Stepless speed change zeroing control system based on direct-axis electric drive axle

Abstract

The invention relates to the technical field of stepless speed change control, and discloses a stepless speed change zeroing control system based on a direct-axis electric drive axle, which comprises a signal acquisition module, a torque analysis module, a zeroing control module, a mode switching module, a reference calibration module and an execution output module. The signal acquisition module acquires a rotating speed signal and torque data and sets a zero setting detection interval; the torque analysis module divides torque compensation nodes and generates feature vectors; the zero setting control module establishes a correction rule and obtains a compensation parameter; the mode switching module identifies an output mode and calculates a rotating speed fluctuation amplitude; the reference calibration module deduces an optimal zeroing threshold value and generates a torque deviation sequence; and the execution output module integrates to form a control scheme. The system achieves torque dynamic compensation, rotation speed fluctuation suppression and threshold value self-adaptive calibration through multi-module cooperation, improves the stability and control precision of an electric drive axle under complex working conditions, and is suitable for stepless speed change control of an electric vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of continuously variable transmission control, and particularly to a continuously variable transmission zero adjustment control system based on a direct-axis electric drive axle. Background Art

[0002] In the field of the drive systems of new energy vehicles and electric vehicles, as a core power component, the transmission efficiency and control accuracy of a direct-axis electric drive axle directly affect the power performance, energy consumption level, and driving comfort of the vehicle. In the face of complex working conditions, traditional electric drive axle variable speed control systems generally have problems such as unstable torque output and large rotational speed fluctuations. Especially during the zero adjustment control process, it is difficult to achieve precise compensation for dynamic torque changes, resulting in phenomena such as power interruption, impact vibration, etc. when the transmission system shifts gears or the load suddenly changes, severely restricting the application and popularization of electric drive axles in high-end equipment and new energy vehicles.

[0003] In the prior art, most zero adjustment control systems adopt a torque compensation strategy with a fixed threshold and lack the ability to adaptively adjust to real-time working conditions. For example, traditional solutions usually perform compensation based on a preset torque-rotation speed mapping table. However, in actual operation, the load volatility rate, phase shift parameter of the drive axle, and dynamic changes during the variable speed stage will cause compensation lag or overcompensation, unable to meet the requirement of smooth torque transition during the continuously variable transmission process. In addition, the analysis of torque characteristics in existing systems stays at a single dimension, and the coupling relationship between torque type, driving frequency, and phase characteristics is not fully considered, making it difficult to establish a zero adjustment correction rule for multi-parameter coordination, resulting in insufficient stability of the system under high-frequency disturbance working conditions.

[0004] With the increasing demand for the intelligence and high efficiency of drive systems in electric vehicles, developing a continuously variable transmission zero adjustment control system that can real-time sense the changes in working conditions and dynamically adjust the compensation strategy has become a technical problem urgently to be solved in the industry. How to achieve multi-node phase analysis of torque data, construct an adaptive mode switching network, and deduce the optimal zero adjustment threshold are the key technical bottlenecks for improving the transmission performance of electric drive axles. The limitations of the prior art in aspects such as multi-module cooperative control, dynamic parameter matching, and adaptability to complex working conditions urgently require a more advanced control solution to break through the shackles of traditional technologies. Summary of the Invention

[0005] The purpose of the present invention is to provide a continuously variable transmission zero adjustment control system based on a direct-axis electric drive axle to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A continuously variable transmission zero adjustment control system based on a direct-axis electric drive axle, the system includes: A signal acquisition module, configured to obtain the rotational speed signal and torque output data of the drive axle, and set a zero adjustment detection interval corresponding to the variable speed working condition; A torque analysis module, which is used to divide multiple torque compensation nodes within a zeroing detection interval, perform phase analysis on the torque data of each node, and generate a torque feature vector corresponding to the node; A zeroing control module, which is used to extract dynamic compensation indicators from the torque feature vector, establish a zeroing correction rule associated with the torque compensation node, and obtain the phase compensation parameters corresponding to the rule; A mode switching module, which is used to identify the drive output mode in the phase compensation parameters, adaptively switch the dynamic compensation indicators according to the output mode, and calculate the rotational speed fluctuation amplitude of each node under different switching strategies; A reference calibration module, which is used to deduce the optimal zeroing threshold based on the rotational speed fluctuation amplitude, and generate a torque deviation sequence by comparing the current torque output value with the optimal zeroing threshold; An execution output module, which is used to analyze the torque deviation sequence, and integrate the torque deviation sequence into a stepless speed change zeroing control scheme based on the rotational speed fluctuation direction of the node.

[0007] Preferably, the implementation manner of the torque analysis module includes: constructing a drive feature library corresponding to the torque compensation node, where the drive feature library contains a drive parameter vector mapped by a rotational speed signal and torque data; Perform equivalent phase matching on the drive parameter vector, and divide the torque compensation interval of the drive parameter vector according to the matching result; extract the distribution critical point of the torque data from the torque compensation interval, and set the distribution critical point as the torque compensation node.

[0008] Preferably, the division of the torque compensation interval of the drive parameter vector further includes: According to the torque type and speed change stage in the drive parameter vector, extract the drive frequency, load volatility, and phase shift parameters, and generate a drive feature label based on the above parameters; Associate the drive feature label with the drive parameter vector, and screen the drive parameter vectors with an equivalent degree higher than the preset torque threshold to form a torque compensation interval by calculating the torque equivalence degree between the feature labels.

[0009] Preferably, the implementation manner of generating the torque feature vector corresponding to the node includes: For each torque compensation node, obtain the rotational speed fluctuation data of the node within a preset period according to the timing position of the node in the zeroing detection interval, and calculate the rotational speed fluctuation coefficient of the node; When the rotational speed fluctuation coefficient exceeds the first zeroing threshold, mark the node as a high-frequency disturbance node, and extract its torque data to form a torque feature vector; when the rotational speed fluctuation coefficient is lower than the first zeroing threshold, mark the node as a steady-state node, and perform phase superposition on the torque data of adjacent nodes of the node, and reconstruct the superimposed data into a torque feature vector.

[0010] Preferably, the implementation method of the zero adjustment control module includes: Separate the driving torque ratio, no-load torque ratio and phase deviation parameters from the torque feature vector, and generate a zero adjustment correction rule for the torque compensation node based on the above parameters; If the number of compensation nodes covered by the current zero adjustment correction rule is less than the preset compensation threshold, traverse the torque feature vectors of adjacent torque compensation nodes, and add the compensation indicators not included in the correction rules of adjacent nodes to the current rule.

[0011] Preferably, the implementation method of the mode switching module includes: obtaining the phase factor of the switching frequency and the switching factor of the load response rate in the driving output mode; Construct a mode transfer network associated with the phase factor and the switching factor, and determine the rotational speed fluctuation amplitude under different switching strategies according to the transfer probabilities of each path in the network.

[0012] Preferably, the construction of the mode transfer network further includes: Identify the periodic characteristics of the phase factor. If the current periodic characteristics exactly match the preset driving period, set the phase factor as the starting node of the mode transfer network; Calculate the switching correlation degree between the phase factor and the switching factor, and generate the intermediate nodes and termination nodes of the mode transfer network in descending order of the correlation degree; Perform phase backtracking on the termination node. When the correlation degree of the termination node is lower than the preset switching threshold, output it as the final path of the mode transfer network.

[0013] Preferably, the implementation method of calculating the rotational speed fluctuation amplitude includes: Statistically calculate the mean value of the phase factor and the range of the switching factor of each termination node in the mode transfer network, and calculate the global covariance of all node factors; Subtract the mean value of the phase factor of a single termination node from the mean value of the phase factor of the adjacent node, and divide by the global covariance to obtain the phase fluctuation coefficient; at the same time, calculate the ratio of the range of the switching factor to the global covariance, and weight and sum the two as the rotational speed fluctuation amplitude of the node.

[0014] Preferably, the implementation method of deriving the optimal zero adjustment threshold includes: Extract the disturbance mode in the historical data that is closest to the current rotational speed fluctuation amplitude, and calculate the Euclidean distance in the phase distribution between the two as the first calibration reference value; Statistically calculate the difference in the number of peak points between the current rotational speed fluctuation amplitude and the historical disturbance mode, and use the difference quantity as the second calibration reference value; Based on the linear combination of the first calibration reference value and the second calibration reference value, match the optimal zero adjustment threshold in the preset zero adjustment threshold table.

[0015] Preferably, the implementation method of the execution output module includes: dividing a positive compensation interval and a negative compensation interval according to the rotational speed fluctuation direction of each node in the torque deviation sequence; extracting the attenuation rate of the torque deviation in the positive compensation interval and the amplification rate of the torque deviation in the negative compensation interval, and performing weighted fusion on the two according to the load weight of the torque compensation node to generate a compensation parameter for the stepless speed change zero adjustment control scheme.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: Through the collaborative work of multiple modules, precise dynamic control of the torque output of the electric drive axle is achieved, significantly improving the stability and response speed of the transmission system under complex working conditions. The signal acquisition module obtains the rotational speed signal and torque data in real time and sets a zero adjustment detection interval, providing basic data support for subsequent precise analysis and enabling the system to specifically process the key data interval under variable speed conditions. The torque analysis module realizes multi-dimensional analysis of torque data by constructing a drive feature library, dividing torque compensation intervals, and setting compensation nodes. It not only considers the influence of torque type and variable speed stage, but also accurately screens out parameter vectors with similar torque characteristics through drive feature labels and equivalence calculation, making the generation of torque feature vectors more in line with actual working conditions and providing a reliable basis for dynamic compensation.

[0017] The zero adjustment control module extracts multiple parameters from the torque feature vector to generate correction rules, and supplements compensation indicators by traversing adjacent nodes, ensuring the comprehensiveness and adaptability of the rules. It can dynamically adjust the compensation strategy according to the real-time torque characteristics, avoiding the hysteresis and limitations of the traditional fixed threshold scheme. The mode switching module realizes intelligent identification of the drive output mode and optimization of the dynamic switching strategy by constructing a mode transition network and analyzing the correlation degree of the phase factor and the switching factor, accurately calculating the rotational speed fluctuation amplitude of each node, enabling the system to automatically select the optimal compensation path under different working conditions, effectively suppressing rotational speed fluctuations, and improving transmission smoothness.

[0018] The reference calibration module realizes dynamic adaptive calibration of the threshold by comparing and analyzing historical data with the current fluctuation amplitude, and matching the optimal zero adjustment threshold through linear combination. Compared with the traditional preset threshold method, it can better adapt to the real-time changes of working conditions and improve the accuracy of torque deviation detection. The execution output module divides the compensation interval according to the rotational speed fluctuation direction, combines the attenuation and amplification rates and the load weight to generate compensation parameters, realizing refined processing of torque deviation, enabling the stepless speed change zero adjustment control scheme to dynamically adjust the compensation intensity according to actual needs, and further improving the control accuracy and energy efficiency of the system.

[0019] Overall, through the organic coordination of multiple modules and in-depth mining and analysis of data, the system forms a complete closed-loop from data acquisition, feature analysis, rule establishment to dynamic control. It not only solves the problems of compensation lag and insufficient stability of traditional systems under complex working conditions, but also significantly improves the transmission efficiency and driving comfort of the electric drive axle, providing an advanced technical solution for the optimization of the transmission systems of electric vehicles and high-end equipment, and having significant engineering application value and market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is the working principle diagram of the stepless speed change zero adjustment control system based on the direct-axis electric drive axle of the present invention; Figure 2 is the working principle diagram for the torque analysis module to construct torque compensation nodes; Figure 3 is the working principle diagram for the generation of torque feature vectors; Figure 4 is the working principle diagram for the establishment of zero adjustment correction rules and the expansion of compensation indicators. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to Figures 1-4 , the stepless speed change zero adjustment control system based on the direct-axis electric drive axle involved in the present invention includes: a signal acquisition module, a torque analysis module, a zero adjustment control module, a mode switching module, a reference calibration module, and an execution output module. The specific implementation steps are as follows: Signal acquisition module: Real-time obtains the rotational speed signal and torque output data of the drive axle through sensors, and at the same time sets corresponding zero adjustment detection intervals according to different speed change working conditions (such as starting, accelerating, decelerating, etc.). For example, in the vehicle starting stage, the zero adjustment detection interval can be set as the interval where the rotational speed ranges from 0 to 30% of the rated rotational speed to specifically monitor the torque output stability under low rotational speed conditions.

[0023] Torque analysis module: Divides multiple torque compensation nodes within the zero adjustment detection interval. Each node corresponds to a specific torque output state. The module performs phase analysis on the torque data of each node to generate a vector reflecting the torque characteristics of this node, and this vector includes parameters such as torque amplitude, phase offset, and fluctuation frequency.

[0024] Zero adjustment control module: Extract dynamic compensation indicators (such as torque deviation rate, phase correction coefficient, etc.) from the torque feature vector, establish zero adjustment correction rules associated with each torque compensation node, and obtain the phase compensation parameters corresponding to the rules. For example, the phase lead compensation parameters for high-frequency disturbance nodes or the phase lag compensation parameters for steady-state nodes.

[0025] Mode switching module: Identify the drive output mode (such as constant torque mode, variable torque mode) in the phase compensation parameters, and adaptively switch the dynamic compensation indicators according to the output mode. At the same time, calculate the rotational speed fluctuation amplitude of each node under different switching strategies, and this amplitude reflects the influence degree of torque adjustment on rotational speed stability.

[0026] Reference calibration module: Deduce the optimal zero adjustment threshold based on the rotational speed fluctuation amplitude, and this threshold is determined by comparing the torque output performance under similar working conditions in historical data. The module generates sequence data representing torque deviation by comparing the current torque output value with the optimal zero adjustment threshold.

[0027] Execution output module: Analyze the torque deviation sequence, and based on the rotational speed fluctuation direction (positive fluctuation or negative fluctuation) of each node, integrate the torque deviation sequence into a specific continuously variable transmission zero adjustment control scheme. This scheme includes the compensation direction and compensation amount of each node to achieve real-time correction of the torque output of the drive axle.

[0028] The technical solution of the present invention will be further described in detail below in conjunction with specific embodiments.

[0029] Embodiment 1: In this embodiment, the specific implementation manner of the torque analysis module is as follows: First, construct a drive feature library corresponding to the torque compensation node. The construction of this drive feature library is based on the historical operation data of the direct-axis electric drive axle under different working conditions, and includes the rotational speed signals and torque data collected in real time by the rotational speed sensor and torque sensor and preprocessed. These data are mapped into multi-dimensional drive parameter vectors and stored in the library. The drive parameter vectors cover basic features such as rotational speed amplitude, torque mean, phase angle, etc., as well as derivative features such as trend terms and periodic components obtained through time series analysis, forming a comprehensive description of the operating state of the drive axle.

[0030] When performing equivalent phase matching on the drive parameter vector, a similarity calculation method based on the absolute value of the phase difference is adopted. Specifically, for any two drive parameter vectors, the absolute value of the point-by-point difference of their phase angle sequences is calculated, and the mean value is taken as the phase matching degree index. According to the preset phase matching threshold, the vectors with a phase matching degree higher than the threshold are divided into the same group, thereby forming different torque compensation intervals. For example, under a certain variable speed condition, the drive parameter vectors can be divided into multiple torque compensation intervals such as the starting acceleration interval, the constant speed cruising interval, and the deceleration braking interval through this matching method. The vectors within each interval have similar phase characteristics, reflecting the common operating characteristics of the drive axle at this stage.

[0031] When extracting the distribution critical points of the torque data from the torque compensation intervals, first, statistical analysis is performed on the torque data within each interval, and the probability density distribution of the torque values is calculated. By finding the local minimum points of the probability density function, the boundary points of the torque data distribution are determined, and these boundary points are the distribution critical points. For example, in the torque data distribution of the starting acceleration interval, when the torque changes from a slow increase to a rapid increase, the corresponding torque value is a distribution critical point, which is set as the torque compensation node to identify the transition node of the drive axle operating state.

[0032] When dividing the torque compensation intervals of the drive parameter vector, characteristic parameters also need to be extracted according to the torque type and variable speed stage in the drive parameter vector. The torque types include driving torque, no-load torque, braking torque, etc., and the variable speed stages include starting, low speed, medium speed, high speed, deceleration, and parking. For different torque types and variable speed stages, the drive frequency (the number of torque changes per unit time), the load volatility (the ratio of the load change amount to the average load), and the phase offset parameter (the difference between the actual phase and the ideal phase) are extracted. For example, in the case of the driving torque type at the high-speed constant speed stage, the drive frequency is low, the load volatility is small, and the phase offset parameter is stable within a small range. These parameters are combined to generate the corresponding drive characteristic label, such as "high speed - driving torque - low fluctuation - stable phase".

[0033] After associating the drive feature tags with the drive parameter vectors, a torque compensation interval is formed by screening vectors through calculating the torque equivalence degree between the feature tags. The calculation of the torque equivalence degree is based on the parameter differences in each dimension of the feature tags. Using the weighted Euclidean distance formula, different weights are assigned to dimensions such as torque type, gear shift stage, drive frequency, load volatility, and phase shift parameter, and the distance between two feature tags is calculated. The smaller the distance, the higher the torque equivalence degree. The preset torque threshold is the distance critical value set by experience. When the distance between two feature tags is less than this threshold, the corresponding drive parameter vectors are considered to have high equivalence and are grouped into the same torque compensation interval. For example, within a certain torque compensation interval, the torque equivalence degrees of multiple drive feature tags are all higher than the preset threshold, indicating that the drive parameter vectors corresponding to these tags have high similarity in torque characteristics and operating stages and can be used as data samples within the same interval for unified analysis and processing.

[0034] Through the above steps, the torque analysis module can accurately divide multiple torque compensation nodes within the zeroing detection interval and analyze the torque data of each node based on phase and characteristic parameters, providing basic data support for subsequent generation of torque feature vectors and establishment of zeroing correction rules. This process realizes the refined modeling of the operating state of the direct-axis electric drive axle through multi-dimensional analysis and feature extraction of drive parameters, ensuring that the division of torque compensation nodes can accurately reflect the torque change characteristics of the drive axle under different working conditions and laying a foundation for subsequent processing links of the continuously variable transmission zeroing control system.

[0035] Embodiment 2: In this embodiment, the implementation method of generating the torque feature vector corresponding to the node is as follows: For each torque compensation node within the zeroing detection interval, first determine its time range within the preset period according to the position of the node in the time series. The preset period can be set according to the characteristics of the gear shift working conditions. For example, it is set to 0.5 seconds in the starting working condition and 2 seconds in the high-speed cruising working condition to ensure that the obtained rotational speed fluctuation data can completely reflect the dynamic characteristics of the node in the typical operating stage. The rotational speed signal within this period is collected by a rotational speed sensor, and after removing high-frequency noise through digital filtering, the deviation sequence of the real-time value and the mean value of the rotational speed is calculated, and then the rotational speed fluctuation data is obtained.

[0036] The calculation of the rotational speed fluctuation coefficient is based on the statistical characteristics of the deviation sequence, specifically calculating the ratio of the standard deviation to the mean value of the deviation sequence. This coefficient is used to quantify the relative amplitude of the rotational speed fluctuation. For example, the rotational speed mean value of a certain node within the preset period is 1000 rpm and the standard deviation is 50 rpm, then the rotational speed fluctuation coefficient is 5%. By setting the first zeroing threshold (such as 3%), the nodes are divided into two categories: high-frequency disturbance nodes and steady-state nodes.

[0037] When the rotational speed fluctuation coefficient exceeds the first zero - adjustment threshold, determine that node as a high - frequency disturbance node. At this time, directly extract the output data of the torque sensor of this node within the preset period, including parameters such as the instantaneous value sequence, peak value, and valley value of the torque, and arrange them in chronological order to form a torque feature vector. For example, the torque data of the high - frequency disturbance node may show periodic fluctuations or sudden spikes, and its feature vector can be expressed as [t1, t2, t3,..., tn], where ti is the torque value at the i - th sampling moment, reflecting the dynamic change details of the torque at this node and used for formulating subsequent zero - adjustment correction rules.

[0038] When the rotational speed fluctuation coefficient is lower than the first zero - adjustment threshold, mark this node as a steady - state node. Since the rotational speed fluctuation of the steady - state node is small, it is difficult to capture significant features by analyzing its torque data alone. Therefore, it is necessary to perform collaborative processing by combining the torque data of adjacent nodes. Specifically, select N adjacent nodes before and after this steady - state node (N can be set according to the complexity of the working conditions, such as 2 - 5), and perform phase superposition on the torque data of these nodes. The method of phase superposition is: taking the phase of the steady - state node as the reference, translate the torque data of adjacent nodes in time according to their phase differences so that the torque waveforms of each node are aligned on the time axis, and then perform arithmetic mean or weighted mean (the weight can be set according to the distance from the steady - state node, with higher weights for nearer nodes) to obtain the superposed torque data sequence.

[0039] When reconstructing the superposed data to form a torque feature vector, it is necessary to perform dimensionality reduction on the superposition result to retain the main feature components. For example, extract the first k principal components (k is determined according to the data complexity, such as 2 - 3) through principal component analysis (PCA), and use the coefficients of the principal components as the elements of the feature vector. The reconstructed torque feature vector can comprehensively reflect the torque distribution trend of the steady - state node and its adjacent areas, avoiding the problem of feature loss caused by small fluctuations in the data of a single node.

[0040] In practical applications, the determination of the timing position needs to be combined with the operating state signals of the drive axle, such as the gear signal and the accelerator pedal position signal obtained through the CAN bus, to ensure that the preset period is synchronized with the time node of the current working condition. For example, when detecting the signal of stepping on the accelerator pedal, set the preset period starting from this moment, and collect the rotational speed and torque data during the starting and accelerating stages to ensure that the node analysis is accurately corresponding to the working condition stage.

[0041] In addition, the sampling frequency of the rotational speed fluctuation data needs to meet the Nyquist sampling theorem to avoid frequency aliasing. Usually, a sampling frequency higher than twice the highest frequency of the rotational speed signal (such as 100Hz) is used to ensure the accuracy of the calculation of the rotational speed fluctuation coefficient. For the extraction and superposition processing of torque data, it is necessary to perform synchronous clock calibration to ensure the consistency of data of different nodes on the time axis and avoid phase misalignment problems caused by clock deviation.

[0042] Through this method, the generated torque feature vector can adaptively select different processing strategies based on the node's disturbance characteristics: for high-frequency disturbance nodes, the original torque data is directly extracted to retain its dynamic details; for steady-state nodes, the regionalized torque distribution characteristics are mined by superimposing and reconstructing the data of adjacent nodes. This differentiated processing method not only ensures a rapid response to abnormal fluctuations, but also improves the reliability of data features under steady-state conditions, providing accurate input data for the subsequent zeroing control module to extract dynamic compensation indicators. The entire process achieves efficient analysis of torque data and accurate generation of feature vectors through the synergistic effects of timing synchronization, data filtering, feature calculation, and reconstruction.

[0043] Example 3: In this embodiment, the zeroing control module is implemented as follows: First, the driving torque ratio, no-load torque ratio and phase deviation parameter are separated from the torque characteristic vector. The torque characteristic vector is a multidimensional data sequence generated by the torque analysis module, which contains information such as the instantaneous value, mean value, peak value, etc. of the torque on the time axis. The driving torque ratio is obtained by calculating the ratio of the numerical sum of the driving torque interval in the characteristic vector to the numerical sum of the entire vector, and the no-load torque ratio is determined by the ratio of the numerical sum of the no-load torque interval to the vector sum. The phase deviation parameter is obtained by comparing the difference between the actual phase sequence of the torque characteristic vector and the ideal phase sequence (preset based on the design working conditions of the direct-axis electric drive bridge), reflecting the degree of phase deviation of the torque output.

[0044] When generating zeroing correction rules for the torque compensation node based on the aforementioned parameters, a mapping relationship between the parameters and the correction strategy must be established. For example, when the driving torque ratio exceeds a preset threshold (e.g., 70%) and the phase deviation parameter is greater than the allowable range (e.g., ±5°), the node is determined to need to increase the no-load torque compensation to balance the phase deviation. When the no-load torque ratio is too high (e.g., exceeding 30%) and the phase deviation is in the negative range, the driving torque output ratio is determined to be reduced. These rules are developed through expert experience and historical data training and stored in the zeroing control module's rule library as tables or conditional statements for easy real-time access and matching.

[0045] If the number of compensation nodes covered by the current zeroing correction rule is less than the preset compensation threshold (e.g., 50% of the total number of nodes), the adjacent node rule traversal mechanism is triggered. During the traversal process, first obtain the torque feature vectors of the adjacent torque compensation nodes of the current node (determined according to the timing position, such as 3 nodes each for the previous and subsequent nodes), extract the drive torque ratio, no-load torque ratio, and phase deviation parameters in these vectors, and compare them with the parameters in the current rule. If there is a compensation index in the parameters of the adjacent nodes that is not included in the current rule (e.g., the phase deviation parameter of a certain adjacent node exceeds the processing range of the current rule), then add the correction strategy corresponding to this index (such as a stepped compensation method for out-of-range phase deviation) to the current rule.

[0046] During the rule extension process, it is necessary to ensure the compatibility of the newly added compensation index with the existing rules. For example, if the current rule is mainly for the positive compensation of phase deviation and the negative phase deviation index of the adjacent nodes is not covered, then add a processing branch for negative deviation to the existing rule to form a two-way compensation rule. At the same time, by calculating the correlation between the new index and the existing parameters (such as the Pearson correlation coefficient), judge its influence weight on the zeroing control. If the correlation is lower than the preset value (e.g., 0.3), then ignore this index to avoid rule redundancy.

[0047] The update frequency of the zeroing correction rule is synchronized with the division period of the zeroing detection interval, and usually triggers a rule optimization process at the end of each zeroing detection interval. For example, after a complete variable speed working condition (such as from start to stop), the system counts the rule coverage of all torque compensation nodes in this interval. If it does not reach the preset compensation threshold, start the adjacent node traversal and rule extension to ensure that the rule coverage in the next detection interval meets the control requirements.

[0048] In practical applications, the calculation of the drive torque ratio and no-load torque ratio needs to consider the dynamic switching of torque types. For example, when the straight-axis electric drive axle switches from the driving state to the braking state, the torque type changes from drive torque to braking torque. At this time, it is necessary to redefine the torque interval and adjust the ratio calculation method. The calculation of the phase deviation parameter is based on a real-time phase tracking algorithm. The actual phase is extracted from the speed signal through phase-locked loop (PLL) technology and compared with the preset ideal phase in real time to ensure the accuracy of the deviation calculation.

[0049] The zeroing control module and the torque analysis module interact through a real-time data interface. After the torque feature vector is generated, it is immediately transmitted to the zeroing control module for parameter separation and rule matching. For the torque feature vectors of high-frequency disturbance nodes, the system uses a priority queue for processing to ensure a quick response to sudden torque fluctuations; for the reconstructed feature vectors of steady-state nodes, a batch processing method is used to improve the calculation efficiency.

[0050] In the above - mentioned manner, the zero - adjustment control module can dynamically generate and expand zero - adjustment correction rules according to the real - time parameters of the torque feature vector, achieving precise control of the torque compensation nodes. This process, through the collaborative action of parameter separation, rule matching, adjacent node traversal, and index expansion, ensures that the zero - adjustment correction rules can adapt to the torque output characteristics of the straight - axis electric drive axle under different working conditions, providing a reliable rule basis for the subsequent mode - switching module and reference calibration module. At the same time, by presetting the compensation threshold and correlation filtering mechanism, the over - complexity and redundancy of the rules are avoided, ensuring the real - time performance and stability of the control system.

[0051] Embodiment 4: In this embodiment, the mode - switching module is implemented as follows: First, obtain the phase factor of the switching frequency and the switching factor of the load response rate in the drive output mode. The phase factor characterizes the phase periodic characteristics during the drive output mode switching, which can be obtained by extracting the phase - angle sequence of the main frequency components through Fourier transform of the drive - axle speed signal; the switching factor reflects the speed of the system response when the load changes, and is determined by calculating the ratio of the time required for the torque to reach the stable value after a load step change to the preset reference time. For example, in the economy mode, the switching frequency is low, the phase factor shows long - period characteristics, the load response rate is slow, and the switching - factor value is large; in the power mode, the switching frequency is high, the phase - factor period is short, the load response rate is fast, and the switching - factor value is small.

[0052] When constructing a mode - transfer network associated with the phase factor and the switching factor, first establish a network - node model. The nodes are divided into three categories: starting nodes, intermediate nodes, and terminating nodes. The starting node represents the initial state of the drive output mode, the intermediate node represents the transition state during the mode - switching process, and the terminating node represents the stable target - mode state. When identifying the periodic characteristics of the phase factor, determine its main period by calculating the autocorrelation function of the phase - factor sequence. If the current main period completely matches the preset drive period (such as the standard switching period under the design working conditions), set the corresponding phase factor as the starting node of the mode - transfer network. For example, when it is detected that the main period of the phase factor is 1.5 seconds, which is consistent with the preset drive period, use the state node corresponding to this phase factor as the network starting point.

[0053] When calculating the switching correlation degree between the phase factor and the switching factor, the mutual information algorithm is used to measure the dependence degree between the two. The larger the mutual information value, the higher the correlation degree between the two factors. The intermediate nodes and termination nodes of the mode transition network are generated in descending order of correlation degree. For example, the phase factor-switching factor combination with the highest correlation degree is used as the first intermediate node, and so on, until the combination with a correlation degree lower than the preset switching threshold (such as the mutual information value is less than 0.2) is used as the termination node. While generating the nodes, the transition probabilities between the nodes are statistically calculated based on the historical switching data to form the network path weights. For example, if the number of historical switches from the starting node to a certain intermediate node is 100 times and the number of successful times is 85 times, the transition probability of this path is set to 0.85.

[0054] When performing phase backtracking on the termination node, trace back from the termination node to the starting node in the reverse direction, and check whether the correlation degrees of all nodes on the path are higher than the preset switching threshold. If the correlation degree of a certain termination node is lower than the threshold, it is determined that this node is in an invalid state and this path is excluded; if the correlation degrees of all nodes meet the conditions, it is output as the final path of the mode transition network. For example, if the mutual information value of a certain termination node is 0.15, which is lower than the threshold of 0.2, the corresponding path will be excluded to ensure that the final network path only contains state transitions with high correlation degrees.

[0055] When calculating the rotational speed fluctuation amplitude, first, the mean value of the phase factor and the range of the switching factor of each termination node in the mode transition network are statistically calculated. The mean value of the phase factor is the arithmetic mean of all historical phase factors of this node, which reflects the phase characteristics under the stable state; the range of the switching factor is the difference between the maximum value and the minimum value of the switching factor, which reflects the fluctuation range of the load response rate. The global covariance is obtained by calculating the covariance matrix of the phase factor and the switching factor of all nodes, which characterizes the overall change correlation degree between the two factors.

[0056] For a single termination node, calculate the absolute value of the difference between the mean value of its phase factor and the mean value of the phase factors of the adjacent nodes (the previous and subsequent nodes), and divide it by the square root of the global covariance to obtain the phase fluctuation coefficient. This coefficient is dimensionless and is used to quantify the relative amplitude of the phase change. At the same time, calculate the ratio of the range of the switching factor to the global covariance to obtain the load fluctuation coefficient. The phase fluctuation coefficient and the load fluctuation coefficient are weighted and summed according to the preset weights (such as the phase fluctuation accounts for 60% and the load fluctuation accounts for 40%) as the rotational speed fluctuation amplitude of this node. For example, if the phase fluctuation coefficient of a certain termination node is 0.3, the load fluctuation coefficient is 0.2, and the weights are 0.6 and 0.4 respectively, then the rotational speed fluctuation amplitude is 0.3×0.6 + 0.2×0.4 = 0.26.

[0057] In practical applications, the acquisition of the phase factor and the switching factor needs to be synchronized with the Controller Area Network (CAN) bus data of the drive axle to ensure that the factor calculation is based on the operating state at the same moment. For example, the gear position signal of the transmission, the motor torque command, etc. are obtained in real time through the CAN bus, and combined with the speed and torque sensor data, the phase factor and the switching factor are dynamically updated. The construction of the mode transition network adopts an incremental learning method. As new operating data accumulates, the node association degree and the path transition probability are continuously updated to improve the adaptability of the network to real-time working conditions.

[0058] The calculation frequency of the rotational speed fluctuation amplitude is consistent with the switching frequency of the drive output mode, and usually triggers a calculation process after each mode switching is completed. For the mode transition network with multiple paths, a parallel computing architecture is adopted to simultaneously process the calculation of the fluctuation amplitude of each termination node, shortening the response time. The statistical quantities involved in the calculation process (such as mean, range, covariance) are updated using a sliding window algorithm, and the window size is dynamically adjusted according to the working condition stability (for example, the window size is 100 sampling points under stable working conditions and 50 sampling points under fluctuating working conditions) to ensure that the statistical results can reflect the current operating state in real time.

[0059] Through the above method, the mode switching module can construct a dynamic mode transition network based on the phase factor and the switching factor, accurately identify the state transition path of the drive output mode, and quantify the influence of different switching strategies through the calculation of the rotational speed fluctuation amplitude. This process realizes the adaptive switching control of the direct-axis electric drive axle output mode through the coordinated action of factor extraction, network construction, path optimization, and amplitude calculation, provides the rotational speed fluctuation data support under multiple strategies for the reference calibration module, and ensures that the continuously variable transmission zero adjustment control system can select the optimal switching strategy according to the actual working conditions, reduce the rotational speed fluctuation, and improve the operation stability.

[0060] Embodiment 5: In this embodiment, the implementation method for deriving the optimal zero adjustment threshold is as follows: First, extract the disturbance mode in the historical data that is closest to the current rotational speed fluctuation amplitude. The historical data is stored in the system database, and each disturbance mode includes features such as the rotational speed fluctuation amplitude, the phase distribution sequence, and the number of peak points. The amplitude difference between the current rotational speed fluctuation amplitude and each mode in the historical data is calculated through the Euclidean distance, and the first N (such as N = 5) historical modes with the smallest difference are selected as the candidate set.

[0061] Calculate the Euclidean distance between the current disturbance mode and each historical mode in the candidate set in terms of phase distribution as the first calibration reference value . The phase distribution is represented by the phase spectrum after discrete Fourier transform. Let the phase spectrum of the current mode be , and the phase spectrum of the historical mode be , then the Euclidean distance formula is:

[0062] Among them, represents the phase value of the -th frequency component of the current mode, represents the phase value of the -th frequency component of the historical mode, and n is the total number of frequency components.

[0063] Statistically analyze the difference in the number of peak points between the disturbance mode corresponding to the current rotational speed fluctuation amplitude and the historical disturbance mode, and use it as the second calibration reference value . The peak point is defined as the local maximum point in the rotational speed fluctuation curve, which is determined by comparing the values of adjacent sampling points. If the value of the current point is greater than the values of the points before and after it, it is determined as a peak point. is the number of peak points in the current mode and the number of peak points in the historical mode The absolute value difference, that is .

[0064] Based on the linear combination of the first calibration reference value and the second calibration reference value , match the optimal zeroing threshold in the preset zeroing threshold table. The preset zeroing threshold table is constructed through off-line simulation and engineering experience, and stores the thresholds corresponding to different and combinations . The linear combination expression is , where and are weight coefficients (automatically adjusted by the system according to the working condition type, such as under the acceleration working condition), and the optimal zeroing threshold is obtained by interpolating the K value in the threshold table.

[0065] The implementation method of the execution output module includes: according to the rotational speed fluctuation direction (positive fluctuation or negative fluctuation) of each node in the torque deviation sequence, divide the torque compensation nodes into a positive compensation interval and a negative compensation interval. Positive fluctuation means that the rotational speed is higher than the target rotational speed, and the corresponding torque deviation is negative; negative fluctuation means that the rotational speed is lower than the target rotational speed, and the corresponding torque deviation is positive. By traversing the torque deviation sequence, the nodes with continuous same-direction fluctuations are divided into the same interval. For example, if the torque deviations of 5 consecutive nodes are all negative, they are divided into the positive compensation interval.

[0066] Extract the attenuation rate of the torque deviation within the positive compensation interval and the increase rate of the torque deviation within the negative compensation interval. The attenuation rate is defined as the change rate of the absolute value of the torque deviation within the positive compensation interval, which is obtained by linearly fitting the absolute value of the slope of the torque deviation sequence within the interval; the increase rate is defined as the change rate of the absolute value of the torque deviation within the negative compensation interval, which is obtained by linearly fitting the absolute value of the slope of the torque deviation sequence within the interval. Weightedly fuse the two according to the load weights of the torque compensation nodes. The load weights are determined according to the load sizes of the drive axles corresponding to the nodes (such as the light load weight of 0.3, the medium load weight of 0.6, and the heavy load weight of 0.9). The calculation formula is:

[0067] where C is the compensated parameter after fusion, is the rate (attenuation rate or increase rate) of the th interval, is the load weight of the corresponding interval, and m is the total number of intervals. The finally generated continuously variable transmission zeroing control scheme adjusts the torque compensation amount of each node according to the C value to achieve closed-loop control of the rotational speed fluctuation.

[0068] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0069] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A continuously variable transmission zeroing control system based on a direct-axis electric drive axle, characterized in that, Including: A signal acquisition module, which is used to obtain the rotational speed signal and torque output data of the drive axle, and set a zeroing detection interval corresponding to the shifting working condition; A torque analysis module, which is used to divide multiple torque compensation nodes within the zeroing detection interval, perform phase analysis on the torque data of each node, and generate a torque feature vector corresponding to the node; A zeroing control module, which is used to extract dynamic compensation indicators from the torque feature vector, establish a zeroing correction rule associated with the torque compensation node, and obtain the phase compensation parameter corresponding to the rule; A mode switching module, which is used to identify the drive output mode in the phase compensation parameter, adaptively switch the dynamic compensation indicator according to the output mode, and calculate the rotational speed fluctuation amplitude of each node under different switching strategies; A reference calibration module, which is used to deduce the optimal zeroing threshold based on the rotational speed fluctuation amplitude, and generate a torque deviation sequence by comparing the current torque output value with the optimal zeroing threshold; An execution output module, which is used to analyze the torque deviation sequence, and integrate the torque deviation sequence into a continuously variable transmission zeroing control scheme based on the rotational speed fluctuation direction of the node.

2. The continuously variable transmission zeroing control system based on a direct-axis electric drive bridge according to claim 1, wherein, The implementation method of the torque analysis module includes: constructing a drive feature library corresponding to the torque compensation node, and the drive feature library contains a drive parameter vector mapped by the rotational speed signal and torque data; Performing equivalent phase matching on the drive parameter vector, and dividing the torque compensation interval of the drive parameter vector according to the matching result; extracting the distribution critical point of the torque data from the torque compensation interval, and setting the distribution critical point as the torque compensation node.

3. The stepless speed change and zero adjustment control system based on a direct-axis electric drive axle according to claim 2, wherein, Dividing the torque compensation interval of the drive parameter vector further includes: Extracting the drive frequency, load volatility and phase offset parameter according to the torque type and shifting stage in the drive parameter vector, and generating a drive feature label based on the above parameters; Associating the drive feature label with the drive parameter vector, and screening the drive parameter vectors with an equivalence degree higher than the preset torque threshold through calculating the torque equivalence degree between the feature labels to form a torque compensation interval.

4. The stepless speed change and zero adjustment control system based on a direct-axis electric drive axle according to claim 1, wherein The implementation method of generating a torque feature vector corresponding to the node includes: For each torque compensation node, according to the timing position of the node in the zeroing detection interval, obtaining the rotational speed fluctuation data of the node within a preset period, and calculating the rotational speed fluctuation coefficient of the node; When the rotational speed fluctuation coefficient exceeds the first zeroing threshold, marking the node as a high-frequency disturbance node, and extracting its torque data to form a torque feature vector; when the rotational speed fluctuation coefficient is lower than the first zeroing threshold, marking the node as a steady-state node, and performing phase superposition on the torque data of the adjacent nodes of the node, and reconstructing the superposed data into a torque feature vector.

5. A stepless speed change and zero adjustment control system based on a direct-axis electric drive axle according to claim 1, characterized in that, The implementation method of the zeroing control module includes: Separating the drive torque ratio, no-load torque ratio and phase deviation parameter from the torque feature vector, and generating a zeroing correction rule for the torque compensation node based on the above parameters; If the number of compensation nodes covered by the current zeroing correction rule is less than the preset compensation threshold, traversing the torque feature vectors of adjacent torque compensation nodes, and adding the compensation indicators not included in the correction rules of the adjacent nodes to the current rule.

6. The stepless speed change and zero adjustment control system based on a direct-axis electric drive axle according to claim 1, wherein, The implementation method of the mode switching module includes: obtaining the phase factor of the switching frequency and the switching factor of the load response rate in the drive output mode; Construct a mode transition network associated with phase factors and switching factors, and determine the rotational speed fluctuation amplitude under different switching strategies according to the transition probabilities of each path in the network.

7. A stepless speed change zeroing control system based on a direct-axis electric drive axle according to claim 6, characterized in that, Constructing the mode transition network also includes: Identifying the periodic characteristics of the phase factor. If the current periodic characteristics exactly match the preset driving period, set the phase factor as the starting node of the mode transition network; Calculating the switching correlation degree between the phase factor and the switching factor, and generating the intermediate nodes and termination nodes of the mode transition network in descending order of the correlation degree; Performing phase backtracking on the termination node. When the correlation degree of the termination node is lower than the preset switching threshold, output it as the final path of the mode transition network.

8. A stepless speed change and zero adjustment control system based on a direct-axis electric drive axle according to claim 7, characterized in that, The implementation method of calculating the rotational speed fluctuation amplitude includes: Statistical mean value of the phase factor and the range of the switching factor of each termination node in the mode transition network, and calculate the global covariance of all node factors; Subtract the mean value of the phase factor of a single termination node from the mean value of the phase factor of the adjacent node, and divide by the global covariance to obtain the phase fluctuation coefficient; at the same time, calculate the ratio of the range of the switching factor to the global covariance, and weight and sum the two as the rotational speed fluctuation amplitude of this node.

9. The continuously variable speed zeroing control system based on a direct-axis electric drive axle according to claim 1, characterized in that, The implementation method of deriving the optimal zeroing threshold includes: Extract the disturbance mode in the historical data that is closest to the current rotational speed fluctuation amplitude, and calculate the Euclidean distance in the phase distribution between the two as the first calibration reference value; Statistical difference in the number of peak points between the current rotational speed fluctuation amplitude and the historical disturbance mode, and use the difference number as the second calibration reference value; Based on the linear combination of the first calibration reference value and the second calibration reference value, match the optimal zeroing threshold in the preset zeroing threshold table.

10. The continuously variable speed zeroing control system based on a direct-axis electric drive axle according to claim 1, characterized in that, The implementation method of executing the output module includes: dividing the positive compensation interval and the negative compensation interval according to the rotational speed fluctuation direction of each node in the torque deviation sequence; Extract the attenuation rate of the torque deviation in the positive compensation interval and the increase rate of the torque deviation in the negative compensation interval, and weight and fuse the two according to the load weight of the torque compensation node to generate the compensation parameters of the continuously variable transmission zeroing control scheme.

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