Method for controlling permanent magnet synchronous motor to realize driving mode switching

Abstract

A method for controlling a permanent magnet synchronous motor to realize driving mode switching relates to a new energy automobile motor control method, and comprises the following steps: S1, sensor data acquisition: a microcontroller acquires image data of a camera, acceleration and angle data of an inertial measurement unit and data of a seat pressure sensor at regular time; s2, data processing and analysis: the collected data are processed and analyzed by the microcontroller 32K144; s3, driving mode decision making: according to the processed data, adopting a driving mode switching decision-making mechanism based on a fuzzy rule to make three driving mode decisions of motion, economy and comfort; and S4, motor control strategy adjustment: the microcontroller adjusts the motor control strategy according to the selected driving mode. The driving mode can be automatically adjusted by comprehensively considering different road condition information and driving behavior information at the same time, the method has the advantages of being good in flexibility, high in intelligent degree, high in reliability, low in power consumption and the like, and driving comfort and power response can be comprehensively improved.

Description

Technical Field

[0001] The present invention relates to a method for controlling a motor of a new energy vehicle, and in particular to a method for controlling a permanent magnet synchronous motor to switch driving modes. Background Art

[0002] In the field of new energy vehicles, the diversity of driving modes is crucial to meet different driving needs and improve the driving experience. However, the current driving mode switching method has many defects. The driving mode switching of traditional electric vehicles is usually achieved through mechanical switches or simple electronic signal switching. For example, the driver sends a discrete signal to the controller through the mode selection button in the car, and the controller changes the torque and speed control strategy of the motor according to the preset parameters; although this method is relatively direct, it lacks flexibility and intelligence.

[0003] At present, there are some control methods on the market that can realize automatic switching of driving modes, but the automatic switching decision-making of these methods often relies on a single or a few parameters, and cannot comprehensively consider complex factors such as road conditions and driving behavior. For example, judging traffic conditions only based on vehicle speed ignores key information such as road type and vehicle distribution, resulting in inaccurate and untimely driving mode switching, and it is difficult to adapt to the diversity of actual driving scenarios.

[0004] In addition, the existing methods use a simple and crude motor control strategy when controlling a permanent magnet synchronous motor to switch driving modes. In different driving modes, the motor speed and torque output cannot be accurately adjusted, and the power performance, energy efficiency and driving comfort cannot be taken into account. In the economic mode, the motor efficiency cannot be effectively optimized, resulting in energy waste; in the sports mode, it is difficult to give full play to the power advantages of the motor; in the comfort mode, the vibration and noise of the motor cannot be well suppressed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for controlling a permanent magnet synchronous motor to achieve driving mode switching, so as to solve the shortcomings of the prior art such as lack of intelligence and flexibility and inaccurate driving mode switching.

[0006] The technical solution to the above technical problem is: a method for controlling a permanent magnet synchronous motor to achieve driving mode switching, comprising the following steps: S1: Sensor Data Collection After the vehicle is started, various sensors start working, and the microcontroller regularly collects image data from the camera, acceleration and angle data from the inertial measurement unit, and data from the seat pressure sensor; S2: Data processing and analysis The microcontroller processes and analyzes the collected data; S3: Driving mode decision According to the processed data, a driving mode switching decision mechanism based on fuzzy rules is used to make decisions on the three driving modes: sport, economy, and comfort. S4: Motor control strategy adjustment The microcontroller adapts the motor control strategy according to the selected driving mode.

[0007] A further technical solution of the present invention is: in step S1, before starting the vehicle, the following preparations are performed: The microcontroller is connected to the permanent magnet synchronous motor through a motor driver, and then the camera is installed on the car to identify road conditions. The inertial measurement unit is installed on the vehicle chassis to sense the dynamic characteristics of the vehicle, and the pressure sensor is installed on the seat to sense changes in the driver's sitting posture.

[0008] A further technical solution of the present invention is: the specific contents of step S2 include: S21. Data transmission and preprocessing: The data of each sensor is transmitted to the microcontroller. The ADC and related signal processing units inside the microcontroller preprocess the data, convert the analog signal into a digital signal, and filter and amplify it to obtain accurate sensor data; S22. Data processing and analysis: The image data from the camera is processed by the CNN algorithm to identify the road type. The acceleration and angle data from the inertial measurement unit are used to calculate the dynamic parameters of the vehicle's acceleration rate of change. The seat pressure sensor data is used to analyze the pressure variation. Driving behavior analysis is performed based on the dynamic parameters of the acceleration rate of change and the seat pressure variation.

[0009] A further technical solution of the present invention is: the data processing in step S22 is implemented by constructing a layered sensor fusion architecture; at the bottom layer, the data of the camera, inertial measurement unit and seat pressure sensor are time synchronized and spatially calibrated; timestamp technology is used to ensure that the data from different sensors are in the same time reference system, and the spatial data of different sensors are unified into the vehicle coordinate system through coordinate transformation; at the middle layer, Kalman filtering or particle filtering data fusion algorithm is used to fuse the sensor data processed at the bottom layer; at the high layer, a comprehensive judgment of driving intention and road conditions is made based on the fused data.

[0010] A further technical solution of the present invention is: in step S22, data analysis includes the following specific steps: S221. Road type identification S2211. Data processing: First, the road image collected by the camera is processed, including cropping, normalization and size adjustment of the image; S2212. Construct network architecture: adopt VGG-Net architecture, which includes a combination of multiple consecutive convolutional layers and pooling layers, and finally maps the extracted features to different road type categories through a fully connected layer; S2213. Data training: training using annotated road image data, wherein the annotated road image data includes highway image data, urban road image data, and mountain road image data; during the training process, adjusting the weight of the network through the back propagation algorithm to minimize the error between the road type prediction result output by the network and the true label; S222. Traffic Condition Identification S2221. Use the optical flow method to estimate the vehicle speed and indirectly judge the traffic conditions. Specifically, by calculating the optical flow between adjacent frame images, the speed of the vehicle relative to the surrounding environment is estimated; if the speed of most vehicles is low and relatively stable, it indicates traffic congestion; if the vehicle speed is high and varies greatly, it indicates smooth traffic; S2222. Use target detection algorithms to identify vehicles and traffic signs and assist in judging traffic conditions. Specifically, the image is divided into multiple grid cells, each of which is responsible for predicting a certain number of targets. The prediction results of each cell are post-processed to obtain the accurate positions and categories of vehicles and traffic signs. If a large number of vehicles are detected waiting in line or stop signs appear frequently, it indicates traffic congestion. If the vehicles are sparsely distributed and the traffic signs are mainly signs with high speed limits, it indicates smooth traffic. S223. Driving behavior analysis S2231. Driving behavior analysis based on IMU data S22311. Acceleration data processing: The acceleration data collected by the inertial measurement unit (IMU) contains the acceleration information of the vehicle in the three axes of X, Y, and Z. The speed and displacement information of the vehicle are obtained by integrating the acceleration data. The acceleration data is filtered to remove noise interference and obtain more accurate vehicle dynamic information. S22312. Angular velocity data processing: The inertial measurement unit (IMU) collects the angular velocity information of the vehicle to determine the steering situation of the vehicle. If the angular velocity has a continuous non-zero value, it means that the vehicle is turning. By analyzing the magnitude and duration of the angular velocity, the intensity of the turning can be determined. A large angular velocity that lasts for a period of time indicates that the vehicle is making a sharp turn; a small angular velocity indicates that the vehicle is making a gentle turn or a slight direction adjustment. S2232. Driving behavior analysis based on seat pressure sensor data S22321. Pressure change monitoring: The pressure data collected by the seat pressure sensor reflects the changes in the driver's sitting posture. By performing differential operations on the pressure data, the pressure change amplitude and frequency are monitored. If the pressure changes significantly in a short period of time, it means that the driver's body is leaning forward or moving left and right, which is related to the tense driving operation. S22322. Pressure distribution analysis: By analyzing the pressure distribution in different areas of the seat, if the pressure in the front of the seat increases and the pressure in the back decreases, it means that the driver's body is leaning forward, and the analysis is that he is accelerating or focusing on the road conditions; if the pressure on both sides of the seat is uneven, it means that the driver's body is leaning to one side, and the analysis is related to the steering operation.

[0011] A further technical solution of the present invention is: the step S3 comprises the following specific steps: S31. Parameter fuzzification: Fuzzify the relevant parameters of road conditions and driving behavior; determine the degree of membership of each fuzzy set based on actual data; S32. Establish a fuzzy rule base: Establish a fuzzy rule base based on experience and understanding of the characteristics of different driving modes; S33. Fuzzy reasoning: Reasoning based on fuzzy rules to obtain fuzzy output of driving mode; S34. Defuzzification: A defuzzification method is used to convert the fuzzy output into a definite driving mode.

[0012] A further technical solution of the present invention is: the fuzzy rule base in step S32 is: if the traffic condition is smooth and the driving behavior is aggressive, the driving mode is the sports driving mode; if the traffic condition is congested and the driving behavior is conservative, the driving mode is the economic mode or the comfortable driving mode.

[0013] A further technical solution of the present invention is: the step S4 includes the following contents: The microcontroller changes the control method of the motor by adjusting the PWM signal sent to the permanent magnet synchronous motor driver; in economy mode, the motor speed and torque output are reduced to optimize efficiency; in sports mode, the motor speed and torque output are increased to provide stronger power; in comfort mode, the motor speed and torque are maintained at a moderate level to reduce vibration and noise.

[0014] A further technical solution of the present invention is: the method further comprises step S5. Adaptive motor control parameter adjustment: adaptively adjusting the motor control parameters based on real-time feedback of the motor operating state.

[0015] A further technical solution of the present invention is: the step S5 includes the following contents: During the operation of the motor, the temperature, current and voltage parameters of the motor are monitored in real time. When the temperature of the motor rises, the power output of the motor is reduced and the current reference value in the magnetic field oriented control algorithm is adjusted to avoid overheating of the motor. At the same time, the control parameters of the motor are adaptively adjusted according to the different stages of vehicle driving, such as starting, acceleration, constant speed and deceleration, to improve the efficiency and performance of the motor and better adapt to the switching of driving modes.

[0016] Due to the adoption of the above structure, the method of controlling a permanent magnet synchronous motor to realize driving mode switching of the present invention has the following beneficial effects compared with the prior art: 1. High intelligence and good flexibility The present invention collects the image data of the camera, the acceleration and angle data of the inertial measurement unit (i.e., IMU), and the data of the seat pressure sensor at regular intervals, analyzes and processes the data, and then makes decisions on the three driving modes of sport, economy, and comfort. The microcontroller controls the motor according to the selected driving mode. Therefore, the present invention can automatically adjust the driving mode according to different road conditions and driving behavior information, without the need for switching through a mechanical switch, and has a high degree of intelligence. Moreover, this ability to make comprehensive judgments and adjustments based on multiple information makes the driving mode switching more flexible and can better adapt to various actual driving scenarios.

[0017] In addition, since the present invention can obtain dynamic change information of road conditions and driving behaviors in real time, the system can continuously and adaptively adjust the driving mode without the need for manual intervention by the driver, thereby improving the convenience and flexibility of driving.

[0018] 2. Can improve the accuracy of driving mode switching decisions The present invention realizes data processing and analysis by constructing a layered sensor fusion architecture. At the bottom layer, the data of the camera, inertial measurement unit (IMU) and seat pressure sensor are synchronized in time and space calibrated; the timestamp technology is used to ensure that the data from different sensors are in the same time reference system, and the spatial data of different sensors are unified into the vehicle coordinate system through coordinate transformation; at the middle layer, the Kalman filter or particle filter data fusion algorithm is used to fuse the sensor data processed at the bottom layer; for example, the Kalman filter can fuse the road curvature information recognized by the camera and the vehicle angular velocity information measured by the IMU to more accurately predict the vehicle's driving trajectory. At the high level, the driving intention and road condition are comprehensively judged based on the fused data. This layered architecture can more efficiently utilize sensor data and improve the accuracy of driving mode switching decisions.

[0019] 3. Take into account power performance, energy efficiency and driving comfort The present invention can finely adjust the control mode of the permanent magnet synchronous motor according to different driving modes. In the economic mode, the motor speed and torque output are reduced to effectively optimize energy utilization efficiency and reduce energy consumption; in the sports mode, the motor speed and torque are increased to fully release the motor power to meet the driver's pursuit of passionate driving; in the comfort mode, the motor speed and torque are maintained at a moderate level, significantly reducing vibration and noise, improving driving comfort, and providing passengers with a more comfortable driving environment.

[0020] In addition, the present invention monitors the motor's operating parameters such as temperature, current, and voltage in real time. When the motor temperature rises, it automatically reduces the power output and adjusts the current reference value in the magnetic field oriented control algorithm to prevent the motor from overheating, ensure the safe and stable operation of the motor, and extend the motor's service life. At the same time, in different stages of vehicle driving, such as starting, acceleration, constant speed, and deceleration, the motor control parameters are adaptively adjusted to keep the motor in a good working state at all times, improve the motor's efficiency and performance, further enhance the smoothness and stability of driving mode switching, and improve the overall vehicle performance and driving experience.

[0021] Below, the technical features of a method for controlling a permanent magnet synchronous motor to achieve driving mode switching according to the present invention are further described in conjunction with embodiments. DETAILED DESCRIPTION Example

[0022] A method for controlling a permanent magnet synchronous motor to achieve driving mode switching comprises the following steps: S1: Sensor Data Collection After the vehicle is started, various sensors start working, and the microcontroller regularly collects image data from the camera, acceleration and angle data from the IMU, and data from the seat pressure sensor; S2: Data processing and analysis The S32K144 microcontroller processes and analyzes the collected data; S3: Driving mode decision According to the processed data, a driving mode switching decision mechanism based on fuzzy rules is used to make decisions on the three driving modes: sport, economy, and comfort. S4: Motor control strategy adjustment The microcontroller adjusts the motor control strategy according to the selected driving mode; S5. Adaptive motor control parameter adjustment Based on real-time feedback of the motor's operating status, the motor control parameters are adaptively adjusted.

[0023] In step S1, before starting the vehicle, the following preparations are performed: The microcontroller is connected to the permanent magnet synchronous motor through a motor driver, and then the camera is installed on the car to identify road conditions. The inertial measurement unit is installed on the vehicle chassis to sense the dynamic characteristics of the vehicle, and the pressure sensor is installed on the seat to sense changes in the driver's sitting posture.

[0024] The specific contents of step S2 include: S21. Data transmission and preprocessing: The data of each sensor is transmitted to the microcontroller. The ADC and related signal processing units inside the microcontroller preprocess the data, convert the analog signal into a digital signal, and filter and amplify it to obtain accurate sensor data; S22. Data processing and analysis: The image data from the camera is processed by the CNN algorithm to identify the road type. The acceleration and angle data from the inertial measurement unit (IMU) are used to calculate the dynamic parameters of the vehicle's acceleration rate of change. The seat pressure sensor data is used to analyze the pressure variation. Driving behavior analysis is performed based on the dynamic parameters of the acceleration rate of change and the seat pressure variation.

[0025] The data processing in step S22 is achieved by constructing a hierarchical sensor fusion architecture. At the bottom layer, the data of the camera, inertial measurement unit (IMU) and seat pressure sensor are synchronized and spatially calibrated; the timestamp technology is used to ensure that the data from different sensors are in the same time reference system, and the spatial data of different sensors are unified into the vehicle coordinate system through coordinate transformation; at the middle layer, the Kalman filter or particle filter data fusion algorithm is used to fuse the sensor data processed at the bottom layer; for example, the Kalman filter can fuse the road curvature information recognized by the camera and the vehicle angular velocity information measured by the IMU to more accurately predict the vehicle's driving trajectory. At the high level, the driving intention and road condition are comprehensively judged based on the fused data. This hierarchical architecture can more efficiently utilize sensor data and improve the accuracy of driving mode switching decisions.

[0026] In step S22, the specific steps of data analysis are as follows: S221. Road type identification S2211. Data processing: First, the road image collected by the camera is processed, including cropping, normalization and size adjustment of the image; S2212. Construct network architecture: adopt VGG-Net architecture, which includes a combination of multiple consecutive convolutional layers and pooling layers, and finally maps the extracted features to different road type categories through a fully connected layer; S2213. Data training: training using annotated road image data, wherein the annotated road image data includes highway image data, urban road image data, and mountain road image data; during the training process, adjusting the weight of the network through the back propagation algorithm to minimize the error between the road type prediction result output by the network and the true label; S222. Traffic Condition Identification S2221. Use the optical flow method to estimate the vehicle speed, and then indirectly judge the traffic conditions; the optical flow is the speed vector of the object moving in the image; by calculating the optical flow between adjacent frame images, the speed of the vehicle relative to the surrounding environment is estimated; the specific algorithm is such as the Lucas-Kanade optical flow algorithm, which assumes that the movement of the object is linear in a small local area; for each pixel in the image, calculate its displacement between adjacent frames, and then convert the pixel displacement into the actual speed according to the camera parameters (such as focal length); if the speed of most vehicles is low and relatively stable, it may indicate traffic congestion; if the vehicle speed is high and varies greatly, it may indicate smooth traffic; S2222. Use target detection algorithm to identify vehicles and traffic signs and assist in judging traffic conditions: divide the image into multiple grid cells, each of which is responsible for predicting a certain number of targets; obtain accurate locations and categories of vehicles and traffic signs by post-processing the prediction results of each cell; if a large number of vehicles are detected waiting in line or stop signs appear frequently, it indicates traffic congestion; if vehicles are sparsely distributed and traffic signs are mainly signs with high speed limits, it indicates smooth traffic; S223. Driving behavior analysis S2231. Driving behavior analysis based on IMU data S22311. Acceleration data processing: The acceleration data collected by the inertial measurement unit (IMU) contains the acceleration information of the vehicle in the three axes of X, Y, and Z. By integrating the acceleration data, the speed and displacement information of the vehicle are obtained. For example, in the straight-line acceleration stage, the acceleration of the X axis will have a significant positive value; when turning, the acceleration of the Y axis will change. The acceleration data is filtered to remove noise interference to obtain more accurate vehicle dynamic information.

[0027] S22312. Angular velocity data processing: The inertial measurement unit (IMU) collects the angular velocity information of the vehicle to determine the steering situation of the vehicle. If the angular velocity has a continuous non-zero value, it means that the vehicle is turning. By analyzing the magnitude and duration of the angular velocity, the intensity of the turning can be determined. A large angular velocity that lasts for a period of time indicates that the vehicle is making a sharp turn; a small angular velocity indicates that the vehicle is making a gentle turn or a slight direction adjustment. S2232. Driving behavior analysis based on seat pressure sensor data S22321. Pressure change monitoring: The pressure data collected by the seat pressure sensor reflects the changes in the driver's sitting posture. By performing differential operations on the pressure data, the pressure change amplitude and frequency are monitored. If the pressure changes significantly in a short period of time, it may mean that the driver's body is leaning forward or moving left and right, which is related to tense driving operations; S22322. Pressure distribution analysis: By analyzing the pressure distribution in different areas of the seat, if the pressure in the front of the seat increases and the pressure in the back decreases, it means that the driver's body is leaning forward, and the analysis is that he is accelerating or focusing on the road conditions; if the pressure on both sides of the seat is uneven, it means that the driver's body is leaning to one side, and the analysis is related to the steering operation.

[0028] The step S3 comprises the following specific steps: S31. Parameter fuzzification: Fuzzify the relevant parameters of road conditions and driving behavior. For example, for traffic conditions, define three fuzzy sets: "congested", "general", and "smooth"; for the aggressiveness of driving behavior, define three fuzzy sets: "conservative", "moderate", and "aggressive". Determine the membership of each fuzzy set based on actual data such as the average speed of the vehicle, the rate of change of acceleration, and the amplitude of the change in seat pressure; for example, if the average speed of the vehicle is 20km / h, in the traffic condition fuzzy set, its membership to "congested" may be 0.8, its membership to "general" is 0.2, and its membership to "smooth" is 0.

[0029] S32. Establish a fuzzy rule base: Establish a fuzzy rule base based on experience and understanding of the characteristics of different driving modes, such as "if the traffic condition is smooth and the driving behavior is aggressive, then the driving mode is sports driving mode", "if the traffic condition is congested and the driving behavior is conservative, then the driving mode is economic mode or comfort driving mode", etc.; S33. Fuzzy reasoning: Reasoning based on fuzzy rules to obtain fuzzy output of driving mode. For example, by matching and reasoning the fuzzy input of traffic conditions and driving behavior, the membership of sports mode is 0.6, the membership of economy mode is 0.3, and the membership of comfort mode is 0.1. S34. Defuzzification: Using a defuzzification method such as a centroid method, the fuzzy output is converted into a determined driving mode. For example, according to the above membership degree, the driving mode is finally determined to be a sports mode.

[0030] The step S4 includes the following contents: The microcontroller changes the control method of the motor by adjusting the PWM signal sent to the permanent magnet synchronous motor driver; in economy mode, the motor speed and torque output are reduced to optimize efficiency; in sports mode, the motor speed and torque output are increased to provide stronger power; in comfort mode, the motor speed and torque are maintained at a moderate level to reduce vibration and noise.

[0031] The step S5 includes the following contents: During the operation of the permanent magnet synchronous motor, the temperature, current and voltage parameters of the motor are monitored in real time. When the temperature of the motor rises, the power output of the motor is reduced and the current reference value in the field oriented control (FOC) algorithm is adjusted to avoid overheating of the motor. At the same time, the control parameters of the motor are adaptively adjusted according to the different stages of vehicle driving, such as starting, acceleration, constant speed and deceleration, to improve the efficiency and performance of the motor and better adapt to the switching of driving modes.

Claims

1. A method for controlling a permanent magnet synchronous motor to achieve driving mode switching, characterized in that: The following steps are involved: S1: Sensor Data Collection After the vehicle is started, various sensors start working, and the microcontroller regularly collects image data from the camera, acceleration and angle data from the inertial measurement unit, and data from the seat pressure sensor; S2: Data processing and analysis The microcontroller processes and analyzes the collected data; S3: Driving mode decision According to the processed data, a driving mode switching decision mechanism based on fuzzy rules is used to make decisions on the three driving modes: sport, economy, and comfort. S4: Motor control strategy adjustment The microcontroller adapts the motor control strategy according to the selected driving mode.

2. The method for controlling a permanent magnet synchronous motor to switch driving modes according to claim 1, characterized in that: In step S1, before starting the vehicle, the following preparations are performed: The microcontroller is connected to the permanent magnet synchronous motor through a motor driver, and then the camera is installed on the car to identify road conditions. The inertial measurement unit is installed on the vehicle chassis to sense the dynamic characteristics of the vehicle, and the pressure sensor is installed on the seat to sense changes in the driver's sitting posture.

3. The method for controlling a permanent magnet synchronous motor to switch driving modes according to claim 1, characterized in that: The specific contents of step S2 include: S21. Data transmission and preprocessing: The data of each sensor is transmitted to the microcontroller. The ADC and related signal processing units inside the microcontroller preprocess the data, convert the analog signal into a digital signal, and filter and amplify it to obtain accurate sensor data; S22. Data processing and analysis: The image data from the camera is processed by the CNN algorithm to identify the road type. The acceleration and angle data from the inertial measurement unit are used to calculate the dynamic parameters of the vehicle's acceleration rate of change. The seat pressure sensor data is used to analyze the pressure variation. Driving behavior analysis is performed based on the dynamic parameters of the acceleration rate of change and the seat pressure variation.

4. The method for controlling a permanent magnet synchronous motor to switch driving modes according to claim 3, characterized in that: The data processing in step S22 is achieved by constructing a layered sensor fusion architecture; at the bottom layer, the data of the camera, the inertial measurement unit and the seat pressure sensor are time synchronized and spatially calibrated; Use timestamp technology to ensure that data from different sensors are in the same time reference system, and use coordinate transformation to unify spatial data from different sensors into the vehicle coordinate system; at the middle level, use Kalman filter or particle filter data fusion algorithm to fuse sensor data processed at the bottom level; At a high level, a comprehensive judgment of driving intention and road conditions is made based on the fused data.

5. The method for controlling a permanent magnet synchronous motor to switch driving modes according to claim 4, characterized in that: In step S22, data analysis includes the following specific steps: S221. Road type identification S2211. Data processing: First, the road image collected by the camera is processed, including cropping, normalization and size adjustment of the image; S2212. Construct network architecture: adopt VGG-Net architecture, which includes a combination of multiple consecutive convolutional layers and pooling layers, and finally maps the extracted features to different road type categories through a fully connected layer; S2213. Data training: training using annotated road image data, wherein the annotated road image data includes highway image data, urban road image data, and mountain road image data; during the training process, adjusting the weight of the network through the back propagation algorithm to minimize the error between the road type prediction result output by the network and the true label; S222. Traffic Condition Identification S2221. Use the optical flow method to estimate the vehicle speed and indirectly judge the traffic conditions. Specifically, by calculating the optical flow between adjacent frame images, the speed of the vehicle relative to the surrounding environment is estimated; if the speed of most vehicles is low and relatively stable, it indicates traffic congestion; if the vehicle speed is high and varies greatly, it indicates smooth traffic; S2222. Use target detection algorithms to identify vehicles and traffic signs and assist in judging traffic conditions. Specifically, the image is divided into multiple grid cells, each of which is responsible for predicting a certain number of targets. The prediction results of each cell are post-processed to obtain the accurate positions and categories of vehicles and traffic signs. If a large number of vehicles are detected waiting in line or stop signs appear frequently, it indicates traffic congestion. If the vehicles are sparsely distributed and the traffic signs are mainly signs with high speed limits, it indicates smooth traffic. S223. Driving behavior analysis S2231. Driving behavior analysis based on IMU data S22311. Acceleration data processing: The acceleration data collected by the inertial measurement unit (IMU) contains the acceleration information of the vehicle in the three axes of X, Y, and Z. The speed and displacement information of the vehicle are obtained by integrating the acceleration data. The acceleration data is filtered to remove noise interference and obtain more accurate vehicle dynamic information. S22312. Angular velocity data processing: The inertial measurement unit (IMU) collects the angular velocity information of the vehicle to determine the steering situation of the vehicle. If the angular velocity has a continuous non-zero value, it means that the vehicle is turning. By analyzing the magnitude and duration of the angular velocity, the intensity of the turning can be determined. A large angular velocity that lasts for a period of time indicates that the vehicle is making a sharp turn; a small angular velocity indicates that the vehicle is making a gentle turn or a slight direction adjustment. S2232. Driving behavior analysis based on seat pressure sensor data S22321. Pressure change monitoring: The pressure data collected by the seat pressure sensor reflects the changes in the driver's sitting posture. By performing differential operations on the pressure data, the pressure change amplitude and frequency are monitored. If the pressure changes significantly in a short period of time, it means that the driver's body is leaning forward or moving left and right, which is related to the tense driving operation. S22322. Pressure distribution analysis: By analyzing the pressure distribution in different areas of the seat, if the pressure in the front of the seat increases and the pressure in the back decreases, it means that the driver's body is leaning forward, and the analysis is that he is accelerating or focusing on the road conditions; if the pressure on both sides of the seat is uneven, it means that the driver's body is leaning to one side, and the analysis is related to the steering operation.

6. The method for controlling a permanent magnet synchronous motor to switch driving modes according to claim 5, characterized in that: The step S3 comprises the following specific steps: S31. Parameter fuzzification: Fuzzify the relevant parameters of road conditions and driving behavior; determine the degree of membership of each fuzzy set based on actual data; S32. Establish a fuzzy rule base: Establish a fuzzy rule base based on experience and understanding of the characteristics of different driving modes; S33. Fuzzy reasoning: Reasoning based on fuzzy rules to obtain fuzzy output of driving mode; S34. Defuzzification: A defuzzification method is used to convert the fuzzy output into a definite driving mode.

7. The method for controlling a permanent magnet synchronous motor to switch driving modes according to claim 6, characterized in that: The fuzzy rule base in step S32 is: if the traffic condition is smooth and the driving behavior is aggressive, the driving mode is the sports driving mode; if the traffic condition is congested and the driving behavior is conservative, the driving mode is the economic mode or the comfortable driving mode.

8. The method for controlling a permanent magnet synchronous motor to switch driving modes according to claim 1, characterized in that: The step S4 includes the following contents: The microcontroller changes the motor control method by adjusting the PWM signal sent to the permanent magnet synchronous motor driver; in economic mode, the motor speed and torque output are reduced to optimize efficiency; In sports mode, the motor speed and torque output are increased to provide stronger power; In comfort mode, the motor speed and torque are maintained at moderate levels to reduce vibration and noise.

9. The method for controlling a permanent magnet synchronous motor to switch driving modes according to claim 1, characterized in that: The method further comprises step S5. Adaptive motor control parameter adjustment: adaptively adjusting the motor control parameters based on real-time feedback of the motor operating state.

10. The method for controlling a permanent magnet synchronous motor to switch driving modes according to claim 9, characterized in that: The step S5 includes the following contents: During the operation of the motor, the temperature, current and voltage parameters of the motor are monitored in real time. When the temperature of the motor rises, the power output of the motor is reduced and the current reference value in the magnetic field oriented control algorithm is adjusted to avoid overheating of the motor. At the same time, the control parameters of the motor are adaptively adjusted according to the different stages of vehicle driving, such as starting, acceleration, constant speed and deceleration, to improve the efficiency and performance of the motor and better adapt to the switching of driving modes.