Self-adaptive intelligent control method and system for new energy automobile motor

Abstract

The invention relates to the technical field of automobile motor control, in particular to a self-adaptive intelligent control method and system for a new energy automobile motor. The method comprises the following steps: respectively deploying temperature sensor groups on the surface of a permanent magnet and the end part of a winding of an automobile motor rotor, collecting the real-time temperature of the permanent magnet and the real-time temperature of the winding, and generating a temperature gradient according to the real-time temperature of the permanent magnet and the real-time temperature of the winding; if the temperature gradient is larger than or equal to the preset temperature gradient value, the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding are obtained; calculating a local eddy current loss compensation coefficient of the permanent magnet according to the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; and adjusting the stator resistance according to the permanent magnet local eddy current loss compensation coefficient based on a preset trigger condition, and generating a global stator resistance correction value. According to the invention, the stability and response speed of the output torque of the motor can be ensured under the complex working conditions of frequent start-stop and acceleration brake cycle.

Description

Technical Field

[0001] This invention relates to the field of automotive motor control technology, and in particular to an adaptive intelligent control method and system for motors in new energy vehicles. Background Technology

[0002] In the development of new energy vehicle technology, motor control strategies have evolved from scalar control to vector control. Vector control significantly improves the dynamic response and control accuracy of the motor by decoupling the d-axis and q-axis currents. However, with the increasing complexity of new energy vehicle application scenarios, especially in the frequent start-stop conditions of urban high-temperature and high-humidity environments, the limitations of traditional vector control strategies have gradually become apparent.

[0003] In existing technologies, the real-time temperature of the permanent magnet in the drive motor of new energy vehicles exhibits a dynamic decoupling effect from the winding resistance parameters, leading to significant errors in traditional vector control strategies based on fixed temperature compensation. When a vehicle undergoes continuous acceleration-braking cycles, the local temperature rise rate of the permanent magnet due to eddy current losses can reach 3-5 times the overall temperature rise of the winding. However, existing temperature sensors can only monitor the temperature of the motor casing or winding ends, failing to reflect the real-time magnetic flux density decay state of the permanent magnet. This non-uniformity of the temperature field distribution causes systematic deviations in the online stator resistance identification algorithm, especially under low-speed, high-torque conditions, where the angle compensation of the rotor position observer deviates from the actual requirement by more than 8°. These problems directly affect the control accuracy and operating efficiency of the motor, limiting the performance of new energy vehicles under complex operating conditions. Summary of the Invention

[0004] Therefore, it is necessary for the present invention to provide an adaptive intelligent control method and system for motors in new energy vehicles to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, an adaptive intelligent control method for electric motors in new energy vehicles includes the following steps:

[0006] Step S1: Deploy temperature sensor groups on the surface of the permanent magnet and the end of the winding of the automobile motor rotor to collect the real-time temperature of the permanent magnet and the real-time temperature of the winding, and generate a temperature gradient based on the real-time temperature of the permanent magnet and the real-time temperature of the winding.

[0007] Step S2: If the temperature gradient is greater than or equal to the preset temperature gradient value, obtain the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; calculate the local eddy current loss compensation coefficient of the permanent magnet based on the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; adjust the stator resistance based on the local eddy current loss compensation coefficient of the permanent magnet according to the preset triggering conditions, and generate a global stator resistance correction value. The triggering conditions for adjusting the stator resistance also include full parameter identification mode and partition correction mode.

[0008] Step S3: Obtain the real-time motor speed and real-time motor output torque; adjust the d-axis and q-axis current distribution of the automotive motor according to the global stator resistance correction value, real-time motor speed and real-time motor output torque to obtain the d-axis-q-axis current distribution parameters;

[0009] Step S4: Obtain the angle compensation deviation value of the rotor position observer; based on the preset collaborative control of the automotive motor cooling system and the preset automotive motor fault diagnosis and fault tolerance mechanism, perform segmented current closed-loop control of the automotive motor according to the angle compensation deviation value and the d-axis-q-axis current distribution parameters.

[0010] This invention addresses the problem of inaccurate reflection of permanent magnet flux density decay in existing technologies by deploying high-precision temperature sensor arrays on the surface of the permanent magnet and at the winding ends to monitor temperature gradients in real time, providing reliable data support for subsequent dynamic compensation. By calculating eddy current loss compensation coefficients based on temperature gradients and rates of change, and dynamically adjusting stator resistance, the influence of non-uniform temperature field distribution on the online stator resistance identification algorithm can be effectively eliminated, significantly reducing angle compensation deviations under low-speed, high-torque conditions, thereby improving the accuracy and stability of vector control. The introduction of coordinated control of the cooling system and a fault diagnosis mechanism further enhances the motor's operational safety under high-temperature conditions. When the temperature of the permanent magnet or winding exceeds a preset threshold, the cooling system's flow rate enhancement mechanism responds quickly, ensuring effective temperature control of critical motor components. Furthermore, the liquid nitrogen injection mechanism provides additional cooling protection under extreme high-temperature conditions, while the fault diagnosis mechanism can promptly detect and limit motor output power, preventing system failures due to overheating, thus significantly improving the reliability and safety of the motor system. The optimized d-axis and q-axis current distribution strategy combined with segmented current closed-loop control adapts to dynamic requirements under complex operating conditions. By adjusting the current distribution parameters in real time, this invention ensures the stability and response speed of the motor's output torque under complex operating conditions involving frequent start-stop cycles and acceleration-braking cycles. In summary, this invention significantly improves the motor's dynamic response capability through multi-dimensional dynamic monitoring and intelligent compensation, while reducing torque ripple amplitude and further optimizing the motor's operating efficiency.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S1: At least 6 miniature thermocouple sensors are evenly arranged circumferentially on the surface of the permanent magnet of the motor rotor. The measurement accuracy of the miniature thermocouple sensors is ±0.5℃, the sampling frequency is greater than or equal to 100Hz, and the installation position is no more than 2mm away from the edge of the permanent magnet.

[0013] Step S2: Embed a distributed optical fiber sensor in the insulation layer at the end of the winding to collect the real-time temperature of the permanent magnet and the real-time temperature of the winding. Subtract the real-time temperature of the permanent magnet from the real-time temperature of the winding to obtain the temperature gradient.

[0014] This invention achieves high-precision, high-frequency monitoring of the permanent magnet and winding temperatures by uniformly arranging high-precision miniature thermocouple sensors circumferentially on the surface of the permanent magnet of the motor rotor and embedding distributed fiber optic sensors at the winding ends. The high measurement accuracy (±0.5℃) and high sampling frequency (greater than or equal to 100Hz) of the miniature thermocouple sensors can capture local temperature rise changes in the permanent magnet, while the distributed fiber optic sensors provide comprehensive monitoring of the winding temperature. This accurately reflects the temperature gradient between the permanent magnet and the winding, providing reliable data support for subsequent eddy current loss compensation and stator resistance correction. By monitoring temperature changes in real time, the problem of stator resistance identification deviation caused by uneven temperature field distribution in existing technologies can be effectively solved, while providing a basis for dynamic optimization of vector control strategies.

[0015] Preferably, step S2 includes the following steps:

[0016] Step S21: If the temperature gradient is greater than or equal to the preset temperature gradient value, then obtain the initial temperature and current temperature of the permanent magnet, and obtain the initial temperature and current temperature of the winding.

[0017] Step S22: Calculate the real-time temperature change rate of the permanent magnet based on its initial temperature and current temperature; calculate the real-time temperature change rate of the winding based on its initial temperature and current temperature.

[0018] Step S23: Calculate the local eddy current loss compensation coefficient of the permanent magnet based on the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding. The specific calculation formula is as follows:

[0019]

[0020] Among them, K eddy dT is the local eddy current loss compensation coefficient for permanent magnets. pm / dt represents the real-time temperature change rate of the permanent magnet, dT w / dt is the real-time temperature change rate of the winding, and ΔT is the temperature gradient. pm ΔT is the difference between the current temperature and the initial temperature of the permanent magnet. w This is the difference between the current winding temperature and the initial winding temperature.

[0021] Step S24: Obtain the rated current value of the stator winding; inject a high-frequency test current value with a frequency of 1kHz and an amplitude of 10% of the rated current value of the stator winding into the stator winding, and measure the amplitude of the first voltage response signal of the stator winding. The sampling period is 100ms, and the accuracy error is less than or equal to 0.5%.

[0022] Step S25: Calculate the first stator winding resistance deviation value based on the high-frequency test current value and the amplitude of the first voltage response signal;

[0023] Step S26: Calculate the global stator resistance correction value based on the local eddy current loss compensation coefficient of the permanent magnet and the stator winding resistance deviation value. The triggering conditions for adjusting the stator resistance also include full parameter identification mode and zone correction mode. The specific calculation formula is as follows:

[0024] R adj =R nominal +K eddy ×ΔR×α;

[0025] Among them, R adj R is the global stator resistance correction value. nominal ΔR is the nominal resistance value of the stator, ΔR is the resistance deviation value of the first stator winding, and α is the thermal balance correction factor, which ranges from 0.8 to 1.2.

[0026] This invention calculates the rate of temperature change by combining the initial and current temperatures of the permanent magnet and windings when the temperature gradient exceeds a preset value, and further calculates the eddy current loss compensation coefficient, thus accurately quantifying the impact of temperature changes on the eddy current losses of the permanent magnet. This effectively corrects the magnetic flux density attenuation caused by temperature changes, ensuring the accuracy of the vector control strategy. Simultaneously, by measuring high-frequency test current injection and voltage response signals, the resistance deviation of the stator winding can be monitored in real time, and the global stator resistance correction value can be dynamically adjusted in conjunction with the eddy current loss compensation coefficient. This significantly improves the stator resistance identification accuracy, especially under low-speed, high-torque conditions, effectively reducing the angle compensation deviation of the rotor position observer and improving the stability and response speed of vector control. The introduction of a thermal balance correction factor further optimizes the adaptability of the stator resistance correction value, ensuring the robustness and reliability of the system under different operating conditions.

[0027] Preferably, the full-parameter identification mode in step S2 includes:

[0028] The duration for which the temperature gradient is greater than or equal to a preset temperature gradient value is collected;

[0029] Obtain the instantaneous rate of change of the current temperature of the permanent magnet;

[0030] Obtain the rated current value of the stator winding;

[0031] If the duration exceeds the preset temperature gradient continuous trigger threshold or the instantaneous change rate of the current temperature of the permanent magnet is greater than or equal to the preset permanent magnet temperature rise rate threshold, a high-frequency test current with a frequency of 1kHz and an amplitude of 10% of the rated current value of the stator winding is injected into the stator winding, and the amplitude of the second voltage response signal of the stator winding is measured.

[0032] The second stator winding resistance deviation value is calculated based on the high-frequency test current value and the amplitude of the second voltage response signal.

[0033] Obtain the nominal stator resistance value, and record the sum of the nominal stator resistance value and the second stator winding resistance deviation value as the global stator resistance correction value.

[0034] This invention, through a full-parameter identification mode, can trigger high-frequency test current injection and monitor the voltage response signal amplitude of the stator winding in real time when the temperature gradient continuously exceeds the limit or the permanent magnet temperature rise rate is abnormal. This effectively captures minute changes in the stator winding resistance, providing a reliable basis for the accurate calculation of stator resistance deviation. By combining the nominal stator resistance value with the real-time calculated resistance deviation value, a global stator resistance correction value is generated, which can significantly improve the accuracy and adaptability of the vector control strategy. Under complex operating conditions, such as frequent start-stop and acceleration-braking cycles, this correction mechanism can effectively reduce control errors caused by temperature changes and improve the stability and response speed of the motor system.

[0035] Preferably, the partition correction mode in step S2 includes:

[0036] By using distributed fiber optic sensors embedded in the insulation layer at the winding end, temperature data at the winding end is collected at 10cm intervals to generate a real-time temperature distribution thermal map of the winding.

[0037] Identify whether there are local hot spots in the real-time temperature distribution heatmap of the winding. If so, calculate the temperature difference of a single hot spot and the proportion of the total area of ​​the hot spot based on the real-time temperature distribution heatmap of the winding.

[0038] If the temperature difference of a single hot spot is greater than or equal to 3℃ and the total area of ​​the hot spots is greater than or equal to 15%, a local hot spot distribution map is generated based on the real-time temperature distribution heat map of the winding.

[0039] A hotspot distribution weight mapping table is generated based on the location map of local hotspot areas, wherein the weight coefficients in the hotspot distribution weight mapping table range from 0.7 to 1.3;

[0040] The local stator resistance correction value is calculated based on the hotspot distribution weight mapping table, and the specific calculation formula is as follows:

[0041]

[0042] Among them, Rlocal_adj Here, w represents the local stator resistance correction value, n is the total number of detected hot spots, and w is the total number of detected hot spots. i Let ΔR be the weight coefficient of the i-th hotspot region. i This represents the local resistance deviation value of the i-th sub-region;

[0043] Generate a global stator resistance correction value based on the local stator resistance correction value;

[0044] Record the continuous trigger count of local hotspots in the same local hotspot area. If the number of consecutive triggers reaches 3, activate the full parameter identification mode for global calibration.

[0045] This invention utilizes a partitioned correction mode to generate a real-time temperature distribution thermal map of the winding using distributed fiber optic sensors. This accurately identifies local hotspot areas and dynamically adjusts the stator resistance correction value based on the hotspot distribution characteristics. This effectively captures abnormal temperature distributions within the winding, avoiding control deviations caused by localized overheating. By introducing a hotspot distribution weight mapping table, the correction weights can be dynamically adjusted according to the location and temperature difference of the hotspot area, ensuring the accuracy of the stator resistance correction value. This not only improves the adaptability of the vector control strategy but also significantly reduces torque ripple and control errors caused by localized temperature rises. By recording the number of consecutive hotspot triggers and combining this with a full-parameter identification mode for global calibration, the stability and reliability of the system can be further improved based on localized corrections. This dynamic monitoring and correction mechanism is particularly suitable for the control of new energy vehicle motors under complex operating conditions, ensuring that the motor maintains efficient and stable operation even in complex environments such as high temperatures and frequent start-stop cycles.

[0046] Preferably, step S3 includes the following steps:

[0047] Step S31: Obtain the real-time motor speed and real-time motor output torque;

[0048] Step S32: If the real-time motor speed is less than or equal to the preset low speed operating condition threshold and the real-time motor output torque is greater than or equal to the preset high torque operating condition threshold, then proceed to the next step; otherwise, proceed directly to step S310.

[0049] Step S33: Obtain the initial magnetic flux density value and the current magnetic flux density value of the permanent magnet. Calculate the magnetic flux density attenuation rate of the first permanent magnet based on the initial magnetic flux density value and the current magnetic flux density value of the permanent magnet. Obtain the current timestamp and record the current timestamp as the magnetic flux density attenuation rate calculation timestamp.

[0050] Step S34: When the magnetic flux density attenuation rate of the first permanent magnet is less than the preset first magnetic flux density attenuation threshold, the preset first angle compensation amount is set as the angle compensation amount of the rotor position observer to obtain the first current angle compensation amount.

[0051] Step S35: When the magnetic flux density attenuation rate of the first permanent magnet is greater than or equal to the preset first magnetic flux density attenuation threshold and less than the preset second magnetic flux density attenuation threshold, the preset second angle compensation amount is set as the angle compensation amount of the rotor position observer to obtain the second current angle compensation amount.

[0052] Step S36: When the magnetic flux density attenuation rate of the first permanent magnet is greater than or equal to the preset second magnetic flux density attenuation threshold, the preset third angle compensation amount is set as the angle compensation amount of the rotor position observer to obtain the third current angle compensation amount, wherein the first current angle compensation amount < the second current angle compensation amount < the third current angle compensation amount.

[0053] Step S37: Perform a second-order low-pass filter on the first current angle compensation amount / second current angle compensation amount / third current angle compensation amount to obtain the filtered rotor angle compensation amount;

[0054] Step S38: Obtain the raw angle data from the rotor position observer; superimpose the filtered rotor angle compensation amount onto the raw angle data from the rotor position observer to generate compensated rotor position data;

[0055] Step S39: Adjust the d-axis and q-axis current distribution of the automotive motor based on the compensated rotor position data to obtain the d-axis-q-axis current distribution parameters;

[0056] Of particular importance, step S39 also includes the following steps:

[0057] Step S391: Obtain the three-phase stator current and stationary coordinate system of the automotive motor;

[0058] Step S392: Perform a Park transformation on the stationary coordinate system based on the compensated rotor position data to generate a rotating coordinate system;

[0059] Step S393: Perform Clarke-Park transformation on the three-phase stator current of the automotive motor based on the rotating coordinate system to obtain dynamic current distribution parameters;

[0060] Step S394: Calculate the timestamp based on the magnetic flux density attenuation rate. After the preset dynamic monitoring time window, recalculate the magnetic flux density attenuation rate of the second permanent magnet.

[0061] Step S395: Calculate the change in magnetic flux density attenuation rate based on the magnetic flux density attenuation rate of the first permanent magnet and the magnetic flux density attenuation rate of the second permanent magnet;

[0062] Step S396: If the change in magnetic flux density attenuation rate is greater than 0, then negative d-axis current injection is performed on the dynamic current distribution parameters according to the preset field weakening compensation coefficient, and the q-axis current ratio is increased according to the preset torque gain coefficient to generate dynamic field weakening compensation current distribution parameters.

[0063] Step S397: If the change in magnetic flux density attenuation rate is less than or equal to 0, then the dynamic current distribution parameters remain unchanged;

[0064] Step S398: Record the dynamic field weakening compensation current allocation parameters or dynamic current allocation parameters as d-axis-q-axis current allocation parameters.

[0065] Step S310: Based on the global stator resistance correction value, real-time motor speed and real-time motor output torque, generate d-axis-q-axis current distribution parameters using a preset steady-state current distribution strategy.

[0066] Of particular importance, step S310 also includes the following steps:

[0067] When the real-time motor speed is greater than the preset low speed operating threshold or the real-time motor output torque is less than the preset high torque operating threshold, the preset steady-state current distribution mapping table is queried based on the real-time motor speed and the real-time motor output torque to obtain the initial d-axis current reference value and q-axis current reference value.

[0068] Based on the global stator resistance correction value, the initial d-axis current reference value is compensated for stator resistance sensitivity to generate a corrected d-axis current reference value.

[0069] Based on the real-time temperature of the winding, the temperature rise compensation d-axis current reference value is performed to generate a temperature rise compensation d-axis current value.

[0070] The corrected d-axis current reference value and the temperature rise compensated d-axis current value are combined to form the d-axis-q-axis current distribution parameters.

[0071] This invention, by real-time monitoring of motor speed and output torque, can quickly identify low-speed, high-torque operating conditions and trigger corresponding magnetic flux density attenuation rate calculations. This ensures the timeliness of the control strategy and avoids control lag caused by changes in operating conditions. Secondly, by setting angle compensation in stages and combining it with second-order low-pass filtering, the rotor position observation error caused by magnetic flux density attenuation can be effectively reduced, improving the accuracy of current distribution. Through a dynamic field weakening compensation mechanism, the motor's field weakening performance can be optimized by injecting negative d-axis current and increasing the q-axis current ratio when the magnetic flux density attenuation rate changes, ensuring efficient operation of the motor under different operating conditions. By combining steady-state current distribution strategies with dynamic adjustments, smooth transitions in current distribution can be achieved under complex operating conditions, significantly reducing torque ripple and improving the motor's response speed and operating efficiency.

[0072] Preferably, step S4 includes the following steps:

[0073] Step S41: Obtain the actual position of the rotor and compare it with the preset theoretical position of the rotor to generate the original angle deviation value of the rotor;

[0074] Step S42: Calculate the rate of change of the original rotor angle deviation value;

[0075] Step S43: If the original rotor angle deviation value is less than or equal to the preset low deviation stage trigger threshold, the PID control algorithm is used to fine-tune the d-axis-q-axis current distribution parameters to generate dynamic current compensation parameters.

[0076] Step S44: If the original rotor angle deviation value is greater than the low deviation stage trigger threshold and the original rotor angle deviation value is less than or equal to the high deviation stage trigger threshold, then the rotor angle deviation trend will be predicted according to the angle deviation change rate, and the feedforward slope of the d-axis-q-axis current distribution parameters will be adjusted according to the rotor angle deviation trend to generate the predicted current distribution parameters.

[0077] Step S45: If the original rotor angle deviation value is greater than the preset high deviation stage trigger threshold, the d-axis current ratio is increased in the d-axis-q-axis current distribution parameters to generate emergency correction current parameters.

[0078] Step S46: Obtain the rotating coordinate system, and perform inverse Clarke-Park transformation on the dynamic current compensation parameters / predicted current distribution parameters / emergency correction current parameters based on the rotating coordinate system to obtain the real-time drive current parameters of the automotive motor.

[0079] Step S47: Perform current control on the automotive motor based on the real-time drive current parameters of the automotive motor.

[0080] The current control loop takes the drive current parameter as input and outputs a control signal to the motor driver through the PWM generation module.

[0081] This invention employs a phased deviation response mechanism to flexibly adjust the d-axis to q-axis current distribution parameters based on the magnitude of the rotor's initial angle deviation, ensuring stable motor operation under various deviation conditions. Firstly, the graded processing mechanism effectively reduces torque fluctuations caused by angle deviations. Through PID fine-tuning, feedforward slope adjustment, and emergency correction strategies, it ensures that the motor obtains appropriate control parameters at low, medium, and high deviation stages. This significantly improves the accuracy and response speed of vector control, particularly under frequent start-stop and acceleration-braking cycle conditions, effectively reducing torque pulsation and improving motor operating efficiency. The inverse Clarke-Park transform converts the dynamically adjusted current parameters into real-time drive current parameters, ensuring the real-time performance and accuracy of current control.

[0082] Preferably, the pre-defined coordinated control of the automotive motor cooling system includes:

[0083] When the real-time temperature of the permanent magnet is greater than or equal to the preset real-time temperature warning threshold of the permanent magnet or the real-time temperature of the winding is greater than or equal to the preset real-time temperature warning threshold of the winding, the flow rate of the coolant in the motor active cooling circuit will be increased to 150% of the nominal value for 60 seconds.

[0084] After the coolant flow rate increase phase ends, the current temperature of the permanent magnet and the current temperature of the winding are obtained. If the current temperature of the permanent magnet is greater than the preset liquid nitrogen trigger temperature threshold or the current temperature of the winding is greater than the preset winding liquid nitrogen trigger temperature threshold, liquid nitrogen injection is triggered. The single injection volume is less than or equal to 10ml. The automotive motor cooling system and the automotive motor controller communicate via CANFD bus with a data transmission rate greater than or equal to 2Mbps and a response delay less than or equal to 100ms.

[0085] This invention, through coordinated control of the cooling system, can rapidly increase the coolant flow rate to 150% of the nominal value when the temperature of the permanent magnet or winding exceeds a warning threshold, ensuring effective temperature control of critical motor components. This reduces temperature quickly, preventing performance degradation or malfunction due to overheating. An additional cooling guarantee is provided through a liquid nitrogen injection mechanism when the temperature exceeds a higher threshold, ensuring safe operation of the motor even under extreme high-temperature conditions. CANFD bus communication between the cooling system and the controller optimizes data transmission rate and response latency, ensuring the real-time performance and reliability of the cooling strategy.

[0086] Preferably, the preset automotive motor fault diagnosis and fault tolerance mechanism includes:

[0087] When the absolute value of the deviation between the real-time temperature of the permanent magnet and the real-time temperature of the winding is greater than or equal to 10℃, the preset real-time temperature gradient abnormal protection mechanism of the permanent magnet-winding is triggered.

[0088] Obtain motor torque recording data, calculate the motor torque ripple amplitude based on the motor torque recording data, and generate a motor torque ripple amplitude statistics table. The specific calculation formula for the motor torque ripple amplitude is as follows:

[0089]

[0090] Where, ΔT ripple T represents the torque ripple amplitude of the motor. peak T represents the peak torque within the sampling period. vally T represents the torque valley value. avg This is the average torque;

[0091] If the torque ripple amplitude in the motor torque ripple amplitude statistics table exceeds 15% for three consecutive times, the current output power of the car motor is obtained, and the current output power of the car motor is limited to 70% of the rated output power of the car motor. A maintenance warning is also pushed through the preset HMI interface.

[0092] This invention enables timely detection and response to abnormal conditions during motor operation by real-time monitoring of the temperature gradient between the permanent magnet and windings, as well as the torque ripple amplitude. The temperature gradient anomaly protection mechanism ensures safe motor operation even with uneven temperature distribution, preventing performance degradation or malfunctions due to localized overheating. Monitoring and statistical analysis of torque ripple amplitude provides a real-time assessment of motor operational stability, helping to identify potential control errors or mechanical problems early. When excessive torque ripple amplitude is detected, limiting motor output power and sending maintenance warnings effectively reduces the risk of failure and ensures vehicle safety. This significantly improves the reliability and maintenance efficiency of the motor system, reducing repair costs and time losses caused by sudden failures.

[0093] Preferably, the present invention also provides an adaptive intelligent control system for a new energy vehicle motor, for executing the adaptive intelligent control method for a new energy vehicle motor as described above, the adaptive intelligent control system for a new energy vehicle motor comprising:

[0094] The temperature monitoring module can deploy temperature sensor groups on the surface of the permanent magnet and the end of the winding of the automotive motor rotor to collect the real-time temperature of the permanent magnet and the real-time temperature of the winding, and generate a temperature gradient based on the real-time temperature of the permanent magnet and the real-time temperature of the winding.

[0095] The eddy current compensation module can obtain the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding if the temperature gradient is greater than or equal to the preset temperature gradient value; calculate the local eddy current loss compensation coefficient of the permanent magnet based on the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; adjust the stator resistance based on the local eddy current loss compensation coefficient of the permanent magnet and generate a global stator resistance correction value. The triggering conditions for adjusting the stator resistance include full parameter identification mode and zone correction mode.

[0096] The current distribution module can obtain the real-time motor speed and real-time motor output torque; based on the global stator resistance correction value, the real-time motor speed and real-time motor output torque, the d-axis and q-axis current distribution of the automotive motor is adjusted to obtain the d-axis-q-axis current distribution parameters.

[0097] The motor control module can obtain the angle compensation deviation value from the rotor position observer; and perform segmented current closed-loop control of the automotive motor based on the angle compensation deviation value and the d-axis-q-axis current distribution parameters.

[0098] In this invention, the temperature monitoring module can collect real-time temperature data of the permanent magnet and windings, generating a temperature gradient to provide data support for subsequent compensation and adjustment. The eddy current compensation module calculates the compensation coefficient based on the temperature change rate and dynamically adjusts the stator resistance, effectively solving the control error problem caused by temperature changes. The current distribution module combines real-time motor parameters and the corrected stator resistance value to optimize the current distribution on the d-axis and q-axis, improving the accuracy and efficiency of vector control. The motor control module ensures stable motor operation under different operating conditions through segmented current closed-loop control. In summary, this invention significantly improves the accuracy, stability, and adaptability of motor control, enhances the performance of new energy vehicles under complex operating conditions, and improves the safety and reliability of the system through real-time monitoring and dynamic adjustment. Attached Figure Description

[0099] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings:

[0100] Figure 1 A flowchart illustrating the steps of an embodiment of an adaptive intelligent control method for a new energy vehicle motor is shown.

[0101] Figure 2 A detailed flowchart of step S2 of one embodiment is shown.

[0102] Figure 3 A detailed flowchart of step S4 of one embodiment is shown. Detailed Implementation

[0103] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0104] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0105] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0106] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides an adaptive intelligent control method for electric motors in new energy vehicles, comprising the following steps:

[0107] Step S1: Deploy temperature sensor groups on the surface of the permanent magnet and the end of the winding of the automobile motor rotor to collect the real-time temperature of the permanent magnet and the real-time temperature of the winding, and generate a temperature gradient based on the real-time temperature of the permanent magnet and the real-time temperature of the winding.

[0108] Step S2: If the temperature gradient is greater than or equal to the preset temperature gradient value, obtain the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; calculate the local eddy current loss compensation coefficient of the permanent magnet based on the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; adjust the stator resistance based on the local eddy current loss compensation coefficient of the permanent magnet according to the preset triggering conditions, and generate a global stator resistance correction value. The triggering conditions for adjusting the stator resistance also include full parameter identification mode and partition correction mode.

[0109] Step S3: Obtain the real-time motor speed and real-time motor output torque; adjust the d-axis and q-axis current distribution of the automotive motor according to the global stator resistance correction value, real-time motor speed and real-time motor output torque to obtain the d-axis-q-axis current distribution parameters;

[0110] Step S4: Obtain the angle compensation deviation value of the rotor position observer; based on the preset collaborative control of the automotive motor cooling system and the preset automotive motor fault diagnosis and fault tolerance mechanism, perform segmented current closed-loop control of the automotive motor according to the angle compensation deviation value and the d-axis-q-axis current distribution parameters.

[0111] In this embodiment, firstly, at least six miniature thermocouple sensors (such as K-type miniature thermocouple sensors with a measurement accuracy of ±0.5℃, a sampling frequency greater than or equal to 100Hz, and an installation position no more than 2mm from the edge of the permanent magnet) are uniformly arranged circumferentially on the surface of the permanent magnet of the motor rotor. Distributed fiber optic sensors are also embedded in the insulation layer at the winding ends to collect temperature data of the permanent magnet and windings in real time. A data acquisition card collects temperature data at a frequency of 100Hz and transmits it to the motor controller. The controller uses LabVIEW software to process the temperature data in real time and calculate the temperature gradient between the permanent magnet and the windings. When the temperature gradient reaches or exceeds a preset temperature gradient value (e.g., 5℃), the system triggers further processing through the LabVIEW condition judgment module. At this time, the real-time temperature change rate of the permanent magnet and windings is calculated using the MATLAB signal processing toolbox, and the local eddy current loss compensation coefficient of the permanent magnet is calculated according to the formula. This compensation coefficient is used to adjust the stator resistance and generate a global stator resistance correction value. The triggering conditions for adjusting the stator resistance include a full-parameter identification mode and a partition correction mode, which are implemented through the LabVIEW logic control module. The motor's speed and output torque data are acquired in real time using current and torque sensors. This data, along with a global stator resistance correction value, is used to adjust the d-axis and q-axis current distribution of the automotive motor. Specifically, the controller uses a preset current distribution mechanism, combined with real-time speed and torque data, to generate d-axis-q-axis current distribution parameters. Angle compensation deviation values ​​are obtained through a rotor position observer, and combined with preset cooling system collaborative control and fault diagnosis and tolerance mechanisms, segmented current closed-loop control of the motor is implemented. The cooling system collaborative control communicates with the controller via a CANFD bus (data transmission rate greater than or equal to 2Mbps, response delay less than or equal to 100ms). When the temperature exceeds a warning threshold, the coolant flow rate is automatically increased to 150% of the nominal value. The fault diagnosis and tolerance mechanism monitors temperature gradients and torque ripple amplitude, promptly triggering protection mechanisms to limit motor output power and pushing maintenance warnings through the HMI interface.

[0112] Preferably, step S1 includes the following steps:

[0113] Step S1: At least 6 miniature thermocouple sensors are evenly arranged circumferentially on the surface of the permanent magnet of the motor rotor. The measurement accuracy of the miniature thermocouple sensors is ±0.5℃, the sampling frequency is greater than or equal to 100Hz, and the installation position is no more than 2mm away from the edge of the permanent magnet.

[0114] Step S2: Embed a distributed optical fiber sensor in the insulation layer at the end of the winding to collect the real-time temperature of the permanent magnet and the real-time temperature of the winding. Subtract the real-time temperature of the permanent magnet from the real-time temperature of the winding to obtain the temperature gradient.

[0115] In this embodiment, a K-type miniature thermocouple sensor is selected, with a measurement accuracy of ±0.5℃ and a sampling frequency of 200Hz, meeting the requirements of high-precision and high-frequency monitoring. The sensor is installed no more than 2mm away from the edge of the permanent magnet. High-temperature heat-resistant adhesive is used to uniformly fix the miniature thermocouple sensors onto the surface of the permanent magnet, with at least six sensors evenly distributed circumferentially. Shielded cables are used for the sensor leads. After installation, the sensors are calibrated using a portable calibration device (such as a constant-temperature water bath). In actual operation, the temperature data of the permanent magnet surface is collected in real time at a sampling frequency of 200Hz using a data acquisition card, and the data is transmitted to the motor controller for processing. The controller uses the MATLAB / Simulink platform for data visualization and preliminary analysis. A distributed fiber optic temperature sensor (such as a fiber Bragg grating-based DTS system) is selected, with a sampling interval of 10cm, which can accurately monitor the temperature distribution at the winding end. A laser drilling machine is used to uniformly drill holes in the insulation layer surface at the winding end, with a hole diameter of 1mm and a depth of 2mm. The fiber optic sensor uses high-temperature resistant optical fiber (such as silica fiber) and is encased in a protective sheath to prevent mechanical damage and chemical corrosion. The sensor is fixed within the insulation layer with epoxy resin to ensure a tight fit with the winding. A fiber optic demodulator (such as Micron Optics' SM130) acquires real-time temperature data at the winding ends at a sampling frequency of 100Hz and transmits it to the motor controller via optical fiber. The controller subtracts the real-time winding temperature from the real-time permanent magnet temperature to calculate the temperature gradient, and stores this gradient data in the controller's non-volatile memory.

[0116] Preferably, step S2 includes the following steps:

[0117] Step S21: If the temperature gradient is greater than or equal to the preset temperature gradient value, then obtain the initial temperature and current temperature of the permanent magnet, and obtain the initial temperature and current temperature of the winding.

[0118] Step S22: Calculate the real-time temperature change rate of the permanent magnet based on its initial temperature and current temperature; calculate the real-time temperature change rate of the winding based on its initial temperature and current temperature.

[0119] Step S23: Calculate the local eddy current loss compensation coefficient of the permanent magnet based on the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding. The specific calculation formula is as follows:

[0120]

[0121] Among them, K eddy dT is the local eddy current loss compensation coefficient for permanent magnets. pm / dt represents the real-time temperature change rate of the permanent magnet, dT w / dt is the real-time temperature change rate of the winding, and ΔT is the temperature gradient. pm ΔT is the difference between the current temperature and the initial temperature of the permanent magnet. w This is the difference between the current winding temperature and the initial winding temperature.

[0122] Step S24: Obtain the rated current value of the stator winding; inject a high-frequency test current value with a frequency of 1kHz and an amplitude of 10% of the rated current value of the stator winding into the stator winding, and measure the amplitude of the first voltage response signal of the stator winding. The sampling period is 100ms, and the accuracy error is less than or equal to 0.5%.

[0123] Step S25: Calculate the first stator winding resistance deviation value based on the high-frequency test current value and the amplitude of the first voltage response signal;

[0124] Step S26: Calculate the global stator resistance correction value based on the local eddy current loss compensation coefficient of the permanent magnet and the stator winding resistance deviation value. The triggering conditions for adjusting the stator resistance also include full parameter identification mode and zone correction mode. The specific calculation formula is as follows:

[0125] R adj =R nominal +K eddy ×ΔR×α;

[0126] Among them, R adj R is the global stator resistance correction value. nominal ΔR is the nominal resistance value of the stator, ΔR is the resistance deviation value of the first stator winding, and α is the thermal balance correction factor, which ranges from 0.8 to 1.2.

[0127] In this embodiment, when the temperature gradient reaches or exceeds a preset temperature gradient value (e.g., 10°C), temperature data of the permanent magnet and windings are acquired via a data acquisition card (e.g., NI USB-6212). The initial and current temperatures of the permanent magnet are acquired by a miniature thermocouple sensor, while the initial and current temperatures of the windings are acquired by a distributed fiber optic sensor. The acquired temperature data is processed and stored in real time using the MATLAB / Simulink platform. The initial temperature data is recorded when the motor starts, while the current temperature data is updated in real time within each sampling period (100ms). The temperature data is processed using LabVIEW software. First, the temperature data of the permanent magnet and windings are imported into the time series analysis module of LabVIEW. By setting the time interval to 100ms, the rate of temperature change over time is calculated. Specifically, LabVIEW's "numerical derivative" function is used to input the temperature data and time interval, and output the rate of temperature change. The rate of temperature change of the permanent magnet and the rate of temperature change of the windings are calculated separately and stored in the controller's memory. Formula calculations are performed using Excel or MATLAB. Import the temperature change rates of the permanent magnet and windings calculated in step S22, and the temperature gradient data obtained in step S21, into Excel. In Excel, use formulas... The calculations are performed. Specifically, the temperature change rate and temperature gradient values ​​are entered into cells, the compensation coefficient is calculated using a formula, and the results are saved in designated cells. The calculation results are then transmitted to the motor controller via a data interface. When injecting a high-frequency test current into the stator winding, a high-frequency current signal with a frequency of 1kHz and an amplitude of 10% of the stator winding's rated current is generated using a signal generator (such as a Keysight 33500B). The rated current value of the stator winding (e.g., 200A) is preset by the motor controller. The high-frequency current signal is amplified by a power amplifier (such as an Amplifier Research 10W) ​​before being injected into the stator winding. Simultaneously, a power analyzer (such as a Yokogawa WT3000) is used to measure the amplitude of the first voltage response signal of the stator winding, with a sampling period set to 100ms, ensuring that the measurement accuracy error is less than or equal to 0.5%. The high-frequency test current value and the amplitude of the first voltage response signal are then imported into MATLAB. In MATLAB, the actual resistance value of the stator winding is calculated using Ohm's law formula and compared with the nominal resistance value of the stator winding (e.g., 0.5Ω) to obtain the resistance deviation value. The calculation results are stored in the controller's database. The formula calculation is then performed again using Excel or MATLAB. The eddy current loss compensation coefficient calculated in step S23 and the resistance deviation value calculated in step S25 are imported into Excel. In Excel, the formula R... adj =R nominal +K eddyThe calculation is performed using ×ΔR×α. The specific steps include: inputting the nominal resistance value, compensation coefficient, resistance deviation value, and thermal balance correction factor (range 0.8–1.2), setting the formula to calculate the corrected resistance value, and saving the result in a specified cell. The calculation result is then transmitted to the motor controller via a data interface.

[0128] Preferably, the full-parameter identification mode in step S2 includes:

[0129] The duration for which the temperature gradient is greater than or equal to a preset temperature gradient value is collected;

[0130] Obtain the instantaneous rate of change of the current temperature of the permanent magnet;

[0131] Obtain the rated current value of the stator winding;

[0132] If the duration exceeds the preset temperature gradient continuous trigger threshold or the instantaneous change rate of the current temperature of the permanent magnet is greater than or equal to the preset permanent magnet temperature rise rate threshold, a high-frequency test current with a frequency of 1kHz and an amplitude of 10% of the rated current value of the stator winding is injected into the stator winding, and the amplitude of the second voltage response signal of the stator winding is measured.

[0133] The second stator winding resistance deviation value is calculated based on the high-frequency test current value and the amplitude of the second voltage response signal.

[0134] Obtain the nominal stator resistance value, and record the sum of the nominal stator resistance value and the second stator winding resistance deviation value as the global stator resistance correction value.

[0135] In this embodiment, when the temperature gradient reaches or exceeds a preset temperature gradient value (e.g., 10°C), temperature data is acquired in real time via a data acquisition card. The data acquisition card acquires the temperature of the permanent magnet and windings at a frequency of 100Hz and transmits the data to the controller. The controller uses LabVIEW software to process the temperature data in real time and records the duration for which the temperature gradient exceeds the preset value. Specifically, a conditional judgment module is set up in LabVIEW to start a timer when the temperature gradient is greater than or equal to 10°C and to stop the timer when the temperature gradient is below the threshold. The timing results are stored in the controller's memory. The temperature data is processed using MATLAB's signal processing toolbox. First, the temperature data of the permanent magnet is imported into MATLAB, and the "gradient" function is used to calculate the temperature gradient, thereby obtaining the instantaneous rate of change. Specifically, the temperature data and time data are imported into the MATLAB workspace, the "gradient" function is used to calculate the rate of change of temperature, and the result is stored in a variable. In motor controllers, the rated current value of the stator winding is usually indicated on the motor's nameplate. For example, the rated current value of the stator winding of a certain model of new energy vehicle motor is 200A. This can be confirmed by reading the motor nameplate or consulting the motor's technical manual. In the controller's configuration file, set the rated current value as a constant parameter. Specifically, enter the rated current value in the controller's configuration interface and save the configuration file. When the duration of the temperature gradient exceeds a preset threshold (e.g., 5 seconds) or the instantaneous rate of change of the permanent magnet temperature exceeds a preset threshold (e.g., 2℃ / s), initiate high-frequency test current injection. Use a signal generator (e.g., Keysight 33500B) to generate a high-frequency current signal with a frequency of 1kHz and an amplitude of 10% of the stator winding's rated current. For example, if the rated current is 200A, the test current amplitude is 20A. Amplify the signal using a power amplifier (e.g., Amplifier Research 10W) ​​and inject it into the stator winding. Simultaneously, use a power analyzer (e.g., Yokogawa WT3000) to measure the amplitude of the second voltage response signal of the stator winding, with a sampling period of 100ms, ensuring the measurement accuracy error is less than or equal to 0.5%. Import the high-frequency test current value and the second voltage response signal amplitude acquired in the steps into MATLAB. The specific steps include: calculating the actual resistance value of the stator winding using Ohm's Law formula and comparing it with the nominal resistance value of the stator winding (e.g., 0.5Ω) to obtain the resistance deviation value. The calculation results are stored in the controller's database. When calculating the global stator resistance correction value, Excel or MATLAB is used for formula calculation. The resistance deviation value and the nominal resistance value of the stator winding calculated in the previous steps are then imported into Excel.

[0136] Preferably, the partition correction mode in step S2 includes:

[0137] By using distributed fiber optic sensors embedded in the insulation layer at the winding end, temperature data at the winding end is collected at 10cm intervals to generate a real-time temperature distribution thermal map of the winding.

[0138] Identify whether there are local hot spots in the real-time temperature distribution heatmap of the winding. If so, calculate the temperature difference of a single hot spot and the proportion of the total area of ​​the hot spot based on the real-time temperature distribution heatmap of the winding.

[0139] If the temperature difference of a single hot spot is greater than or equal to 3℃ and the total area of ​​the hot spots is greater than or equal to 15%, a local hot spot distribution map is generated based on the real-time temperature distribution heat map of the winding.

[0140] A hotspot distribution weight mapping table is generated based on the location map of local hotspot areas, wherein the weight coefficients in the hotspot distribution weight mapping table range from 0.7 to 1.3;

[0141] The local stator resistance correction value is calculated based on the hotspot distribution weight mapping table, and the specific calculation formula is as follows:

[0142]

[0143] Among them, R local_adj Here, w represents the local stator resistance correction value, n is the total number of detected hot spots, and w is the total number of detected hot spots. i Let ΔR be the weight coefficient of the i-th hotspot region. i This represents the local resistance deviation value of the i-th sub-region;

[0144] Generate a global stator resistance correction value based on the local stator resistance correction value;

[0145] Record the continuous trigger count of local hotspots in the same local hotspot area. If the number of consecutive triggers reaches 3, activate the full parameter identification mode for global calibration.

[0146] In this embodiment, a distributed fiber optic temperature sensor system can be used to collect temperature data at the winding ends at 10cm intervals. The fiber optic sensors use high-temperature resistant optical fibers (such as silica fibers) and are encased in a protective sheath to prevent mechanical damage and chemical corrosion. A fiber optic demodulator (such as Micron Optics' SM130) is used to collect temperature data in real time at a sampling frequency of 100Hz and transmits it to the motor controller via optical fiber. The controller uses the matplotlib library in MATLAB or Python to visualize the temperature data, generating a real-time temperature distribution heatmap of the winding. Specific operations include: importing the temperature data into MATLAB or Python, using the "imagesc" or "imshow" function to generate the heatmap, and setting color mapping to visually display the temperature distribution. Image processing software (such as ImageJ) is used to analyze the heatmap. Specific operations include: importing the heatmap into ImageJ, setting a temperature threshold (e.g., 3°C above the average temperature) to identify hotspot areas. Using ImageJ's "Analyze Particles" function, the area and location of each hotspot area are calculated. The area and location data of the hotspot areas are then exported to Excel or MATLAB. In Excel, import the temperature and area data of hotspot areas into the worksheet. Specific operations include: setting formulas in Excel to calculate the temperature difference (the difference between the hotspot temperature and the average temperature) and the percentage of the total area of ​​a single hotspot (the ratio of the hotspot area to the total winding area). For example, use the formula =MAX(temperature data) - AVERAGE(temperature data) to calculate the temperature difference, and use the formula =SUM(hotspot area) / total area to calculate the area percentage. The calculation results are stored in designated cells and transmitted to the motor controller via a data interface. Use heatmap generation software (such as FLIR Tools) to process the temperature data. Specific operations include: importing the temperature data into FLIR Tools, setting temperature thresholds to highlight hotspot areas, and generating a detailed hotspot distribution map. The location map can display the specific location and temperature distribution of each hotspot area. In Excel, generate a hotspot distribution weight mapping table based on the location and temperature difference of the hotspot areas. Specific operations include: importing the temperature difference and location data of the hotspot areas into Excel, and setting weight coefficients in a new column. The weight coefficients are assigned according to the importance of the temperature difference and location, ranging from 0.7 to 1.3. For example, hotspot areas with larger temperature differences are assigned higher weighting coefficients (e.g., 1.2), while hotspot areas with smaller temperature differences are assigned lower weighting coefficients (e.g., 0.8). The weighting mapping table is then exported to the motor controller. In MATLAB, the hotspot distribution weighting mapping table and local resistance deviation values ​​are imported into the workspace. Specific operations include: using formulas... The local stator resistance correction value is calculated, and the result is stored in the MATLAB workspace and transmitted to the motor controller via a data interface. In the motor controller, a counter is configured using a configuration file to record the number of consecutive triggers of the same local hotspot area. Specifically, the counter is initialized to 0 in the controller's configuration interface, and incremented by 1 each time a hotspot area is detected. When the count reaches 3 times, the controller automatically activates the full parameter identification mode for comprehensive resistance calibration. The counter's status and trigger events are recorded in the controller's log file.

[0147] Preferably, step S3 includes the following steps:

[0148] Step S31: Obtain the real-time motor speed and real-time motor output torque;

[0149] Step S32: If the real-time motor speed is less than or equal to the preset low speed operating condition threshold and the real-time motor output torque is greater than or equal to the preset high torque operating condition threshold, then proceed to the next step; otherwise, proceed directly to step S310.

[0150] Step S33: Obtain the initial magnetic flux density value and the current magnetic flux density value of the permanent magnet. Calculate the magnetic flux density attenuation rate of the first permanent magnet based on the initial magnetic flux density value and the current magnetic flux density value of the permanent magnet. Obtain the current timestamp and record the current timestamp as the magnetic flux density attenuation rate calculation timestamp.

[0151] Step S34: When the magnetic flux density attenuation rate of the first permanent magnet is less than the preset first magnetic flux density attenuation threshold, the preset first angle compensation amount is set as the angle compensation amount of the rotor position observer to obtain the first current angle compensation amount.

[0152] Step S35: When the magnetic flux density attenuation rate of the first permanent magnet is greater than or equal to the preset first magnetic flux density attenuation threshold and less than the preset second magnetic flux density attenuation threshold, the preset second angle compensation amount is set as the angle compensation amount of the rotor position observer to obtain the second current angle compensation amount.

[0153] Step S36: When the magnetic flux density attenuation rate of the first permanent magnet is greater than or equal to the preset second magnetic flux density attenuation threshold, the preset third angle compensation amount is set as the angle compensation amount of the rotor position observer to obtain the third current angle compensation amount, wherein the first current angle compensation amount < the second current angle compensation amount < the third current angle compensation amount.

[0154] Step S37: Perform a second-order low-pass filter on the first current angle compensation amount / second current angle compensation amount / third current angle compensation amount to obtain the filtered rotor angle compensation amount;

[0155] Step S38: Obtain the raw angle data from the rotor position observer; superimpose the filtered rotor angle compensation amount onto the raw angle data from the rotor position observer to generate compensated rotor position data;

[0156] Step S39: Adjust the d-axis and q-axis current distribution of the automotive motor based on the compensated rotor position data to obtain the d-axis-q-axis current distribution parameters;

[0157] Of particular importance, step S39 also includes the following steps:

[0158] Step S391: Obtain the three-phase stator current and stationary coordinate system of the automotive motor;

[0159] Step S392: Perform a Park transformation on the stationary coordinate system based on the compensated rotor position data to generate a rotating coordinate system;

[0160] Step S393: Perform Clarke-Park transformation on the three-phase stator current of the automotive motor based on the rotating coordinate system to obtain dynamic current distribution parameters;

[0161] Step S394: Calculate the timestamp based on the magnetic flux density attenuation rate. After the preset dynamic monitoring time window, recalculate the magnetic flux density attenuation rate of the second permanent magnet.

[0162] Step S395: Calculate the change in magnetic flux density attenuation rate based on the magnetic flux density attenuation rate of the first permanent magnet and the magnetic flux density attenuation rate of the second permanent magnet;

[0163] Step S396: If the change in magnetic flux density attenuation rate is greater than 0, then negative d-axis current injection is performed on the dynamic current distribution parameters according to the preset field weakening compensation coefficient, and the q-axis current ratio is increased according to the preset torque gain coefficient to generate dynamic field weakening compensation current distribution parameters.

[0164] Step S397: If the change in magnetic flux density attenuation rate is less than or equal to 0, then the dynamic current distribution parameters remain unchanged;

[0165] Step S398: Record the dynamic field weakening compensation current allocation parameters or dynamic current allocation parameters as d-axis-q-axis current allocation parameters.

[0166] Step S310: Based on the global stator resistance correction value, real-time motor speed and real-time motor output torque, generate d-axis-q-axis current distribution parameters using a preset steady-state current distribution strategy.

[0167] Of particular importance, step S310 also includes the following steps:

[0168] When the real-time motor speed is greater than the preset low speed operating threshold or the real-time motor output torque is less than the preset high torque operating threshold, the preset steady-state current distribution mapping table is queried based on the real-time motor speed and the real-time motor output torque to obtain the initial d-axis current reference value and q-axis current reference value.

[0169] Based on the global stator resistance correction value, the initial d-axis current reference value is compensated for stator resistance sensitivity to generate a corrected d-axis current reference value.

[0170] Based on the real-time temperature of the winding, the temperature rise compensation d-axis current reference value is performed to generate a temperature rise compensation d-axis current value.

[0171] The corrected d-axis current reference value and the temperature rise compensated d-axis current value are combined to form the d-axis-q-axis current distribution parameters.

[0172] In this embodiment, in the automotive motor control system, real-time motor speed and output torque data are acquired by the motor controller. Speed ​​data is provided by a photoelectric encoder mounted on the motor shaft, which outputs a speed signal with a resolution of 1024 pulses / revolution. Output torque data is acquired by a torque sensor with a measurement range of 0-500 Nm and an accuracy of ±0.5%. A data acquisition card acquires signals from the encoder and torque sensor in real time at a frequency of 100 Hz and transmits the data to the motor controller. The LabVIEW conditional judgment module is used to determine whether the real-time motor speed and output torque meet the conditions for low-speed, high-torque operation. Specifically, the low-speed threshold is set to 300 rpm, and the high-torque threshold is set to 300 Nm. When the real-time speed is less than or equal to 300 rpm and the real-time torque is greater than or equal to 300 Nm, the next step is triggered; otherwise, step S310 is executed directly. The LabVIEW "Case Structure" module is used to implement the conditional judgment logic, taking real-time speed and torque data as input and outputting the judgment result. A Hall effect fluxmeter is used to measure the initial and current magnetic flux density of the permanent magnet. The initial magnetic flux density value is recorded by the calibration equipment and stored in the controller's configuration file when the motor leaves the factory. The current magnetic flux density value is acquired in real time by a Hall sensor and transmitted to the controller. Specifically, the operation includes: reading the initial magnetic flux density value from the configuration file when the motor starts; and measuring the current magnetic flux density value of the permanent magnet in real time using a Hall sensor (such as the LEM LH series) during operation. The magnetic flux density attenuation rate is calculated using the formula: Attenuation Rate = (Initial Magnetic Flux Density - Current Magnetic Flux Density) / Initial Magnetic Flux Density, and the current timestamp is recorded. The calculation result is stored in the controller's database. A first magnetic flux density attenuation threshold is set to 5%. When the calculated magnetic flux density attenuation rate is less than 5%, a preset first angle compensation amount (e.g., 2°) is used as the angle compensation amount for the rotor position observer. Specifically, the operation includes: presetting the first angle compensation amount in the controller's configuration file, and outputting the compensation amount to the rotor position observer through the LabVIEW "Case Structure" module when the conditions are met. The rotor position observer (such as an observer based on an extended Kalman filter) uses this compensation amount to correct the original angle data. When the magnetic flux density attenuation rate is greater than or equal to 5% and less than 10%, a preset second angle compensation amount (e.g., 5°) is used as the angle compensation amount for the rotor position observer. Specifically, the second angle compensation amount is preset in the controller's configuration file, and when the conditions are met, this compensation amount is output to the rotor position observer via LabVIEW's "Case Structure" module. When the magnetic flux density attenuation rate is greater than or equal to 10%, a preset third angle compensation amount (e.g., 8°) is used as the angle compensation amount for the rotor position observer.The specific operations include: Pre-setting the third angle compensation amount in the controller's configuration file, and outputting this compensation amount to the rotor position observer via LabVIEW's "Case Structure" module when conditions are met. Importing the angle compensation data into MATLAB, designing a second-order low-pass filter using the "butter" function with a cutoff frequency of 1Hz. Filtering the angle compensation data using the "filter" function to obtain the filtered rotor angle compensation amount. Obtaining the original angle data from the rotor position observer and superimposing the filtered angle compensation amount onto the original angle data. Specifically, using LabVIEW's "Add" function to add the filtered angle compensation amount to the original angle data to generate the compensated rotor position data. In the automotive motor control system, acquiring the three-phase stator current and stationary coordinate system data of the automotive motor. The three-phase stator current is acquired through current sensors installed on the motor's three-phase output lines, capable of real-time monitoring of the current magnitude and direction. The stationary coordinate system data is provided by the coordinate system module inside the motor controller, configured based on the motor's physical structure and initial position. The data acquisition card acquires current data in real-time at a frequency of 100Hz and transmits the data to the controller. Import the compensated rotor position data and three-phase stator current data into LabVIEW, and use the "Park Transform" module to convert the stationary coordinate system to a rotating coordinate system. This module converts the three-phase current data into current components on the d-axis and q-axis, generating current data in the rotating coordinate system. In the rotating coordinate system, perform a Clarke-Park transformation on the three-phase stator current of the automotive motor to obtain dynamic current distribution parameters. The transformation is performed using MATLAB's signal processing toolbox. Specifically, import the current data from the rotating coordinate system into MATLAB and use the "Clarke-Park Transform" function. This function converts the current data from the rotating coordinate system into current components in the stationary coordinate system, generating dynamic current distribution parameters. The transformed parameters are stored in the MATLAB workspace and transmitted to the motor controller via the data interface. Recalculate the magnetic flux density attenuation rate of the second permanent magnet and measure it using a Hall effect fluxmeter after a preset dynamic monitoring time window (e.g., 10 seconds). Specifically, set a timer in the controller; when the preset time window is reached, trigger the Hall sensor to collect the current magnetic flux density value. The attenuation rate of the second permanent magnet is calculated using the formula: Attenuation rate = (Initial magnetic flux density - Current magnetic flux density) / Initial magnetic flux density, and the result is stored in the controller's database. Within the controller, the change in the attenuation rate of the first and second permanent magnets is calculated. Specifically, the attenuation rate values ​​of the first and second permanent magnets are read from the database, and the change is calculated using the formula: Change = Second attenuation rate - First attenuation rate. The calculation results are stored in the controller's memory.When the change in magnetic flux density attenuation rate is greater than 0, negative d-axis current injection is performed on the dynamic current allocation parameters according to the preset field weakening compensation coefficient, and the q-axis current ratio is increased. Specifically, the field weakening compensation coefficient (e.g., -0.1) and torque gain coefficient (e.g., 1.1) are preset in the controller's configuration file. When the change is greater than 0, the "Scale by Factor" module of LabVIEW is used to perform negative d-axis current injection, and the "Multiply" module is used to increase the q-axis current ratio. The adjusted current allocation parameters are stored in the controller's memory. When the change in magnetic flux density attenuation rate is less than or equal to 0, the dynamic current allocation parameters remain unchanged. Specifically, a condition judgment module is set in the controller; when the change is less than or equal to 0, the dynamic current allocation parameters generated in step S393 are used directly without any adjustment. The dynamic field weakening compensation current allocation parameters or the dynamic current allocation parameters are denoted as the d-axis-q-axis current allocation parameters. Specifically, a selection module is set in the controller; based on the judgment result of the change in magnetic flux density attenuation rate, the corresponding current allocation parameters are selected. In automotive motor control systems, when the real-time motor speed exceeds a preset low-speed threshold (e.g., 300 rpm) or the real-time motor output torque falls below a preset high-torque threshold (e.g., 300 Nm), the motor controller queries a preset steady-state current distribution mapping table. This mapping table is pre-created in Excel based on the motor's performance curve and operating requirements and stored in the controller's configuration file. Specific operations include: using the "Case Structure" module in LabVIEW to determine if the real-time speed and torque meet the conditions; when the conditions are met, calling the "Interpolate 1D Array" module to query the mapping table and obtain the initial d-axis current reference value and q-axis current reference value. In the motor controller, MATLAB's signal processing toolbox is used to perform stator resistance sensitivity compensation on the initial d-axis current reference value. Specific operations include: importing the initial d-axis current reference value and the global stator resistance correction value into MATLAB, using the formula: Compensation will be provided, of which I d_initial As the initial d-axis current reference value, R nominal R is the nominal resistance value of the stator. adj This is the global stator resistance correction value. The compensated d-axis current reference value I. d_correctedThe data is stored in the MATLAB workspace and transmitted to the controller via a data interface. Real-time winding temperature data is acquired from a temperature sensor (such as NI's 9211 module) and imported into LabVIEW. Using a pre-set temperature-current derating curve (e.g., 10% derating at 60°C), the corrected d-axis current reference value is adjusted using the "Scale by Factor" module. The compensated d-axis current value is stored in the controller's memory. The corrected d-axis current reference value is combined with the temperature-compensated d-axis current value to form the final d-axis-q-axis current allocation parameters. Specifically, the "Bundle" module in LabVIEW is used to combine the two parameters into a cluster, which is then output to the motor drive module. The combined result is stored in the controller's memory and transmitted to the motor driver via a data interface for real-time control.

[0173] Preferably, step S4 includes the following steps:

[0174] Step S41: Obtain the actual position of the rotor and compare it with the preset theoretical position of the rotor to generate the original angle deviation value of the rotor;

[0175] Step S42: Calculate the rate of change of the original rotor angle deviation value;

[0176] Step S43: If the original rotor angle deviation value is less than or equal to the preset low deviation stage trigger threshold, the PID control algorithm is used to fine-tune the d-axis-q-axis current distribution parameters to generate dynamic current compensation parameters.

[0177] Step S44: If the original rotor angle deviation value is greater than the low deviation stage trigger threshold and the original rotor angle deviation value is less than or equal to the high deviation stage trigger threshold, then the rotor angle deviation trend will be predicted according to the angle deviation change rate, and the feedforward slope of the d-axis-q-axis current distribution parameters will be adjusted according to the rotor angle deviation trend to generate the predicted current distribution parameters.

[0178] Step S45: If the original rotor angle deviation value is greater than the preset high deviation stage trigger threshold, the d-axis current ratio is increased in the d-axis-q-axis current distribution parameters to generate emergency correction current parameters.

[0179] Step S46: Obtain the rotating coordinate system, and perform inverse Clarke-Park transformation on the dynamic current compensation parameters / predicted current distribution parameters / emergency correction current parameters based on the rotating coordinate system to obtain the real-time drive current parameters of the automotive motor.

[0180] Step S47: Perform current control on the automotive motor based on the real-time drive current parameters of the automotive motor.

[0181] In this embodiment, in the automotive motor control system, the data for acquiring the actual rotor position is provided by a high-precision photoelectric encoder mounted on the motor shaft. The preset theoretical rotor position is calculated based on the motor's operating state and control algorithm. Specifically, the motor controller calculates the theoretical rotor position through the following steps: The controller collects the motor's speed and output torque data in real time, which are provided by current and torque sensors. The controller uses a vector control algorithm to calculate the theoretical rotor position based on the collected speed and torque data. The vector control algorithm precisely controls the motor's magnetic field and torque by decoupling the motor's d-axis and q-axis currents, thereby calculating the ideal rotor position. Based on the calculation results of the vector control algorithm, the controller generates theoretical rotor position data, which represents the position the rotor should be in under ideal conditions. The vector control algorithm is also known as field-oriented control (FOC). This is an advanced method widely used in AC motor control. By decomposing the stator current into excitation current (d-axis current) and torque current (q-axis current), it achieves independent control of the motor's magnetic field and torque. The data acquisition card acquires encoder signals in real time at a frequency of 200Hz and transmits the actual position data to the controller. In LabVIEW, the actual position data is compared with the theoretical position data to calculate the rotor's original angle deviation. Specifically, the "Subtract" module in LabVIEW is used to subtract the two position data points to obtain the deviation value, which is then stored in the controller's memory. The deviation value data is imported into MATLAB, and the "gradient" function is used to calculate the rate of change of the deviation value over time. When the rotor's original angle deviation value is less than or equal to a preset low-deviation stage trigger threshold (e.g., 2°), the MATLAB PID controller module is used to fine-tune the d-axis and q-axis current allocation parameters. Specifically, the PID controller parameters are set in MATLAB (e.g., Kp = 0.5, Ki = 0.1, Kd = 0.05), with the deviation value as input, and dynamic current compensation parameters are output. The compensated parameters are transmitted to the controller via the data interface. When the original rotor angle deviation value is between the low deviation stage trigger threshold (e.g., 2°) and the high deviation stage trigger threshold (e.g., 5°), linear regression prediction is performed using Python's scikit-learn library. Specifically, the historical deviation values ​​and their rate of change data are organized into a feature matrix, where each row represents the observation data at a given time point, including the deviation value and the rate of change.Using the LinearRegression model from Python's scikit-learn library, historical data is used as the training set to fit a model of the relationship between deviation values ​​and time. During training, the model finds the optimal linear relationship parameters to minimize the error between predicted and actual values. Using the trained linear regression model, the deviation values ​​and rates of change at the current and recent time points are input to predict the deviation trend over the next few time steps. The model outputs the predicted deviation values ​​to help the system adjust control parameters in advance. Based on the prediction results, the feedforward slope of the d-axis and q-axis current allocation parameters is adjusted using LabVIEW's "Scale by Factor" module to generate predicted current allocation parameters. When the rotor's original angle deviation value exceeds the preset high deviation stage trigger threshold (e.g., 5°), the emergency correction mechanism is triggered using LabVIEW's conditional judgment module. Specifically, in LabVIEW, conditional judgment logic is set up; when the deviation value exceeds the threshold, the d-axis current proportion is increased (e.g., by 10%) using the "Multiply" module to generate emergency correction current parameters. The correction parameters are stored in the controller's memory and immediately used for current control operations. Data from a rotating coordinate system is acquired, and an inverse Clarke-Park transformation is performed using MATLAB's Simulink module. Specifically, this involves importing dynamic current compensation parameters, predicted current distribution parameters, and emergency correction current parameters into Simulink, performing coordinate transformation using the "Inverse Clarke-Park Transform" module to obtain the real-time drive current parameters of the automotive motor. Based on these transformed real-time drive current parameters, a Motor Control Blockset (such as MathWorks' Motor ControlBlockset) is used to control the current of the automotive motor. This involves constructing a current control loop in Simulink, using the drive current parameters as input, and outputting control signals to the motor driver via a PWM generation module.

[0182] Preferably, the pre-defined coordinated control of the automotive motor cooling system includes:

[0183] When the real-time temperature of the permanent magnet is greater than or equal to the preset real-time temperature warning threshold of the permanent magnet or the real-time temperature of the winding is greater than or equal to the preset real-time temperature warning threshold of the winding, the flow rate of the coolant in the motor active cooling circuit will be increased to 150% of the nominal value for 60 seconds.

[0184] After the coolant flow rate increase phase ends, the current temperature of the permanent magnet and the current temperature of the winding are obtained. If the current temperature of the permanent magnet is greater than the preset liquid nitrogen trigger temperature threshold or the current temperature of the winding is greater than the preset winding liquid nitrogen trigger temperature threshold, liquid nitrogen injection is triggered. The single injection volume is less than or equal to 10ml. The automotive motor cooling system and the automotive motor controller communicate via CANFD bus with a data transmission rate greater than or equal to 2Mbps and a response delay less than or equal to 100ms.

[0185] In this embodiment, in the automotive motor cooling system, when the real-time temperature of the permanent magnet reaches or exceeds a preset temperature warning threshold (e.g., 80°C) or the real-time temperature of the winding reaches or exceeds a preset temperature warning threshold (e.g., 120°C), the coolant flow rate increase mechanism is triggered by the motor controller. Specific operations include: using temperature sensors to monitor the temperature of the permanent magnet and winding in real time and transmitting the data to the controller. The controller uses LabVIEW's conditional decision module; when the temperature exceeds the threshold, it sends a command to the cooling system via the CANFD bus (data transmission rate 2Mbps, response delay less than or equal to 100ms) to increase the coolant pump flow rate to 150% of the nominal value. Upon receiving the command, the coolant pump immediately adjusts the flow rate and maintains it for 60 seconds. During the flow rate increase, the system monitors the flow rate in real time using a flow sensor to ensure the expected value is reached. After the coolant flow rate increase phase ends, the current temperature of the permanent magnet and winding is again obtained via the temperature sensor. If the current temperature of the permanent magnet exceeds a preset liquid nitrogen trigger temperature threshold (e.g., 90°C) or the current temperature of the winding exceeds a preset winding liquid nitrogen trigger temperature threshold (e.g., 130°C), the liquid nitrogen injection mechanism is triggered. The specific operation includes: the controller sends a spray command to the liquid nitrogen spraying system via the CANFD bus; after receiving the command, the liquid nitrogen spraying system performs a single spray, with the spray volume controlled within 10ml. The spraying process is precisely controlled by a solenoid valve to ensure that the spray volume meets the requirements. After spraying is completed, the spraying event is recorded and notification is sent to maintenance personnel through the HMI interface.

[0186] Preferably, the preset automotive motor fault diagnosis and fault tolerance mechanism includes:

[0187] When the absolute value of the deviation between the real-time temperature of the permanent magnet and the real-time temperature of the winding is greater than or equal to 10℃, the preset real-time temperature gradient abnormal protection mechanism of the permanent magnet-winding is triggered.

[0188] Obtain motor torque recording data, calculate the motor torque ripple amplitude based on the motor torque recording data, and generate a motor torque ripple amplitude statistics table. The specific calculation formula for the motor torque ripple amplitude is as follows:

[0189]

[0190] Where, ΔTripple T represents the torque ripple amplitude of the motor. peak T represents the peak torque within the sampling period. vally T represents the torque valley value. avg This is the average torque;

[0191] If the torque ripple amplitude in the motor torque ripple amplitude statistics table exceeds 15% for three consecutive times, the current output power of the car motor is obtained, and the current output power of the car motor is limited to 70% of the rated output power of the car motor. A maintenance warning is also pushed through the preset HMI interface.

[0192] In this embodiment, in the automotive motor control system, when the absolute value of the deviation between the real-time temperature of the permanent magnet and the real-time temperature of the winding reaches or exceeds 10°C, a temperature gradient anomaly protection mechanism is triggered by the motor controller. Specific operations include: using a temperature sensor to monitor the temperature of the permanent magnet and winding in real time and transmitting the data to the controller. The controller uses LabVIEW's conditional judgment module to trigger the protection mechanism when the temperature deviation exceeds a threshold. The protection mechanism includes reducing the motor output power to 70% of the rated power and pushing a maintenance warning through the HMI interface. In the motor controller, motor torque recording data is acquired, and the motor torque ripple amplitude is calculated using MATLAB's signal processing toolbox. Specific operations include: importing torque data collected by a torque sensor (such as the SST series from Sensor Technology Co., Ltd.) into MATLAB and using formulas... Calculations are performed. The results are stored in the MATLAB workspace, generating a statistical table of motor torque ripple amplitude. LabVIEW's conditional decision module is used to monitor this statistical table. Specifically, conditional logic is set to trigger a power limiting mechanism when the torque ripple amplitude exceeds 15% three consecutive times. The controller sends a command to the motor driver via the CANFD bus (data transmission rate 2Mbps, response delay less than or equal to 100ms) to limit the current automotive motor output power to 70% of its rated output power. Simultaneously, a maintenance warning is pushed through the HMI interface to remind the driver to perform inspection and maintenance.

[0193] Preferably, the present invention also provides an adaptive intelligent control system for a new energy vehicle motor, for executing the adaptive intelligent control method for a new energy vehicle motor as described above, the adaptive intelligent control system for a new energy vehicle motor comprising:

[0194] The temperature monitoring module can deploy temperature sensor groups on the surface of the permanent magnet and the end of the winding of the automotive motor rotor to collect the real-time temperature of the permanent magnet and the real-time temperature of the winding, and generate a temperature gradient based on the real-time temperature of the permanent magnet and the real-time temperature of the winding.

[0195] The eddy current compensation module can obtain the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding if the temperature gradient is greater than or equal to the preset temperature gradient value; calculate the local eddy current loss compensation coefficient of the permanent magnet based on the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; adjust the stator resistance based on the local eddy current loss compensation coefficient of the permanent magnet and generate a global stator resistance correction value. The triggering conditions for adjusting the stator resistance include full parameter identification mode and zone correction mode.

[0196] The current distribution module can obtain the real-time motor speed and real-time motor output torque; based on the global stator resistance correction value, the real-time motor speed and real-time motor output torque, the d-axis and q-axis current distribution of the automotive motor is adjusted to obtain the d-axis-q-axis current distribution parameters.

[0197] The motor control module can obtain the angle compensation deviation value from the rotor position observer; and perform segmented current closed-loop control of the automotive motor based on the angle compensation deviation value and the d-axis-q-axis current distribution parameters.

[0198] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0199] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for adaptive intelligent control of motors in new energy vehicles, characterized in that, Includes the following steps: Step S1: Deploy temperature sensor groups on the surface of the permanent magnet and the end of the winding of the automobile motor rotor to collect the real-time temperature of the permanent magnet and the real-time temperature of the winding, and generate a temperature gradient based on the real-time temperature of the permanent magnet and the real-time temperature of the winding. Step S2: If the temperature gradient is greater than or equal to the preset temperature gradient value, obtain the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; calculate the local eddy current loss compensation coefficient of the permanent magnet based on the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; adjust the stator resistance based on the local eddy current loss compensation coefficient of the permanent magnet according to the preset triggering conditions, and generate a global stator resistance correction value. The triggering conditions for adjusting the stator resistance also include full parameter identification mode and partition correction mode. Step S3: Obtain the real-time motor speed and real-time motor output torque; adjust the d-axis and q-axis current distribution of the automotive motor according to the global stator resistance correction value, real-time motor speed and real-time motor output torque to obtain the d-axis-q-axis current distribution parameters; Step S4: Obtain the angle compensation deviation value of the rotor position observer; based on the preset collaborative control of the automotive motor cooling system and the preset automotive motor fault diagnosis and fault tolerance mechanism, perform segmented current closed-loop control of the automotive motor according to the angle compensation deviation value and the d-axis-q-axis current distribution parameters.

2. The adaptive intelligent control method for new energy vehicle motors according to claim 1, characterized in that, Step S1 includes the following steps: Step S1: At least 6 miniature thermocouple sensors are evenly arranged circumferentially on the surface of the permanent magnet of the motor rotor. The measurement accuracy of the miniature thermocouple sensors is ±0.5℃, the sampling frequency is greater than or equal to 100Hz, and the installation position is no more than 2mm away from the edge of the permanent magnet. Step S2: Embed a distributed optical fiber sensor in the insulation layer at the end of the winding to collect the real-time temperature of the permanent magnet and the real-time temperature of the winding. Subtract the real-time temperature of the permanent magnet from the real-time temperature of the winding to obtain the temperature gradient.

3. The adaptive intelligent control method for new energy vehicle motors according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: If the temperature gradient is greater than or equal to the preset temperature gradient value, then obtain the initial temperature and current temperature of the permanent magnet, and obtain the initial temperature and current temperature of the winding. Step S22: Calculate the real-time temperature change rate of the permanent magnet based on its initial temperature and current temperature; calculate the real-time temperature change rate of the winding based on its initial temperature and current temperature. Step S23: Calculate the local eddy current loss compensation coefficient of the permanent magnet based on the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding. The specific calculation formula is as follows: Among them, K eddy dT is the local eddy current loss compensation coefficient for permanent magnets. pm / dt is the real-time temperature change rate of the permanent magnet, dT w / dt is the real-time temperature change rate of the winding, and ΔT is the temperature gradient. pm ΔT is the difference between the current temperature and the initial temperature of the permanent magnet. w This is the difference between the current winding temperature and the initial winding temperature. Step S24: Obtain the rated current value of the stator winding; inject a high-frequency test current value with a frequency of 1kHz and an amplitude of 10% of the rated current value of the stator winding into the stator winding, and measure the amplitude of the first voltage response signal of the stator winding. The sampling period is 100ms, and the accuracy error is less than or equal to 0.5%. Step S25: Calculate the first stator winding resistance deviation value based on the high-frequency test current value and the amplitude of the first voltage response signal; Step S26: Calculate the global stator resistance correction value based on the local eddy current loss compensation coefficient of the permanent magnet and the stator winding resistance deviation value. The triggering conditions for adjusting the stator resistance also include full parameter identification mode and zone correction mode. The specific calculation formula is as follows: R adj =R nominal +K eddy ×ΔR×α; Among them, R adj R is the global stator resistance correction value. nominal ΔR is the nominal resistance value of the stator, ΔR is the resistance deviation value of the first stator winding, and α is the thermal balance correction factor, which ranges from 0.8 to 1.

2.

4. The adaptive intelligent control method for new energy vehicle motors according to claim 1, characterized in that, The full-parameter identification mode in step S2 includes: The duration for which the temperature gradient is greater than or equal to a preset temperature gradient value is collected; Obtain the instantaneous rate of change of the current temperature of the permanent magnet; Obtain the rated current value of the stator winding; If the duration exceeds the preset temperature gradient continuous trigger threshold or the instantaneous change rate of the current temperature of the permanent magnet is greater than or equal to the preset permanent magnet temperature rise rate threshold, a high-frequency test current with a frequency of 1kHz and an amplitude of 10% of the rated current value of the stator winding is injected into the stator winding, and the amplitude of the second voltage response signal of the stator winding is measured. The second stator winding resistance deviation value is calculated based on the high-frequency test current value and the amplitude of the second voltage response signal. Obtain the nominal stator resistance value, and record the sum of the nominal stator resistance value and the second stator winding resistance deviation value as the global stator resistance correction value.

5. The adaptive intelligent control method for new energy vehicle motors according to claim 1, characterized in that, The partition correction mode in step S2 includes: By using distributed fiber optic sensors embedded in the insulation layer at the winding end, temperature data at the winding end is collected at 10cm intervals to generate a real-time temperature distribution thermal map of the winding. Identify whether there are local hot spots in the real-time temperature distribution heatmap of the winding. If so, calculate the temperature difference of a single hot spot and the proportion of the total area of ​​the hot spot based on the real-time temperature distribution heatmap of the winding. If the temperature difference of a single hot spot is greater than or equal to 3℃ and the total area of ​​the hot spots is greater than or equal to 15%, a local hot spot distribution map is generated based on the real-time temperature distribution heat map of the winding. A hotspot distribution weight mapping table is generated based on the location map of local hotspot areas, wherein the weight coefficients in the hotspot distribution weight mapping table range from 0.7 to 1.3; The local stator resistance correction value is calculated based on the hotspot distribution weight mapping table, and the specific calculation formula is as follows: Among them, R local_adj Here, w represents the local stator resistance correction value, n is the total number of detected hot spots, and w is the total number of detected hot spots. i Let ΔR be the weight coefficient of the i-th hotspot region. i This represents the local resistance deviation value of the i-th sub-region; Generate a global stator resistance correction value based on the local stator resistance correction value; Record the continuous trigger count of local hotspots in the same local hotspot area. If the number of consecutive triggers reaches 3, activate the full parameter identification mode for global calibration.

6. The adaptive intelligent control method for new energy vehicle motors according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Obtain the real-time motor speed and real-time motor output torque; Step S32: If the real-time motor speed is less than or equal to the preset low speed operating condition threshold and the real-time motor output torque is greater than or equal to the preset high torque operating condition threshold, then proceed to the next step; otherwise, proceed directly to step S310. Step S33: Obtain the initial magnetic flux density value and the current magnetic flux density value of the permanent magnet. Calculate the magnetic flux density attenuation rate of the first permanent magnet based on the initial magnetic flux density value and the current magnetic flux density value of the permanent magnet. Obtain the current timestamp and record the current timestamp as the magnetic flux density attenuation rate calculation timestamp. Step S34: When the magnetic flux density attenuation rate of the first permanent magnet is less than the preset first magnetic flux density attenuation threshold, the preset first angle compensation amount is set as the angle compensation amount of the rotor position observer to obtain the first current angle compensation amount. Step S35: When the magnetic flux density attenuation rate of the first permanent magnet is greater than or equal to the preset first magnetic flux density attenuation threshold and less than the preset second magnetic flux density attenuation threshold, the preset second angle compensation amount is set as the angle compensation amount of the rotor position observer to obtain the second current angle compensation amount. Step S36: When the magnetic flux density attenuation rate of the first permanent magnet is greater than or equal to the preset second magnetic flux density attenuation threshold, the preset third angle compensation amount is set as the angle compensation amount of the rotor position observer to obtain the third current angle compensation amount, wherein the first current angle compensation amount < the second current angle compensation amount < the third current angle compensation amount. Step S37: Perform a second-order low-pass filter on the first current angle compensation amount / second current angle compensation amount / third current angle compensation amount to obtain the filtered rotor angle compensation amount; Step S38: Obtain the raw angle data from the rotor position observer; superimpose the filtered rotor angle compensation amount onto the raw angle data from the rotor position observer to generate compensated rotor position data; Step S39: Adjust the d-axis and q-axis current distribution of the automotive motor based on the compensated rotor position data to obtain the d-axis-q-axis current distribution parameters; Step S310: Based on the global stator resistance correction value, real-time motor speed and real-time motor output torque, generate d-axis-q-axis current distribution parameters using a preset steady-state current distribution strategy.

7. The adaptive intelligent control method for new energy vehicle motors according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Obtain the actual position of the rotor and compare it with the preset theoretical position of the rotor to generate the original angle deviation value of the rotor; Step S42: Calculate the rate of change of the original rotor angle deviation value; Step S43: If the original rotor angle deviation value is less than or equal to the preset low deviation stage trigger threshold, the PID control algorithm is used to fine-tune the d-axis-q-axis current distribution parameters to generate dynamic current compensation parameters. Step S44: If the original rotor angle deviation value is greater than the low deviation stage trigger threshold and the original rotor angle deviation value is less than or equal to the high deviation stage trigger threshold, then the rotor angle deviation trend will be predicted according to the angle deviation change rate, and the feedforward slope of the d-axis-q-axis current distribution parameters will be adjusted according to the rotor angle deviation trend to generate the predicted current distribution parameters. Step S45: If the original rotor angle deviation value is greater than the preset high deviation stage trigger threshold, the d-axis current ratio is increased in the d-axis-q-axis current distribution parameters to generate emergency correction current parameters. Step S46: Obtain the rotating coordinate system, and perform inverse Clarke-Park transformation on the dynamic current compensation parameters / predicted current distribution parameters / emergency correction current parameters based on the rotating coordinate system to obtain the real-time drive current parameters of the automotive motor. Step S47: Perform current control on the automotive motor based on the real-time drive current parameters of the automotive motor.

8. The adaptive intelligent control method for new energy vehicle motors according to claim 1, characterized in that, The pre-defined coordinated control of the automotive motor cooling system includes: When the real-time temperature of the permanent magnet is greater than or equal to the preset real-time temperature warning threshold of the permanent magnet or the real-time temperature of the winding is greater than or equal to the preset real-time temperature warning threshold of the winding, the flow rate of the coolant in the motor active cooling circuit will be increased to 150% of the nominal value for 60 seconds. After the coolant flow rate increase phase ends, the current temperature of the permanent magnet and the current temperature of the winding are obtained. If the current temperature of the permanent magnet is greater than the preset liquid nitrogen trigger temperature threshold or the current temperature of the winding is greater than the preset winding liquid nitrogen trigger temperature threshold, liquid nitrogen injection is triggered. The single injection volume is less than or equal to 10ml. The automotive motor cooling system and the automotive motor controller communicate via CANFD bus with a data transmission rate greater than or equal to 2Mbps and a response delay less than or equal to 100ms.

9. The adaptive intelligent control method for new energy vehicle motors according to claim 1, characterized in that, The pre-defined automotive motor fault diagnosis and fault tolerance mechanisms include: When the absolute value of the deviation between the real-time temperature of the permanent magnet and the real-time temperature of the winding is greater than or equal to 10℃, the preset real-time temperature gradient abnormal protection mechanism of the permanent magnet-winding is triggered. Obtain motor torque recording data, calculate the motor torque ripple amplitude based on the motor torque recording data, and generate a motor torque ripple amplitude statistics table. The specific calculation formula for the motor torque ripple amplitude is as follows: Where, ΔT ripple T represents the torque ripple amplitude of the motor. peak T represents the peak torque within the sampling period. vally T represents the torque valley value. avg This is the average torque; If the torque ripple amplitude in the motor torque ripple amplitude statistics table exceeds 15% for three consecutive times, the current output power of the car motor is obtained, and the current output power of the car motor is limited to 70% of the rated output power of the car motor. A maintenance warning is also pushed through the preset HMI interface.

10. An adaptive intelligent control system for a new energy vehicle motor, characterized in that, For executing the adaptive intelligent control method for a new energy vehicle motor as described in claim 1, the adaptive intelligent control system for a new energy vehicle motor includes: The temperature monitoring module can deploy temperature sensor groups on the surface of the permanent magnet and the end of the winding of the automotive motor rotor to collect the real-time temperature of the permanent magnet and the real-time temperature of the winding, and generate a temperature gradient based on the real-time temperature of the permanent magnet and the real-time temperature of the winding. The eddy current compensation module can obtain the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding if the temperature gradient is greater than or equal to the preset temperature gradient value; calculate the local eddy current loss compensation coefficient of the permanent magnet based on the real-time temperature change rate of the permanent magnet and the real-time temperature change rate of the winding; adjust the stator resistance based on the local eddy current loss compensation coefficient of the permanent magnet and generate a global stator resistance correction value. The triggering conditions for adjusting the stator resistance include full parameter identification mode and zone correction mode. The current distribution module can obtain the real-time motor speed and real-time motor output torque; based on the global stator resistance correction value, the real-time motor speed and real-time motor output torque, the d-axis and q-axis current distribution of the automotive motor is adjusted to obtain the d-axis-q-axis current distribution parameters. The motor control module can obtain the angle compensation deviation value from the rotor position observer; and perform segmented current closed-loop control of the automotive motor based on the angle compensation deviation value and the d-axis-q-axis current distribution parameters.

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