Shielding and noise reduction method and system for high-voltage wiring harness of new energy automobile

Abstract

The invention relates to the technical field of electromagnetic compatibility, and discloses a shielding and noise reduction method and system for a high-voltage wiring harness of a new energy automobile, and the method comprises the steps: obtaining current fluctuation data, working state codes and environment condition data; performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics; performing environment analysis according to the environment condition data to obtain environment state characteristics; inputting the current fluctuation characteristics, the working state codes and the environment state characteristics into a preset current prediction model to obtain predicted current data; performing noise extraction according to the predicted current data to obtain high-frequency noise data; and performing impedance matching according to the high-frequency noise data, and outputting a target impedance parameter. The method has the following effect that the shielding and noise reduction accuracy of the high-voltage wire harness of the new energy automobile can be improved.

Description

Technical Field

[0001] The present invention relates to the field of electromagnetic compatibility technology, and in particular to a shielding and noise reduction method and system for a high-voltage wire harness of a new energy vehicle. Background Art

[0002] At present, new energy vehicles are gradually becoming mainstream transportation as a substitute for traditional fuel vehicles. High-voltage wiring harness is one of the most important components in new energy vehicles. It is responsible for transmitting power and signals between various electrical systems of the vehicle. Due to its high operating voltage and large current, the design of high-voltage wiring harness must not only consider electrical performance, but also pay special attention to electromagnetic compatibility (EMC) and noise reduction. Especially in the context of the increasing number of electronic devices in modern cars, it is particularly important to ensure that the high-voltage wiring harness can effectively resist external electromagnetic interference and reduce the noise generated by itself. In addition, the high-voltage wiring harness must also have good mechanical strength and durability to cope with complex driving environments and long-term use requirements. Therefore, research and development of high-efficiency shielding and noise reduction technology is of great significance to improving the overall performance of new energy vehicles.

[0003] In one prior art, a shielding and noise reduction method is to add one or more metal shielding layers to the outside of the high-voltage wire harness, and on this basis, combine an absorbing layer made of magnetic material to jointly construct a composite protective structure. The specific steps are as follows: First, select a suitable metal material, such as aluminum foil or copper braided mesh, and wrap it around the high-voltage cable to form the first line of defense, which is mainly used to block high-frequency electromagnetic interference signals. Next, apply a layer of absorbing layer composed of ferrite or other magnetic materials outside the metal shielding layer. This step is intended to further weaken the intensity of electromagnetic waves in the low-frequency band to prevent them from penetrating the shielding layer and causing interference. In addition, in order to enhance the reliability of the overall system, filters are installed at both ends of the cable to eliminate common-mode noise on the conduction path.

[0004] Although the above-mentioned shielding and noise reduction methods can theoretically provide good protection, their disadvantage is the lack of dynamic matching capabilities for environmental conditions and the operating status of the vehicle body. For example, under different driving conditions (such as acceleration, deceleration, turning, etc.), the load of the electrical system in the car will change significantly, causing the frequency and intensity of the generated electromagnetic interference to change accordingly. However, the existing shielding scheme cannot adjust its own protection strategy in real time according to these changes, resulting in poor EMI suppression effect under different working conditions. At the same time, the external environment in which the car is located during driving will also have an important impact on electromagnetic interference. For example, when approaching a substation or a radio transmission tower, the strong external electromagnetic field will exceed the original design range, making the original effective shielding measures no longer applicable. More importantly, the complex electromagnetic environment changes caused by the interaction between the various subsystems inside the car are not taken into account, which also limits the applicability of existing technologies in a wider range of scenarios. In summary, the existing high-voltage wiring harness shielding and noise reduction technology has low accuracy. Summary of the invention

[0005] The present invention provides a shielding and noise reduction method and system for a high-voltage wire harness of a new energy vehicle, so as to improve the shielding and noise reduction accuracy of the high-voltage wire harness.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides a shielding and noise reduction method for a high-voltage wire harness of a new energy vehicle, comprising: Obtain current fluctuation data, working status code and environmental condition data; Performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics; Performing environmental analysis according to the environmental condition data to obtain environmental state characteristics; Inputting the current fluctuation characteristics, the working state code and the environmental state characteristics into a preset current prediction model to obtain predicted current data; Perform noise extraction according to the predicted current data to obtain high-frequency noise data; Impedance matching is performed according to the high-frequency noise data, and a target impedance parameter is output.

[0007] In an optional implementation manner, the obtaining of current fluctuation data, working state code and environmental condition data includes: Get the electromagnetic interference frequency; When the electromagnetic interference frequency is less than a preset first sampling frequency, data acquisition is performed according to the first sampling frequency to obtain current fluctuation data; When the electromagnetic interference frequency is greater than or equal to the first sampling frequency, performing frequency updating according to the electromagnetic interference frequency to obtain a second sampling frequency; When the electromagnetic interference frequency is greater than the second sampling frequency, data acquisition is performed according to the second sampling frequency to obtain current fluctuation data; Get the car acceleration; When the acceleration of the vehicle is greater than a preset first working threshold, determining that the working state code is a preset first code; When the vehicle acceleration is less than or equal to the first working threshold and greater than a preset second working threshold, determining that the working state code is a preset second code; When the vehicle acceleration is less than or equal to the second working threshold, the working state code is determined to be a preset third code.

[0008] In an optional implementation, performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics includes: Perform basis function matching according to the current fluctuation data to obtain a decomposed basis function; Recursively decomposing the decomposition basis function and the current fluctuation data to obtain a sub-band coefficient set; Reconstructing a signal according to the sub-band coefficient set to obtain a sub-band time domain signal; The entropy value is quantized according to the sub-band time domain signal to obtain the current fluctuation characteristics.

[0009] In an optional implementation, performing environmental analysis according to the environmental condition data to obtain environmental state characteristics includes: Perform spectrum analysis according to the environmental condition data to obtain the proportion of high-frequency energy; When the high-frequency energy ratio is greater than a preset high-frequency threshold, determining that the electromagnetic propagation path is radiation propagation; Perform periodic detection according to the environmental condition data to obtain a propagation period; When the propagation period is an integer, it is determined that the electromagnetic propagation path is waveguide propagation; Extracting field strength according to the electromagnetic propagation path and the environmental condition data to obtain field strength data; Feature fusion is performed based on the electromagnetic propagation path and the field strength data to obtain environmental state features.

[0010] In an optional implementation, the training process of the current prediction model includes: A current prediction model is constructed based on a long short-term memory neural network, where the input layer includes historical current characteristics, historical working conditions, and historical environmental characteristics; The output layer is the target current prediction value; In the iterative process, the mean square error between the predicted value and the true value is used as the loss function; Complete one round of iteration to update parameters; When it is detected that the loss function of the model meets the conditions or the number of training times reaches the set upper limit, the training is considered to be completed and the trained model is obtained.

[0011] In an optional implementation, performing noise extraction according to the predicted current data to obtain high-frequency noise data includes: Performing baseline correction according to the predicted current data to obtain residual waveform data; Perform frequency domain transformation according to the residual waveform data to obtain energy spectrum density distribution; Perform high-frequency filtering according to the energy spectrum density distribution to obtain high-frequency noise distribution; Time-frequency joint labeling is performed according to the high-frequency noise distribution to obtain high-frequency noise data.

[0012] In an optional implementation, performing impedance matching according to the high-frequency noise data and outputting target impedance parameters includes: Performing spectrum extraction according to the high-frequency noise data to obtain a noise spectrum; Performing sensitivity calculation according to the noise spectrum to obtain a sensitivity distribution curve; Performing deviation calculation according to the sensitivity distribution curve to obtain an impedance deviation index; Multi-parameter optimization is performed according to the impedance deviation index to obtain the target impedance parameters.

[0013] In a second aspect, the present invention provides a shielding and noise reduction system for a high-voltage wire harness of a new energy vehicle, comprising: A data acquisition module, used to acquire current fluctuation data, working status code and environmental condition data; A current characteristic module, used for performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics; An environmental characteristic module, used to perform environmental analysis based on the environmental condition data to obtain environmental state characteristics; A current prediction module, used for inputting the current fluctuation characteristics, the working state code and the environmental state characteristics into a preset current prediction model to obtain predicted current data; A noise extraction module, used for performing noise extraction according to the predicted current data to obtain high-frequency noise data; The impedance matching module is used to perform impedance matching according to the high-frequency noise data and output target impedance parameters.

[0014] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the shielding and noise reduction method for the high-voltage wire harness of a new energy vehicle as described above is implemented.

[0015] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned shielding and noise reduction methods for high-voltage wire harnesses of new energy vehicles.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The process of acquiring current fluctuation data, working status coding, and environmental condition data ensures comprehensive monitoring of the system operation status. By accurately collecting this data, a solid foundation is provided for subsequent analysis. This step not only improves the integrity and accuracy of the data, but also provides high-quality data input for subsequent steps such as wavelet decomposition and environmental analysis, thereby enhancing the reliability and stability of the overall system.

[0017] (2) Performing wavelet decomposition on the current fluctuation data to obtain current fluctuation characteristics. The wavelet decomposition method can effectively extract key characteristic information from complex current fluctuation signals, including energy distribution in different frequency bands. This step significantly improves the ability to understand current fluctuation patterns, helps to more accurately identify potential problems, and provides accurate input data for the current prediction model, thereby improving the reliability of the prediction results.

[0018] (3) Performing environmental analysis based on the environmental condition data to obtain environmental state characteristics. Through a detailed analysis of environmental conditions, various external factors that affect system performance can be captured. This analysis helps to adjust system parameters to adapt to different working environments and reduce the impact of environmental changes on system performance. As one of the important input information, environmental state characteristics play a key role in improving the accuracy of the current prediction model.

[0019] (4) Inputting the current fluctuation characteristics, the working state code and the environmental state characteristics into a preset current prediction model to obtain predicted current data. By integrating multiple characteristic information into the current prediction model, the system can more accurately simulate the actual current change trend. This method not only improves the accuracy of the prediction, but also better reflects the response characteristics of the system under different working conditions, providing a reliable basis for subsequent noise extraction.

[0020] (5) Noise extraction is performed based on the predicted current data to obtain high-frequency noise data. Advanced signal processing technology is used to separate high-frequency noise components from the predicted current data. This process helps to identify the interference sources in the system and their impact. Accurate extraction of high-frequency noise data is crucial for subsequent impedance matching because it is directly related to whether noise can be effectively suppressed and the stable operation of the system can be ensured.

[0021] (6) Perform impedance matching according to the high-frequency noise data and output the target impedance parameters. By analyzing the high-frequency noise data and performing impedance matching, the electrical characteristics of the system can be optimized and the impact of noise on system performance can be reduced. This method can not only improve the anti-interference ability of the system, but also improve the quality of the current waveform and improve the overall work efficiency. Finally, based on the calculated target impedance parameters, adjustments can be made to achieve the optimal operating state of the system and meet the needs of high-efficiency applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic flow chart of a shielding and noise reduction method for a high-voltage wire harness of a new energy vehicle provided by the first embodiment of the present invention; Figure 2 It is a structural schematic diagram of a shielding and noise reduction system for a high-voltage wire harness of a new energy vehicle provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Reference Figure 1 The first embodiment of the present invention provides a shielding and noise reduction method for a high-voltage wire harness of a new energy vehicle, comprising the following steps: S11, obtaining current fluctuation data, working state code and environmental condition data; S12, performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics; S13, performing environmental analysis according to the environmental condition data to obtain environmental state characteristics; S14, inputting the current fluctuation characteristics, the working state code and the environmental state characteristics into a preset current prediction model to obtain predicted current data; S15, performing noise extraction according to the predicted current data to obtain high-frequency noise data; S16, performing impedance matching according to the high-frequency noise data, and outputting target impedance parameters.

[0025] In step S11, current fluctuation data, working state code and environmental condition data are acquired.

[0026] It is worth noting that the current fluctuation data is collected in real time through the Hall sensor array terminated at the high-voltage harness, and the transient current changes of the motor when working are recorded at a sampling frequency of 1MHz, and stored as a CSV time series data file with a timestamp (for example, 20230313_1530_BatteryCurrent.csv, containing a millisecond timestamp and a current value in the ±500A range); the working status code is transmitted by the vehicle controller through the CAN bus, using the PGN63488 message in the J1939 protocol, and the vehicle operation mode is represented in hexadecimal encoding (such as 0x01 for acceleration conditions, 0x02 for energy recovery conditions), which is stored as a binary log file with a message ID; the environmental condition data is obtained through the temperature and humidity sensor and EMI detection module integrated in the BMS system, including a temperature gradient of -40℃~125℃, a humidity value of 0-100%RH and an electromagnetic field strength in the frequency band of 0-200MHz, and is stored as a structured record table in the SQLite database, each record containing 20 fields such as longitude and latitude coordinates, timestamp and three-axis magnetic field strength. The three types of data are time-synchronized through the on-board gateway to form a fusion collection system for multi-source heterogeneous data.

[0027] In one embodiment, an electromagnetic interference frequency is obtained; when the electromagnetic interference frequency is less than a preset first sampling frequency, data is obtained according to the first sampling frequency to obtain current fluctuation data; when the electromagnetic interference frequency is greater than or equal to the first sampling frequency, frequency is updated according to the electromagnetic interference frequency to obtain a second sampling frequency; when the electromagnetic interference frequency is greater than or equal to the second sampling frequency, data is obtained according to the second sampling frequency to obtain current fluctuation data; a vehicle acceleration is obtained; when the vehicle acceleration is greater than a preset first working threshold, the working state code is determined to be a preset first code; when the vehicle acceleration is less than or equal to the first working threshold and greater than a preset second working threshold, the working state code is determined to be a preset second code; when the vehicle acceleration is less than or equal to the second working threshold, the working state code is determined to be a preset third code.

[0028] It is worth noting that the key parameters involved in this implementation are analyzed as follows: the electromagnetic interference frequency refers to the main frequency band of electromagnetic noise generated when the high-voltage system is working, such as the 20kHz fundamental frequency and its third harmonic 60kHz generated by the PWM modulation of the drive motor; the first sampling frequency is the preset basic data acquisition rate (such as 50kHz), and when the interference frequency is detected to reach 80kHz, the second sampling frequency (such as 100kHz) is enabled for anti-aliasing sampling. The acceleration of the vehicle is obtained through the vehicle longitudinal acceleration sensor, with a range of ±1g. When the acceleration exceeds 0.3g (the first working threshold), it is marked as a sudden acceleration state (the first code 0x01), between 0.1g (the second working threshold) and 0.3g is a normal working condition (the second code 0x02), and below 0.1g is determined to be a sliding state (the third code 0x03). This parameter system implements a dynamic sampling strategy, such as automatically increasing the sampling rate to 200kHz when a 100kHz motor harmonic is detected, and triggering a high-frequency data acquisition mode during the sudden acceleration stage to ensure that the current ripple caused by the transient state of the IGBT switch is captured.

[0029] It is worth noting that in the subsequent current prediction model, the first, second and third codes, as the core characterization parameters of the vehicle operating conditions, affect the prediction accuracy through a multi-dimensional mechanism: at the level of physical mapping of the code, the first code (0x01) represents the high-frequency harmonic offset of the current caused by the motor IGBT switching frequency rising to above 15kHz under emergency acceleration conditions, the second code (0x02) represents the ±50A current ripple stability range corresponding to a load rate of 40-70% during normal driving, and the third code (0x03) reflects the current direction reversal and 60kHz characteristic interference in the energy recovery gliding mode; at the feature coupling level, a 32-dimensional LSTM input matrix is ​​constructed by combining the three-dimensional vector converted by the unique hot encoding with the 8-layer wavelet decomposition current features and the normalized environmental parameters, in which the weight of the operating condition code accounts for 18%; in time series modeling, changes in the code sequence within the 500ms sliding window trigger state transition detection. For example, three consecutive 0x01 codes will enhance the model's prediction sensitivity to transient overcurrent.

[0030] In step S12, wavelet decomposition is performed on the current fluctuation data to obtain current fluctuation characteristics.

[0031] In one embodiment, basis function matching is performed according to the current fluctuation data to obtain a decomposition basis function; recursive decomposition is performed according to the decomposition basis function and the current fluctuation data to obtain a sub-band coefficient set; signal reconstruction is performed according to the sub-band coefficient set to obtain a sub-band time domain signal; entropy quantization is performed according to the sub-band time domain signal to obtain a current fluctuation feature.

[0032] It is worth noting that in the current fluctuation analysis, it is first necessary to select a basis function that matches the signal characteristics as a decomposition tool. The selection of basis functions is based on the morphological characteristics of the current data. For example, if the current waveform presents a short-time pulse characteristic (such as the transient caused by the IGBT switch), a basis function with high local time domain resolution (such as the Daubechies series) is used; if the signal has periodic oscillations (such as PWM modulation harmonics), a basis function with good symmetry (such as the Symlets series) is preferred. After the matching is completed, the system will lock the optimal decomposition basis function, which is essentially a set of waveform templates with specific time-frequency characteristics for subsequent multi-scale signal decomposition.

[0033] It is worth noting that based on the selected basis function, the system performs multi-level recursive decomposition on the current fluctuation data. Each level of decomposition breaks down the signal into a low-frequency approximate part (reflecting trend changes) and a high-frequency detail part (capturing transient disturbances). For example, when performing 8-level decomposition, the first level corresponds to the highest frequency noise (such as switching noise), and the eighth level corresponds to the lowest frequency load fluctuation trend. The sub-band coefficient set is composed of high- and low-frequency coefficients obtained by decomposition at each level. Its structure presents a tree-like distribution. Each coefficient represents the energy distribution and time series correlation characteristics of the signal in a specific frequency band. For example, a sudden increase in the amplitude of the high-frequency coefficient indicates a motor stall event.

[0034] It is worth noting that through the inverse transform algorithm, the system restores the sub-band coefficient set to a time domain signal that can be analyzed intuitively. Each sub-band signal corresponds to the component of the original current fluctuation in a specific frequency band. For example, the reconstructed 3rd layer sub-band signal contains PWM carrier harmonics in the range of 10-20kHz, while the 6th layer sub-band signal reflects the low-frequency oscillation of hundreds of hertz caused by the motor torque pulsation. These sub-band time domain signals not only retain the original timing characteristics, but also enhance the identifiability of specific interference components through frequency band isolation, for example, the periodic current distortion caused by the inverter dead zone effect can be clearly separated.

[0035] It is worth noting that, finally, the system calculates the entropy value of the sub-band time domain signal to quantify its complexity and randomness. For example, the energy entropy of the high-frequency sub-band (such as the second layer) can be calculated to characterize the burst intensity of the switching noise; the approximate entropy of the low-frequency sub-band (such as the seventh layer) is calculated to evaluate the stability of the load fluctuation. These entropy values ​​together constitute the current fluctuation feature vector. For example, the energy entropy of the high-frequency sub-band increases significantly under rapid acceleration conditions, while the low-frequency approximate entropy tends to be stable under gliding conditions. The feature vector is finally input into the prediction model to achieve accurate prediction of abnormal current fluctuations (such as resonant overshoot) under different working conditions.

[0036] In step S13, an environmental analysis is performed based on the environmental condition data to obtain environmental state characteristics.

[0037] In one implementation, spectrum analysis is performed based on the environmental condition data to obtain a high-frequency energy ratio; when the high-frequency energy ratio is greater than a preset high-frequency threshold, the electromagnetic propagation path is determined to be radiation propagation; periodic detection is performed based on the environmental condition data to obtain a propagation period; when the propagation period is an integer, the electromagnetic propagation path is determined to be waveguide propagation; field strength extraction is performed based on the electromagnetic propagation path and the environmental condition data to obtain field strength data; feature fusion is performed based on the electromagnetic propagation path and the field strength data to obtain environmental state features.

[0038] It is worth noting that the system first performs a spectrum energy distribution scan on the environmental condition data (such as electromagnetic interference spectrum, temperature and humidity, etc.), focusing on calculating the proportion of high-frequency band (for example, above 1GHz) energy to total energy. The proportion of high-frequency energy reflects the activity level of radiation-type interference in the environment. For example, this value will be significantly increased when the radar module is working in a vehicle-mounted scenario. When the proportion exceeds the preset threshold (set to 30%), the system determines that the current electromagnetic interference is mainly propagated through radiation, which manifests as spatial electromagnetic field coupling. Typical scenarios include spatial radiation generated by unshielded sections of high-voltage wiring harnesses.

[0039] It is worth noting that for the electromagnetic wave components in the environmental data, the system uses an autocorrelation algorithm to detect its periodicity. If the detected propagation period shows an integer value characteristic (for example, the period is strictly equal to the inverse of the vehicle's metal cavity resonance frequency), it is determined to be a waveguide propagation mode. This type of propagation often occurs in waveguides formed by structures such as body gaps and wiring harness grooves. It is characterized by the directional propagation of electromagnetic energy along the surface of the conductor or in a confined space. For example, the crosstalk between the motor controller and the battery pack often manifests as fluctuations with a period of integer multiples of 2ms.

[0040] It is worth noting that the system extracts field strength data in a differentiated manner based on the identified propagation paths: for radiation propagation, the focus is on the spatial field strength peak and its distribution gradient (such as the field strength in the instrument panel area reaching 120V / m); for waveguide propagation, the field strength attenuation curve on the conductor surface is extracted (such as 15dB attenuation per meter along the shielded wire bundle). At the same time, the field strength value is corrected in combination with the ambient temperature and humidity data. For example, when the dielectric constant of the insulating material changes due to high temperature, the system automatically compensates for the field strength measurement error.

[0041] It is worth noting that the final system fuses the propagation path identifier (radiation / waveguide) with the corrected field strength data to generate an environmental state feature vector. For example: in the radiation propagation-dominated scenario, the feature vector includes the high-frequency energy proportion, spatial field strength gradient, and temperature and humidity coupling coefficient; while in the waveguide propagation scenario, it integrates parameters such as the periodic stability index, conductor attenuation rate, and resonant frequency deviation. This feature vector can accurately characterize the electromagnetic environment state under complex working conditions (such as moisture on rainy days aggravating waveguide leakage), providing an anti-interference correction benchmark for subsequent prediction models.

[0042] In step S14, the current fluctuation characteristics, the working state code and the environmental state characteristics are input into a preset current prediction model to obtain predicted current data.

[0043] In one embodiment, the training process of the current prediction model includes: constructing a current prediction model based on a long short-term memory neural network, wherein the input layer includes historical current characteristics, historical working status, and historical environmental characteristics; The output layer is the target current prediction value; In the iterative process, the mean square error between the predicted value and the true value is used as the loss function; Complete one round of iteration to update parameters; When it is detected that the loss function of the model meets the conditions or the number of training times reaches the set upper limit, the training is considered to be completed and the trained model is obtained.

[0044] It is worth noting that the current prediction model is built based on the LSTM model. The data set of the LSTM current prediction model consists of three parts: current fluctuation characteristics (including sub-band entropy values ​​and time-domain statistics such as high-frequency energy entropy and low-frequency approximate entropy), working state encoding (using unique hot encoding or numerical mapping to represent equipment operation mode and control instructions) and environmental state characteristics (including standardized parameters such as temperature and humidity, electromagnetic field strength and propagation mode identifiers). It is organized in the form of a sliding window (such as predicting the current value in the next 5 seconds with the feature sequence of the past 60 seconds), and is constructed through a 2-3 layer LSTM unit (64-256 neurons per layer, Dropout rate 0.2-0 .5) neural network architecture for training, using Adam optimizer (initial learning rate 0.001 with cosine annealing strategy) and MAE and RMSE composite loss function (weight ratio 6:4), combined with early stopping method (patience value 10 epochs) and model checkpoint mechanism to achieve efficient training; its purpose is to achieve equipment protection (microsecond abnormal response), energy efficiency optimization (working condition adaptation to improve efficiency by 5-8%), fault warning (identifying device aging 3-6 months in advance) and environmental adaptability (automatic compensation for temperature drift and electromagnetic interference) through multi-factor coupling prediction.

[0045] In step S15, noise extraction is performed based on the predicted current data to obtain high-frequency noise data.

[0046] In one embodiment, baseline correction is performed based on the predicted current data to obtain residual waveform data; frequency domain transformation is performed based on the residual waveform data to obtain energy spectral density distribution; high-frequency filtering is performed based on the energy spectral density distribution to obtain high-frequency noise distribution; time-frequency joint annotation is performed based on the high-frequency noise distribution to obtain high-frequency noise data.

[0047] It is worth noting that the system predicts the long-term trend of current changes (such as minute-level fluctuations) based on the sliding time window analysis, and uses an adaptive weight algorithm to fit a dynamic baseline. By performing baseline correction on the predicted current data, the system first eliminates the steady-state current component under normal operating conditions of the equipment. The residual waveform data characterizes the instantaneous deviation of the actual current from the predicted benchmark. The abnormal forms such as spikes and oscillations highlighted in the waveform can reflect transient disturbances caused by electromagnetic interference (such as current glitches caused by power device switching). The time resolution of the data is accurate to microseconds, which can clearly capture the non-periodic fluctuation characteristics caused by events such as relay bounce and capacitor charging and discharging, providing the original signal basis for subsequent noise separation.

[0048] It is worth noting that the original current data is subtracted from the dynamic baseline point by point to obtain a residual waveform containing only transient fluctuations. The residual waveform data is converted into an energy spectrum density distribution through frequency domain transformation. This distribution uses frequency as the horizontal axis and energy intensity as the vertical axis to intuitively display the aggregation characteristics of electromagnetic energy in different frequency bands. The low-frequency bands in the energy spectrum (such as 0-100kHz) correspond to the power frequency harmonics of normal operation of the equipment, while the energy peaks in the high-frequency band (above 1MHz) are mostly derived from the radiation noise of the switching power supply or the out-of-band leakage of the wireless communication module. By analyzing the steepness and peak position of the spectral density, the frequency characteristics and propagation path of the noise source can be preliminarily determined.

[0049] It is worth noting that based on the energy spectral density distribution, the system uses a high-pass filter with an adjustable cutoff frequency to separate the high-frequency noise distribution. This distribution focuses on the 1MHz-1GHz frequency band, and its morphological characteristics include three typical modes: narrowband spikes (such as CAN bus clock harmonics); broadband noise floor (such as electromagnetic radiation caused by IGBT switching); periodic comb spectrum (such as sideband leakage generated by PWM modulation). Each distribution mode corresponds to a specific interference mechanism. For example, the energy diffusion degree of the broadband noise floor can directly reflect the impact of the switching speed of the power device on the electromagnetic compatibility.

[0050] It is worth noting that the high-frequency noise distribution is jointly labeled in time and frequency, and finally structured high-frequency noise data is generated. The data contains three core dimensions: time mark (noise burst moment accurate to nanoseconds); frequency mark (noise main frequency and -3dB bandwidth); intensity mark (equivalent radiation intensity in dBμA). In particular, the transient high-frequency noise recorded in the data (such as pulse groups with a duration of less than 1μs) can be associated with the parasitic parameter resonance phenomenon of specific circuit nodes, while continuous high-frequency noise (such as broadband radiation of more than 10ms) is mostly related to structural defects such as poor grounding of the radiator. These data provide a quantitative basis for electromagnetic compatibility rectification. For example, by identifying dense noise clusters in the 200-400MHz frequency band, the problem of excessive radiation caused by the motor drive harness without magnetic rings can be located.

[0051] In step S16, impedance matching is performed according to the high-frequency noise data, and a target impedance parameter is output.

[0052] In one implementation, spectrum extraction is performed based on the high-frequency noise data to obtain a noise spectrum; sensitivity calculation is performed based on the noise spectrum to obtain a sensitivity distribution curve; deviation calculation is performed based on the sensitivity distribution curve to obtain an impedance deviation index; multi-parameter optimization is performed based on the impedance deviation index to obtain a target impedance parameter.

[0053] It is worth noting that the system generates a noise spectrum based on spectrum extraction of high-frequency noise data, which fully presents the noise characteristics in the three-dimensional form of frequency-time-intensity. Different color blocks in the spectrum represent the noise intensity distribution in a specific frequency band. For example, the red area represents the continuous radiation noise in the 1-5MHz frequency band, and the blue stripes correspond to transient pulse groups above 10MHz. The noise spectrum can intuitively identify the main frequency position of the interference source (such as the fundamental frequency and harmonics of the switching power supply) and its evolution pattern over time (such as the periodic fluctuations caused by PWM modulation), providing a frequency domain feature benchmark for subsequent impedance matching.

[0054] It is worth noting that by analyzing the energy share of different frequency bands in the noise spectrum and the correlation with the device function, the system calculates and generates a sensitivity distribution curve. The vertical axis of the curve represents the sensitivity of the device performance to noise of a specific frequency. For example, the sensitivity peak in the wireless communication band (such as 2.4GHz) indicates that the noise here will directly interfere with the signal transmission quality. The shape of the curve can reveal the key vulnerable frequency bands: the steep rising edge indicates that narrowband interference is prone to cause a surge in bit error rate, and the flat area represents the cumulative effect of broadband noise on the system. Sensitivity data provides a quantitative basis for impedance optimization priorities.

[0055] In one implementation, the sensitivity calculation quantifies the sensitivity of the device performance to electromagnetic noise of different frequencies, and generates a frequency-sensitivity mapping curve based on the correlation between the energy distribution of each frequency band in the noise spectrum and the core functions of the device (such as wireless communication and signal sampling). The peak value corresponds to the key vulnerable frequency band (such as the sensitivity of the 2.4GHz band is 0.9, indicating that slight noise here can significantly reduce the communication quality), and the low sensitivity area (such as the sensitivity of the frequency band below 10MHz is less than 0.1) reflects that the device has a strong tolerance to noise in this range. This data provides a basis for frequency band priority sorting and tolerance threshold setting for subsequent impedance optimization.

[0056] It is worth noting that based on the sensitivity distribution curve, the system calculates the deviation between the measured impedance characteristics and the theoretical ideal value to generate an impedance deviation index. This index reflects the current impedance matching defects in the form of a percentage. For example, a deviation of 30% in the 100MHz frequency band indicates that the parasitic inductance here causes the high-frequency return path impedance to be too high. The index includes two key parameters: frequency domain deviation (impedance deviation in a specific frequency band) and time domain stability (impedance drift with temperature / load fluctuations), which together evaluate the actual performance of the circuit design in noise suppression.

[0057] It is worth noting that the system generates the target impedance parameters through multi-parameter optimization based on the comprehensive impedance deviation index and equipment operating condition constraints. This parameter defines the impedance range that meets the electromagnetic compatibility requirements. For example, in sensitive frequency bands (such as 500MHz-1GHz), the impedance value is required to be less than 5 ohms to suppress common-mode noise. The optimization process balances three factors: electrical performance (such as insertion loss and return loss), physical feasibility (such as PCB trace width and stacking structure), and cost constraints (such as the amount of high-frequency magnetic materials). The final target impedance parameters will guide the design of the filter circuit. For example, by adjusting the impedance-frequency characteristics of ferrite beads, the impedance matching degree in the key frequency band can be increased to more than 95%.

[0058] In summary, the present invention discloses a shielding and noise reduction method for a high-voltage wiring harness of a new energy vehicle, the method comprising obtaining current fluctuation data, working state coding and environmental condition data; performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics; performing environmental analysis according to the environmental condition data to obtain environmental state characteristics; inputting the current fluctuation characteristics, the working state coding and the environmental state characteristics into a preset current prediction model to obtain predicted current data; performing noise extraction according to the predicted current data to obtain high-frequency noise data; performing impedance matching according to the high-frequency noise data to output target impedance parameters. In the specific implementation process, current fluctuation data is first collected in real time through a Hall sensor array, and the working state coding is transmitted through the vehicle controller and the CAN bus, while the temperature and humidity sensor and EMI detection module integrated in the BMS system are used to collect environmental condition data. After time synchronization processing, these multi-source heterogeneous data provide a high-quality data foundation for subsequent wavelet decomposition, environmental analysis and other steps.

[0059] For the extraction of current fluctuation characteristics, the basis function matching method is adopted. The appropriate basis function (such as Daubechies or Symlets series) is selected according to the signal characteristics, and then recursive decomposition is performed to obtain the sub-band coefficient set. The sub-band time domain signal is obtained by reconstructing the sub-band coefficient set, and finally the current fluctuation characteristics are extracted from the sub-band time domain signal using entropy quantization technology. This process can effectively capture the energy distribution of different frequency bands, which helps to more accurately identify potential problems and provide accurate input for the current prediction model. At the same time, the analysis of environmental condition data is also crucial. It not only includes the monitoring of electromagnetic interference frequency, but also includes the consideration of complex environmental factors such as determining the proportion of high-frequency energy based on spectrum analysis and identifying whether the propagation path is radiation propagation or waveguide propagation through periodic detection, so as to adjust the system parameters to adapt to different working environments and reduce the impact of external changes on system performance.

[0060] Furthermore, by integrating the current fluctuation characteristics, working state encoding and environmental state characteristics into the pre-trained current prediction model, the actual current change trend can be simulated more accurately, which not only improves the prediction accuracy, but also better reflects the response characteristics of the system under different working conditions, providing a reliable basis for subsequent noise extraction. In the noise extraction stage, advanced signal processing techniques such as baseline correction, frequency domain transformation and high-frequency filtering are used to separate high-frequency noise components from the predicted current data, which is particularly critical for subsequent impedance matching, because correct impedance matching can effectively suppress noise and ensure the stable operation of the system. Finally, in the process of impedance matching based on high-frequency noise data, the noise spectrum is obtained by spectrum extraction, and the sensitivity distribution curve is calculated to determine the impedance deviation index, and finally multi-parameter optimization is achieved to obtain the optimal target impedance parameters. In this way, the shielding noise reduction accuracy of the high-voltage wiring harness of new energy vehicles is improved.

[0061] Reference Figure 2 The second embodiment of the present invention provides a shielding and noise reduction system for a high-voltage wire harness of a new energy vehicle, comprising: A data acquisition module, used to acquire current fluctuation data, working status code and environmental condition data; A current characteristic module, used for performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics; An environmental characteristic module, used to perform environmental analysis based on the environmental condition data to obtain environmental state characteristics; A current prediction module, used for inputting the current fluctuation characteristics, the working state code and the environmental state characteristics into a preset current prediction model to obtain predicted current data; A noise extraction module, used for performing noise extraction according to the predicted current data to obtain high-frequency noise data; The impedance matching module is used to perform impedance matching according to the high-frequency noise data and output target impedance parameters.

[0062] It should be noted that the shielding and noise reduction system for the high-voltage wire harness of a new energy vehicle provided in an embodiment of the present invention is used to execute all the process steps of the shielding and noise reduction method for the high-voltage wire harness of a new energy vehicle in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0063] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, the steps in the above-mentioned shielding and noise reduction method embodiments for high-voltage wiring harnesses of new energy vehicles are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0064] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0065] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0066] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0067] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0068] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0069] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0070] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A shielding and noise reduction method for a high-voltage wire harness of a new energy vehicle, characterized in that: include: Obtain current fluctuation data, working status code and environmental condition data; Performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics; Performing environmental analysis according to the environmental condition data to obtain environmental state characteristics; Inputting the current fluctuation characteristics, the working state code and the environmental state characteristics into a preset current prediction model to obtain predicted current data; Perform noise extraction according to the predicted current data to obtain high-frequency noise data; Impedance matching is performed according to the high-frequency noise data, and a target impedance parameter is output.

2. The shielding and noise reduction method for high-voltage wire harness of new energy vehicles according to claim 1 is characterized in that: The obtaining of current fluctuation data, working state code and environmental condition data includes: Get the electromagnetic interference frequency; When the electromagnetic interference frequency is less than a preset first sampling frequency, data acquisition is performed according to the first sampling frequency to obtain current fluctuation data; When the electromagnetic interference frequency is greater than or equal to the first sampling frequency, performing frequency updating according to the electromagnetic interference frequency to obtain a second sampling frequency; When the electromagnetic interference frequency is greater than the second sampling frequency, data acquisition is performed according to the second sampling frequency to obtain current fluctuation data; Get the car acceleration; When the acceleration of the vehicle is greater than a preset first working threshold, determining that the working state code is a preset first code; When the vehicle acceleration is less than or equal to the first working threshold and greater than a preset second working threshold, determining that the working state code is a preset second code; When the vehicle acceleration is less than or equal to the second working threshold, the working state code is determined to be a preset third code.

3. The shielding and noise reduction method for high-voltage wire harness of new energy vehicles according to claim 1 is characterized in that: The step of performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics includes: Perform basis function matching according to the current fluctuation data to obtain a decomposed basis function; Recursively decomposing the decomposition basis function and the current fluctuation data to obtain a sub-band coefficient set; Reconstructing a signal according to the sub-band coefficient set to obtain a sub-band time domain signal; The entropy value is quantized according to the sub-band time domain signal to obtain the current fluctuation characteristics.

4. The shielding and noise reduction method for high-voltage wire harness of new energy vehicles according to claim 1 is characterized in that: The performing of environmental analysis according to the environmental condition data to obtain environmental state characteristics includes: Perform spectrum analysis according to the environmental condition data to obtain the proportion of high-frequency energy; When the high-frequency energy ratio is greater than a preset high-frequency threshold, determining that the electromagnetic propagation path is radiation propagation; Perform periodic detection according to the environmental condition data to obtain a propagation period; When the propagation period is an integer, it is determined that the electromagnetic propagation path is waveguide propagation; Extracting field strength according to the electromagnetic propagation path and the environmental condition data to obtain field strength data; Feature fusion is performed based on the electromagnetic propagation path and the field strength data to obtain environmental state features.

5. The shielding and noise reduction method for high-voltage wire harness of new energy vehicles according to claim 1 is characterized in that: The training process of the current prediction model includes: A current prediction model is constructed based on a long short-term memory neural network, where the input layer includes historical current characteristics, historical working conditions, and historical environmental characteristics; The output layer is the target current prediction value; In the iterative process, the mean square error between the predicted value and the true value is used as the loss function; Complete one round of iteration to update parameters; When it is detected that the loss function of the model meets the conditions or the number of training times reaches the set upper limit, the training is considered to be completed and the trained model is obtained.

6. The shielding and noise reduction method for high-voltage wire harness of new energy vehicles according to claim 1 is characterized in that: The step of extracting noise according to the predicted current data to obtain high-frequency noise data includes: Performing baseline correction according to the predicted current data to obtain residual waveform data; Perform frequency domain transformation according to the residual waveform data to obtain energy spectrum density distribution; Perform high-frequency filtering according to the energy spectrum density distribution to obtain high-frequency noise distribution; Time-frequency joint labeling is performed according to the high-frequency noise distribution to obtain high-frequency noise data.

7. The shielding and noise reduction method for high-voltage wire harness of new energy vehicles according to claim 1 is characterized in that: The performing impedance matching according to the high-frequency noise data and outputting target impedance parameters comprises: Performing spectrum extraction according to the high-frequency noise data to obtain a noise spectrum; Performing sensitivity calculation according to the noise spectrum to obtain a sensitivity distribution curve; Performing deviation calculation according to the sensitivity distribution curve to obtain an impedance deviation index; Multi-parameter optimization is performed according to the impedance deviation index to obtain the target impedance parameters.

8. A shielding and noise reduction system for high-voltage wiring harnesses of new energy vehicles, characterized in that: include: A data acquisition module, used to acquire current fluctuation data, working status code and environmental condition data; A current characteristic module, used for performing wavelet decomposition according to the current fluctuation data to obtain current fluctuation characteristics; An environmental characteristic module, used to perform environmental analysis based on the environmental condition data to obtain environmental state characteristics; A current prediction module, used for inputting the current fluctuation characteristics, the working state code and the environmental state characteristics into a preset current prediction model to obtain predicted current data; A noise extraction module, used for performing noise extraction according to the predicted current data to obtain high-frequency noise data; The impedance matching module is used to perform impedance matching according to the high-frequency noise data and output target impedance parameters.

9. An electronic device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the shielding and noise reduction method for the high-voltage wire harness of a new energy vehicle as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the shielding and noise reduction method for the high-voltage wire harness of a new energy vehicle as described in any one of claims 1 to 7.

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