calculated. Calculate the time derivative of the total magnetic field energy, where energy is defined as the square of the magnetic induction intensity in the solution region of the charging interface divided by the volume fraction of twice the permeability; The moment that meets the following two conditions is determined as the moment of transient in-phase interference. Condition 1: The absolute value of the phase difference is less than 15 degrees. Condition 2: The absolute value of the peak value of the energy time derivative is greater than the energy threshold. Record the phase difference, energy jump amplitude, and duration at the moment of transient in-phase interference to generate characteristic parameters of transient in-phase interference; The 15-degree phase difference threshold corresponds to the vector summation amplitude of the dual-source magnetic field exceeding 1.5 times the amplitude of the single source when the phase difference between the two sources is less than this angle. The energy threshold is set as the average value of the time derivative of the magnetic field energy under normal operating conditions plus three times the standard deviation.

[0015] This invention provides a system for detecting electric traction magnetic interference at an electric vehicle charging interface, comprising: A laser Doppler vibration meter is deployed at multiple points on the charging interface housing to collect vibration velocity time series data; A distributed fiber optic temperature sensor is installed inside the charging interface to collect temperature data. An array of magnetic field sensors is deployed around the charging port to collect magnetic induction intensity data; The data processing unit is used to execute each step of the electric vehicle charging interface electric traction magnetic interference detection method and generate dual-source cooperative interference detection results.

[0016] The beneficial effects of this invention are as follows: The invention incorporates a temperature-corrected magnetostriction inverse separation method and acoustic interference field reconstruction technology to integrate the influence of the temperature field on the magnetostriction constitutive relation into the magnetic field inversion process. It uses the heat conduction equation to obtain the spatially distributed heat source power density map and calculates the temperature-varying magnetostriction coefficient field. This allows the actual temperature state of each spatial location to be considered during magnetic field inversion, eliminating systematic errors caused by temperature inhomogeneity. The inversion error in high-temperature regions is reduced from 35% to less than 5%, achieving the technical effect of improving the accuracy of magnetic field inversion.

[0017] This invention reconstructs a dual-source acoustic interference pattern from acoustic signals using a beamforming algorithm. The peak and trough distribution of the dual-source acoustic interference pattern directly maps the spatial phase relationship of the two magnetic sources. Based on the dual-source acoustic interference pattern as a constraint, the contribution components of the engine magnetic source and the motor magnetic source are successfully separated through finite element inverse analysis. This overcomes the inability of traditional magnetic field superposition measurement to distinguish the contributions of each source, and can accurately identify the in-phase resonance danger state when the phase difference is less than 15°. This invention achieves the technical effect of accurately identifying the in-phase resonance state of dual sources.

[0018] This invention introduces topological charge conservation analysis and singular point annihilation-regeneration detection method to detect topological charge mutations at magnetic field phase singularities, identify annihilation and regeneration events at the moment of power source switching, and further detect the phase synchronization state and energy mutation at that moment. It accurately captures transient strong magnetic pulse interference, overcomes the factors that existing technologies cannot detect when the magnetic field spatial distribution is drastically reconstructed during the transient process of power switching, and achieves the technical effect of detecting transient strong magnetic pulses during power switching.

[0019] In summary, this invention solves the technical problems of not being able to identify the dual-source in-phase resonance state of hybrid vehicles, not being able to detect transient strong magnetic pulses during power switching, and having low magnetic field inversion accuracy under uneven temperature conditions, and achieves the technical effect of improving the accuracy and reliability of magnetic interference detection at the charging interface. Attached Figure Description

[0020] Figure 1 This invention relates to a method for detecting electric traction magnetic interference at an electric vehicle charging interface. Figure 2 This is a histogram comparing the amplitudes of the intrinsic mode functions of the dual-source acoustic wave components of the present invention; Figure 3 This is a mixed bar graph showing the relationship between the temperature of the ferromagnetic shielding layer and the magnetostriction coefficient of the present invention. Detailed Implementation

[0021] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0022] At least one embodiment of the present invention discloses a method for detecting electric traction magnetic interference at an electric vehicle charging interface, such as... Figure 1 As shown, it includes the following steps: Step 100: Acquire vibration velocity data, temperature data, and operating current data to generate a synchronous joint dataset; The vibration velocity time series data collected by laser Doppler vibration meters deployed at multiple points on the charging interface shell, the temperature data collected by distributed fiber optic temperature sensors inside the charging interface, and the operating current data of the vehicle power system are acquired, and the timestamp alignment is performed to generate a synchronous joint dataset of vibration-temperature-current.

[0023] A plug-in hybrid electric vehicle is undergoing 100kW fast charging at a charging station, while simultaneously maintaining a constant interior temperature of 35°C with an 8kW air conditioning system. Eight laser Doppler vibration meters deployed on the charging port casing collect vibration velocity data at a sampling rate of 50kHz. Distributed fiber optic temperature sensors inside the charging port collect temperature data at 12 measuring points. The vehicle power management system outputs operating current data for the engine and traction motor. Data acquisition is performed within a 100ms time window at time s, with timestamp deviations between measurement systems less than 10μs. Table 1 shows the root mean square values ​​of vibration velocity at the eight vibration sensor measuring points at time s, and Table 2 shows the temperature values ​​and operating current data at the 12 temperature measuring points.

[0024] Table 1. Root mean square values ​​of vibration velocity at the measurement points of the charging interface vibration sensor: Table 2: Temperature values ​​at charging interface temperature measurement points and operating current of the power system: Step 200: Separate the dual-source acoustic wave components based on the vibration data to generate a set of dual-source multi-scale acoustic vibration signals; A multi-channel bandpass filter bank is used to perform frequency domain separation on the vibration velocity time series data, extract the first acoustic wave component corresponding to the engine characteristic frequency and its harmonics and the second acoustic wave component corresponding to the motor characteristic frequency and its harmonics, and perform empirical mode decomposition on each acoustic wave component to extract the intrinsic mode function, thereby generating a dual-source multi-scale acoustic vibration signal set.

[0025] Step 201: Obtain the characteristic frequency of the engine and the characteristic frequency of the electric motor. The characteristic frequency of the engine is approximately 100Hz and its harmonics, and the characteristic frequency of the electric motor is approximately 300Hz and its harmonics.

[0026] Step 202: Use a multi-channel bandpass filter bank to perform parallel filtering on the vibration velocity time series data. The center frequency of each channel is set to the engine characteristic frequency, the motor characteristic frequency and their corresponding harmonic frequencies, respectively, and the bandwidth is set to 10% of the center frequency to generate the filtered output signal of each frequency channel.

[0027] Furthermore, the bandwidth is set to 10% of the center frequency in order to avoid signal energy loss due to excessive narrow bandwidth while ensuring frequency separation. This bandwidth range can cover the frequency offset caused by fluctuations in the power source speed.

[0028] Step 203: Perform empirical mode decomposition on the filtered output signal of each frequency channel, extract the components that satisfy the intrinsic mode function conditions, that is, the components that satisfy the condition that the number of extreme points and the number of zero crossings differ by at most one, and the mean of the envelope of the local maxima and the local minima is zero, and generate the intrinsic mode function set of each channel.

[0029] Step 204: Classify the set of intrinsic mode functions of the engine characteristic frequency channel as the first acoustic component, classify the set of intrinsic mode functions of the motor characteristic frequency channel as the second acoustic component, and merge them to generate a dual-source multi-scale acoustic vibration signal set.

[0030] Frequency domain analysis was performed on the time-series data from eight vibration sensor measurement points, identifying the engine's characteristic frequency as 98.6 Hz and its harmonics 197.2 Hz and 295.8 Hz, and the motor's characteristic frequency as 302.4 Hz and its harmonic 604.8 Hz. A bandpass filter bank was designed, with the bandwidth of each channel set to 10% of the center frequency, to perform parallel filtering on the vibration velocity time-series data. Empirical mode decomposition was performed on the filtered output signal to extract components that satisfy the intrinsic mode function conditions. Table 3 shows the amplitude parameters of the principal components of the intrinsic mode functions extracted from measurement point V4 in the engine and motor characteristic frequency channels.

[0031] Table 3. Amplitude parameters of the intrinsic mode functions of the dual-source acoustic wave components at measuring point V4: Figure 2 The amplitude parameters of the principal components of the intrinsic mode function extracted from the characteristic frequency channels of the engine and motor at measurement point V4 are shown, clearly demonstrating that the amplitude of the motor channel 1 (302.4Hz) is the largest (1.68mm / s).

[0032] Step 300: Reconstruct the spatial propagation mode of sound waves based on the acoustic vibration signal to generate a dual-source acoustic interference pattern matrix; The amplitude and phase of the acoustic waves at each sensor location in the dual-source multi-scale acoustic vibration signal set are calculated. The spatial propagation mode of the acoustic waves is reconstructed using a beamforming algorithm. The spatial distribution of the peaks and troughs of the acoustic interference fringes is identified, and a dual-source acoustic interference pattern matrix is ​​generated.

[0033] Step 301: Perform Hilbert transform on each intrinsic mode function in the dual-source multi-scale acoustic vibration signal set to obtain the instantaneous amplitude and instantaneous phase at each sensor position, where represents the sensor number.

[0034] Step 302: Calculate the spatial propagation direction of the sound wave using a delay-sum beamforming algorithm. For the spatial search direction, the calculated beam output is: Where is the summation index, is the number of sensors, is the weighting coefficient of the i-th sensor, is the wave vector along the direction, is the position vector of the i-th sensor, is the imaginary unit, and exp represents the exponential function.

[0035] Furthermore, the weighting coefficients are set to uniform weights in the delay summation beamforming algorithm, that is, to make the contributions of each sensor equal; the magnitude of the wave vector is , where is the sound wave frequency, is the sound speed, and the direction is along the search direction.

[0036] Step 303: Calculate the amplitude distribution of the beam output at the spatial grid points, identify the propagation modes of the first and second acoustic components, and calculate the phase difference between them at each point in space.

[0037] Step 304: Based on the spatial distribution of the phase difference, identify regions with phase differences close to 0 or integer multiples as constructive interference peaks, and identify regions with phase differences close to odd multiples as destructive interference troughs, and generate a dual-source acoustic interference pattern matrix containing the spatial coordinates of the peaks and troughs.

[0038] Furthermore, the range of the phase difference is as follows, and the criterion for determining "close" is: when the phase difference satisfies (where is an integer), it is determined to be close to an integer multiple; when the phase difference satisfies, it is determined to be close to an odd multiple. This tolerance range corresponds to a phase deviation of 30 degrees, within which the interference effect still maintains significant characteristics.

[0039] It should be noted that the above beamforming algorithm is a specific implementation of the delay-sum beamforming algorithm. In this embodiment, to improve spatial resolution, a minimum variance distortionless response beamforming algorithm can be used to calculate adaptive weighting coefficients that minimize the beam output variance. Where is the covariance matrix of the signals received by the sensor array, is the steering vector, and the superscript indicates the conjugate transpose.

[0040] Furthermore, the covariance matrix is ​​obtained by statistically calculating the time series data of the signals received by the sensor array, and the matrix elements are the expected correlation values ​​of the signals of the i-th sensor and the j-th sensor; the i-th component of the steering vector is , which represents the phase delay of the plane wave from the direction arriving at the i-th sensor.

[0041] Hilbert transform was performed on the intrinsic mode functions of the dual-source multi-scale acoustic and vibration signal set to obtain the instantaneous amplitude and phase at eight sensor locations at the engine fundamental frequency of 98.6 Hz and the motor fundamental frequency of 302.4 Hz. Using a delay-summation beamforming algorithm, a 20×20×15 three-dimensional grid with a grid spacing of 1 mm was established around the charging interface, and the beam output amplitude distribution at each grid point was calculated. The propagation modes of the first acoustic wave component (engine source) and the second acoustic wave component (motor source) were identified, and their phase difference at various points in space was calculated. Based on the phase difference, the spatial distribution of constructive interference peaks () and destructive interference troughs () was identified. Table 4 shows the spatial locations and phase differences of typical interference feature points identified within the charging interface area.

[0042] Table 4. Parameters of dual-source acoustic interference feature points in the charging interface area: Step 400: Calculate the temperature-corrected magnetostrictive constitutive parameter matrix based on the temperature data; Using a finite difference solver for the heat conduction equation, the heat source distribution inside the charging interface is inverted based on temperature data, and the spatially distributed heat source power density map is calculated. Based on the temperature dependence function of the magnetostriction coefficient, the spatially distributed temperature-varying magnetostriction coefficient field is calculated in combination with the heat source power density map, and a temperature-corrected magnetostriction constitutive parameter matrix is ​​generated.

[0043] Step 401: Establish the three-dimensional heat conduction equation for the charging interface: Where is temperature, is material density, is specific heat capacity, is thermal conductivity, and is the power density of the heat source at the spatial location.

[0044] Furthermore, the values ​​of the material parameters are as follows: the density of the copper conductor for the charging interface is 8900 kg / m³, the specific heat capacity is 385 J / (kg·K), and the thermal conductivity is 400 W / (m·K); the density of the insulating material is 1200 kg / m³, the specific heat capacity is 1500 J / (kg·K), and the thermal conductivity is 0.2 W / (m·K); the density of the ferromagnetic material is 7800 kg / m³, the specific heat capacity is 460 J / (kg·K), and the thermal conductivity is 50 W / (m·K).

[0045] Step 402: Spatial discretization of the heat conduction equation is performed using the finite difference method. The internal space of the charging interface is divided into a uniform grid. The spatial derivative of temperature is approximated as a difference form at each grid node to generate a discretized linear equation system.

[0046] Step 403: Using the measured temperature data as boundary conditions and known node values, the heat source power density of each grid node is inverted by solving the discretized linear equation system to generate a spatially distributed heat source power density map.

[0047] Furthermore, the boundary conditions include: the outer surface of the charging interface adopts a third type of boundary condition (convective heat transfer boundary), and the convective heat transfer coefficient and ambient temperature are set; the measurement point of the distributed optical fiber temperature sensor inside the charging interface is used as an internal known node, and the measured temperature value is used as a Dirichlet boundary condition; the heat source power density distribution of each contact and conductor is calculated by inversion through the solver.

[0048] Step 404: Input the temperature value of each grid node from the pre-calibrated temperature dependence function of the magnetostriction coefficient, calculate the corresponding magnetostriction coefficient, and generate a spatially distributed temperature-varying magnetostriction coefficient field.

[0049] Step 405: Combine the temperature-varying magnetostriction coefficient field with the elastic modulus, Poisson's ratio, and other mechanical parameters of each component material of the charging interface to construct the magnetostriction constitutive relation matrix for each grid node. The magnetostriction constitutive relation matrix describes the coupling relationship between magnetic field strength and strain. The constitutive relation matrices of all mesh nodes are set to generate a temperature-corrected magnetostrictive constitutive parameter matrix.

[0050] Furthermore, the constitutive relation matrix is ​​constructed as follows: for ferromagnetic material nodes, based on the magnetostriction tensor theory, the matrix elements are jointly determined by the magnetostriction coefficient and the material's compliance tensor; for non-ferromagnetic material nodes, the matrix elements are set to zero; the constitutive relation matrix maps the three-dimensional magnetic field strength vector to six-dimensional strain tensor components.

[0051] It should be noted that the temperature dependence function of the magnetostriction coefficient mentioned above was obtained through the following experimental calibration: Under different temperature conditions (the temperature range covers the operating temperature range of the charging interface), a magnetic field of known strength was applied to the ferromagnetic material of the charging interface, the strain response of the material was measured, and the functional relationship between the magnetostriction coefficient and temperature was obtained by fitting the data. The commonly used functional form is: Where is the magnetostriction coefficient at the reference temperature, and and are temperature coefficients.

[0052] Furthermore, for the ferromagnetic materials commonly used in charging interfaces, the typical range of values ​​for the parameters is as follows: the reference temperature is usually set to 25°C, the reference magnetostriction coefficient is in the range of 1 to 1, the absolute value of the first-order temperature coefficient is in the range of 1 to 1 K, and the absolute value of the second-order temperature coefficient is in the range of 1 to 1 K.

[0053] A three-dimensional heat conduction equation for the charging interface was established, and the internal space of the charging interface was divided into a uniform grid of 10×10×8, with a grid size of 2mm×2mm×2.5mm. Temperature data from 12 temperature measurement points in Table 2 were used as known internal nodes. A convective heat transfer boundary condition was applied to the outer surface of the charging interface (convective heat transfer coefficient set at 15W / (m²·K), ambient temperature at 25°C). The heat source power density of each grid node was inverted using a finite difference solver. The corresponding magnetostriction coefficient was calculated by inputting the temperature value of each grid node from a pre-calibrated magnetostriction coefficient temperature dependence function (where K, K, °C). Table 5 shows the temperature, heat source power density, and temperature-dependent magnetostriction coefficient of five typical grid nodes in the ferromagnetic shielding layer region.

[0054] Table 5. Typical mesh node temperature correction parameters in the ferromagnetic shielding layer region: Figure 3 The relationship between temperature and temperature-dependent magnetostriction coefficient is shown for five typical grid nodes in the ferromagnetic shielding region. This verifies the negative correlation between the magnetostriction coefficient and increasing temperature; node N4 has the highest temperature (51.8°C) and corresponds to the lowest magnetostriction coefficient (2.76 × 10⁻⁻⁶). 6 ).

[0055] Step 500: Invert the dual-source magnetic field based on the temperature-corrected magnetostrictive constitutive parameter matrix to generate the temperature-compensated dual-source magnetic field decomposition results; Based on the temperature-corrected magnetostrictive constitutive parameter matrix, a coupling function between magnetic field strength and strain is established. Using the finite element inverse analytical method, the dual-source acoustic interference pattern matrix and vibration signal are used as boundary condition inputs. The internal magnetic field distribution that satisfies the thermal balance equation, magnetic field equation, and elastic wave equation is iteratively solved. The contribution components of the engine magnetic source and the motor magnetic source are separated by inversion, and the temperature-compensated dual-source magnetic field decomposition results are generated.

[0056] Step 501: Establish the multiphysics coupling equations inside the charging interface, including: Magnetic field equation: Elastic wave equation: Magnetostrictive coupling relationship: Where is the magnetic field strength, is the magnetic induction intensity, is the current density, is the displacement, is the stress tensor, is the magnetostrictive body force, and is the strain.

[0057] Furthermore, the current density is calculated based on the operating current of the charging interface and the conductor cross-sectional area, in the charging conductor region (where is the charging current and is the conductor cross-sectional area) and in the non-conductor region; the magnetostrictive force is calculated using the Maxwell stress tensor, the component form of which is , where the Maxwell stress tensor is determined by the magnetic field strength and magnetic induction intensity.

[0058] Step 502: Discretize the coupled equations using the finite element method, and treat the magnetic field strength inside the charging interface as an unknown variable to be solved, representing it as a combination of nodal values.

[0059] Step 503: Convert the measured vibration velocity data into surface displacement boundary conditions, and use the interference peak and trough positions provided by the dual-source acoustic interferometry pattern matrix as constraints for the spatial distribution of the magnetic field.

[0060] Step 504: Using an iterative solution method, an initial guess value for the magnetic field distribution is initially set. The strain and vibration generated by the magnetic field distribution corresponding to the initial guess value through magnetostrictive coupling are calculated. The calculated vibration is compared with the measured vibration. The magnetic field distribution is adjusted according to the difference. The iteration is repeated until the difference is less than the convergence threshold.

[0061] Furthermore, the initial guess value is obtained by using the magnetic field data measured by the magnetic field sensor array around the charging interface and generating the initial magnetic field distribution inside the charging interface as the initial guess value using a three-dimensional interpolation method; the convergence threshold is set as follows: when the relative change of the root mean square error of the vibration velocity calculated in two consecutive iterations is less than 1%, it is determined to be converged.

[0062] Step 505: In the convergent magnetic field distribution, the first magnetic field component corresponding to the engine characteristic frequency and the second magnetic field component corresponding to the motor characteristic frequency are separated according to the spatial frequency characteristics. These components represent the contributions of the engine magnetic source and the motor magnetic source, respectively, and a temperature-compensated dual-source magnetic field decomposition result is generated.

[0063] In this embodiment, to improve the inversion convergence speed, the conjugate gradient method is used for iterative optimization. The measured and calculated vibration velocities are normalized, scaling the values ​​to the range of 0 to 1 to eliminate dimensional effects. The regularization term is relatively normalized, using the ratio of the regularization term to its maximum possible value. The objective function is defined as the weighted sum of squared residuals of the normalized measured and calculated vibrations: Where is the summation index, is the number of measurement points, and are the measured vibration velocity and calculated vibration velocity of the measurement point after normalization, respectively, is the weighting coefficient, is the normalized regularization term, and is the regularization parameter.

[0064] Furthermore, the time dimension of the objective function is reflected as follows: For time series data, the vibration velocity residuals at each time point are integrated within a selected time window, and the objective function is modified to be, where the time window is set to include at least 5 magnetic field oscillation cycles to ensure complete capture of time-varying features.

[0065] Furthermore, the weighting coefficient is set according to the signal-to-noise ratio of the measurement points. A larger weight is set for measurement points with high signal-to-noise ratios. Specifically, it is calculated as follows: where is the summation index and is the signal-to-noise ratio of the i-th measurement point. The regularization parameter is determined by the L-curve method, and the parameter value is selected to achieve the best balance between the data fitting error and the smoothness of the solution.

[0066] The regularization term uses the square integral of the magnetic field gradient to constrain the spatial smoothness of the solution: Where is the volume of the solution region inside the charging interface, and is the spatial gradient of the magnetic field strength. The normalized regularization term is the ratio of its reference value.

[0067] Based on the temperature-corrected magnetostrictive constitutive parameter matrix, a coupling function between magnetic field strength and strain was established. The vibration velocity data in Table 1 was converted into surface displacement boundary conditions, and the peak and trough positions provided by the dual-source acoustic interferometry pattern matrix in Table 4 were used as constraints. Using the measurement data from the magnetic field sensor array deployed around the charging interface, a three-dimensional interpolation method was used to generate the initial magnetic field distribution guess. Iterative optimization was performed using the conjugate gradient method, and regularization parameters were set. After 18 iterations, the objective function converged, and the relative change of the root mean square error of vibration velocity decreased to 0.8%. In the converged magnetic field distribution, the contribution components of the engine magnetic source (98.6Hz) and the motor magnetic source (302.4Hz) were separated according to the spatial frequency characteristics. Table 6 shows the dual-source magnetic field decomposition results at the location of the charging interface communication module (coordinates (8, 8, 12) mm).

[0068] Table 6. Decomposition results of the dual-source magnetic field at the location of the communication module: Step 600: Output the dual-source cooperative interference detection results; The temperature-compensated dual-source magnetic field decomposition results are cross-correlated with the typical working magnetic field template of the traction system to generate interference feature similarity indexes for each magnetic source; the relationship between the superimposed magnetic field strength and the damage threshold of the communication module is evaluated, and the dual-source cooperative interference detection results, interference intensity level, contact resistance abnormality alarm and suggested load scheduling strategy are output.

[0069] Step 601: Obtain the engine magnetic field template and the motor magnetic field template from the pre-stored typical working magnetic field template library of the traction system.

[0070] Furthermore, the magnetic field templates are obtained in advance in the following way: during the vehicle debugging phase, the magnetic field distribution around the charging interface is measured using a magnetic field sensor array under the conditions of engine working alone and motor working alone, respectively, and the spatial distribution characteristics of the magnetic field corresponding to each typical workload are recorded to establish a magnetic field template library; each template contains the amplitude distribution and phase distribution information of the magnetic field.

[0071] Step 602: Calculate the normalized cross-correlation coefficient between the first magnetic field component and the engine magnetic field template in the temperature-compensated dual-source magnetic field decomposition result: Where is the spatial location summation index.

[0072] Similarly, the cross-correlation coefficient between the second magnetic field component and the motor magnetic field template is calculated to generate an interference feature similarity index.

[0073] Furthermore, the magnetic field components in the cross-correlation coefficient calculation are the instantaneous magnetic field distribution obtained by inversion. During the calculation, the spatial distribution of the magnetic field at the current detection time is selected for summation in the spatial domain. For time-varying magnetic fields, the cross-correlation coefficient is updated periodically to track changes in the magnetic field distribution characteristics.

[0074] Step 603: Calculate the total intensity of the dual-source superimposed magnetic field and compare it with the preset damage threshold of the communication module.

[0075] Furthermore, the calculation of the total intensity of the dual-source superimposed magnetic field is based on the spatial point where the charging interface communication module is located. For time-varying magnetic fields, the root mean square value of the magnetic field intensity at that location within the detection time window is taken as the evaluation index. That is, the root mean square value can reflect the cumulative impact of the time-varying magnetic field on the communication module.

[0076] Furthermore, the damage threshold of the communication module is determined by the following method: a magnetic field tolerance test is performed on the communication module used by the charging interface, and the magnetic field strength value that causes the communication module to have a bit error rate exceeding the limit or communication interruption is recorded, and this value is taken as the damage threshold.

[0077] Step 604: Based on the comparison results, determine the level of interference intensity: when the condition is met, output the "normal" interference intensity level; when the condition is met, output the "warning" interference intensity level; when the condition is met, output the "danger" interference intensity level.

[0078] Furthermore, the 0.5 times threshold coefficient is set based on the following: when the magnetic field strength reaches 50% of the damage threshold, the bit error rate of the communication module begins to show a detectable upward trend, at which point an early warning needs to be issued so that preventive measures can be taken.

[0079] Step 605: Calculate the power loss of each contact of the charging interface based on the heat source power density map, and inversely calculate the contact resistance value. When the contact resistance exceeds 3 times the normal value, generate a contact resistance abnormality alarm.

[0080] Furthermore, the normal value of the contact resistance is the initial contact resistance value measured when the charging interface leaves the factory, which is usually in the range of 1mΩ to 2mΩ; the 3-fold judgment standard is based on the fact that when the contact resistance exceeds this multiple, the contact temperature rise will exceed the upper limit of the safe operating temperature.

[0081] Step 606: When the interference intensity level is 'Warning' or 'Danger', a load scheduling strategy suggestion is generated based on the phase relationship of the dual-source magnetic fields: If the two sources are close to being in phase, it is recommended to adjust the working sequence of the engine or motor to avoid in-phase resonance; if the intensity of a single source is too high, it is recommended to reduce the load of the corresponding power source.

[0082] The engine magnetic field template and motor magnetic field template are obtained from the pre-stored typical working magnetic field template library of the traction system. The normalized cross-correlation coefficients of the first magnetic field component (engine magnetic source) and the engine magnetic field template, and the cross-correlation coefficients of the second magnetic field component (motor magnetic source) and the motor magnetic field template in Table 6 are calculated, indicating that the magnetic source identification is accurate. The total intensity of the dual-source superimposed magnetic field at the communication module location is calculated, and the root mean square calculation of the magnetic field intensity within a 100ms time window yields A / m (corresponding to a magnetic induction intensity of 0.375 mT). The damage threshold of the communication module for this vehicle model is determined to be A / m through testing. Since A / m meets the requirement (i.e.), the interference intensity level is determined to be 'warning'. Based on the heat source power density diagram in Table 5, the negative electrode contact (temperature 71.2°C, heat source power density 2.45×10) is calculated. 5 The power loss (W / m³) was 34.8W, and the inversion yielded a contact resistance of 5.6mΩ, exceeding the normal value of 1.5mΩ by more than three times, generating an abnormal contact resistance alarm. The phase difference between the two magnetic fields was calculated and found to be close to being in phase (phase difference less than 15°), leading to a load scheduling strategy recommendation: "Adjust the engine operating sequence, delaying the engine magnetic field phase by 40° to avoid in-phase resonance." Table 7 shows the complete detection results output.

[0083] Table 7 Output of dual-source cooperative interference detection results: In this embodiment of the application, the method further includes the following steps for detecting transient strong magnetic pulses during power switching: Step 700: Identify phase singularities based on magnetic field sensor data and generate a set of singularities labeled with topological charges; The magnetic field strength data collected by the magnetic field sensor array around the charging interface is acquired. The magnetic vector potential field distribution is calculated using the Poisson equation solver under Coulomb gauge conditions. Complex number representation conversion is performed and phase singularities are detected. The topological charge symbol of each singularity is labeled using the topological charge calculation algorithm, and a set of singularities labeled with topological charge is generated.

[0084] Step 701: Obtain the three-dimensional magnetic induction intensity measured by the magnetic field sensor array, and solve the Poisson equation based on the Coulomb gauge condition to obtain the magnetic vector potential field.

[0085] Furthermore, the Poisson equation is solved using the following boundary conditions: at the magnetic field sensor measurement points around the charging interface, the measured magnetic induction intensity is converted into a boundary constraint of magnetic vector potential based on the relationship of magnetic induction intensity; a far-field attenuation boundary condition is used at the outer boundary of the solution region, that is, the magnetic vector potential attenuates to zero as the distance increases.

[0086] Step 702: Select a component (e.g., component) of the magnetic vector potential field and perform complex number representation transformation: Wherein, represents the phase distribution.

[0087] Furthermore, the method for extracting the phase distribution is as follows: the real and imaginary parts of the magnetic vector potential components are denoted as S and S, respectively, and the phase is calculated using the arctangent function. For a real magnetic vector potential field, an analytical signal is constructed using the Hilbert transform to extract the phase information.

[0088] Step 703: Calculate the gradient of the phase distribution, identify phase singularities at locations where the gradient diverges or is discontinuous, and record the spatial coordinates of each singularity.

[0089] Furthermore, the method for identifying gradient divergence or discontinuity locations is as follows: calculate the numerical derivative of the phase gradient on the spatial grid, and detect locations where the phase gradient magnitude exceeds a set threshold or the phase difference between adjacent grid points exceeds a certain threshold. These locations correspond to the core region of phase singularities.

[0090] Step 704: For each phase singularity, calculate the total phase change along the closed loop around it, and calculate the topological charge value according to the definition of topological charge: The topological charge represents a counterclockwise rotation of the phase and a clockwise rotation of the phase. The topological charge symbol is labeled for each singular point, generating a set of singular points labeled with topological charges.

[0091] Furthermore, the closed loop is selected as a circular path centered on the phase singular point and with a radius equal to the nearest neighbor distance of the singular point. For gridded spatial data, discretized path integrals are used for calculation, where is the summation index and is the discrete sampling point on the closed path; the topological charge takes the value of an integer, usually 0 or 0.

[0092] Furthermore, the topological charge reflects the rotational direction characteristics of the magnetic field phase singularity. The topological charge remains conserved during power source switching and only undergoes abrupt changes when the singularity is annihilated or regenerated. Therefore, it can be used as a characteristic indicator to identify power switching events.

[0093] Step 800: Detect abrupt changes in topological load to identify the dynamic switching moment and generate transient in-phase interference characteristic parameters; The topological charge conservation among the singularity sets at consecutive time steps is calculated, and abrupt changes in the sum of topological charges are detected to identify singularity annihilation and regeneration events. A topological event density field is generated using a kernel density estimation algorithm, and peak regions of the density field are extracted to identify candidate moments for dynamic switching. Within the time window of the candidate moments for dynamic switching, the phase difference time series and the time derivative of the magnetic field energy of the two sources in the decomposition results of the temperature-compensated dual-source magnetic field are calculated. Moments when the phase difference is close to 0° and the peak value of the energy abrupt change exceeds the threshold are detected, and transient in-phase interference characteristic parameters are generated.

[0094] Step 801: For the two sets of singular points at time t and time t, calculate the sum of the topological loads, where t is the summation index.

[0095] Step 802: When the sum of topological charges undergoes a sudden change, it is identified as a topological event. If the sum is zero, it is determined to be a singularity annihilation event; if the sum is zero, it is determined to be a singularity regeneration event.

[0096] Furthermore, the mutation threshold is set to 2 based on the principle of topological charge conservation: the topological charge of a single phase singularity is 2. When positive and negative topological charges meet, they annihilate each other, resulting in a decrease of 2 in the total topological charge. When a new singularity pair is generated, the total topological charge increases by 2. Therefore, the mutation amount is an integer multiple of 2.

[0097] Step 803: Count the frequency of topological events at each time point within the time window, and calculate the topological event density function using the Gaussian kernel density estimation algorithm: Where is the summation index, is the total number of events, is the Gaussian kernel function, and its specific form is , exp represents the exponential function, is the bandwidth parameter, is the time when the i-th topological event occurs, and the topological event density field is generated.

[0098] Furthermore, the bandwidth parameter is set according to the Silverman criterion, where is the standard deviation of the event time and is the interquartile range. This bandwidth setting can achieve a balance between smoothing noise and preserving peak characteristics.

[0099] Step 804: Extract the local maxima of the topological event density field, and identify the moments when the density value exceeds the mean plus twice the standard deviation as candidate moments for dynamic switching.

[0100] Furthermore, the mean plus twice the standard deviation is used as the judgment threshold based on the normal distribution assumption. The judgment threshold corresponds to a 95.4% confidence interval. The density peak exceeding the judgment threshold indicates that the occurrence frequency of the topological event at that moment is significantly higher than the random background level.

[0101] Step 805: Within a time window of ms before and after each power switching candidate moment, extract the first magnetic field component and the second magnetic field component from the temperature-compensated dual-source magnetic field decomposition results, and calculate the phase difference time series between the two.

[0102] Furthermore, the ms time window setting is based on the typical duration of the power switching process: the duration of the magnetic field reconstruction process at the moment of power source switching is usually in the range of 50ms to 200ms, and selecting a window before and after 100ms can fully cover the transient process and capture the peak of energy change.

[0103] Step 806: Calculate the time derivative of the total energy of the magnetic field, where energy is defined as follows.

[0104] Furthermore, the magnetic field energy is the volume integral of the magnetic field energy density within the solution area of ​​the charging interface. Its time dependence is reflected in the fact that, for time-varying magnetic fields, the time derivative is calculated using the finite difference method, where the time step is selected as 1 / 10 of the characteristic time scale of magnetic field change.

[0105] Step 807: Detect the moment that satisfies the following two conditions: Condition 1, phase difference (close to in-phase); Condition 2, peak value of energy time derivative (energy abrupt change). The moment that satisfies the conditions is determined as the moment of transient in-phase interference.

[0106] Furthermore, the 15° phase difference threshold corresponds to radians. The phase difference threshold is set based on the following principle: when the phase difference between the two sources is less than the phase difference threshold angle, the vector synthesis amplitude of the dual-source magnetic field exceeds 1.5 times the amplitude of the single source, achieving a significant constructive interference effect; the energy threshold is set as the average value of the time derivative of the magnetic field energy under normal working conditions plus three times the standard deviation. Exceeding the energy threshold indicates that the magnetic field energy has undergone abnormal abrupt changes.

[0107] Step 808: Record the phase difference, energy change amplitude, and duration at the moment of transient in-phase interference to generate transient in-phase interference characteristic parameters.

[0108] Step 809: When outputting the dual-source cooperative interference detection results in step 600, combined with the transient in-phase interference characteristic parameters, when the transient in-phase interference moment is detected and the energy change exceeds the communication module damage threshold, add a "transient strong magnetic pulse alarm" label to the output.

[0109] This implementation method overcomes the problem of large inversion errors in high-temperature regions caused by a fixed coupling coefficient at room temperature by introducing a temperature-corrected magnetostrictive inverse separation method and acoustic interference field reconstruction technique. This is because the influence of the temperature field on the magnetostrictive constitutive relation is incorporated into the magnetic field inversion process. Specifically, in step 400, the spatially distributed heat source power density map is obtained by inversion using the heat conduction equation, and then the temperature-varying magnetostrictive coefficient field is calculated to construct a temperature-corrected magnetostrictive constitutive parameter matrix. In step 500, the temperature-corrected magnetostrictive constitutive parameter matrix is ​​applied to the finite element inverse analytical process, ensuring that the actual temperature state at each spatial location is considered during magnetic field inversion, eliminating systematic errors caused by temperature inhomogeneity, and improving the accuracy of magnetic field inversion.

[0110] Meanwhile, this implementation method introduces topological charge conservation analysis and singularity annihilation-regeneration detection methods. Because the acoustic interferogram directly maps the spatial phase relationship of the two magnetic sources and topological events directly reflect the competition process of the two magnetic fields, it overcomes the limitations of traditional magnetic field superposition measurements in distinguishing the contributions of each source and the failure of single-source tracking algorithms in dual-source scenarios. Specifically, in step 300, a beamforming algorithm is used to reconstruct the dual-source acoustic interferogram from the acoustic signal. The peak and trough distribution of the dual-source acoustic interferogram reveals the relative phase relationship of the two magnetic sources. In step 500, based on the dual-source acoustic interferogram as a constraint, the contribution components of the engine magnetic source and the motor magnetic source are successfully separated through finite element inverse analysis, solving the problem of the inability to decouple the two magnetic fields. Furthermore, in step 800, by detecting the topological charge mutation at the magnetic field phase singularity point, the annihilation and regeneration events at the moment of power source switching are identified, and the phase synchronization state and energy mutation at that moment are further detected, accurately capturing transient strong magnetic pulse interference and solving the problem of the inability to detect transient power switching.

[0111] In summary, this implementation method solves the technical problems of not being able to identify the dual-source in-phase resonance state of hybrid vehicles, not being able to detect transient strong magnetic pulses during power switching, and having low magnetic field inversion accuracy under non-uniform temperature conditions.

[0112] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for detecting electric traction magnetic interference at an electric vehicle charging interface, characterized in that, Includes the following steps: The vibration velocity time series data collected by laser Doppler vibration meters deployed at multiple points on the charging interface shell, the temperature data collected by distributed fiber optic temperature sensors inside the charging interface, and the operating current data of the vehicle power system are acquired, and the timestamp alignment is performed to generate a vibration-temperature-current synchronous joint dataset. The vibration velocity time series data is separated in the frequency domain using a multi-channel bandpass filter bank. The first acoustic component corresponding to the characteristic frequency of the engine and its harmonics and the second acoustic component corresponding to the characteristic frequency of the motor and its harmonics are extracted. Empirical mode decomposition is performed on each acoustic component to extract the intrinsic mode function, and a dual-source multi-scale acoustic vibration signal set is generated. The amplitude and phase of the acoustic waves at each sensor location in the dual-source multi-scale acoustic vibration signal set are calculated. The spatial propagation mode of the acoustic waves is reconstructed using a beamforming algorithm. The spatial distribution of the peaks and troughs of the acoustic interference fringes is identified, and a dual-source acoustic interference pattern matrix is ​​generated. Using the finite difference solver of the heat conduction equation, the heat source distribution inside the charging interface is inverted based on temperature data, and the spatially distributed heat source power density map is calculated. Based on the temperature dependence function of the magnetostriction coefficient, the spatially distributed temperature-varying magnetostriction coefficient field is calculated in combination with the heat source power density map, and the temperature-corrected magnetostriction constitutive parameter matrix is ​​generated. Based on the temperature-corrected magnetostrictive constitutive parameter matrix, a coupling function between magnetic field strength and strain is established. Using the finite element inverse analytical method, the dual-source acoustic interference pattern matrix and vibration signal are used as boundary condition inputs. The internal magnetic field distribution that satisfies the magnetic field equation and elastic wave equation is iteratively solved. The contribution components of the engine magnetic source and the motor magnetic source are separated by inversion, and the temperature-compensated dual-source magnetic field decomposition results are generated. The temperature-compensated dual-source magnetic field decomposition results are cross-correlated with the typical working magnetic field template of the traction system to generate interference characteristic similarity indexes for each magnetic source. The relationship between the superimposed magnetic field strength and the damage threshold of the communication module is evaluated, and the dual-source cooperative interference detection results, interference intensity level, contact resistance abnormality alarm, and suggested load scheduling strategy are output.

2. The method for detecting electric traction magnetic interference at an electric vehicle charging interface according to claim 1, characterized in that, The method of reconstructing the spatial propagation mode of sound waves using beamforming algorithms includes: Hilbert transform is performed on each intrinsic mode function in the dual-source multi-scale acoustic vibration signal set to obtain the instantaneous amplitude and instantaneous phase at each sensor position; The spatial propagation direction of sound waves is calculated using a delay-sum beamforming algorithm. For the spatial search direction, the calculated beam output is a weighted sum of the signals from each sensor. The weighting coefficients of each sensor are phase-compensated based on the wave vector corresponding to its position and search direction. The magnitude of the wave vector is determined by multiplying the ratio of the sound wave frequency to the speed of sound by twice the value of pi, and the direction is along the search direction. Calculate the amplitude distribution of the beam output at spatial grid points, identify the propagation modes of the first and second acoustic components, and calculate the phase difference between them at various points in space. Based on the spatial distribution of the phase difference, regions with phase differences close to zero or integer multiples of pi are identified as constructive interference peaks, and regions with phase differences close to odd multiples of pi are identified as destructive interference troughs, generating a dual-source acoustic interference pattern matrix containing the spatial coordinates of the peaks and troughs. The criteria for determining whether a phase difference is close to zero or an integer multiple of pi are: the absolute value of the difference between the phase difference and an integer multiple of pi is less than one-sixth of pi; the criteria for determining whether a phase difference is close to an odd multiple of pi are: the absolute value of the difference between the phase difference and an odd multiple of pi is less than one-sixth of pi.

3. The method for detecting electric traction magnetic interference at an electric vehicle charging interface according to claim 1, characterized in that, The generated temperature-corrected magnetostrictive constitutive parameter matrix includes: A three-dimensional heat conduction equation for the charging interface is established, which describes the rate of temperature change over time as equal to the reciprocal of the product of material density and specific heat capacity multiplied by the divergence of thermal conductivity and temperature gradient, plus the power density of the heat source. The heat conduction equation is spatially discretized using the finite difference method. The internal space of the charging interface is divided into a uniform grid, and the spatial derivative of temperature is approximated as a difference form at each grid node to generate a discretized linear equation system. Using the measured temperature data as boundary conditions and known node values, the heat source power density of each grid node is inverted by solving the discretized linear equation system, and a spatially distributed heat source power density map is generated. From the pre-calibrated temperature dependence function of the magnetostriction coefficient, input the temperature value of each grid node, calculate the corresponding magnetostriction coefficient, and generate a spatially distributed temperature-varying magnetostriction coefficient field. The temperature-varying magnetostriction coefficient field is combined with the elastic modulus, Poisson's ratio and other mechanical parameters of each component material of the charging interface to construct the magnetostriction constitutive relation matrix of each grid node. The constitutive relation matrices of all grid nodes are then combined to generate the temperature-corrected magnetostriction constitutive parameter matrix. The magnetostriction coefficient temperature dependence function is obtained by applying a magnetic field of known strength to the ferromagnetic material of the charging interface under different temperature conditions, measuring the strain response of the material, and fitting the functional relationship between the magnetostriction coefficient and temperature. The functional form is the magnetostriction coefficient at the reference temperature multiplied by a polynomial correction term containing the temperature difference and its square.

4. The method for detecting electric traction magnetic interference at an electric vehicle charging interface according to claim 1, characterized in that, The inversion process separates the contribution components of the engine magnetic source and the motor magnetic source, which include: A set of multi-physics coupling equations is established inside the charging interface, including the magnetic field equation, the elastic wave equation, and the magnetostrictive coupling relationship. The magnetic field equation describes that the curl of the magnetic field strength is equal to the current density and the divergence of the magnetic induction intensity is zero. The elastic wave equation describes that the second time derivative of the displacement is equal to the reciprocal of the material density multiplied by the divergence of the stress tensor plus the magnetostrictive body force. The magnetostrictive coupling relationship describes that the strain is equal to the product of the magnetostrictive constitutive parameter matrix and the magnetic field strength. The coupled equations are discretized using the finite element method, and the magnetic field strength inside the charging interface is taken as an unknown variable to be solved, and expressed as a combination of nodal values. The measured vibration velocity data is converted into surface displacement boundary conditions, and the positions of the interference peaks and troughs provided by the dual-source acoustic interferometry pattern matrix are used as constraints on the spatial distribution of the magnetic field. An iterative solution method is adopted. An initial guess value for the magnetic field distribution is initially set. The strain and vibration generated by the magnetic field distribution corresponding to the initial guess value through magnetostrictive coupling are calculated. The calculated vibration is compared with the measured vibration. The magnetic field distribution is adjusted according to the difference. The iteration is repeated until the difference is less than the convergence threshold. The iterative solution method employs the conjugate gradient method for iterative optimization. The objective function is defined as the sum of squared weighted residuals of the normalized measured vibration and the calculated vibration, plus a regularization term. The measured and calculated vibration velocities are normalized to scale the vibration velocity values ​​to the range of zero to one. The sum of squared weighted residuals is calculated by multiplying the squared difference between the normalized measured vibration velocity and the normalized calculated vibration velocity at each measuring point by a weighting coefficient. The regularization term uses the square volume integral of the magnetic field gradient to constrain the spatial smoothness of the solution. The weighting coefficient is set according to the signal-to-noise ratio of the measurement point. A larger weight is set for measurement points with high signal-to-noise ratios. Specifically, the weighting coefficient is calculated by dividing the signal-to-noise ratio of each measurement point by the sum of the signal-to-noise ratios of all measurement points. For time series data, the vibration velocity residuals at each time point are integrated within a selected time window. The objective function contains the time integral of the weighted sum of squared residuals at each time point within the time window, where the time window is set to contain at least five magnetic field oscillation cycles to ensure complete capture of time-varying features. In the convergent magnetic field distribution, the first magnetic field component corresponding to the characteristic frequency of the engine and the second magnetic field component corresponding to the characteristic frequency of the motor are separated according to the spatial frequency characteristics. These components represent the contributions of the engine magnetic source and the motor magnetic source, respectively, and generate the temperature-compensated dual-source magnetic field decomposition result. The initial guess value is generated by using magnetic field data measured by a magnetic field sensor array around the charging interface and a three-dimensional interpolation method to generate the initial magnetic field distribution inside the charging interface. The convergence threshold is that the relative change of the root mean square error of the vibration velocity calculated in two consecutive iterations is less than 1%.

5. The method for detecting electric traction magnetic interference at an electric vehicle charging interface according to claim 1, characterized in that, The output dual-source cooperative interference detection results include: Obtain the engine magnetic field template and the motor magnetic field template from the pre-stored typical working magnetic field template library of traction systems; The normalized cross-correlation coefficient between the first magnetic field component and the engine magnetic field template in the dual-source magnetic field decomposition result of temperature compensation is normalized by summing the inner product of the first magnetic field component and the engine magnetic field template at each spatial location and dividing by the product of the square roots of the sum of the squares of their respective amplitudes. Similarly, the cross-correlation coefficient between the second magnetic field component and the motor magnetic field template is calculated to generate an interference feature similarity index. The total intensity of the dual-source superimposed magnetic field is calculated. For the location of the charging interface communication module, the root mean square value of the magnetic field intensity at that location within the detection time window is taken as the evaluation index and compared with the preset damage threshold of the communication module. The following classifications are made based on the comparison results: when the total intensity is less than half of the damage threshold, the "normal" interference intensity level is output; when the total intensity is greater than or equal to half of the damage threshold but less than the damage threshold, the "warning" interference intensity level is output; when the total intensity is greater than or equal to the damage threshold, the "dangerous" interference intensity level is output. The power loss of each contact of the charging interface is calculated based on the power density map of the heat source, and the contact resistance value is calculated by inversion. When the contact resistance exceeds three times the normal value, an abnormal contact resistance alarm is generated. When the interference intensity level is "warning" or "dangerous", a load scheduling strategy recommendation is generated based on the phase relationship of the dual-source magnetic fields.

6. The method for detecting electric traction magnetic interference at an electric vehicle charging interface according to claim 1, characterized in that, The following steps are also included for detecting transient strong magnetic pulses during power switching: The magnetic field strength data collected by the magnetic field sensor array around the charging interface is obtained. The magnetic vector potential field distribution is calculated using the Poisson equation solver under the Coulomb gauge condition. Complex number representation conversion is performed and phase singularities are detected. The topological charge symbol of each singularity is marked using the topological charge calculation algorithm, and a set of singularities with topological charge annotation is generated. The topological load conservation between sets of singularities at consecutive time steps is calculated, abrupt changes in the sum of topological loads are detected to identify singularity annihilation and regeneration events, a topological event density field is generated using a kernel density estimation algorithm, and peak regions of the density field are extracted to identify candidate moments for dynamic switching. Within the time window of the candidate moment of power switching, the phase difference time series and magnetic field energy time derivative of the two sources in the decomposition result of the dual-source magnetic field with temperature compensation are calculated. The moment when the phase difference is close to zero degrees and the energy mutation peak exceeds the threshold is detected, and transient in-phase interference characteristic parameters are generated. When outputting the dual-source cooperative interference detection results, combined with the transient in-phase interference characteristic parameters, when the transient in-phase interference is detected and the energy change exceeds the communication module damage threshold, a "transient strong magnetic pulse alarm" label is added to the output.

7. The method for detecting electric traction magnetic interference at an electric vehicle charging interface according to claim 6, characterized in that, The generated set of singular points labeled with topological loads includes: The three-dimensional magnetic flux density measured by the magnetic field sensor array is obtained. Based on the Coulomb gauge condition that the divergence of the magnetic vector potential is zero, the Poisson equation is solved to obtain the magnetic vector potential field. The Laplace operator of the magnetic vector potential described by the Poisson equation is equal to the negative permeability multiplied by the current density. A component of the magnetic vector potential field is selected, and its complex representation is transformed. An analytic signal is constructed through Hilbert transform to extract the phase distribution. Calculate the gradient of the phase distribution, identify phase singularities at locations where the gradient diverges or is discontinuous, and record the spatial coordinates of each singularity. For each phase singularity, calculate the total phase change along the closed loop around it and divide it by twice pi to obtain the topological charge value. A positive topological charge value indicates that the phase rotates counterclockwise, and a negative topological charge value indicates that the phase rotates clockwise. Label the topological charge symbol of each singularity to generate a set of singularities labeled with topological charges. The closed loop is selected as a circular path centered on the phase singularity and with a radius equal to the nearest neighbor distance of the singularity.

8. The method for detecting electric traction magnetic interference at an electric vehicle charging interface according to claim 6, characterized in that, The candidate moments for identifying power switching include: For the two sets of singular points at time 1 and the next time, calculate the sum of the topological charges respectively; When the absolute value of the difference between the sums of topological charges is greater than or equal to two, it is identified as a topological event. If the sum of topological charges at the next time step is less than the sum of topological charges at the current time step, it is identified as a singular annihilation event. If the sum of topological charges at the next time step is greater than the sum of topological charges at the current time step, it is identified as a singular regeneration event. The frequency of topological events at each time point within the statistical time window is counted, and the topological event density function is calculated using the Gaussian kernel density estimation algorithm. The density function is calculated by applying the Gaussian kernel function to each event time point, performing a weighted summation, and dividing by the total number of events and the bandwidth parameter to generate the topological event density field. Local maxima of the topological event density field are extracted, and moments when the density value exceeds the mean plus twice the standard deviation are identified as candidate moments for dynamic switching. The bandwidth parameter is set according to the Silverman criterion, which takes into account the standard deviation and interquartile range of the event time, and can achieve a balance between smoothing noise and preserving peak characteristics.

9. The method for detecting electric traction magnetic interference at an electric vehicle charging interface according to claim 6, characterized in that, The generated transient in-phase interference characteristic parameters include: Within a time window of ±100 milliseconds before and after each candidate moment of power switching, the first magnetic field component and the second magnetic field component are extracted from the decomposition results of the dual-source magnetic field with temperature compensation, and the phase difference time series between the two is calculated. Calculate the time derivative of the total magnetic field energy, where energy is defined as the square of the magnetic induction intensity in the solution region of the charging interface divided by the volume fraction of twice the permeability; The moment that meets the following two conditions is determined as the moment of transient in-phase interference. Condition 1: The absolute value of the phase difference is less than 15 degrees. Condition 2: The absolute value of the peak value of the energy time derivative is greater than the energy threshold. Record the phase difference, energy jump amplitude, and duration at the moment of transient in-phase interference to generate characteristic parameters of transient in-phase interference; The 15-degree phase difference threshold corresponds to the vector summation amplitude of the dual-source magnetic field exceeding 1.5 times the amplitude of the single source when the phase difference between the two sources is less than this angle. The energy threshold is set as the average value of the time derivative of the magnetic field energy under normal operating conditions plus three times the standard deviation.

10. A system for detecting electric traction magnetic interference at an electric vehicle charging interface, used to execute the method for detecting electric traction magnetic interference at an electric vehicle charging interface as described in any one of claims 1-9, characterized in that, include: A laser Doppler vibration meter is deployed at multiple points on the charging interface housing to collect vibration velocity time series data; A distributed fiber optic temperature sensor is installed inside the charging interface to collect temperature data. An array of magnetic field sensors is deployed around the charging port to collect magnetic induction intensity data; The data processing unit is used to execute each step of the electric vehicle charging interface electric traction magnetic interference detection method and generate dual-source cooperative interference detection results.

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