A method and system for electric steering control of new energy vehicles

Abstract

This invention relates to the field of automotive electronic control technology, and more particularly to a method and system for electronically controlled steering in new energy vehicles. The method includes: acquiring and preprocessing a four-wheel vibration data sequence; segmenting and evaluating the consistency of the vibration data sequence for each wheel; selecting the current data sequence from the optimal segment to calculate the road condition influence coefficient; matching the current data sequence with historical vibration data sequences; calculating the similarity to determine the influence weight; adjusting the influence coefficient based on the steering angle change rate of the historical vibration data sequence; inputting the adjusted road condition influence coefficient into the ECU; and dynamically adjusting the steering assist torque to achieve precise steering control. This invention selects the optimal segment through segmentation and consistency evaluation to accurately reflect the current road conditions, and dynamically adjusts the steering assist torque based on historical vibration data sequence matching and steering angle change rate, thereby improving driving safety and comfort.

Description

Technical Field

[0001] This invention relates to the field of automotive electronic control technology. In particular, it relates to an electronic steering control method and system for new energy vehicles. Background Technology

[0002] New energy vehicles, as a green and efficient means of transportation, are gradually becoming the mainstream of the automotive industry. They not only reduce reliance on traditional fossil fuels and lower emissions, but also provide users with a more comfortable and convenient driving experience due to their efficient power systems and intelligent driver assistance technologies. Among the many key technologies, electric steering systems, as an important component for improving driving safety and comfort, are receiving increasing attention.

[0003] Steering control is an indispensable and crucial aspect of vehicle operation, directly affecting its handling, stability, and safety. Good steering control allows drivers to easily and accurately maneuver the vehicle under various driving conditions, effectively preventing traffic accidents. In new energy vehicles, the electronic steering system uses an electronic control unit (ECU) to intelligently manage and control the steering system. It automatically adjusts the steering assist based on the vehicle's driving status and the driver's intentions, achieving more precise, flexible, and efficient steering. This not only improves driving comfort and convenience but also enhances vehicle stability in complex road conditions.

[0004] While existing electronic steering systems have improved steering control performance to some extent, they still rely solely on steering torque and vehicle speed to calculate output torque, neglecting the influence of road conditions. This results in the system failing to accurately adapt to changes in road surface requirements for assist torque under varying conditions. For example, on uneven or gravel roads, the increased impact between the tires and the road surface necessitates greater assist torque to maintain stable steering. However, existing electronic steering systems cannot adjust and provide sufficient steering assistance in a timely manner based on current road conditions. Consequently, a discrepancy exists between the actual output torque and the desired effect, impacting the stability of the assisted steering and reducing driving safety and comfort. Summary of the Invention

[0005] To address the problem that while existing electronic steering systems improve steering performance, they rely solely on steering torque and vehicle speed to calculate output torque, neglecting road conditions and failing to accurately adjust auxiliary torque under different road conditions. This results in insufficient or excessive steering assistance, affecting steering stability and reducing driving safety and comfort. The present invention provides solutions in the following aspects.

[0006] In a first aspect, a method for electric steering control of a new energy vehicle includes: acquiring vibration data sequences of the four wheels during vehicle operation and preprocessing them; segmenting the vibration data sequences of each wheel based on amplitude to obtain segmented sequences; performing consistency evaluation on each segmented sequence and selecting the segmented sequence corresponding to the maximum consistency evaluation value as the optimal segment; selecting vibration data containing the current moment from the optimal segment as the current data sequence to assist in determining road conditions; calculating the road condition influence coefficient of the current data sequence; matching the current data sequence with each segment of historical vibration data sequences, calculating the similarity between the current data sequence and each segment of historical vibration data sequences, determining the influence weight of each segment of historical vibration data sequences on the current data sequence based on the similarity, calculating the rate of change of wheel steering angle during driving corresponding to each segment of historical vibration data sequences, and adjusting the road condition influence coefficient of the current data sequence by combining the influence weight and the rate of change; inputting the adjusted road condition influence coefficient into the ECU to dynamically adjust the output steering assist torque to achieve steering control.

[0007] By segmenting and evaluating the consistency of vibration data sequences, the current data sequence in the optimal segment is selected to calculate the road condition influence coefficient, thus providing real-time road information for steering control. Furthermore, through matching and similarity analysis with historical vibration data sequences, and by incorporating the wheel steering angle change rate in the historical vibration data sequences, the current road condition influence coefficient is adjusted, achieving dynamic optimization of the steering assist torque. This effectively solves the problem of insufficient or excessive steering assistance in existing electronic steering systems under different road conditions, significantly improving steering stability and accuracy, thereby enhancing driving safety and comfort.

[0008] Preferably, the consistency evaluation of each segment sequence includes:

[0009] The vibration data sequence is divided into preset time windows, with the vibration data in each time window being a data segment. The amplitude of the data segment in each preset time window is calculated and used as the feature value of the time window for input to cluster analysis.

[0010] The number of clusters is determined based on the preset range of the number of clusters. The number of different clusters is traversed within the preset range to complete hierarchical clustering and obtain the clustering result corresponding to the preset number of clusters.

[0011] Take any clustering result as the result to be analyzed, and take any cluster in the result to be analyzed as the cluster to be analyzed. Calculate the time difference between the clusters to be analyzed in each pair of vibration data sequences of each wheel, and select the ratio between the largest time difference and the mean of the time length in all time windows in the cluster to be analyzed as the hysteresis difference.

[0012] The ratio between the standard deviation and the mean of the information entropy of all time window amplitudes in each cluster to be analyzed is used as the coefficient of variation. The product of the hysteresis difference and the coefficient of variation is exponentially decayed using a negative exponential function to obtain the consistency contribution value of each cluster to be analyzed. The consistency contribution values ​​of all clusters to be analyzed are averaged to obtain the consistency of the analysis results.

[0013] By segmenting vibration data sequences according to preset time windows and calculating the amplitude within each time window, continuous vibration data can be transformed into discrete data points. Hierarchical clustering effectively segments the vibration data, ensuring relatively stable road conditions within each segment. By calculating hysteresis differences and coefficients of variation, combined with an exponential decay function, the consistency of wheel vibration data within each cluster can be quantified, allowing for the selection of the optimal clustering result. This significantly improves the accuracy and robustness of road condition assessment, providing more reliable road information for electronic steering systems, thereby enhancing the stability and adaptability of steering control and improving driving safety and comfort.

[0014] Preferably, the method for calculating the road surface condition influence coefficient includes:

[0015] Taking any wheel as the target wheel, obtain the vibration data sequence of the target wheel, including the information entropy value of the amplitude, the standard deviation of the amplitude, and the mean of the amplitude for all time windows. The ratio between the standard deviation of the amplitude and the mean is used as the degree of dispersion. Calculate the product of the information entropy value of all wheels and the degree of dispersion and sum them to obtain the road condition influence coefficient.

[0016] Preferably, the method for calculating the similarity between the historical vibration data sequence and the current data sequence includes:

[0017] Using any wheel as the target wheel, the dynamic time bending algorithm is used to calculate the shortest path distance between each segment of the historical vibration data sequence of the target wheel and the current data sequence;

[0018] Calculate the total number of matching point pairs between each segment of the historical vibration data sequence and the current data sequence of the target wheel. Normalize the ratio between the shortest path distance and the total number of matching point pairs. Sum all the normalization results and take the average value as the similarity between each segment of the historical vibration data sequence and the current data sequence.

[0019] Preferably, the calculation method for the adjusted road surface condition influence coefficient includes:

[0020] Using any historical vibration data sequence as the target data sequence, calculate the average rate of change of wheel steering angle in the target data sequence. Use the similarity between the target data sequence and the current data sequence as the influence weight, and weightedly fuse the contribution values ​​of all target data sequences to obtain a comprehensive adjustment factor. Multiply the comprehensive adjustment factor by the road condition influence coefficient of the current data sequence to obtain the adjusted road condition influence coefficient.

[0021] Preferably, the dynamically adjusted steering assist torque includes:

[0022] The product of the adjusted road condition influence coefficient and the correction coefficient plus 1, and the sum of these, is multiplied by the original steering assist torque to obtain the corrected steering assist torque.

[0023] Preferably, the preprocessing step includes:

[0024] Wavelet denoising technology was applied to the collected wheel vibration data sequence to filter out high-frequency noise and improve data quality. The denoised vibration data sequence was then aligned in chronological order and smoothed to reduce data fluctuations and make the data curve smoother.

[0025] Secondly, a new energy vehicle electric steering control system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned new energy vehicle electric steering control method is implemented.

[0026] The present invention has the following effects:

[0027] 1. This invention segments the vibration data sequence and evaluates the consistency of each segment, selecting the segment with the highest consistency as the optimal segment. This ensures that the current data sequence accurately reflects the current road surface conditions, effectively improving the accuracy of road surface condition assessment and providing reliable data support for subsequent steering assist torque adjustments.

[0028] 2. This invention dynamically adjusts the steering assist torque by matching the current data sequence with historical vibration data sequences, calculating the similarity, and adjusting the road condition influence coefficient of the current data sequence based on the steering angle change rate of the historical vibration data sequences. This allows the steering assist torque to be optimized according to real-time road conditions, improving the accuracy and adaptability of steering control. Attached Figure Description

[0029] Figure 1 This is a flowchart of steps S1-S4 in an embodiment of the present invention for a new energy vehicle electronic steering control method.

[0030] Figure 2This is a structural block diagram of an electric steering control system for a new energy vehicle according to an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0032] Reference Figure 1 A method for controlling the electric steering of a new energy vehicle includes steps S1-S4, as detailed below:

[0033] S1: Obtain the vibration data sequence of the four wheels during vehicle operation and perform preprocessing.

[0034] To meet the stability requirements of vehicle driving under different road conditions, vibration sensors are installed at the four wheel hubs to collect vibration data sequences during vehicle driving and perform preprocessing.

[0035] Specifically, the preprocessing steps include: firstly, applying wavelet denoising technology to the wheel vibration data sequence to filter out high-frequency noise, thereby improving the quality and purity of the data. Then, aligning the wavelet-denoised vibration data sequence according to time sequence and further smoothing it to reduce fluctuations in the data, making the data curve smoother and facilitating subsequent analysis.

[0036] Because each wheel is positioned differently, its vibration data characteristics also differ. Therefore, the interaction force between the tire and the road surface changes with road conditions. For example, when a vehicle travels on a smooth road, the contact between the tire and the road surface is relatively stable, and the vibration is small; however, when the vehicle travels on an uneven road surface (such as potholes, gravel roads, etc.), the impact between the tire and the road surface intensifies, and the vibration amplitude increases significantly. Wheel vibration sensors can capture the current vibration data sequence and reflect the interaction force between the tire and the road surface. Therefore, by analyzing wheel vibration data, transient events on the road surface (such as potholes, gravel, seams, etc.) can be sensitively captured, thereby determining the road surface condition.

[0037] To further illustrate, vibration data under different road surface conditions exhibit different characteristics. For example, vibration data from smooth roads has smaller amplitudes and less fluctuation, while vibration data from bumpy roads has larger amplitudes and more pronounced fluctuations. By analyzing these characteristics, the smoothness and roughness of the road surface can be quantified. Since the four wheels are in different positions during vehicle movement, their vibration data characteristics also differ.

[0038] When assessing road conditions, the key factors significantly impacting vehicle handling and steering stability are the smoothness and roughness of the road surface. Wheel vibration data effectively reflects the smoothness or roughness of the road surface. However, because the specific contact conditions between each wheel and the ground differ, the vibration data of each wheel must be analyzed independently. Therefore, weights can be assigned to the vibration data of each wheel, and the vibration data of all four wheels can be comprehensively considered to more accurately reflect the road conditions traversed by the entire vehicle. The specific logical steps are as follows:

[0039] S2: The vibration data sequence of each wheel is segmented based on the amplitude to obtain a segmented sequence. The consistency of each segmented sequence is evaluated, and the segmented sequence corresponding to the maximum consistency evaluation value is selected as the best segment. The vibration data containing the current moment is selected from the best segment as the current data sequence to help determine the road surface condition. The road surface condition influence coefficient of the current data sequence is calculated.

[0040] Because vehicle wheel vibration data varies significantly when traversing different types of road surfaces, segmentation based on the trend of vibration data ensures that the road conditions within each segment are approximately stable, thereby improving the accuracy of the analysis. In particular, when a vehicle travels over uneven road surfaces (such as potholes or gravel roads), the impact between the tire and the road surface intensifies, leading to a significant increase in vibration amplitude; conversely, on relatively smooth roads, the vibration amplitude is relatively smaller. Therefore, changes in vibration amplitude can effectively reflect the roughness of the road surface traversed by the vehicle.

[0041] The vibration data sequence is divided into preset time windows, with the vibration data in each time window being a data segment. The amplitude of the data segment in each preset time window is calculated and used as the feature value of the time window for input to cluster analysis.

[0042] Within a preset range, different numbers of clusters are traversed to complete hierarchical clustering and obtain the clustering results corresponding to the preset number of clusters.

[0043] Take any clustering result as the result to be analyzed, and take any cluster in the result to be analyzed as the cluster to be analyzed. Calculate the time difference between the clusters to be analyzed in each pair of vibration data sequences of each wheel, and select the ratio between the largest time difference and the mean of the time length in all time windows in the cluster to be analyzed as the hysteresis difference.

[0044] The ratio between the standard deviation and the mean of the information entropy of all time window amplitudes in each cluster to be analyzed is used as the coefficient of variation. The product of the hysteresis difference and the coefficient of variation is exponentially decayed using a negative exponential function to obtain the consistency contribution value of each cluster to be analyzed. The consistency contribution values ​​of all clusters to be analyzed are averaged to obtain the consistency of the analysis results.

[0045] Specifically, the hierarchical clustering process includes:

[0046] The amplitude of each time window is initialized as a separate cluster. The distance between all data points is calculated and a distance matrix is ​​generated. The two closest clusters are obtained from the distance matrix and merged into a new cluster. At the same time, the distance matrix is ​​updated to reflect the distance between the new cluster and other clusters. The merging steps are repeated until the predetermined number of clusters is reached to obtain the clustering results.

[0047] For example, a preset time window The range of the number of clusters is This corresponds to multiple clustering results, that is, the consistency of clustering results with 3, 4, 5, 6, 7, 8, 9 and 10 clusters is calculated respectively.

[0048] Specifically, consistency satisfies the following relationship:

[0049] ;

[0050] In the formula, This indicates the consistency of the segments in the wheel vibration data sequence. Indicates the number of clusters. Indicates the first In the clustering segmentation results of the wheel vibration data sequence, the first... The start time of the time window in each cluster. Indicates the first In the clustering segmentation results of the wheel vibration data sequence, the first... The start time of the time window in each cluster. This indicates the first segment of the clustering results for the four wheel vibration data sequences. Each cluster contains the mean length of a time window. Indicates the first The standard deviation of the information entropy of the amplitude of all time windows in a cluster. Indicates the first The mean information entropy of the amplitude of all time windows in a cluster. Represented by natural numbers An exponential function with base 0. Represents the maximum value function. It is the first The and the first In the clustering segmentation results of the wheel vibration data sequence, the first... The maximum value of the time difference between the two sides of the i-th cluster, i.e., the i-th sequence in the two sequences. The larger of the left and right time differences of each cluster represents the lag time difference between two wheel vibration data sequences. The larger this time difference, the worse the consistency of the segmentation results of each sequence.

[0051] Within a preset range of the number of clusters (e.g., 3 to 10), select the cluster with the highest consistency as the optimal cluster. Ensure that the selected cluster is the most reliable and best reflects the true structure of the data. Among the optimal clusters, find the cluster containing the current data sequence and use it as the basis for road condition analysis when the electronic steering system controls assisted steering.

[0052] Given that the vibration data of the four wheels during vehicle operation differ and are not entirely consistent, the analysis process calculates and assigns corresponding weights to each wheel's vibration data sequence based on its characteristics. These weights are then used to calculate the road condition influence coefficient. The specific steps are as follows:

[0053] Taking any wheel as the target wheel, obtain the vibration data sequence of the target wheel, including the information entropy value of the amplitude, the standard deviation of the amplitude, and the mean of the amplitude for all time windows. The ratio between the standard deviation of the amplitude and the mean is used as the degree of dispersion. Calculate the product of the information entropy value of all wheels and the degree of dispersion and sum them to obtain the road condition influence coefficient.

[0054] Specifically, the road surface condition influence coefficient satisfies the following relationship:

[0055] ;

[0056] In the formula, This indicates the influence coefficient of road surface conditions. This indicates the total number of wheels. Indicates the current road surface condition of the first... The vibration data sequence of a wheel contains the information entropy values ​​of the amplitude across all time windows, reflecting the degree of disorder in the vibration data sequence, i.e., the information entropy values ​​of the first wheel vibration data sequence. The roughness of the road surface after each wheel passes through it is used as a weight when calculating the road condition influence coefficient. Indicates the current road surface condition of the first... Each wheel vibration data sequence contains the standard deviation of the amplitude across all time windows. Indicates the current road surface condition of the first... The individual wheel vibration data sequence contains the mean amplitude across all time windows; It reflects the degree of dispersion of the amplitude; the rougher the road surface, the more obvious the amplitude change.

[0057] By assigning corresponding weights to each wheel vibration data sequence based on its characteristics, the road condition influence coefficient can be calculated, which can accurately present the actual road conditions on which the vehicle is traveling and effectively avoid misjudgments caused by deviations in a single wheel vibration data sequence.

[0058] The road condition influence coefficient reflects changes in road conditions, enabling the electronic steering system to dynamically adjust steering assist torque based on different road conditions, thus significantly improving vehicle steering stability in complex road conditions. Especially when driving on uneven or gravel roads, greater steering assist torque is needed to maintain a stable driving direction. In these situations, the road condition influence coefficient ensures that the electronic steering system provides sufficient assist torque, thereby reducing the driver's workload and enhancing driving safety.

[0059] S3: Match the current data sequence with each segment of historical vibration data sequence, calculate the similarity between the current data sequence and each segment of historical vibration data sequence, determine the influence weight of each segment of historical vibration data sequence on the current data sequence based on the similarity, calculate the rate of change of wheel steering angle during driving corresponding to each segment of historical vibration data sequence, and adjust the road condition influence coefficient of the current data sequence by combining the influence weight and the rate of change.

[0060] Using any wheel as the target wheel, the dynamic time bending algorithm is used to calculate the shortest path distance between each segment of the historical vibration data sequence of the target wheel and the current data sequence;

[0061] Calculate the total number of matching point pairs between each segment of the historical vibration data sequence and the current data sequence of the target wheel. Normalize the ratio between the shortest path distance and the total number of matching point pairs. Sum all the normalization results and take the average value as the similarity between each segment of the historical vibration data sequence and the current data sequence.

[0062] Specifically, the similarity coefficient satisfies the following relationship:

[0063] ;

[0064] In the formula, Indicates the first The degree of similarity between a segment of historical vibration data sequence and the current data sequence. Indicates the number of wheels. This indicates that the DTW algorithm is used to obtain the first... The first wheel The shortest path distance between a historical vibration data sequence and the current data sequence is used to quantify the similarity between the two sequences, since the two data sequences are not necessarily of equal length. This allows for a more accurate calculation of the similarity between the historical vibration data sequence and the current data sequence. This indicates that the DTW algorithm is used to obtain the first... The first wheel The total number of matching point pairs between a segment of historical vibration data sequence and the current data sequence. This indicates normalization processing.

[0065] Using any historical vibration data sequence as the target data sequence, calculate the average rate of change of wheel steering angle in the target data sequence. Use the similarity between the target data sequence and the current data sequence as the influence weight, and weightedly fuse the contribution values ​​of all target data sequences to obtain a comprehensive adjustment factor. Multiply the comprehensive adjustment factor by the road condition influence coefficient of the current data sequence to obtain the adjusted road condition influence coefficient.

[0066] Specifically, the adjusted road surface condition influence coefficient satisfies the following relationship:

[0067] ;

[0068] In the formula, This indicates the adjusted road surface condition impact coefficient. This represents the road surface condition influence coefficient of the current data sequence. This represents the total number of segments in the extracted historical vibration data sequence. Indicates the first The degree of similarity between a segment of historical vibration data sequence and the current data sequence. Indicates the first A sequence of historical vibration data includes the length of a time window. Indicates the first The rate of change of wheel steering angle in a segment of historical vibration data sequence This represents the time derivative; For the first The rate of change of wheel steering angle acceleration in a historical vibration data sequence can reflect the stability of the vehicle when it is driving in that segment of the historical vibration data sequence. The lower the rate of change, the higher the stability, the closer the electronic steering system's control of the assisted steering effect is to the ideal state, the smaller the error, and the smaller the impact of road conditions.

[0069] S4: Input the adjusted road condition influence coefficient into the ECU to dynamically adjust the output steering assist torque and achieve steering control.

[0070] The product of the adjusted road condition influence coefficient and the correction coefficient plus 1 is added to the sum, and then multiplied by the original steering assist torque to obtain the corrected steering assist torque.

[0071] Specifically, the modified steering assist torque satisfies the following relationship:

[0072] ;

[0073] In the formula, This indicates the adjusted steering assist torque. This represents the original steering assist torque, i.e., the output torque without considering the influence of road conditions. This indicates the adjusted road surface condition impact coefficient. This represents the correction factor, which controls the amplification of the output torque by the road surface condition influence factor. In this embodiment, it is taken as... You can choose based on your specific needs or experience.

[0074] This invention can dynamically adjust the steering assist torque according to real-time road conditions, significantly improving the stability and precision of steering control, and enhancing driving safety and comfort.

[0075] This invention also provides an electronic steering control system for new energy vehicles. For example... Figure 2 As shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a new energy vehicle electronic steering control method according to the first aspect of the present invention. The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. Their configuration and functions are known in the art and will not be described in detail here.

[0076] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A new energy vehicle electric control steering control method, characterized in that, The method comprises the following steps: Obtain the vibration data sequence of the four wheels in the vehicle driving state and preprocess the vibration data sequence; Segment the vibration data sequence of each wheel based on the amplitude to obtain a segmented sequence, evaluate the consistency of each segmented sequence, and select the segmented sequence corresponding to the maximum consistency evaluation value as the optimal segmented sequence. The vibration data containing the current time in the optimal segmented sequence is selected as the current data sequence to assist in determining the road surface condition, and the road surface condition influence coefficient of the current data sequence is calculated; Match the current data sequence with each historical vibration data sequence, calculate the similarity between the current data sequence and each historical vibration data sequence, determine the influence weight of each historical vibration data sequence on the current data sequence based on the similarity, calculate the change rate of the wheel steering angle in the driving process corresponding to each historical vibration data sequence, and adjust the road surface condition influence coefficient of the current data sequence in combination with the influence weight and the change rate; Input the adjusted road surface condition influence coefficient into the ECU to dynamically adjust the output steering assist torque and realize steering control; Divide the vibration data sequence according to a preset time window, wherein the vibration data in each time window is a data segment, calculate the amplitude of each data segment in the preset time window, and use the amplitude as the characteristic value of the time window as the input for clustering analysis; Determine the number of clustering clusters according to the range of the preset number of clustering clusters, iterate through different numbers of clustering clusters within the preset range, complete hierarchical clustering, and obtain the clustering result corresponding to the preset number of clustering clusters; Take any clustering result as an analysis result, take any clustering cluster in the analysis result as an analysis cluster, calculate the time difference between the analysis clusters of the vibration data sequences of each wheel, and select the ratio between the maximum time difference and the average time length of all time windows in the analysis cluster as the lag difference; Take the ratio between the standard deviation and the average of the information entropy of the amplitudes of all time windows in each analysis cluster as the coefficient of variation, and use the product of the lag difference and the coefficient of variation to perform exponential decay using a negative exponential function to obtain the consistency contribution value of each analysis cluster. Average the consistency contribution values of all analysis clusters to obtain the consistency of the analysis result; Take any wheel as a target wheel, obtain the information entropy value of the amplitudes of all time windows in the vibration data sequence of the target wheel, the standard deviation and the average of the amplitudes, take the ratio between the standard deviation and the average of the amplitudes as the dispersion degree, calculate the sum of the products of the information entropy values and the dispersion degrees of all wheels to obtain the road surface condition influence coefficient; Take any wheel as a target wheel, and use the dynamic time warping algorithm to calculate the shortest path distance between each historical vibration data sequence and the current data sequence of the target wheel; Calculate the total number of matching point pairs between each historical vibration data sequence and the current data sequence of the target wheel, normalize the ratio between the shortest path distance and the total number of matching point pairs, and average the sum of all normalized results to obtain the similarity between each historical vibration data sequence and the current data sequence of the target wheel. The average value of the wheel steering angle change rate of each historical vibration data sequence is calculated, the similarity between the target data sequence and the current data sequence is taken as an influence weight, the contribution values of all target data sequences are weighted and fused to obtain a comprehensive adjustment factor, and the comprehensive adjustment factor is multiplied by the road surface condition influence coefficient of the current data sequence to obtain an adjusted road surface condition influence coefficient; The product of the adjusted road surface condition influence coefficient and the correction coefficient is added by 1, and the product of the original steering auxiliary torque is taken as a corrected steering auxiliary torque.

2. The electric control steering control method of a new energy vehicle according to claim 1, characterized in that, The preprocessing step comprises: The wavelet denoising technology is applied to the collected wheel vibration data sequence to filter high-frequency noise and improve data quality; the denoised vibration data sequence is aligned in time sequence and smoothed to reduce data fluctuation and make the data curve smoother.

3. A new energy vehicle electric control steering control system, characterized in that, The method comprises: A processor and a memory, wherein the memory stores computer program instructions which, when executed by the processor, implement the new energy automobile electric control steering control method according to any one of claims 1-2.

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