A vehicle braking torque adjustment method and system based on multiple input signals

Abstract

The present invention discloses a vehicle braking torque adjustment method and system based on multi-input signals, aiming to improve the safety, driving experience and braking efficiency of vehicles in complex environments. The system collects the vehicle driving state, driver intention and road surface friction conditions in real time through multiple sensor modules, providing accurate data support for braking control. According to these diversified inputs, the system can intelligently identify the road surface state and dynamically adjust the braking torque gradient, optimize the braking response, and ensure the stability of the vehicle in various driving environments. By using a feedforward control algorithm to smooth the mutation and step phenomena during the braking process, the driving experience is improved, and the instability caused by the system switching control strategies is avoided. Based on the motor back electromotive force spectrum analysis, the system can accurately adjust the energy recovery and improve the energy utilization efficiency of the vehicle. It provides strong support for enhancing the braking performance, safety and autonomous driving technology of modern vehicles.

Description

Technical Field

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

[0002] In modern vehicle braking systems, safety and driving experience are of utmost importance. With the continuous development of automotive intelligence and autonomous driving technologies, traditional braking systems have gradually been unable to meet the safety requirements in complex environments. To improve braking efficiency, avoid accidents, and enhance the driving experience, more intelligent and dynamic control strategies are needed to adjust the braking torque of the vehicle. Different road surface friction coefficients affect braking performance. On wet and slippery roads, vehicles are more prone to skidding, 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 operation intention (such as gentle braking, emergency braking, etc.), are prone to misjudgment, and affect the driver's control experience, especially in complex traffic environments. Summary of the Invention

[0003] A method for adjusting vehicle braking torque based on multiple input signals includes the following steps;

[0004] S1. Signal input acquisition: Obtain the real-time vehicle driving speed through vehicle speed sensors (such as wheel speed sensors, GPS, etc.), monitor the driver's braking pedal input, and obtain its position (pedal depth, pressure, etc.) through a 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 slip or lock-up. On-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 surface friction and climate information through on-vehicle sensors or external systems;

[0005] 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 passed through a filter 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 approaching lock-up or skidding;

[0006] S3. Multi - system collaborative arbitration mechanism: According to the real - time vehicle speed, road surface adhesion coefficient, and driver's operation intention, use machine - learning algorithms to calculate the activation weights of IEBS (Intelligent Electronic Brake System), AEBS (Automatic Emergency Braking System), and ABS (Anti - lock Braking System). Specifically, based on the current state of the vehicle, the system assigns priorities to different subsystems to ensure the maximization of braking efficiency:

[0007] In high - speed and emergency braking situations, the priority of AEBS increases;

[0008] In low - speed and slippery road surface situations, the priority of ABS increases;

[0009] When ABS is activated, the system dynamically calculates the feedback torque threshold according to the real - time wheel slip rate and the current road surface friction characteristics, and adjusts the control strategy of ABS, breaking through the traditional "full prohibition" strategy, allowing appropriate feedback torque, and improving braking efficiency;

[0010] S4. Dynamic optimization of torque gradient: Through the spectrum analysis of the motor back - electromotive force, combined with the road surface state (such as wet or dry road surface), the torque gradient is adjusted in real - time. When IEBS brakes, the system sets a basic gradient (such as 620 Nm / s) and corrects the gradient value according to the vehicle pitch - angle sensor data, with a range within ±20%;

[0011] S5. Energy recovery collaborative control: When IEBS requests braking force, the system decomposes the requested torque into two components: mechanical braking and electric braking:

[0012] Mechanical braking: Provided by the Electronic Hydraulic Braking System (EHB);

[0013] Electric braking: Achieved through motor feedback, especially during light braking and deceleration;

[0014] When the ABS system is activated, to maintain the adhesion between the tire and the road surface, a pulsed feedback strategy is adopted. The intermittent control at the 5 - ms level can reduce the instantaneous impact force generated by the braking system and help maintain stability;

[0015] S6. Fault - safe interaction mechanism: When there is a large deviation (10% - 15%) between the torque requested by IEBS and the actual output torque of the motor, cross - verify the signals between each subsystem 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, a feed - forward control algorithm is used to smooth the instability phenomena of torque occurrence: mutation, step - change, and reduce the impact of mutation on the driving experience and vehicle stability;

[0016] S7. Braking Torque Adjustment Strategy: Based on various signals collected in real time (such as vehicle speed, road surface information, driver intention, wheel speed, etc.), the system automatically adjusts the braking torque:

[0017] When performing emergency braking or driving at high speed, the system provides stronger braking force by increasing the priority of AEBS and ABS;

[0018] When driving at low speed, IEBS is preferentially used to ensure energy recovery and reduce the driver's burden;

[0019] Under slippery or poor road conditions, the system dynamically adjusts the braking gradient and feedback torque to prevent the vehicle from losing control

[0020] S8. Feedback and Adjustment: According to the real-time vehicle dynamics and input signals, adjust the braking torque. During emergency braking, automatically adjust a higher braking force according to the acceleration sensor information.

[0021] Furthermore, a vehicle braking torque adjustment method based on multiple input signals,

[0022] In step S4, through the spectrum analysis of the motor back electromotive force, combined with the road surface state: slippery and dry road surfaces, the torque gradient is adjusted in real time. The specific steps are as follows;

[0023] S41. Motor Back Electromotive Force Spectrum Analysis: Obtain the real-time working state of the motor through back electromotive force measurement, including motor speed, load condition, and possible changes in road surface adhesion. Use the Fourier transform spectrum analysis method to analyze the back electromotive force signal, convert the time-domain signal into a frequency-domain signal, judge the change of the road surface state, estimate the load state of the motor according to the frequency characteristics and vibration modes appearing in the spectrum, and then infer the road surface adhesion situation;

[0024] Slippery road surface: The high-frequency noise is strong, and the motor load fluctuates greatly;

[0025] Dry road surface: The spectrum is relatively stable, and the motor load fluctuates little;

[0026] S42. Road Surface State Judgment and Classification:

[0027] Slippery road surface: The slippery road surface has a low friction coefficient, and the tires are prone to slipping during braking. The feedback torque of the motor needs to be smaller;

[0028] Dry road surface: The dry road surface has strong friction, and the motor can output a large braking force;

[0029] Road surface state classification: Using the spectrum analysis result: Based on the motor back electromotive force spectrum, the system can judge the friction state of the current road surface in real time and classify it as slippery, dry, and ice and snow road surfaces;

[0030] S43. Torque gradient dynamic adjustment: Basic gradient setting. Under normal conditions, the reference torque gradient, such as 620 Nm / s, is used to adjust the torque response of the motor. The basis for gradient adjustment is:

[0031] Slippery road surface: On a slippery road surface, due to the small friction force, the tires are prone to slipping. The braking feedback of the motor needs to be smoother, and the torque gradient needs to be reduced. The set basic gradient value of 620 Nm / s is adjusted down to within the range of ±20%;

[0032] Dry road surface: On a dry road surface, the friction force is stronger, the braking feedback needs to be more rapid, and the motor can output a stronger feedback torque to increase the torque gradient, which is adjusted up to ±20% on a dry road surface.

[0033] Torque gradient adjustment strategy:

[0034] Dynamically correct the gradient value: Through the spectrum analysis of the motor back electromotive force and the estimation of the road surface condition, the gradient is adjusted in real time. When the system recognizes a slippery road surface, the gradient is adjusted to a lower value: ±10% - 20%, and when on a dry road surface, it is adjusted to a higher value: ±15% - 25%;

[0035] S44. Torque output adjustment: Under slippery road surface conditions, the system will dynamically reduce the feedback torque to avoid excessive braking torque of the motor, which may cause the tires to lock or slip;

[0036] Under dry road surface conditions, the system will enhance the feedback torque to increase the braking force of the motor to achieve a more efficient braking effect;

[0037] S45. Verification and optimization: Continuously optimize the adjustment process through the feedback loop to ensure that the torque output matches the vehicle's dynamic state and road surface conditions. At the end of each control cycle, automatic adjustment is performed based on the vehicle's motion state, torque deviation, and feedback information. Field tests are carried out under various road conditions, and the road surface recognition algorithm and gradient adjustment strategy are adjusted through feedback to ensure the stability and braking performance of the system under different driving conditions.

[0038] Furthermore, a vehicle braking torque adjustment method based on multiple input signals

[0039] In step S6, the feedforward control algorithm is used to smooth the unstable phenomena of torque occurrence: mutations and steps, and reduce the impact of mutations on the driving experience and vehicle stability. The specific steps are as follows;

[0040] S61. Torque deviation detection and tolerance interval setting: Monitoring signal acquisition:

[0041] IEBS requested torque: Obtain the requested braking torque from the IEBS system;

[0042] Actual output torque of the motor: Obtain 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 where T IEBS is the IEBS requested torque, T motor is the actual output torque of the motor, and Δ T is the deviation between the requested torque and the actual output torque of the motor. T thres is the upper limit of the set threshold range. Within the range of 10% - 15% of the deviation value, the system will cross - verify through the CAN bus to check the data consistency between subsystems. When the deviation within the tolerance zone lasts for a long time, it is determined as abnormal;

[0043] S62. Verify signal consistency: Cross - verify the real - time signals of IEBS, the motor control system, and the ABS subsystem through the CAN bus. Adopt the method of redundant signal verification to compare the signals from different sources: the torque requested by IEBS, the wheel speed sensor feedback, and the feedback from other control systems. When signal inconsistency is detected: there is a large deviation between the torque requested by IEBS and the motor feedback torque and it exceeds 15%, the system automatically marks it as a fault. When the deviation continuously exceeds 10% - 15% and the signal consistency is abnormal, the system enters the standby control strategy;

[0044] S63. Switch to the standby control strategy: The standby control strategy includes falling back to the standard braking control: pure mechanical braking, simple electro - hydraulic braking. In the standby mode, the torque output is restricted to maintain the safety of the vehicle and avoid system failure caused by excessive braking force;

[0045] S64. Feed - forward control algorithm: According to the input signals of the IEBS requested torque, motor speed, and vehicle dynamic information, pre - calculate the torque value to be applied in the next control cycle. Use the vehicle dynamic model: vehicle suspension, powertrain, torque transfer model to calibrate the parameters of the feed - forward control, predict the target torque in advance. When the control strategy switches, calculate the expected torque change amount through the feed - forward control algorithm and adjust the torque output of the motor in advance to reduce the torque mutation. The specific method is: T a = T motor +Δ T feed, where T a is the expected torque change, Δ T feed is the adjustment calculated according to the feedforward control. When switching the standby control strategy, the feedforward control will slowly and smoothly adjust the torque output of the motor to avoid instability caused by sudden changes or step changes;

[0046] S65. System Recovery and Fallback Mechanism: After the system has been operating stably under the standby 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%), attempt to restore to the main control strategy. When the restoration process cannot operate stably (for example, the torque deviation exceeds the standard again), the system will enter the standby control state again and continue to smoothly transition through the feedforward control algorithm;

[0047] S66. Real-time Feedback and Regulation: Throughout the process, the system continuously adjusts the control strategy through real-time feedback signals: vehicle dynamic data, vehicle speed, wheel speed, and brake pedal depth to ensure that the stability of the vehicle is not affected during the switching process.

[0048] A vehicle braking torque adjustment system based on multi-input signals, where the vehicle braking torque adjustment system based on multi-input signals is used for any vehicle braking torque adjustment method based on multi-input signals; the vehicle braking torque adjustment system based on multi-input signals includes: 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 adjustment module;

[0049] Signal Acquisition Module: Collect data through various sensors to accurately identify the vehicle state, driver's operation intention, and road conditions;

[0050] Signal Preprocessing and Fusion Module: Filter, denoise, and normalize the collected signals: vehicle speed, pedal depth, and wheel speed to ensure the quality and stability of the signals, and fuse multiple signals to calibrate the errors of different sensors through algorithms;

[0051] Multi-System Collaborative Arbitration Mechanism Module: Dynamically calculate the activation priorities and weights of IEBS, AEBS, and ABS according to factors such as vehicle speed, road surface friction coefficient, and driver's operation intention;

[0052] Torque Gradient Dynamic Optimization Module: Judge the motor load and road adhesion through the spectral analysis of the motor back electromotive force signal, and then estimate the road surface state. Dynamically adjust the braking gradient according to the road surface state and pitch angle information;

[0053] Collaborative Control Module: Decompose the torque request into two components of mechanical braking and electric braking according to the braking force demand requested by IEBS;

[0054] Fault detection module: Monitor the deviation between the IEBS requested torque and the actual motor output torque in real time. When the deviation exceeds the set threshold: 10% - 15%, trigger fault detection;

[0055] Real-time adjustment module: Adjust the braking torque according to real-time vehicle dynamic information: acceleration, pitch angle, vehicle speed.

[0056] Advantages of the present invention: By monitoring multiple signals such as vehicle speed, pedal depth, wheel speed, and road surface condition in real time, the system can quickly and accurately adjust the braking torque to cope with different driving environments (such as wet and dry road surfaces), greatly reducing the accident risk caused by insufficient or excessive braking. By analyzing the back electromotive force spectrum of the motor and the change of road surface friction, the system can identify the road surface condition in real time and adjust the braking strategy, thereby preventing wheel slip or lock-up and ensuring the stability and safety of the vehicle in complex environments. By monitoring the consistency of sensor data in real time, detecting and correcting system or sensor failures in a timely manner, ensuring that the system can automatically switch to the backup control strategy when an abnormality occurs, and minimizing the safety hazards caused by sensor failures or false alarms. The system smooths the change of braking torque through the feedforward control algorithm, avoiding sudden changes or step phenomena. Especially when the system switches the control strategy, it can reduce instability and provide a smooth braking response, thus enhancing the driver's comfort and trust. Brief Description of the Drawings

[0057] Figure 1 It is a flowchart of a vehicle braking torque adjustment method based on multiple input signals; Detailed Embodiments

[0058] A vehicle braking torque adjustment method based on multiple input signals, comprising the following steps;

[0059] S1. Signal input and acquisition: Obtain the real-time vehicle driving speed through vehicle speed sensors (such as wheel speed sensors, GPS, etc.), monitor the driver's braking pedal input, and obtain its position (pedal depth, pressure, etc.) through the pedal sensor. Wheel speed: Monitor the rotation speed of each wheel through the wheel speed sensors (ABS sensors) of each wheel to judge whether there is a risk of wheel slip or lock-up. On-vehicle control system information: such as engine speed, power system status, etc., to help understand the overall power status of the vehicle. Road surface condition information: Provide road surface friction and climate information through on-vehicle sensors or external systems;

[0060] 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 passes through a filter 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 approaching lock-up or skidding.

[0061] S3. Multi-System Cooperative Arbitration Mechanism: According to the real-time vehicle speed, road surface adhesion coefficient, and driver's operation intention, use machine learning algorithms to calculate the activation weights of IEBS (Intelligent Electronic Brake System), AEBS (Automatic Emergency Braking System), and ABS (Anti-lock Braking System). Specifically, based on the current state of the vehicle, the system assigns priorities to different subsystems to ensure the maximization of braking efficiency:

[0062] In high-speed and emergency braking situations, the priority of AEBS increases;

[0063] In low-speed and slippery road surface situations, the priority of ABS increases;

[0064] When ABS is activated, the system dynamically calculates the feedback torque threshold according to the real-time wheel slip rate and the current road surface friction characteristics, and adjusts the control strategy of ABS, breaking through the traditional "full prohibition" strategy, allowing appropriate feedback torque, and improving braking efficiency;

[0065] S4. Dynamic Optimization of Torque Gradient: Through the spectrum analysis of the motor back electromotive force, combined with the road surface state (such as wet or dry road surface), the torque gradient is adjusted in real time. When IEBS brakes, the system sets a basic gradient (such as 620 Nm / s) and corrects the gradient value according to the data of the vehicle pitch angle sensor, with a range of ±20%;

[0066] S5. Cooperative Control of Energy Recovery: When IEBS requests braking force, the system decomposes the requested torque into two components: mechanical braking and electric braking:

[0067] Mechanical braking: Provided by the Electronic Hydraulic Braking System (EHB);

[0068] Electric braking: Achieved through motor feedback, especially during light braking and deceleration;

[0069] When the ABS system is activated, to maintain the adhesion between the tire and the road surface, a pulsed feedback strategy is adopted. The intermittent control at the 5 ms level can reduce the instantaneous impact force generated by the braking system and help maintain stability;

[0070] S6. Fail-safe interaction mechanism: When there is a large deviation (10%-15%) between the IEBS-requested torque and the actual motor output torque, cross-verify the signals between subsystems through the CAN bus to ensure data consistency. If any abnormality is found, the system will automatically switch to the backup control strategy to ensure safety. When the system switches the control strategy, use the feedforward control algorithm to smooth out the instability phenomena of torque: mutation and step change, and reduce the impact of mutations on the driving experience and vehicle stability;

[0071] S7. Braking torque adjustment strategy: According to various signals collected in real time (such as vehicle speed, road surface information, driver intention, wheel speed, etc.), the system automatically adjusts the braking torque:

[0072] When emergency braking or driving at high speed, the system provides stronger braking force by increasing the priority of AEBS and ABS;

[0073] When driving at low speed, preferentially use IEBS to ensure energy recovery and reduce the driver's burden;

[0074] Under wet or poor road conditions, the system dynamically adjusts the braking gradient and feedback torque to prevent the vehicle from getting out of control

[0075] S8. Feedback and adjustment: Adjust the braking torque according to the real-time vehicle dynamics and input signals. During emergency braking, automatically adjust a higher braking force according to the acceleration sensor information.

[0076] Furthermore, a vehicle braking torque adjustment method based on multiple input signals,

[0077] In step S4, through the spectrum analysis of the motor back electromotive force, combined with the road surface conditions: wet and dry road surfaces, the torque gradient is adjusted in real time. The specific steps are as follows;

[0078] S41. Motor back electromotive force spectrum analysis: Obtain the real-time working state of the motor through back electromotive force measurement, including motor speed, load conditions, and possible changes in road surface adhesion. Use the Fourier transform spectrum analysis method to analyze the back electromotive force signal, convert the time-domain signal into a frequency-domain signal, judge the change of road surface conditions, estimate the load state of the motor according to the frequency characteristics and vibration modes appearing in the spectrum, and then infer the road surface adhesion situation;

[0079] Wet road surface: High-frequency noise is strong, and the motor load fluctuates greatly;

[0080] Dry road surface: The spectrum is relatively stable, and the motor load fluctuates little;

[0081] S42. Road surface condition judgment and classification:

[0082] Slippery road surface: A slippery road surface has a low coefficient of friction. When braking, the tires are prone to skidding, and the feedback torque of the motor needs to be smaller;

[0083] Dry road surface: A dry road surface has strong friction, and the motor can output a large braking force;

[0084] Road surface state classification: Using the spectrum analysis result: Based on the back electromotive force spectrum of the motor, the system can real-time judge the friction state of the current road surface and classify it as slippery, dry, and ice and snow road surface;

[0085] S43. Dynamic adjustment of torque gradient: Basic gradient setting. Under normal conditions, the reference torque gradient: such as 620 Nm / s is used to adjust the torque response of the motor. The basis for gradient adjustment:

[0086] Slippery road surface: On a slippery road surface, due to the small friction, the tires are prone to skidding, and the braking feedback of the motor needs to be more gentle. The torque gradient needs to be reduced, and the set basic gradient value of 620 Nm / s is adjusted down to within the range of ±20%;

[0087] Dry road surface: On a dry road surface, the friction is strong, the braking feedback needs to be more rapid, the motor can output a stronger feedback torque, and the torque gradient is increased. On a dry road surface, it is adjusted up to ±20%,

[0088] Torque gradient adjustment strategy:

[0089] Dynamically correct the gradient value: Through the back electromotive force spectrum analysis of the motor and the estimation of the road surface state, the gradient is adjusted in real-time. When the system recognizes a slippery road surface, the gradient is adjusted to a lower value: ±10% - 20%, while when on a dry road surface, it is adjusted to a higher value: ±15% - 25%;

[0090] S44. Torque output adjustment: Under the condition of a slippery road surface, the system will dynamically reduce the feedback torque to avoid excessive braking torque of the motor, resulting in tire locking or skidding;

[0091] Under the condition of a dry road surface, the system will enhance the feedback torque and increase the braking force of the motor to achieve a more efficient braking effect;

[0092] S45. Verification and optimization: Continuously optimize the adjustment process through the feedback loop to ensure that the torque output matches the vehicle's dynamic state and road surface conditions. At the end of each control cycle, according to the vehicle's motion state, torque deviation, and feedback information, automatic adjustment is performed. Field tests are carried out under various road conditions, and the road surface recognition algorithm and gradient adjustment strategy are adjusted through feedback to ensure the stability and braking performance of the system under different driving conditions.

[0093] Furthermore, a vehicle braking torque adjustment method based on multi-input signals,

[0094] In step S6, a feedforward control algorithm is used to smooth the unstable phenomena of torque generation: mutations and steps, reducing the impact of mutations on the driving experience and vehicle stability. The specific steps are as follows;

[0095] S61. Torque deviation detection and tolerance interval setting: Monitoring signal acquisition:

[0096] IEBS requested torque: Obtain the requested braking torque from the IEBS system;

[0097] Actual motor output torque: Obtain 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 motor output torque: Δ T =∣ T IEBS - T motor ∣, and determine whether the deviation exceeds the set threshold interval of 10% - 15%. Δ T ≥ T thres , where T IEBS is the IEBS requested torque, T motor is the actual motor output torque, Δ T is the deviation between the requested torque and the actual motor output torque, T thres is the upper limit of the set threshold interval. Within the range of 10% - 15% of the deviation value, the system will cross - verify through the CAN bus to check the data consistency between subsystems. When the deviation within the tolerance zone lasts for a long time, it is determined as abnormal;

[0098] S62. Verify signal consistency: Cross - verify the real - time signals of the IEBS, motor control system, and ABS subsystem through the CAN bus. Adopt the method of redundant signal verification to compare signals from different sources: the torque requested by the IEBS, wheel speed sensor feedback, and feedback from other control systems. When signal inconsistency is detected: there is a large deviation between the torque requested by the IEBS and the motor feedback torque and it exceeds 15%, the system automatically marks it as a fault. When the deviation continuously exceeds 10% - 15% and the signal consistency is abnormal, the system enters the backup control strategy;

[0099] S63. Switch to the backup control strategy: The backup control strategy includes falling back to standard braking control: pure mechanical braking, simple electro - hydraulic braking. In the backup mode, torque output is restricted to maintain vehicle safety and avoid system failure caused by excessive braking force;

[0100] Feedforward control algorithm: Based on the input signals of IEBS requested torque, motor speed, and vehicle dynamic information, pre-calculate the torque value to be applied in the next control cycle. Use the vehicle dynamic model: vehicle suspension, powertrain, and torque transfer model to calibrate the parameters of the feedforward control, and predict the target torque in advance. When the control strategy switches, calculate the expected torque change through the feedforward control algorithm, and adjust the torque output of the motor in advance to reduce torque mutations. The specific method is as follows: T a = T motor +Δ T feed , where T a is the expected torque change, and Δ T feed is the adjustment amount calculated according to the feedforward control. When the standby control strategy switches, the feedforward control will slowly and smoothly adjust the torque output of the motor to avoid instability caused by mutations or step changes;

[0101] S65. System recovery and fallback mechanism: When the system has been running stably under the standby 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%), attempt to recover to the main control strategy. When the recovery process cannot run stably (for example, the torque deviation exceeds the standard again), the system will enter the standby control state again and continue to smoothly transition through the feedforward control algorithm;

[0102] S66. Real-time feedback and adjustment: Throughout the process, the system continuously adjusts the control strategy through real-time feedback signals: vehicle dynamic data, vehicle speed, wheel speed, and brake pedal depth to ensure that the stability of the vehicle is not affected during the switching process.

[0103] A vehicle braking torque adjustment system based on multiple input signals, where the vehicle braking torque adjustment system based on multiple input signals is used for any vehicle braking torque adjustment method based on multiple input signals; the vehicle braking torque adjustment system based on multiple input signals includes: 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 adjustment module;

[0104] Signal acquisition module: Collect data through various sensors to accurately identify the vehicle state, driver operation intention, and road surface conditions;

[0105] Signal preprocessing and fusion module: Filter, denoise, and normalize the collected signals: vehicle speed, pedal depth, and wheel speed to ensure the quality and stability of the signals, and fuse multiple signals to calibrate the errors of different sensors through algorithms;

[0106] Multi - system collaborative arbitration mechanism module: Dynamically calculate the activation priorities and weights of IEBS, AEBS, and ABS based on factors such as vehicle speed, road surface friction coefficient, and driver's operation intention;

[0107] Torque gradient dynamic optimization module: Judge the motor load and road adhesion by analyzing the spectrum of the motor back - electromotive force signal, and then estimate the road surface state. Dynamically adjust the braking gradient according to the road surface state and pitch angle information;

[0108] Collaborative control module: Decompose the torque request into two components: mechanical braking and electric braking according to the braking force demand requested by IEBS;

[0109] Fault detection module: Real - time monitor the deviation between the torque requested by IEBS and the actual output torque of the motor. When the deviation exceeds the set threshold: 10% - 15%, trigger fault detection;

[0110] Real - time adjustment module: Adjust the braking torque according to real - time vehicle dynamic information: acceleration, pitch angle, 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 road adhesion condition 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.

Images