Electric drive axle motor and gearbox integrated control system based on SIC module

Abstract

The invention relates to the technical field of electric drive axle control, and discloses an electric drive axle motor and gearbox integrated control system based on an SIC module. The system comprises a power distribution module, a torque coordination module, a temperature field balancing module, an efficiency optimization module and a system integrated control module. The power distribution module integrates the motor power, the transmission ratio of a gearbox and the axle end torque requirement according to the real-time state of the electric drive axle, distributes regulation and control values and parameters, and generates a power control parameter set; the torque coordination module analyzes the influence of power switching on the system stability, adjusts the torque transmission balance and generates a coordination parameter set; the temperature field module analyzes a thermal field of the SIC module, adjusts heat dissipation parameters and generates a temperature regulation and control result; the efficiency optimization module extracts efficiency fluctuation and energy consumption offset to generate an optimization data set; and the system integration module analyzes the overall efficiency and optimization time, and generates an integrated control data table. According to the system, cooperative control of the motor and the gearbox is achieved, SIC heat dissipation is optimized, and the efficiency and reliability of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric drive axle control, in particular to an integrated control system for electric drive axle motor and gearbox based on SIC module. BACKGROUND

[0002] In the field of new energy vehicles and heavy electric commercial vehicles, the electric drive axle system as the core power transmission unit directly determines the vehicle's power performance, economy and reliability. The current mainstream electric drive axle system adopts an independent control architecture of motor and gearbox, and lacks effective cooperative control mechanism between the two, which leads to problems such as power response lag and transmission efficiency fluctuation under dynamic conditions.

[0003] When the vehicle faces complex conditions such as starting, accelerating and climbing, the motor needs to quickly adjust the output torque to meet the demand, while the gearbox needs to switch gears according to the change of vehicle speed and load. Due to the asynchronous adjustment of motor torque and gearbox gear shifting in the independent control mode, power interruption or impact phenomenon is easy to occur, which not only affects the driving comfort, but also aggravates the wear of transmission components and shortens the service life of the system.

[0004] With the development of wide bandgap semiconductor technology, SIC (silicon carbide) modules are widely used in power conversion units of electric drive axle systems due to their advantages of high frequency, high efficiency and high temperature resistance. However, the thermal field distribution of SIC modules is prone to local overheating under high power density operating conditions. The existing cooling system adopts fixed flow rate and single cooling path design, which cannot dynamically adjust according to the real-time thermal field changes of SIC modules, resulting in low cooling efficiency, which not only limits the performance of SIC modules, but also may cause module failure due to long-term overheating, affecting the stability of the electric drive axle system.

[0005] The existing efficiency optimization of electric drive axle system focuses on single components, such as motor efficiency optimization or gearbox transmission efficiency improvement, and lacks comprehensive consideration of the overall efficiency of the system. In actual operation, there is a coupling relationship between motor output efficiency, gearbox transmission efficiency and SIC module energy loss, and the optimization of single components cannot maximize the overall efficiency of the system. For example, adjusting the operating parameters of the motor to improve its output efficiency may result in mismatch between the transmission ratio of the gearbox and the output characteristics of the motor, thereby increasing transmission loss; if only the gearbox gear shifting strategy is optimized, the motor may run in the non-efficient interval for a long time, causing overall energy waste. The existence of these problems makes it difficult for the existing electric drive axle system to meet the comprehensive demand of new energy vehicles for high power, high economy and high reliability, restricting the further improvement of electric vehicle performance. SUMMARY

[0006] The purpose of the present application is to provide an integrated control system for electric drive axle motor and gearbox based on SIC module to solve the problems raised in the background.

[0007] To achieve the above object, the application provides an electric drive axle motor and gearbox integrated control system based on an SIC module, which comprises: a power distribution control module, which, according to real-time running state information of the electric drive axle system, calls power output parameters of the motor, gearbox transmission ratio data and axle end torque demand, analyzes the matching degree of power distribution and transmission efficiency, distributes motor torque control values and gearbox gear shifting parameters, and generates a power distribution control parameter set; a torque coordination management module, which, based on the power distribution control parameter set, extracts motor output shaft speed fluctuation and gearbox input torque gradient change, analyzes the influence of power switching process on system stability, adjusts the torque transmission balance between the motor and the gearbox, and generates a torque coordination optimization parameter set; a temperature field equalization module, which, based on the torque coordination optimization parameter set, analyzes the thermal field distribution characteristics of the SIC module in the electric drive axle system, adjusts the flow rate of the heat dissipation medium and the distribution proportion of the cooling path, redistributes the distribution trend and dynamic parameter values of the temperature field, and generates a temperature field dynamic control result; an efficiency optimization intervention module, which, based on the temperature field dynamic control result, extracts real-time efficiency fluctuation rate and energy loss offset in the running process of the electric drive axle system, analyzes the influence of fluctuation range on the total efficiency of the system, and generates an efficiency optimization adjustment data set; a system integrated control module, which, based on the efficiency optimization adjustment data set, analyzes the overall efficiency distribution proportion and optimization time of the electric drive axle system, and generates an electric drive axle system integrated control data table.

[0008] Preferably, the matching degree of power distribution and transmission efficiency is obtained in the following manner: According to real-time running state information of the electric drive axle system, power output parameters of the motor, gearbox transmission ratio data and axle end torque demand data are extracted, a running time window is set, time point matching data is selected, power output parameters and transmission ratio data are obtained through comparison of data correlation and data screening; Based on the power output parameters and transmission ratio data, the transmission path is matched and verified, the difference between power output and transmission ratio is calculated, the power distribution and transmission ratio distribution are corrected in combination with torque demand changes, the transmission path parameters are adjusted through the influence of torque demand on data, and the power output and transmission ratio matching condition is obtained; Based on the power output and transmission ratio matching condition, transmission efficiency analysis is performed, transmission efficiency analysis standards are set, power distribution under differentiated torque conditions is evaluated in combination with system running dynamic changes, efficiency indicators are compared and torque conditions are optimized, and the matching degree of power distribution and transmission efficiency is obtained.

[0009] Preferably, the power distribution control parameter set is obtained in the following manner: Based on the power distribution and transmission efficiency matching degree, the power transmission and efficiency change of the system under different operating conditions are analyzed, and the power distribution of the system is weighted to obtain the preliminary power regulation requirement of the system; Based on the preliminary power regulation requirement of the system, the power balance between the systems is analyzed, the power transmission efficiency and load distribution relationship between the systems are identified, the system power regulation parameters are corrected, the adjusted power output value of the system is calculated, and the power regulation data set between the systems is obtained; Based on the power regulation data set between the systems and the transmission efficiency matching result, the power between the systems is distributed, and the required balance and efficiency requirements are optimized and matched to obtain the power distribution control parameter set.

[0010] Preferably, the acquisition method of the motor output shaft speed fluctuation and the gearbox input torque gradient change amount is specifically: Based on the power distribution control parameter set, the speed data of the motor output shaft is extracted, the speed points in each time period are selected, the speed fluctuation is analyzed based on the speed change trend of the differentiated position of the output shaft, and the speed data of the motor output shaft is obtained; Based on the speed data of the motor output shaft, each speed point and the corresponding torque value are calculated, the torque change amount of each measurement point is identified by analyzing the relationship between the speed and the torque, the torque distribution and gradient distribution data are obtained by comparing the torque change of the differentiated position based on the system structure parameters. Based on the torque distribution and gradient distribution data, the overall torque distribution of the system is analyzed, the torque gradient is optimized based on the speed data, the influence of torque change on system performance is analyzed, the stable torque configuration under different operating conditions is determined, and the motor output shaft speed fluctuation and gearbox input torque gradient change amount are obtained.

[0011] Preferably, the acquisition method of the torque coordination optimization parameter set is specifically: Based on the motor output shaft speed fluctuation and the gearbox input torque gradient change amount, the time sequence of the torque change is determined, the current torque value and the original torque data are compared, the torque gradient at each moment is analyzed, and the corresponding threshold value is defined according to the system state partition to generate a preliminary torque change parameter set; The preliminary torque change parameter set is analyzed, the influence of the torque in the system on the power output stability is analyzed, the correlation between the torque and the power output is identified, and the section power stability influence coefficient is calculated; By analyzing the section power stability influence coefficient, the torque distribution balance is adjusted based on the system torque change parameter, the torque regulation data is optimized, and the torque coordination optimization parameter set is generated.

[0012] Preferably, the acquisition method of the temperature field dynamic regulation result is specifically: Based on the torque coordination optimization parameter set, temperature distribution data of the SIC module surface is extracted, flow rate of the heat dissipation medium on the surface of the differential material is monitored, diffusion characteristics of the heat dissipation medium are inferred in combination with external environmental factors of time and temperature, flow and diffusion rate coefficients are defined, and a thermal field distribution dynamic parameter set is generated; The influence of the thermal field distribution dynamic parameter set on the flow rate and distribution of the heat dissipation medium is analyzed, the ratio between the flow rate path and the input rate of the heat dissipation medium is optimized according to the demand of the temperature distribution of the SIC module surface, the adjustment coefficient of the temperature field distribution trend is calculated, and the temperature field regulation result is generated; The temperature field regulation result is analyzed, the proportional relationship between the input rate of the heat dissipation medium and the flow rate path is adjusted, the distribution trend of the temperature field is distributed, and the temperature field dynamic regulation result is obtained in combination with the thermal field distribution parameter and the adjustment coefficient.

[0013] Preferably, the efficiency optimization adjustment data set is obtained in the following manner: Based on the temperature field dynamic regulation result, the efficiency fluctuation rate and energy loss offset in the real-time running process of the system are monitored, abnormal values of system failure are identified, average fluctuation rate of the data is analyzed, and efficiency and loss fluctuation data are obtained; The influence of the efficiency and loss fluctuation data on the total efficiency of the system is analyzed, the relationship between efficiency and loss is analyzed by using the known system efficiency characteristics, the system efficiency under the differential fluctuation range is calculated, and the efficiency influence data is obtained; According to the efficiency influence data, the power distribution path and the torque speed ratio in the target range are dynamically adjusted, the power output and speed control range are distributed according to the relationship between the efficiency influence data and the temperature and loss fluctuation range, and the efficiency optimization adjustment data set is generated.

[0014] Preferably, the acquisition method of the electric drive axle system integrated control data table is as follows: Based on the efficiency optimization adjustment data set, the overall efficiency distribution and optimization time of the system are analyzed, efficiency data at different running time points is collected, efficiency time distribution is arranged, efficiency change trend is analyzed and data is classified, and system efficiency distribution data is obtained; Based on the system efficiency distribution data, target control path parameter adjustment is performed, system efficiency optimal running time and efficiency distribution are analyzed, efficiency change under different running conditions is compared, running temperature, time and power output operation conditions are adjusted, and target control path parameters are obtained; Based on the target control path parameters, the running conditions are adjusted according to the current operation parameters, the variable relationship of the running time, temperature and power output is controlled, real-time control is performed according to the adjusted parameters, and the electric drive axle system integrated control data table is obtained.

[0015] Preferably, the system further comprises: The state monitoring feedback module collects the running state data of the electric drive axle system in real time, including motor speed, gearbox gear position, torque output and temperature distribution data, and pre-processes and extracts features from the collected data to generate a system running state feature set; The system running state feature set is transmitted to the power distribution control module for updating the power distribution control parameter set, realizing closed-loop feedback control of the system running state.

[0016] Preferably, the system further comprises: The fault diagnosis and early warning module analyzes the system running abnormal mode and fault characteristics based on the system running state feature set and the electric drive axle system integrated control data table, establishes the mapping relationship between the fault characteristics and the system parameters, and generates the fault diagnosis rule base; Real-time comparison of the current system running state and the abnormal mode in the fault diagnosis rule base is performed, and when the matching fault characteristics are detected, a hierarchical early warning signal is generated and the corresponding fault handling strategy is triggered.

[0017] Compared with the prior art, the beneficial effects of the present application are: By setting the power distribution control module, the motor power output parameter, the gearbox transmission ratio data and the axle end torque demand can be integrated according to the real-time running state information of the electric drive axle system, the matching degree of power distribution and transmission efficiency can be deeply analyzed, and then the motor torque control value and the gearbox gear position switching parameter can be accurately distributed to generate the corresponding power distribution control parameter set. This module breaks the limitation of traditional independent control of motor and gearbox, realizes collaborative control between them, makes the motor output torque and gearbox gear position switching able to be adjusted synchronously according to the actual working condition demand, effectively avoids the power interruption and impact phenomenon in dynamic working condition, reduces the wear of transmission parts, prolongs the service life of the system, and at the same time improves the power response speed and driving comfort.

[0018] The torque coordination management module extracts the motor output shaft speed fluctuation and gearbox input torque gradient change based on the power distribution control parameter set, analyzes the influence of power switching process on system stability in detail, and adjusts the torque transmission balance between the motor and the gearbox, generates the torque coordination optimization parameter set. This module can further optimize the torque matching accuracy in the power transmission process, reduce the system vibration caused by speed fluctuation and torque gradient change, enhance the running stability of the electric drive axle system in complex working conditions, and ensure that the power transmission process is smoother, providing continuous and stable power output for the whole vehicle.

[0019] The temperature field balancing module relies on the torque coordination optimization parameter set, deeply analyzes the thermal field distribution characteristics of the SIC module in the electric drive axle system, adjusts the flow rate of the heat dissipation medium and the distribution proportion of the cooling path, re-distributes the distribution trend and dynamic parameter value of the temperature field, and generates a temperature field dynamic regulation result. This module changes the disadvantages of the traditional fixed heat dissipation mode, can adaptively adjust according to the real-time thermal field change of the SIC module, effectively alleviates the local overheating problem, fully plays the performance advantages of the SIC module such as high frequency, high efficiency and high temperature resistance, reduces the risk of SIC module failure caused by overheating, and ensures the reliability of the electric drive axle system in the high power density operating state.

[0020] The efficiency optimization intervention module extracts the real-time efficiency fluctuation rate and energy loss offset in the running process of the electric drive axle system based on the temperature field dynamic regulation result, analyzes the influence of the fluctuation range on the total efficiency of the system, and generates an efficiency optimization adjustment data set. This module is no longer limited to the efficiency optimization of a single component, but starts from the overall system, comprehensively considers the coupling relationship between the motor output efficiency, the transmission efficiency of the gearbox and the energy loss of the SIC module, realizes the coordinated matching of the running state of each component through optimization and adjustment, reduces energy waste, improves the total efficiency of the system, and helps to reduce the energy consumption of the electric vehicle and prolong the cruising range.

[0021] The system integration control module analyzes the overall performance distribution proportion and optimization time of the electric drive axle system based on the efficiency optimization adjustment data set, and generates an electric drive axle system integration control data table. This module realizes the unified control of each link of the electric drive axle system, can coordinate the operation of each functional module, ensures that the system can maintain the optimal operating state under different working conditions, further improves the comprehensive performance of the electric drive axle system, and provides strong support for the development of new energy vehicles. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 a timing diagram of the SIC module-based electric drive axle motor and gearbox integration control system described in the present application; Figure 2 a flowchart for power distribution and transmission efficiency matching degree analysis; Figure 3 a flowchart for generating a power distribution control parameter set. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] Please refer toFigure 1 The application provides an electric drive axle motor and gearbox integrated control system based on an SIC module. The system includes: a plurality of modules cooperatively work to achieve efficient control; a power distribution control module calls power output parameters of the motor, transmission ratio data of the gearbox and axle end torque demand according to real-time running state information of the electric drive axle system, analyzes power distribution and transmission efficiency matching degree, distributes motor torque control values and gearbox gear switching parameters, and generates a power distribution control parameter set; a torque coordination management module extracts motor output shaft speed fluctuation and gearbox input torque gradient change based on the power distribution control parameter set, analyzes influence of power switching process on system stability, adjusts torque transmission balance between the motor and the gearbox, and generates a torque coordination optimization parameter set; a temperature field equalization module analyzes thermal field distribution characteristics of the SIC module in the electric drive axle system based on the torque coordination optimization parameter set, adjusts cooling medium flow rate and cooling path distribution proportion, reallocates temperature field distribution trend and dynamic parameter values, and generates a temperature field dynamic control result; an efficiency optimization intervention module extracts real-time efficiency fluctuation rate and energy loss offset in the electric drive axle system running process based on the temperature field dynamic control result, analyzes influence of fluctuation range on system total efficiency, and generates an efficiency optimization adjustment data set; and a system integrated control module analyzes electric drive axle system overall efficiency distribution proportion and optimization time based on the efficiency optimization adjustment data set, generates an electric drive axle system integrated control data table, and thus realizes comprehensive optimization control of the electric drive axle system.

[0025] Example 1: refer to Figure 2The acquisition process of the power distribution and transmission efficiency matching degree is realized through hierarchical data processing and dynamic analysis. The system first extracts three core parameters from the real-time running state of the electric drive axle: the motor power output parameter is collected and converted into a power time sequence by the current and voltage sensor, the transmission ratio data of the gearbox is fed back by the gearbox control unit, and the bridge torque demand data is parsed from the demand signal of the vehicle control system. The adaptive time slicing mechanism is used to set the running time window, and the window length is dynamically adjusted according to the vehicle speed. The low-speed working condition is extended to 200 milliseconds, and the high-speed working condition is shortened to 50 milliseconds. The synchronous data points are selected according to the time stamp in the window, the Pearson correlation coefficient of power and transmission ratio is calculated through data correlation analysis, only the effective data segment with a correlation coefficient higher than 0.85 is retained, and the outlier points caused by signal interference are eliminated. In the power output and transmission ratio matching verification stage, a digital twin model of the transmission path is constructed, which includes three physical modules of the motor output shaft, the gearbox gear set and the differential. The absolute difference between the power output value and the theoretical value of the transmission ratio is calculated, and the difference value is input into the torque demand correction algorithm after normalization. The torque demand change correction adopts a double-channel weighting mechanism: the main channel adjusts the power distribution weight according to the torque demand change rate, for example, if the torque demand increases by 10%, the power distribution weight shifts 5% to the high-efficiency transmission zone; the auxiliary channel combines historical transmission efficiency data to correct the transmission ratio distribution curve, and when the historical transmission efficiency is detected to be lower than 85%, the transmission ratio correction strength is automatically increased. The corrected transmission path parameters are output as a two-dimensional matching matrix, the row vector of the matrix represents the power output level, the column vector corresponds to the transmission ratio interval, and the matrix element stores the matching confidence value.

[0026] The transmission efficiency analysis adopts a hierarchical evaluation architecture. The bottom layer sets the ISO21782 standard as the efficiency baseline. The middle layer loads a dynamic working condition feature library containing 12 operating modes such as acceleration, climbing and cruising. The top layer executes differentiated torque condition simulation. The evaluation process first identifies the current operating mode characteristics, calls the torque-efficiency relationship curve of the corresponding mode, marks the real-time power distribution point on the curve, and calculates the Euclidean distance between the point and the theoretical highest efficiency point as the efficiency deviation. The efficiency index comparison introduces three indexes: transmission loss rate, energy conversion rate and heat loss coefficient. The comprehensive efficiency score is calculated by the entropy weight method. The torque condition optimization adopts a closed-loop iteration mechanism: the initial torque value is generated based on the matching matrix, the score is calculated by the efficiency score module, and if the score is lower than the threshold, the torque gradient descent algorithm is triggered. The slope of the torque output curve is adjusted in each iteration until the comprehensive efficiency score reaches more than 92 points or the iteration is terminated after 5 times. The final output includes the matching degree parameter set containing the matching degree score, the optimized torque range and the recommended transmission ratio interval. In the data acquisition link, the motor power output parameters are collected by high-precision Hall sensors to sample three-phase current and voltage at a frequency of 10 kHz. After denoising by the FIR filter, the effective power value is converted; the transmission ratio data of the gearbox are obtained from the TCU through the CAN bus to obtain the real-time gear signal, combined with the pre-stored gear-transmission ratio mapping table to output the value; the bridge torque demand data are intercepted from the signal frame of CANID0x0CF of the vehicle controller, and after analysis, they are smoothed by Kalman filtering. The dynamic adjustment algorithm of the time window is based on the vehicle speed segmentation function: when the vehicle speed is less than 30 km / h, the window length is 200 ms, 30-80 km / h, 100 ms, and more than 80 km / h, 50 ms. The data points in the window are synchronized to the microsecond level through the PTP protocol.

[0027] The digital twin model in the transmission path matching verification adopts a multi-body dynamics simulation kernel, and the gear engagement stiffness coefficient is set to 1.8×10 9N / m, the bearing friction coefficient is taken as 0.002, and the physical parameters of the model are refreshed every 5 ms. The torque demand correction algorithm includes a feedforward control link. When the detected torque demand rate of change exceeds 50 N·m / s, the transmission ratio pre-adjustment mechanism is triggered 0.2 seconds in advance. The dynamic working condition characteristic library of the efficiency analysis layer is constructed by the K-means clustering algorithm. 500 typical working condition segments are extracted from historical operation data, and each working condition is labeled with a torque fluctuation range and an efficiency characteristic label. When calculating the efficiency score by the entropy weight method, the transmission loss rate weight is 0.5, the energy conversion rate weight is 0.3, and the heat loss coefficient weight is 0.2. The score threshold is set to a dynamic value: the cold start stage threshold is 85 points, and the hot stable stage is increased to 92 points. The torque optimization iteration adopts a gradient descent method with constraints, and the torque rate limit is set to ±100 N·m / s. The iteration step size is decreased by the golden section ratio to ensure smooth transition of the torque curve. The final output matching degree parameter set includes three groups of data: matching degree score (0-100 points), optimized torque range (minimum-maximum interval), and recommended transmission ratio interval (including 3 priority recommended gears). The parameter set is refreshed and transmitted to the downstream module at a period of 20 ms. The entire implementation process is accelerated by FPGA, and the data processing delay is controlled within 1.5 ms, meeting the real-time control requirements. The heat dissipation medium regulation link is linked with the efficiency analysis and runs automatically when the heat loss coefficient exceeds 0.15. The cooling system is triggered to strengthen the mode by adjusting the PWM duty cycle of the heat dissipation fan to increase the medium flow rate. The system has two levels of safety fault tolerance mechanisms: the primary fault tolerance starts transmission ratio protective switching when the transmission loss rate suddenly increases by 20%, and the advanced fault tolerance triggers system power reduction when the comprehensive efficiency score is below 70 points for 3 consecutive cycles. All running parameters are stored in a circular buffer, supporting backtracking analysis of the running state in the last 5 seconds. Time-triggered architecture is used in the implementation process, and each processing node strictly follows the global clock cycle of 10 ms to ensure the timing determinacy of the collaborative operation of multiple modules.

[0028] Example 2: see Figure 3, based on the power distribution and transmission efficiency matching degree parameters provided by the upstream module, the parameter set includes matching degree score, optimized torque range and recommended transmission ratio interval, etc. structured data, the analysis process focuses on feature extraction and modeling of differentiated operating conditions, including different vehicle speed intervals, load torque levels, road slope and environmental temperature combinations forming a multi-dimensional working condition space. The system queries the pre-set working condition feature library to label the power transmission coefficient and efficiency change rate label for each working condition combination. The power transmission coefficient calculates the energy transmission ratio from the motor to the bridge end, and the efficiency change rate records the gradient value of the transmission loss fluctuation over time. A dynamic weight distribution algorithm is used for weighted calculation of system power distribution. The weight coefficient is adjusted in real time according to the efficiency matching degree. When the matching degree score is higher than 90 points, the weight is biased to maintain the current power distribution. When the score is between 70-90 points, the weight is shifted to the historical high efficiency interval. When the score is lower than 70 points, the re-distribution mode is started. The weighted calculation introduces the power deviation integral term. The cumulative deviation value of the actual power and the theoretical power in the last 10 cycles is calculated. The preliminary power control requirement output is the target power curve and the torque adjustment suggestion value. The power balance between the systems needs to establish a dynamic coupling model of the motor, gearbox and bridge. The model input includes real-time parameters such as motor output torque, gearbox input shaft speed, bridge resistance torque, etc. The correlation analysis method is used to identify the power transmission efficiency and load distribution relationship between the systems. The correlation coefficient of motor torque and bridge torque is calculated, and the influence of transmission ratio change on load distribution is analyzed. When correcting the system power control parameters, the torque compensation coefficient and transmission ratio fine adjustment amount are mainly adjusted. The torque compensation coefficient is calculated dynamically according to the load mutation, and the transmission ratio fine adjustment amount is limited within ±0.05 to ensure smoothness.

[0029] The adjusted power output value of the computing system adopts a feedforward-feedback composite control strategy. The feedforward link predicts the power demand according to the throttle opening rate of change, and the feedback link adjusts the actual power output through a PID controller. The power regulation data set between systems finally contains the corrected power output curve, the torque compensation coefficient table, and the transmission ratio fine-tuning parameter group. When the data set is fused with the transmission efficiency matching result, a data weighted fusion algorithm is adopted, and the data segment with high confidence in the efficiency matching result is preferentially adopted. The power distribution process is realized through a multi-objective optimization algorithm. The optimization objectives consider the power balance index and the system efficiency index. The power balance index calculates the torque distribution uniformity of the three systems. The system efficiency index integrates the transmission efficiency and the heat loss coefficient. The finally generated power distribution control parameter set contains motor torque control values, transmission gear position switching time, power distribution weight coefficients, and other control instructions. The acquisition of the motor output shaft speed fluctuation is based on the control instruction timing provided by the power distribution control parameter set. The speed data of the motor output shaft is extracted using a high-frequency sampling method with a sampling frequency of 5 kHz to ensure the capture of transient fluctuations. A sliding window mechanism is used to select the speed points in each time period, and the window length is consistent with the power distribution control period. In order to meet the need of differentiated position speed change trend of the output shaft, multiple sets of speed sensors are installed. Optical encoders are arranged at the drive end, intermediate support point, and output end of the output shaft. The time domain and frequency domain combined method is used to analyze the speed fluctuation. The time domain calculates the standard deviation and peak-to-peak value, and the frequency domain performs FFT transform to analyze the characteristic frequency amplitude. The obtained motor output shaft speed data contains original speed values, fluctuation statistical characteristics, and frequency spectrum analysis results. To calculate each speed point and the corresponding torque value, a speed-torque mapping table needs to be established. This mapping table is obtained through bench test calibration and contains characteristic curves at different temperatures. The torque change amount of each measurement point is identified using a difference calculation method, which calculates the torque difference between adjacent sampling points. Combined with system structure parameters, the input shaft stiffness coefficient, gear mesh stiffness, bearing damping, and other mechanical parameters need to be input. The relative difference analysis method is used to compare the torque change of differentiated positions, and the torque gradient difference between different measurement points is calculated. The obtained torque distribution and gradient distribution data contain spatial torque distribution cloud and time gradient curve.

[0030] The analysis of the overall torque distribution of the system requires the construction of a digital twin model of the torque transmission chain, which includes all the transmission components from the motor to the wheels. The model optimizes the torque gradient using model predictive control, taking into account the rotational speed data, with the goal of torque smoothness. To analyze the impact of torque changes on system performance, a vibration and noise mapping model is established. The torque gradient change is used to predict the shaft vibration amplitude and noise decibel value. To determine the stable torque configuration under different operating conditions, torque limit rules need to be established. The torque change rate limit is adjusted dynamically based on vehicle speed and load. The final output of the motor output shaft speed fluctuation includes the fluctuation amplitude and frequency characteristics, and the transmission input torque gradient change includes the maximum gradient value and change trend characteristics. These parameters are transmitted to downstream modules in a fixed format data structure.

[0031] In embodiment 3, the motor output shaft speed fluctuation and transmission input torque gradient change data packets received from the upstream module contain fluctuation amplitude spectrum, gradient time series, and associated timestamp information. The time series of torque changes is determined using a sliding window matching algorithm, with the window length dynamically adjusted within the range of 50-200 milliseconds based on the operating conditions. A difference matrix is established by comparing the current torque value with the original torque data, with the matrix elements recording the torque deviation at each sampling point. The instantaneous change rate is calculated using the central difference method to analyze the torque gradient at each time. The system state partition is divided into four quadrants based on the torque change rate and speed fluctuation amplitude. Each quadrant defines a differentiated threshold parameter, which is dynamically updated through historical data analysis and machine learning. The preliminary torque change parameter set includes gradient statistics, threshold mapping table, and abnormal flag bit.

[0032] In-depth analysis of the preliminary torque change parameter set requires the construction of a torque-stability transfer function model, which maps torque fluctuations to system vibration energy levels. The influence of torque on power output stability within the system is analyzed using the coherence function analysis method, which calculates the coherence coefficient of the torque signal and vibration signal within the 0-1000 Hz frequency band. The correlation between torque and power output is identified by finding the maximum correlation coefficient and corresponding time delay through cross-correlation calculation. The sectional power stability influence coefficient is calculated using the following relationship:

[0033] where: represents the sectional stability influence coefficient, represents the absolute value of the torque gradient at the ith sampling point, represents the speed fluctuation amplitude corresponding to the point, and represent the maximum and minimum torque values of the section, respectively, and N is the number of sampling points. This coefficient reflects the comprehensive impact of torque changes on system stability.

[0034] By analyzing the segment dynamic stability influence coefficient, combined with the gradient distribution characteristics in the system torque change parameter, the torque distribution balance is adjusted by using the weight redistribution algorithm, the torque gradient in the high influence coefficient area is limited in the safe range, the torque control data is optimized by model predictive control, the stability coefficient is minimized as the optimization objective, and the torque coordination optimization parameter set is generated, including the smoothed torque curve, the gradient limit table and the stability compensation coefficient. The temperature field dynamic regulation link provides the heat load data based on the torque coordination optimization parameter set, extracts the temperature distribution data of the SIC module surface using infrared thermal imaging technology, the sampling resolution is set to 320x240 pixels, the temperature measurement accuracy reaches ±1℃, the flow rate of the heat dissipation medium on the surface of the differential material is monitored using an ultrasonic flowmeter, the measurement points are arranged on the silicon carbide substrate, copper substrate and packaging shell of the SIC module, and the diffusion characteristics of the heat dissipation medium are inferred combined with the external environmental factors of time and temperature. A thermal resistance-capacitance network model needs to be established, the model includes 12 nodes representing the thermal characteristics of different material layers, the flow and diffusion rate coefficients are defined by using the dimensionless parameterization method, the flow coefficient represents the relationship between the medium flow rate and the heat convection intensity, and the diffusion coefficient reflects the ratio of the material heat conduction and heat diffusion capacity. A thermal field distribution dynamic parameter set is generated, including a temperature field matrix, a flow rate distribution diagram and a thermal parameter set.

[0035] The influence of the dynamic parameter set of the thermal field distribution on the flow rate and distribution of the heat dissipation medium is analyzed by using computational fluid dynamics simulation. The simulation model includes the micro-channel heat sink structure and the medium flow path. The target temperature field is set according to the requirement of the surface temperature distribution of the SIC module. The ratio between the flow rate path and the input rate of the heat dissipation medium is optimized by using a multi-objective optimization algorithm, considering the heat dissipation efficiency and pump power loss. The adjustment coefficient of the temperature field distribution trend is calculated. The optimal solution is found by using the gradient descent method, and the temperature field regulation result is generated, including the optimized flow rate setting value, path allocation scheme and expected temperature distribution. The feasibility and effectiveness of the temperature field regulation result need to be analyzed by performing a heat-flow coupled simulation verification. The ratio between the input rate of the heat dissipation medium and the flow rate path is adjusted by using a fuzzy control strategy. The input variable is the temperature deviation and its change rate, and the output variable is the pump speed adjustment and the valve opening. The distribution trend of the temperature field is allocated by using an inverse problem solving method, which reverses the required heat dissipation conditions from the target temperature field. The weighted fusion is performed by combining the thermal field distribution parameters and the adjustment coefficient, and finally the temperature field dynamic regulation result is obtained, including the detailed control instruction set, the temperature prediction curve and the safety margin evaluation report. The whole implementation process uses a distributed computing architecture. The torque coordination part runs on a real-time processor with a 100μs period, and the temperature field regulation is executed on a performance processor with a 10ms period. The two subsystems exchange data through shared memory to ensure timing synchronization and data consistency. All output parameters are subjected to effectiveness verification and range check. The abnormal data triggers a re-computation mechanism. The system sets a three-level safety protection strategy, which takes corresponding protection measures when the temperature exceeds the standard, the torque suddenly changes and the heat dissipation fails, to ensure the reliable operation of the system.

[0036] Example 4: The generation process of efficiency optimization adjustment dataset is carried out taking the working condition of a commercial electric vehicle cooling system as an example. The system is based on the temperature field dynamic control results provided by the upstream module (including heat dissipation medium flow rate set value, temperature distribution prediction curve and other parameters). The monitoring link is deployed at the connection between the motor controller and the gearbox. The efficiency fluctuation rate in real-time operation process is calculated by high-precision Hall sensor to collect current voltage phase difference, the sampling frequency is set to 2 kHz, and the energy loss offset is obtained by measuring the input and output power difference, and the data refresh period is 10 ms. When identifying the fluctuation range, the dynamic threshold window is set. The upper limit of the window is 115% of the average efficiency value in the last 30 seconds, and the lower limit is 85%. The improved Z-score algorithm is used to remove system fault abnormal values. The data segment exceeding 3 times the standard deviation of the continuous 5 sampling points is marked and removed. The sliding window weighted average method is used to analyze the average fluctuation rate of the data. The window length is 50 sampling points. The final output efficiency and loss fluctuation data include time stamp, fluctuation rate curve, loss offset sequence and data quality flag. The influence of efficiency and loss fluctuation data on the total efficiency of the system needs to establish a temperature-efficiency correlation model. The model is based on the two-dimensional relationship table calibrated by bench test (as shown in Table 1). The known thermal decay curve in the system efficiency characteristic is used. The relationship between efficiency and loss is analyzed by using piecewise linear interpolation method. When the temperature interval is 80-90℃, the efficiency decreases by 0.15% per 1℃, and above 90℃, it decreases by 0.25% per 1℃. When calculating the system efficiency in the differential fluctuation range, the fluctuation rate is divided into ±5%, ±10% and ±15% three grades, which correspond to different compensation coefficients respectively. The final generated efficiency influence data includes efficiency loss, recommended compensation value and critical temperature warning mark. Refer to Table 1.

[0037] Table 1: Temperature-efficiency characteristic mapping table

[0038] The target range is set as the safe operation area in the temperature field regulation result when dynamically adjusting according to the efficiency influence data, and the path weight redistribution algorithm is adopted to adjust the power distribution path. When it is detected that the efficiency of a branch is lower than a threshold, the power weight of the branch is proportionally transferred to the high-efficiency branch. The torque-speed ratio optimization is based on the pre-stored efficiency MAP, and the torque-speed ratio slope is automatically reduced in the high-temperature region, for example, the ratio is adjusted from 0.85 to 0.78 at 90°C. When adjusting according to the relationship between the efficiency influence data and the temperature and loss fluctuation range, a three-dimensional adjustment coefficient matrix is established: the temperature dimension is divided into 4 grades, the loss fluctuation is divided into 3 grades, and the efficiency deviation is divided into 5 grades. The matrix elements store the power compensation percentage. The power output and speed control range adopt the boundary contraction strategy, and the power output upper limit is reduced by 5-15% and the speed control range is narrowed by 20% in high-temperature conditions. The finally generated efficiency optimization adjustment data set includes the optimized power distribution scheme, the torque-speed ratio curve and the operation boundary parameters. The construction process of the electric drive bridge system integrated control data table takes the urban public transportation condition as the application scenario, and based on the parameter group provided by the efficiency optimization adjustment data set, the system overall efficiency distribution and optimization time analysis are carried out. The efficiency data at three different operating time points of early peak (07:00-09:00), flat peak (10:00-16:00) and late peak (17:00-19:00) are collected. The whole day is divided into 5-minute segments. In addition to the early peak, flat peak and late peak periods, the working condition characteristics of the remaining periods are as follows: the late peak transition period (19:00-22:00) is 3 hours (36 segments). After the end of the late peak (17:00-19:00), the urban road traffic pressure is relieved, and the public transportation operation frequency is gradually reduced. Some vehicles enter the return scheduling stage. The average speed is 18-25 km / h, and the load rate decreases from 50%-60% in the late peak to 10%-15%. The midnight shutdown and idle period (22:00-05:00 the next day) is 7 hours (84 segments). The urban public transportation system enters the night shutdown period, only a small number of emergency connection vehicles are running, and most vehicles are in parking lot or performing idle scheduling tasks. The average speed is 5-10 km / h (only for scheduling vehicles), and the load rate is ≤5% (emergency connection) or 0% (static / idle). The early morning preparation period (05:00-07:00) is 2 hours (24 segments). The public transportation system starts the morning preparation work, the vehicles start to warm up one by one, and some perform the first bus idle debugging. The average speed is 10-15 km / h, and the load rate is 0% (idle debugging) or 5%-10% (first bus warm-up passenger carrying). The early peak after the flat period (09:00-10:00) is 1 hour (12 segments). The early peak commuting flow subsides, the road traffic efficiency returns to normal, and the public transportation passenger carrying capacity decreases significantly.The average vehicle speed is 25-30 km / h, and the load rate decreases from 60-70% in the morning peak to 20-30%. The pre-warming period before the evening peak (16:00-17:00) is 1 hour (12 segments), and the city gradually enters the early evening peak commuting period, with return traffic starting to gather and bus passenger capacity gradually increasing. The average vehicle speed is 20-25 km / h, and the load rate increases from 30-40% in the flat peak period to 40-50%. The efficiency index is calculated as the weighted product of transmission efficiency, thermal efficiency, and mechanical efficiency. The efficiency time distribution is sorted using the time slice statistical method, dividing the whole day into 288 5-minute segments, and the efficiency change trend is analyzed using moving average filtering. When classifying data, three efficiency intervals are set: A (efficiency value ≥ 90), B (80-89), and C (< 80). The output system efficiency distribution data includes period efficiency distribution chart, efficiency level proportion statistics, and trend analysis report. Based on the system efficiency distribution data, the target control path parameter adjustment is performed, and the optimal system efficiency operation time is analyzed by finding consecutive 3-hour A-level efficiency periods. The efficiency distribution optimization uses a genetic algorithm to search for the best parameter combination. By comparing the efficiency changes between the morning peak and flat peak periods, the operating temperature set point is adjusted: the morning peak allows a 2°C upper limit increase due to high load, and the flat peak period strictly implements the standard temperature limit. The power output operating condition is modified to a time-based power upper limit strategy. In the evening peak period (17:00-19:00), due to urban road congestion, high bus passenger capacity, and frequent starts and stops, the electric drive axle system power output needs to be increased to meet the torque demand for starting and accelerating. Therefore, the power upper limit is increased by 8% from the rated power, ensuring timely power response, while the torque coordination management module and temperature field balancing module are coordinated to avoid transmission impact or SIC module overheating problems caused by power increase. The power upper limit adjustment mentioned in this chapter is based on the electric drive axle motor rated power (200kW), and the power upper limit after the 8% increase in the evening peak is 216kW. This value can maintain the system comprehensive efficiency not lower than 90% while meeting the power demand. The target control path parameters include temperature control curve, power output schedule, and optimization priority marker. When adjusting the operating conditions according to the current operating parameters, a three-dimensional control model is established for the variable relationship between control time, temperature, and power output: the time axis is divided into periods, the temperature axis sets gradient thresholds, and the power axis defines dynamic ranges. When real-time control is performed based on the adjusted parameters, a feedforward compensation mechanism is used, and the cooling system power is adjusted 5 minutes in advance when entering the high-efficiency period is detected. The final generated electric drive axle system integrated control data table uses a matrix data structure (see Table 2), with row vectors representing time slice numbers and column vectors containing 12 control parameters such as temperature set value, power upper limit, and speed range. The data refresh period is 1 minute, and the data is broadcast to each execution unit through the CAN bus.

[0039] Table 2: System integrated control data table

[0040] The 288 segment parameters of the electric drive bridge system integrated control data table are all based on the above-mentioned period working condition characteristics setting. In addition to the early morning peak, flat peak part mentioned above, the control parameters of the remaining key period are as follows: the control parameters corresponding to the early morning preparation start period (05:30) are temperature setting 81℃, power upper limit 155kW, basic speed 2100rpm, torque ratio coefficient 0.80, and heat dissipation level 2; the control parameters corresponding to the flat period after early morning peak (09:30) are temperature setting 83℃, power upper limit 195kW, basic speed 2400rpm, torque ratio coefficient 0.84, and heat dissipation level 2; the control parameters corresponding to the preheating period before late peak (16:30) are temperature setting 84℃, power upper limit 205kW, basic speed 2500rpm, torque ratio coefficient 0.85, and heat dissipation level 3; the control parameters corresponding to the late evening transition period (21:10) are temperature setting 78℃, power upper limit 150kW, basic speed 1900rpm, torque ratio coefficient 0.80, and heat dissipation level 2; the control parameters corresponding to the deep night shutdown and no-load period (23:10) are temperature setting 77℃, power upper limit 135kW, basic speed 1600rpm, torque ratio coefficient 0.77, and heat dissipation level 1; the parameter values of all segments follow the period working condition-control demand matching principle to ensure the balance of system efficiency and reliability in different periods.

[0041] In the implementation process, a data verification mechanism is set. When the temperature sensor and the efficiency monitoring data conflict, a secondary calibration program is started. The system reserves 10% control margin to deal with sudden working condition changes. Historical control data is stored in a circular database to support strategy optimization iteration. All control instructions are accompanied by time stamp and version number to ensure traceability.

[0042] Example 5: When the state monitoring feedback module is implemented on a port AGV transport vehicle, the real-time acquisition system contains four groups of sensor networks: the motor speed monitoring uses a magneto-electric encoder installed at the output shaft end, with a resolution of ±0.1° and a sampling frequency of 1 kHz; the gearbox gear data is obtained by a Hall effect sensor to get the gear shift position, with an accuracy of 0.5 mm; the torque output measurement uses a flange strain torque sensor, with a range of 0-5000 N.m; the temperature distribution data is collected by an array of 36 PT100 platinum resistors arranged on the motor winding, gearbox bearing, and SIC module surface, with a temperature measurement range of -40-150°C. In the data preprocessing stage, three operations are performed: the original data is filtered by a FIR low-pass filter to eliminate high-frequency interference, the minimum-maximum normalization is used to unify each parameter to the [0,1] interval, and the multi-source data is aligned to the microsecond level through hardware timestamps. In the feature extraction process, time domain statistics (mean, variance, kurtosis) and frequency domain features (FFT main frequency amplitude) are calculated, generating a system operating state feature set containing 128-dimensional feature vectors, which are refreshed every 100 ms and labeled with environmental working condition codes. The system operating state feature set is transmitted to the power distribution control module through the CANFD bus, with a transmission period of 10 ms and a bandwidth utilization rate controlled within 70%. When updating the power distribution control parameter set, the feature set triggers the parameter reconstruction mechanism: when a sudden increase of 20% in speed variance is detected, the historical high-efficiency working condition parameter template is automatically retrieved; temperature distribution anomalies activate the thermal protection parameter group. The closed-loop feedback control implementation process includes a feedforward compensation link, such as injecting a torque compensation amount 0.2 seconds in advance during gearbox shifting, and the system sets two levels of data verification: primary verification checks the reasonableness range of feature values, and advanced verification compares actual output through twin model simulation to ensure that feedback control error is less than 3%.

[0043] The fault diagnosis and early warning module operates in the scene of a mine heavy-duty truck, and the system operating state feature set input contains real-time data such as vibration spectrum features and temperature gradient distribution. The electric drive bridge system integrated control data table provides reference parameters such as torque ratio safety range and temperature limit table. The analysis system operating abnormal mode uses a decision tree classification algorithm to build a feature fingerprint library containing 17 faults such as motor inter-turn short circuit, bearing wear, and heat dissipation blockage. Fault feature extraction focuses on three dimensions: time domain waveform periodic impact features are marked as bearing damage, frequency domain 250-500 Hz energy surge is determined as gear meshing abnormality, and temperature field >5°C local hot spot is a warning of heat dissipation failure. The mapping relationship between fault features and system parameters is established by association rule mining, for example, when the gearbox input torque gradient exceeds 80 N.m / ms, the confidence degree of bearing failure is 92%; the temperature difference of the SIC module >8°C corresponds to a heat dissipation failure probability of 85%. The generated fault diagnosis rule library contains 256 diagnostic rules, each rule is set with a dynamic weight coefficient, and the weight is automatically adjusted according to the historical diagnosis accuracy. The rule library is updated monthly through cloud new fault cases.

[0044] The real-time comparison module adopts a stream computing architecture, and the current system operation state is compared with the rule base every second to perform 1200 pattern matching operations. When the vibration sensor detects that the amplitude of the characteristic frequency 125 Hz exceeds the baseline by 35%, the gear wear fault feature is matched, and a secondary warning signal (yellow alarm) is generated. If the temperature gradient is also exceeded at the same time, it is upgraded to a primary warning (red alarm). The fault handling strategy performs a hierarchical response: the secondary warning triggers a 15% power reduction and prompts maintenance; the primary warning immediately cuts off the power output and starts the backup cooling system. The historical fault data record contains complete timestamps, fault codes, and disposal records, supporting maintenance personnel to trace back the fault evolution process through the HMI interface. The system integration diagnostic console is deployed on the vehicle instrument panel, and the warning signal is output through multiple modalities: visual warning displays the fault area map on the liquid crystal screen, auditory warning has three levels of buzzer frequency, and tactile feedback transmits emergency signals through steering wheel vibration. The maintenance diagnostic interface supports Bluetooth connection to handheld terminals, and can download the characteristic data stream of the last 72 hours for offline analysis. The self-learning mechanism continuously optimizes the diagnostic rules, automatically increases the priority of the rule when the same fault occurs repeatedly, and reduces the weight coefficient when the false alarm is not confirmed, so that the diagnostic accuracy rate continues to improve with running time.

[0045] It should be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0046] While embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since numerous further modifications and changes can be apparent to one skilled in the art without departing from the scope and spirit of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. A SIC module-based electric drive axle motor and gearbox integrated control system, characterized in that, The system comprises: a power distribution control module, which, according to real-time operation state information of the electric drive axle system, calls power output parameters of the motor, transmission ratio data of the gearbox and torque demand at the axle end, analyzes the matching degree of power distribution and transmission efficiency, distributes motor torque control values and gearbox gear switching parameters, and generates a power distribution control parameter set; a torque coordination management module, which, based on the power distribution control parameter set, extracts motor output shaft speed fluctuation and gearbox input torque gradient change, analyzes the influence of power switching process on system stability, adjusts torque transmission balance between the motor and the gearbox, and generates a torque coordination optimization parameter set; a temperature field balancing module, which, based on the torque coordination optimization parameter set, analyzes thermal field distribution characteristics of the SIC module in the electric drive axle system, adjusts cooling medium flow rate and cooling path distribution proportion, redistributes temperature field distribution trend and dynamic parameter values, and generates temperature field dynamic control results; an efficiency optimization intervention module, which, based on the temperature field dynamic control results, extracts real-time efficiency fluctuation rate and energy loss offset in the electric drive axle system operation process, analyzes the influence of fluctuation range on system total efficiency, and generates an efficiency optimization adjustment data set; a system integrated control module, which, based on the efficiency optimization adjustment data set, analyzes electric drive axle system overall performance distribution proportion and optimization time, and generates an electric drive axle system integrated control data table.

2. The SIC module based electric drive axle motor and transmission integrated control system of claim 1, wherein, The power distribution and transmission efficiency matching degree is obtained in the following manner: According to real-time operation state information of the electric drive axle system, power output parameters of the motor, transmission ratio data of the gearbox and torque demand data are extracted, an operation time window is set, time point matching data is selected, data correlation is compared and data is filtered to obtain power output parameters and transmission ratio data; Based on the power output parameters and transmission ratio data, the transmission path is matched and verified, the difference between power output and transmission ratio is calculated, power distribution and transmission ratio distribution are corrected in combination with torque demand changes, transmission path parameters are adjusted through the influence of torque demand on data, and the power output and transmission ratio matching condition is obtained; Based on the power output and transmission ratio matching condition, transmission efficiency analysis is performed, transmission efficiency analysis standards are set, power distribution under differentiated torque conditions is evaluated in combination with system operation dynamic changes, efficiency indicators are compared and torque conditions are optimized, and the power distribution and transmission efficiency matching degree is obtained.

3. The SIC module based electric drive axle motor and transmission integrated control system of claim 2, wherein, The power distribution control parameter set is obtained in the following manner: Based on the power distribution and transmission efficiency matching degree, power transmission and efficiency changes of the system under differentiated operation conditions are analyzed, and the power distribution of the system is weighted calculated to obtain the preliminary power control demand of the system; Based on the preliminary power control demand of the system, the power balance among the systems is analyzed, the power transmission efficiency and load distribution relationship among the systems are identified, the system power control parameters are corrected, the power output values of the systems after adjustment are calculated, and the inter-system power control data set is obtained; In combination with the inter-system power control data set and transmission efficiency matching results, the power among the systems is distributed, the required balance and efficiency requirements are optimized and matched, and the power distribution control parameter set is obtained.

4. The SIC module based electric drive axle motor and transmission integrated control system of claim 3, wherein, The obtaining method of the motor output shaft speed fluctuation and the gearbox input torque gradient change amount is specifically as follows: Based on the power distribution control parameter set, the speed data of the motor output shaft is extracted, the speed points in each time period are screened, the speed fluctuation is analyzed in combination with the speed change trend of the differentiated position of the output shaft, and the speed data of the motor output shaft is obtained; Based on the speed data of the motor output shaft, each speed point and the corresponding torque value are calculated, the torque change amount of each measurement point is identified by analyzing the relationship between the speed and the torque, the torque change of the differentiated position is compared in combination with the system structure parameters, and the torque distribution and gradient distribution data are obtained; Based on the torque distribution and gradient distribution data, the overall torque distribution of the system is analyzed, the torque gradient is optimized in combination with the speed data, the influence of the torque change on the system performance is analyzed, the stable torque configuration under the differentiated operation condition is determined, and the motor output shaft speed fluctuation and the gearbox input torque gradient change amount are obtained.

5. The SIC module based electric drive axle motor and transmission integrated control system of claim 4, wherein, The obtaining method of the torque coordination optimization parameter set is specifically as follows: Based on the motor output shaft speed fluctuation and the gearbox input torque gradient change amount, the time sequence of the torque change is determined, the current torque value is compared with the original torque data, the torque gradient at each moment is analyzed, and the corresponding threshold value is defined according to the system state partition, and a preliminary torque change parameter set is generated; The preliminary torque change parameter set is analyzed, the influence of the torque in the system on the stability of the power output is analyzed, the correlation between the torque and the power output is identified, and the section power stability influence coefficient is calculated; By analyzing the section power stability influence coefficient, the torque distribution balance is adjusted in combination with the system torque change parameter, the torque regulation data is optimized, and the torque coordination optimization parameter set is generated.

6. The SIC module based electric drive axle motor and transmission integrated control system of claim 5, wherein, The obtaining method of the temperature field dynamic regulation result is specifically as follows: Based on the torque coordination optimization parameter set, the temperature distribution data of the surface of the SIC module is extracted, the flow rate of the heat dissipation medium on the surface of the differentiated material is monitored, the diffusion characteristics of the heat dissipation medium are inferred in combination with the external environmental factors of time and temperature, the flow and diffusion rate coefficients are defined, the thermal field distribution dynamic parameter set is generated; The influence of the thermal field distribution dynamic parameter set on the flow rate and distribution of the heat dissipation medium is analyzed, the proportion between the flow rate path and the input rate of the heat dissipation medium is optimized according to the demand of the temperature distribution of the surface of the SIC module, the adjustment coefficient of the temperature field distribution trend is calculated, and the temperature field regulation result is generated; The temperature field regulation result is analyzed, the proportional relationship between the input rate of the heat dissipation medium and the flow rate path is adjusted, the distribution trend of the temperature field is distributed, the temperature field dynamic regulation result is obtained in combination with the thermal field distribution parameter and the adjustment coefficient.

7. The SIC module based electric drive axle motor and transmission integrated control system of claim 6, wherein, The obtaining method of the efficiency optimization adjustment data set is specifically as follows: Based on the temperature field dynamic regulation result, the efficiency fluctuation rate and the energy loss deviation in the real-time running process of the system are monitored, the fluctuation range is identified, the system fault abnormal value is eliminated, the average fluctuation rate of the data is analyzed, and the efficiency and loss fluctuation data are obtained; The influence of the efficiency and loss fluctuation data on the total efficiency of the system is analyzed, the relationship between the efficiency and the loss is analyzed by using the known system efficiency characteristics, the system efficiency under the differentiated fluctuation range is calculated, and the efficiency influence data is obtained; According to the efficiency influence data, the power distribution path and the torque speed ratio in the target range are dynamically adjusted according to the relationship between the efficiency influence data and the temperature and the loss fluctuation range, the power output and the speed control range are distributed, and an efficiency optimization adjustment data set is generated.

8. The SIC module based electric drive axle motor and transmission integrated control system of claim 7, wherein, The acquisition method of the electric drive axle system integrated control data table is specifically: Based on the efficiency optimization adjustment data set, the system overall efficiency distribution and optimization time analysis are carried out, the efficiency data at different running time points are collected, the efficiency time distribution is arranged, the efficiency change trend is analyzed and the data is classified, and the system efficiency distribution data are obtained; Based on the system efficiency distribution data, the target control path parameters are adjusted, the system efficiency optimal running time and the efficiency distribution are analyzed, the efficiency changes under different running conditions are compared, the operation conditions of running temperature, time and power output are adjusted, and the target control path parameters are obtained; Based on the target control path parameters, the running conditions are adjusted according to the current operation parameters, the variable relationship of running time, temperature and power output is controlled, real-time control is carried out according to the adjusted parameters, and the electric drive axle system integrated control data table is obtained.

9. The SIC module based electric drive axle motor and transmission integrated control system of claim 8, wherein, The system further comprises: The state monitoring feedback module collects the running state data of the electric drive axle system in real time, including the motor speed, the gearbox gear, the torque output and the temperature distribution data, and carries out pretreatment and feature extraction on the collected data to generate a system running state feature set; The system running state feature set is transmitted to the power distribution control module to update the power distribution control parameter set, so as to realize the closed-loop feedback control of the system running state.

10. The SIC module based electric drive axle motor and transmission integrated control system of claim 9, wherein, The system further comprises: The fault diagnosis early warning module analyzes the system running abnormal mode and the fault feature based on the system running state feature set and the electric drive axle system integrated control data table, establishes the mapping relationship between the fault feature and the system parameter, generates a fault diagnosis rule library; Real-time comparison is made between the current system running state and the abnormal mode in the fault diagnosis rule library, when the matched fault feature is detected, a hierarchical early warning signal is generated and the corresponding fault handling strategy is triggered.

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