Vehicle braking torque adjusting method and system based on multiple input signals

Abstract

The invention discloses a vehicle braking torque adjusting method and system based on multiple input signals. The vehicle braking torque adjusting method and system aim at improving the safety, driving experience and braking efficiency of a vehicle in a complex environment. The system collects the vehicle driving state, the intention of a driver and the road friction condition in real time through a plurality of sensor modules, and accurate data support is provided for brake control. According to the diversified input, the system can intelligently recognize the road surface state and dynamically adjust the braking torque gradient, the braking response is optimized, and the stability of the vehicle in various driving environments is ensured. The sudden change and step phenomena in the braking process are smoothed through a feedforward control algorithm, the driving experience is improved, instability caused by system switching control strategies is avoided, and based on motor back electromotive force spectrum analysis, the system can accurately adjust energy recovery and improve the energy utilization efficiency of the vehicle. And powerful support is provided for improving the braking performance, the safety and the automatic driving technology of modern vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle braking torque control, and in particular to a vehicle braking torque adjustment method and system based on multiple input signals. Background Art

[0002] In modern vehicle braking systems, safety and driving experience are crucial. With the continuous development of automobile intelligence and autonomous driving technology, traditional braking systems have gradually failed to meet safety requirements in complex environments. In order to improve braking efficiency, avoid accidents, and enhance driving experience, it is necessary to adjust the vehicle's braking torque through more intelligent and dynamic control strategies. Different road friction coefficients will affect braking performance. On wet and slippery roads, vehicles are more likely to slip, while dry roads have better braking effects. Existing systems may not be able to adapt to these changes in real time, resulting in inaccurate braking and potential safety risks. Traditional braking systems usually rely on single sensor information and cannot comprehensively consider multiple dynamic factors (such as vehicle speed, wheel speed, road surface conditions, etc.) for comprehensive judgment, which may lead to braking delays or loss of control. Traditional braking systems cannot accurately identify the driver's operating intentions (such as light braking, emergency braking, etc.), and are prone to misjudgment, affecting the driver's control experience, especially in complex traffic environments. Summary of the invention

[0003] A vehicle braking torque adjustment method based on multiple input signals comprises the following steps; S1. Signal input acquisition: obtain the real-time vehicle speed through vehicle speed sensors (such as wheel speed sensors, GPS, etc.), monitor the driver's brake pedal input, and obtain its position (pedal depth, pressure, etc.) through the pedal sensor. Wheel speed: monitor the speed of each wheel through the wheel speed sensor (ABS sensor) of each wheel to determine whether there is a risk of wheel slippage or locking. Vehicle control system information: such as engine speed, power system status, etc., to help understand the overall power status of the vehicle. Road surface status information: provide road friction and climate information through vehicle-mounted sensors or external systems; S2. Signal preprocessing and fusion: Preprocess each input signal, such as filtering and normalization, to ensure the quality and stability of the input data. The speed signal is filtered to reduce noise, the pedal signal is calibrated to convert it into the required braking force, and the wheel speed signal is used to determine whether the wheel is close to locking or slipping. S3. Multi-system collaborative arbitration mechanism: Based on the real-time vehicle speed, road adhesion coefficient, and driver's operating intention, the machine learning algorithm is used to calculate the activation weights of IEBS (intelligent electronic braking system), AEBS (automatic emergency braking system), and ABS (anti-lock braking system). Specifically, based on the current state of the vehicle, the system will assign priorities to different subsystems to ensure maximum braking efficiency: At high speeds and in emergency braking situations, AEBS priority is increased; At low speeds and on slippery roads, ABS priority increases; When ABS is activated, the system dynamically calculates the feedback torque threshold based on the real-time wheel slip rate and the current road friction characteristics, and adjusts the ABS control strategy, breaking through the traditional "full ban" strategy, allowing appropriate feedback torque to improve braking efficiency; S4. Dynamic optimization of torque gradient: Through the analysis of the motor back-EMF spectrum and the road conditions (such as wet or dry roads), the torque gradient is adjusted in real time. During IEBS braking, the system sets a basic gradient (such as 620Nm / s) and corrects the gradient value according to the vehicle's pitch angle sensor data within a range of ±20%; S5. Energy recovery cooperative control: When IEBS requests braking force, the system will decompose the requested torque into two components: mechanical braking and electric braking: Mechanical brake: provided by electronic hydraulic brake system (EHB); Electric braking: achieved through motor feedback, especially during light braking and deceleration; When the ABS system is activated, a pulse feedback strategy is used to maintain adhesion between the tire and the road. The 5ms intermittent control can reduce the instantaneous impact force generated by the braking system and help maintain stability. S6. Fail-safe interaction mechanism: When there is a large deviation (10%-15%) between the IEBS requested torque and the actual motor output torque, the signals between the subsystems are cross-verified through the CAN bus to ensure data consistency. If an abnormality is found, the system will automatically switch to the backup control strategy to ensure safety. When the system switches the control strategy, the feedforward control algorithm is used to smooth out the unstable torque phenomenon: mutation and step, reducing the impact of mutation on driving experience and vehicle stability; S7. Braking torque adjustment strategy: Based on multiple signals collected in real time (such as vehicle speed, road information, driver intention, wheel speed, etc.), the system automatically adjusts the braking torque: When emergency braking or driving at high speed, the system provides stronger braking force by increasing the priority of AEBS and ABS; When driving at low speeds, IEBS is used first to ensure energy recovery and reduce the burden on the driver; In slippery or bad road conditions, the system will dynamically adjust the braking gradient and feedback torque to prevent the vehicle from losing control. S8. Feedback and Adjustment: Adjusts the braking torque based on real-time vehicle dynamics and input signals, and automatically adjusts higher braking force based on acceleration sensor information during emergency braking.

[0004] Furthermore, a vehicle braking torque adjustment method based on multiple input signals, In step S4, the torque gradient is adjusted in real time by analyzing the motor back electromotive force spectrum and combining the road surface conditions: wet and dry road surface. The specific steps are as follows; S41. Motor back-EMF spectrum analysis: The real-time working status of the motor is obtained through back-EMF measurement, including motor speed, load condition, and possible changes in road adhesion. The back-EMF signal is analyzed using Fourier transform spectrum analysis method, and the time domain signal is converted into a frequency domain signal to determine the change in road condition. Based on the frequency characteristics and vibration modes appearing in the spectrum, the load state of the motor is estimated, and then the adhesion condition of the road is inferred; Wet and slippery roads: High-frequency noise is strong and motor load fluctuates greatly; Dry road: The spectrum is relatively stable and the motor load fluctuation is small; S42. Road surface condition judgment and classification: Slippery road surface: Slippery road surface has a lower friction coefficient, and the tires are prone to slipping during braking, so the feedback torque of the motor needs to be smaller; Dry road surface: The friction on dry road surface is stronger, and the motor can output greater braking force; Road surface condition classification: Using spectrum analysis results: Based on the motor back-EMF spectrum, the system can determine the friction state of the current road surface in real time and classify it into wet, dry, icy and snowy roads; S43. Torque gradient dynamic adjustment: basic gradient setting. Under normal conditions, the base torque gradient, such as 620Nm / s, is used to adjust the torque response of the motor. The gradient adjustment is based on: Slippery road: On slippery roads, the tires are prone to slipping due to low friction. The motor's braking feedback needs to be smoother and the torque gradient needs to be reduced. The set basic gradient value of 620Nm / s should be lowered to within ±20%; Dry road surface: On dry roads, the friction is stronger and the brake feedback needs to be faster. The motor can output stronger feedback torque and increase the torque gradient, which can be increased to ±20% on dry roads. Torque gradient adjustment strategy: Dynamically correct the gradient value: The gradient is adjusted in real time through motor back-EMF spectrum analysis and road condition estimation. When the system identifies a slippery road surface, the gradient is adjusted to a lower value of ±10%-20%, and when the road surface is dry, it is adjusted to a higher value of ±15%-25%; S44. Torque output adjustment: Under slippery road conditions, the system will dynamically reduce the feedback torque to avoid excessive braking torque of the motor, which may cause the tire to lock or slip; Under dry road conditions, the system will increase the regenerative torque and increase the motor force to achieve a more efficient braking effect; S45. Verification and optimization: The adjustment process is continuously optimized through the feedback loop to ensure that the torque output matches the vehicle dynamic state and road conditions. At the end of each control cycle, automatic adjustments are made based on the vehicle's motion state, torque deviation and feedback information. Field tests are carried out under various road conditions. The road recognition algorithm and gradient adjustment strategy are adjusted through feedback to ensure the stability and braking efficiency of the system under different driving conditions.

[0005] Furthermore, a vehicle braking torque adjustment method based on multiple input signals, In step S6, a feedforward control algorithm is used to smooth out the unstable phenomenon of torque: mutation and step, so as to reduce the impact of mutation on driving experience and vehicle stability. The specific steps are as follows; S61. Torque deviation detection and tolerance interval setting: Monitoring signal acquisition: IEBS request torque: Get the requested braking torque from the IEBS system; Motor actual output torque: Get the current actual output torque of the motor from the motor control system and calculate the deviation between the IEBS requested torque and the actual output torque of the motor: Δ T =∣ T IEBS - T motor ∣, and determine whether the deviation exceeds the set threshold range of 10%-15%, Δ T ≥ T thres ,in T IEBS Request torque for IEBS, T motor is the actual output torque of the motor, Δ T The deviation between the requested torque and the actual output torque of the motor, T thres The upper limit of the threshold interval is set. When the deviation value is within the range of 10%-15%, the system will check the data consistency between each subsystem through CAN bus cross-validation. If the deviation in the tolerance zone lasts for a long time, it is judged as abnormal. S62. Verify signal consistency: Cross-verify the real-time signals of IEBS, motor control system, and ABS subsystem through the CAN bus, and use the redundant signal verification method to compare signals from different sources: IEBS requested torque, wheel speed sensor feedback, and feedback from other control systems. When signal inconsistency is detected: the IEBS requested torque and the motor feedback torque have a large deviation and exceed 15%, the system automatically identifies it as a fault. When the deviation continues to exceed 10%-15% and the signal consistency is abnormal, the system enters the backup control strategy; S63. Switch to backup control strategy: The backup control strategy includes falling back to standard braking control: pure mechanical braking, simple electronic hydraulic braking, limiting torque output in backup mode to maintain vehicle safety and avoid system failure caused by excessive braking force; S64. Feedforward control algorithm: Based on the IEBS request torque, motor speed, and vehicle dynamic information input signal, the torque value to be applied in the next control cycle is calculated in advance. The vehicle dynamic model: vehicle suspension, power system, and torque transfer model are used to calibrate the parameters of the feedforward control, and the target torque is predicted in advance. When the control strategy is switched, the expected torque change is calculated through the feedforward control algorithm, and the torque output of the motor is adjusted in advance to reduce the sudden change of torque. The specific method is as follows: T a = T motor +Δ T feed ,in, T a is the expected torque change, Δ T feed It is the adjustment calculated based on the feedforward control. When the backup control strategy is switched, the feedforward control will slowly and smoothly adjust the torque output of the motor to avoid instability caused by sudden or step changes; S65. System recovery and fallback mechanism: After the system has been running stably under the backup control strategy for a period of time and the torque deviation has returned to the normal range (for example, the deviation is less than 10%), it will try to recover to the main control strategy. If the recovery process cannot run stably (for example, the torque deviation exceeds the standard again), the system will enter the backup control state again and continue to smoothly transition through the feedforward control algorithm; S66. Real-time feedback and adjustment: During the whole process, the system continuously adjusts the control strategy through real-time feedback signals: vehicle dynamic data, vehicle speed, wheel speed, brake pedal depth, to ensure that the stability of the vehicle is not affected during the switching process.

[0006] A vehicle braking torque regulation system based on multiple input signals, wherein the vehicle braking torque regulation system based on multiple input signals is used for any vehicle braking torque regulation method based on multiple input signals; the vehicle braking torque regulation system based on multiple input signals comprises: a signal acquisition module, a signal preprocessing and fusion module, a multi-system collaborative arbitration mechanism module, a torque gradient dynamic optimization module, a collaborative control module, a fault detection module, and a real-time regulation module; Signal acquisition module: collects data through various sensors to accurately identify vehicle status, driver's operating intention and road conditions; Signal preprocessing and fusion module: Filter, denoise and normalize various collected signals: vehicle speed, pedal depth, wheel speed to ensure signal quality and stability, fuse multiple signals, and calibrate the errors of different sensors through algorithms; Multi-system collaborative arbitration mechanism module: dynamically calculates the activation priority and weight of IEBS, AEBS, and ABS based on vehicle speed, road friction coefficient, and driver operation intention factors; Torque gradient dynamic optimization module: By analyzing the spectrum of the motor back-EMF signal, the motor load and road adhesion are judged, and then the road state is estimated. According to the road state and pitch angle information, the braking gradient is dynamically adjusted; Collaborative control module: Decomposes the torque request into two components: mechanical braking and electric braking, based on the braking force demand requested by IEBS; Fault detection module: real-time monitoring of the deviation between the IEBS requested torque and the actual motor output torque. When the deviation exceeds the set threshold: 10%-15%, fault detection is triggered; Real-time adjustment module: adjusts the braking torque according to the real-time vehicle dynamic information: acceleration, pitch angle, and vehicle speed.

[0007] Beneficial effects of the present invention: By real-time monitoring of multiple signals such as vehicle speed, pedal depth, wheel speed, and road surface conditions, the system can quickly and accurately adjust the braking torque to cope with different driving environments (such as wet, dry, and other road surfaces), greatly reducing the risk of accidents caused by insufficient or excessive braking. By analyzing the motor back electromotive force spectrum and the changes in road friction, the system can identify the road surface conditions in real time and adjust the braking strategy to prevent the wheels from slipping or locking, ensuring the stability and safety of the vehicle in complex environments. By real-time monitoring of sensor data consistency, system or sensor failures can be discovered and corrected in a timely manner to ensure that the system can automatically switch to the backup control strategy when an abnormality occurs, thereby minimizing the safety hazards caused by sensor failures or false alarms. The system smoothes the changes in braking torque through a feedforward control algorithm to avoid sudden changes or step phenomena, especially when the system switches control strategies, it can reduce instability and provide a smooth braking response, thereby enhancing the driver's comfort and trust. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a flow chart of a vehicle braking torque adjustment method based on multiple input signals; DETAILED DESCRIPTION

[0009] A vehicle braking torque adjustment method based on multiple input signals comprises the following steps; S1. Signal input acquisition: obtain the real-time vehicle speed through vehicle speed sensors (such as wheel speed sensors, GPS, etc.), monitor the driver's brake pedal input, and obtain its position (pedal depth, pressure, etc.) through the pedal sensor. Wheel speed: monitor the speed of each wheel through the wheel speed sensor (ABS sensor) of each wheel to determine whether there is a risk of wheel slippage or locking. Vehicle control system information: such as engine speed, power system status, etc., to help understand the overall power status of the vehicle. Road surface status information: provide road friction and climate information through vehicle-mounted sensors or external systems; S2. Signal preprocessing and fusion: Preprocess each input signal, such as filtering and normalization, to ensure the quality and stability of the input data. The speed signal is filtered to reduce noise, the pedal signal is calibrated to convert it into the required braking force, and the wheel speed signal is used to determine whether the wheel is close to locking or slipping. S3. Multi-system collaborative arbitration mechanism: Based on the real-time vehicle speed, road adhesion coefficient, and driver's operating intention, the machine learning algorithm is used to calculate the activation weights of IEBS (intelligent electronic braking system), AEBS (automatic emergency braking system), and ABS (anti-lock braking system). Specifically, based on the current state of the vehicle, the system will assign priorities to different subsystems to ensure maximum braking efficiency: At high speeds and in emergency braking situations, AEBS priority is increased; At low speeds and on slippery roads, ABS priority increases; When ABS is activated, the system dynamically calculates the feedback torque threshold based on the real-time wheel slip rate and the current road friction characteristics, and adjusts the ABS control strategy, breaking through the traditional "full ban" strategy, allowing appropriate feedback torque to improve braking efficiency; S4. Dynamic optimization of torque gradient: Through the analysis of the motor back-EMF spectrum and the road conditions (such as wet or dry roads), the torque gradient is adjusted in real time. During IEBS braking, the system sets a basic gradient (such as 620Nm / s) and corrects the gradient value according to the vehicle's pitch angle sensor data within a range of ±20%; S5. Energy recovery cooperative control: When IEBS requests braking force, the system will decompose the requested torque into two components: mechanical braking and electric braking: Mechanical brake: provided by electronic hydraulic brake system (EHB); Electric braking: achieved through motor feedback, especially during light braking and deceleration; When the ABS system is activated, a pulse feedback strategy is used to maintain adhesion between the tire and the road. The 5ms intermittent control can reduce the instantaneous impact force generated by the braking system and help maintain stability. S6. Fail-safe interaction mechanism: When there is a large deviation (10%-15%) between the IEBS requested torque and the actual motor output torque, the signals between the subsystems are cross-verified through the CAN bus to ensure data consistency. If an abnormality is found, the system will automatically switch to the backup control strategy to ensure safety. When the system switches the control strategy, the feedforward control algorithm is used to smooth out the unstable torque phenomenon: mutation and step, reducing the impact of mutation on driving experience and vehicle stability; S7. Braking torque adjustment strategy: Based on multiple signals collected in real time (such as vehicle speed, road information, driver intention, wheel speed, etc.), the system automatically adjusts the braking torque: When emergency braking or driving at high speed, the system provides stronger braking force by increasing the priority of AEBS and ABS; When driving at low speeds, IEBS is used first to ensure energy recovery and reduce the burden on the driver; In slippery or bad road conditions, the system will dynamically adjust the braking gradient and feedback torque to prevent the vehicle from losing control. S8. Feedback and Adjustment: Adjusts the braking torque based on real-time vehicle dynamics and input signals, and automatically adjusts higher braking force based on acceleration sensor information during emergency braking.

[0010] Furthermore, a vehicle braking torque adjustment method based on multiple input signals, In step S4, the torque gradient is adjusted in real time by analyzing the motor back electromotive force spectrum and combining the road surface conditions: wet and dry road surface. The specific steps are as follows; S41. Motor back-EMF spectrum analysis: The real-time working status of the motor is obtained through back-EMF measurement, including motor speed, load condition, and possible changes in road adhesion. The back-EMF signal is analyzed using Fourier transform spectrum analysis method, and the time domain signal is converted into a frequency domain signal to determine the change in road condition. Based on the frequency characteristics and vibration modes appearing in the spectrum, the load state of the motor is estimated, and then the adhesion condition of the road is inferred; Wet and slippery roads: high-frequency noise is strong and motor load fluctuates greatly; Dry road: The spectrum is relatively stable and the motor load fluctuation is small; S42. Road surface condition judgment and classification: Slippery road surface: Slippery road surface has a lower friction coefficient, and the tires are prone to slipping during braking, so the feedback torque of the motor needs to be smaller; Dry road surface: The friction on dry road surface is stronger, and the motor can output greater braking force; Road surface condition classification: Using spectrum analysis results: Based on the motor back-EMF spectrum, the system can determine the friction state of the current road surface in real time and classify it into wet, dry, icy and snowy roads; S43. Torque gradient dynamic adjustment: basic gradient setting. Under normal conditions, the base torque gradient, such as 620Nm / s, is used to adjust the torque response of the motor. The gradient adjustment is based on: Slippery road: On slippery roads, the tires are prone to slipping due to low friction. The motor's braking feedback needs to be smoother and the torque gradient needs to be reduced. The set basic gradient value of 620Nm / s should be lowered to within ±20%; Dry road surface: On dry roads, the friction is stronger and the brake feedback needs to be faster. The motor can output stronger feedback torque and increase the torque gradient, which can be increased to ±20% on dry roads. Torque gradient adjustment strategy: Dynamically correct the gradient value: The gradient is adjusted in real time through motor back-EMF spectrum analysis and road condition estimation. When the system identifies a slippery road surface, the gradient is adjusted to a lower value: ±10%-20%, and when the road surface is dry, it is adjusted to a higher value: ±15%-25%; S44. Torque output adjustment: Under slippery road conditions, the system will dynamically reduce the feedback torque to avoid excessive braking torque of the motor, which may cause the tire to lock or slip; Under dry road conditions, the system will increase the regenerative torque and increase the motor force to achieve a more efficient braking effect; S45. Verification and optimization: The adjustment process is continuously optimized through the feedback loop to ensure that the torque output matches the vehicle dynamic state and road conditions. At the end of each control cycle, automatic adjustments are made based on the vehicle's motion state, torque deviation and feedback information. Field tests are carried out under various road conditions. The road recognition algorithm and gradient adjustment strategy are adjusted through feedback to ensure the stability and braking efficiency of the system under different driving conditions.

[0011] Furthermore, a vehicle braking torque adjustment method based on multiple input signals, In step S6, a feedforward control algorithm is used to smooth out the unstable phenomenon of torque: mutation and step, so as to reduce the impact of mutation on driving experience and vehicle stability. The specific steps are as follows; S61. Torque deviation detection and tolerance interval setting: Monitoring signal acquisition: IEBS requested torque: Get the requested braking torque from the IEBS system; Motor actual output torque: Get the current actual output torque of the motor from the motor control system and calculate the deviation between the IEBS requested torque and the actual output torque of the motor: Δ T =∣ T IEBS - T motor ∣, and determine whether the deviation exceeds the set threshold range of 10%-15%, Δ T ≥ T thres ,in T IEBS Request torque for IEBS, T motor is the actual output torque of the motor, Δ T The deviation between the requested torque and the actual output torque of the motor, T thres The upper limit of the threshold interval is set. When the deviation value is within the range of 10%-15%, the system will check the data consistency between each subsystem through CAN bus cross-validation. If the deviation in the tolerance zone lasts for a long time, it is judged as abnormal. S62. Verify signal consistency: Cross-verify the real-time signals of IEBS, motor control system, and ABS subsystem through the CAN bus, and use the redundant signal verification method to compare signals from different sources: IEBS requested torque, wheel speed sensor feedback, and feedback from other control systems. When signal inconsistency is detected: the IEBS requested torque and the motor feedback torque have a large deviation and exceed 15%, the system automatically identifies it as a fault. When the deviation continues to exceed 10%-15% and the signal consistency is abnormal, the system enters the backup control strategy; S63. Switch to backup control strategy: The backup control strategy includes falling back to standard braking control: pure mechanical braking, simple electronic hydraulic braking, limiting torque output in backup mode to maintain vehicle safety and avoid system failure caused by excessive braking force; S64. Feedforward control algorithm: Based on the IEBS request torque, motor speed, and vehicle dynamic information input signal, the torque value to be applied in the next control cycle is calculated in advance. The vehicle dynamic model: vehicle suspension, power system, and torque transfer model are used to calibrate the parameters of the feedforward control, and the target torque is predicted in advance. When the control strategy is switched, the expected torque change is calculated through the feedforward control algorithm, and the torque output of the motor is adjusted in advance to reduce the sudden change of torque. The specific method is as follows: T a = T motor +Δ T feed ,in, Ta is the expected torque change, Δ T feed It is the adjustment calculated based on the feedforward control. When the backup control strategy is switched, the feedforward control will slowly and smoothly adjust the torque output of the motor to avoid instability caused by sudden or step changes; S65. System recovery and fallback mechanism: After the system has been running stably under the backup control strategy for a period of time and the torque deviation has returned to the normal range (for example, the deviation is less than 10%), it will try to recover to the main control strategy. If the recovery process cannot run stably (for example, the torque deviation exceeds the standard again), the system will enter the backup control state again and continue to smoothly transition through the feedforward control algorithm; S66. Real-time feedback and adjustment: During the whole process, the system continuously adjusts the control strategy through real-time feedback signals: vehicle dynamic data, vehicle speed, wheel speed, brake pedal depth, to ensure that the stability of the vehicle is not affected during the switching process.

[0012] A vehicle braking torque regulation system based on multiple input signals, wherein the vehicle braking torque regulation system based on multiple input signals is used for any vehicle braking torque regulation method based on multiple input signals; the vehicle braking torque regulation system based on multiple input signals comprises: a signal acquisition module, a signal preprocessing and fusion module, a multi-system collaborative arbitration mechanism module, a torque gradient dynamic optimization module, a collaborative control module, a fault detection module, and a real-time regulation module; Signal acquisition module: collects data through various sensors to accurately identify vehicle status, driver's operating intention and road conditions; Signal preprocessing and fusion module: Filter, denoise and normalize various collected signals: vehicle speed, pedal depth, wheel speed to ensure signal quality and stability, fuse multiple signals, and calibrate the errors of different sensors through algorithms; Multi-system collaborative arbitration mechanism module: dynamically calculates the activation priority and weight of IEBS, AEBS, and ABS based on vehicle speed, road friction coefficient, and driver operation intention factors; Torque gradient dynamic optimization module: By analyzing the spectrum of the motor back-EMF signal, the motor load and road adhesion are judged, and then the road state is estimated. According to the road state and pitch angle information, the braking gradient is dynamically adjusted; Collaborative control module: Decomposes the torque request into two components: mechanical braking and electric braking, based on the braking force demand requested by IEBS; Fault detection module: real-time monitoring of the deviation between the IEBS requested torque and the actual motor output torque. When the deviation exceeds the set threshold: 10%-15%, fault detection is triggered; Real-time adjustment module: adjusts the braking torque according to the real-time vehicle dynamic information: acceleration, pitch angle, and vehicle speed.

Claims

1. A vehicle braking torque adjustment method based on multiple input signals, characterized in that: The steps include: S1. Signal input acquisition: obtain real-time vehicle speed through vehicle speed sensor, wheel speed sensor and GPS, monitor the driver's brake pedal input, obtain data through pedal sensor: pedal depth and pressure, monitor the speed of each wheel through the ABS sensor of each wheel, determine whether there is a risk of wheel slippage and locking, and collect vehicle control system information: engine speed, power system status, and provide road friction and climate information through vehicle sensors; S2. Signal preprocessing and fusion: Preprocess, filter and normalize each input signal. The speed signal is filtered to reduce noise. The pedal signal is converted into the required braking force through calibration. The wheel speed signal is used to determine whether the wheel is close to locking or slipping. S3. Multi-system collaborative arbitration mechanism: Based on the real-time vehicle speed, road adhesion coefficient, and driver operation intention, a machine learning algorithm is used to calculate the activation weights of IEBS: Intelligent Electronic Braking System, AEBS: Automatic Emergency Braking System, and ABS: Anti-lock Braking System, and priorities are assigned to different subsystems based on the current state of the vehicle: At high speeds and in emergency braking situations, AEBS priority is increased; At low speeds and on slippery roads, ABS priority increases; When ABS is activated, the system dynamically calculates the feedback torque threshold based on the real-time wheel slip rate and the current road friction characteristics, and adjusts the ABS control strategy to break through the "full ban" strategy, allowing feedback torque to improve braking efficiency; S4. Dynamic optimization of torque gradient: Through the motor back-EMF spectrum analysis, combined with the road conditions: wet and dry roads, the torque gradient is adjusted in real time. During IEBS braking, the system will set a basic gradient of 620Nm / s and correct the gradient value according to the vehicle's pitch angle sensor data within a range of ±20%; S5. Energy recovery cooperative control: When IEBS requests braking force, the system will decompose the requested torque into two components: mechanical braking and electric braking: Mechanical brake: provided by the electronic hydraulic brake system EHB; Electric braking: achieved through motor feedback, provided during slight braking and deceleration; When the ABS system is activated, in order to maintain the adhesion between the tire and the road, a pulse feedback strategy is adopted, and the 5ms intermittent control reduces the instantaneous impact force generated by the braking system; S6. Fail-safe interaction mechanism: When there is a large deviation between the IEBS requested torque and the actual motor output torque: 10%-15%, the signals between the various subsystems are cross-verified through the CAN bus to ensure data consistency. When an abnormality is found, the system will automatically switch to the backup control strategy to ensure safety. When the system switches the control strategy, the feedforward control algorithm is used to smooth the unstable phenomenon of torque: mutation, step; S7. Braking torque adjustment strategy: Automatically adjust the braking torque based on multiple signals collected in real time: vehicle speed, road information, driver intention, wheel speed: When emergency braking or driving at high speed, the system provides stronger braking force by increasing the priority of AEBS and ABS; When driving at low speeds, IEBS is used first to ensure energy recovery and reduce the burden on the driver; In slippery and bad road conditions, the system will dynamically adjust the braking gradient and feedback torque to prevent the vehicle from losing control; S8. Feedback and adjustment: Adjusts the braking torque according to vehicle dynamics and input signals. In emergency braking, it automatically adjusts higher braking force based on acceleration sensor information.

2. A vehicle braking torque adjustment method based on multiple input signals as claimed in claim 1, characterized in that ； In step S4, the torque gradient is adjusted in real time by analyzing the motor back electromotive force spectrum and combining the road surface conditions: wet and dry road surface. The specific steps are as follows; S41. Motor back-EMF spectrum analysis: The real-time working status of the motor is obtained through back-EMF measurement, including motor speed, load condition, and road adhesion change. The back-EMF signal is analyzed using Fourier transform spectrum analysis method, and the time domain signal is converted into a frequency domain signal to determine the change of road state. Based on the frequency characteristics and vibration modes appearing in the spectrum, the load state of the motor is estimated, and then the adhesion condition of the road is inferred; Wet and slippery roads: high-frequency noise is strong and motor load fluctuates greatly; Dry road: The spectrum is relatively stable and the motor load fluctuation is small; S42. Road surface condition judgment and classification: Slippery road surface: Slippery road surface has a lower friction coefficient, and the tires are prone to slipping during braking, so the feedback torque of the motor needs to be smaller; Dry road surface: The friction on dry road surface is stronger, and the motor outputs greater braking force; Road surface condition classification: Using spectrum analysis results: Based on the motor back EMF spectrum, the system determines the friction state of the current road surface in real time and classifies it into wet, dry, icy and snowy roads; S43. Torque gradient dynamic adjustment: basic gradient setting. Under normal conditions, the base torque gradient is set to 620Nm / s to adjust the torque response of the motor. The gradient adjustment is based on: Slippery road surface: On slippery road surfaces, the tires are prone to slipping due to low friction. The motor's braking feedback needs to be smoother and the torque gradient needs to be reduced. The set basic gradient value of 620Nm / s should be lowered to within ±20%; Dry roads: On dry roads, friction is stronger, and brake feedback needs to be faster. The motor outputs stronger feedback torque, increasing the torque gradient, which is adjusted to within ±20% on dry roads. Torque gradient adjustment strategy: Dynamically correct the gradient value: The gradient is adjusted in real time through motor back-EMF spectrum analysis and road condition estimation. When the system identifies a slippery road surface, the gradient is adjusted to a lower value of ±10%-20%, and when the road surface is dry, it is adjusted to a higher value of ±15%-25%; S44. Torque output adjustment: Under slippery road conditions, the system will dynamically reduce the feedback torque to avoid excessive braking torque of the motor, which may cause the tire to lock or slip; Under dry road conditions, the system will increase the regenerative torque and increase the motor force to achieve a more efficient braking effect; S45. Verification and optimization: The adjustment process is continuously optimized through the feedback loop to ensure that the torque output matches the vehicle dynamic state and road conditions. At the end of each control cycle, automatic adjustments are made based on the vehicle's motion state, torque deviation and feedback information. Field tests are carried out under various road conditions. The road recognition algorithm and gradient adjustment strategy are adjusted through feedback to ensure the stability and braking efficiency of the system under different driving conditions.

3. A vehicle braking torque adjustment method based on multiple input signals as claimed in claim 1, characterized in that ； In step S6, a feedforward control algorithm is used to smooth out the unstable phenomenon of torque: mutation and step, so as to reduce the impact of mutation on driving experience and vehicle stability. The specific steps are as follows; S61. Torque deviation detection and tolerance interval setting: Monitoring signal acquisition: IEBS request torque, motor actual output torque, calculate the deviation between IEBS request torque and motor actual output torque: Δ T =∣ T IEBS - T motor ∣, and determine whether the deviation exceeds the set threshold range of 10%-15%, Δ T ≥ T thres ,in T IEBS Request torque for IEBS, T motor is the actual output torque of the motor, Δ T The deviation between the requested torque and the actual output torque of the motor, T thres The upper limit of the threshold interval is set. When the deviation value is within the range of 10%-15%, the system will check the data consistency between each subsystem through CAN bus cross-validation. If the deviation in the tolerance zone lasts for a long time, it is judged as abnormal. S62. Verify signal consistency: Cross-verify the real-time signals of IEBS, motor control system, and ABS subsystem through the CAN bus, and use the redundant signal verification method to compare signals from different sources: IEBS requested torque, wheel speed sensor feedback, and feedback from other control systems. When signal inconsistency is detected: the IEBS requested torque and the motor feedback torque have a large deviation and exceed 15%, the system automatically identifies it as a fault. When the deviation continues to exceed 10%-15% and the signal consistency is abnormal, the system enters the backup control strategy; S63. Switch to backup control strategy: The backup control strategy includes falling back to standard braking control: pure mechanical braking, simple electronic hydraulic braking, limiting torque output in backup mode to maintain vehicle safety and avoid system failure caused by excessive braking force; S64. Feedforward control algorithm: Based on the IEBS request torque, motor speed, and vehicle dynamic information input signal, pre-calculate the torque value to be applied in the next control cycle, use the vehicle dynamic model: vehicle suspension, power system, torque transfer model to calibrate the parameters of the feedforward control, and predict the target torque in advance. When the control strategy is switched, the expected torque change is calculated through the feedforward control algorithm, and the torque output of the motor is adjusted in advance to reduce the sudden change of torque. The specific method is as follows: T a = T motor +Δ T feed ,in, T a is the expected torque change, Δ T feed It is the adjustment amount calculated based on the feedforward control. When the backup control strategy is switched, the feedforward control will slowly and smoothly adjust the torque output of the motor to avoid instability caused by sudden changes or step changes. S65. System recovery and fallback mechanism: After the system has been running stably for a period of time under the backup control strategy, and the torque deviation has returned to the normal range: the deviation is less than 10%, try to restore to the main control strategy. If the recovery process cannot run stably: the torque deviation exceeds the standard again, the system will enter the backup control state again and continue to smoothly transition through the feedforward control algorithm; S66. Real-time feedback and adjustment: During the whole process, the system continuously adjusts the control strategy through real-time feedback signals: vehicle dynamic data, vehicle speed, wheel speed, brake pedal depth, to ensure that the stability of the vehicle is not affected during the switching process.

4. A vehicle braking torque regulation system based on multiple input signals, characterized in that: The vehicle braking torque regulation system based on multiple input signals is used to implement any one of the vehicle braking torque regulation methods based on multiple input signals as claimed in claims 1-3; the vehicle braking torque regulation system based on multiple input signals comprises: a signal acquisition module, a signal preprocessing and fusion module, a multi-system collaborative arbitration mechanism module, a torque gradient dynamic optimization module, a collaborative control module, a fault detection module, and a real-time regulation module; The signal acquisition module collects data through various sensors to accurately identify the vehicle status, driver's operating intention and road conditions; Signal preprocessing and fusion module: Filter, denoise and normalize various collected signals: vehicle speed, pedal depth, wheel speed to ensure signal quality and stability, fuse multiple signals, and calibrate the errors of different sensors through algorithms; Multi-system collaborative arbitration mechanism module: dynamically calculates the activation priority and weight of IEBS, AEBS, and ABS based on vehicle speed, road friction coefficient, and driver operation intention factors; Torque gradient dynamic optimization module: By analyzing the spectrum of the motor back-EMF signal, the motor load and road adhesion are judged, and then the road state is estimated. According to the road state and pitch angle information, the braking gradient is dynamically adjusted; Collaborative control module: Decomposes the torque request into two components: mechanical braking and electric braking, based on the braking force demand requested by IEBS; Fault detection module: real-time monitoring of the deviation between the IEBS requested torque and the actual motor output torque. When the deviation exceeds the set threshold: 10%-15%, fault detection is triggered; Real-time adjustment module: adjusts the braking torque according to the real-time vehicle dynamic information: acceleration, pitch angle, and vehicle speed.

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