EMB position self-learning method for road risk identification

Abstract

The invention is suitable for the technical field of electronic mechanical brake control, and provides an EMB position self-learning method for road risk identification, and the method can monitor the contact position of a brake shoe and a brake disc through the current and other physical signal states of an EMB motor in the vehicle running process. And an expected braking force request command is accurately responded according to a brake rigidity fitting model. According to the method, the initial position of the piston can be calibrated before next braking through real-time operation in the running process of the vehicle, the manufacturing cost of a force sensor and related connecting accessories can be saved, the loss conditions of a brake shoe and a brake disc can be judged in advance, and an estimation basis can be provided for safety redundancy of a system.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic mechanical brake control, and in particular relates to an EMB position self-learning method for road risk identification. Background Art

[0002] Electro-mechanical Brake (EMB) technology was first used in the A-10 Thunderbolt II attack aircraft by the American company Roller Company in the 1980s. So far, many international companies have invested in the research and development of EMB technology for automobiles. The reason why it has not been quickly applied in automobiles is mainly due to the miniaturization of the structure, the accuracy of the braking force control and the reliability of the structure. With the development and progress of technology, the technical bottlenecks such as the miniaturization of servo motors, the precision of vector control, the precision of sensor technology, the enhancement of electronic computing capabilities, and the improvement of the electromagnetic compatibility and temperature resistance of motors have been broken through, making it possible for electro-mechanical brake technology to enter the automotive field.

[0003] The difference between electronic mechanical brakes and current automotive brake technology is that they eliminate the complex oil system to reduce environmental pollution, achieve clamping / release through a servo motor directly driven brake, and use a new wire-controlled brake system driven by a servo motor that converts the motor torque into brake clamping force through a gear mechanism. Compared with the hydraulic braking system, the electronic mechanical brake has obvious advantages: it simplifies the integrated complex control architecture of the hydraulic braking system's "mechanical-electrical-hydraulic-electromagnetic" multi-physical fields, eliminates the brake fluid in the existing vehicle braking system as an energy transfer medium, and can avoid corrosive brake oil from polluting the environment; the electronic mechanical brake uses an electronic brake pedal, which can achieve brake pedal decoupling and help improve the efficiency of brake energy recovery; the electronic mechanical brake is compatible with both the service brake system and the parking brake system, and no longer requires an independent parking brake mechanism; a vehicle is equipped with four electronic mechanical brakes at the same time, which can improve the degree of braking redundancy and reduce the probability of braking failure; the electronic mechanical brake has a more flexible system layout in terms of structural layout, and the modular design is easy to install and maintain; the four wheels have independent braking force adjustment capabilities and fast response speed, which can better realize vehicle dynamics control technologies such as ABS, TCS, and VDC.

[0004] During the use of the existing EMB brakes, the brake shoes and brake discs will be worn out due to friction, and the thickness will gradually decrease, causing the nominal zero position of the working state to change, and then the recognition of the zero drag gap will deviate. If the zero drag gap of the working zero point is large, the braking response time will be long, and if the gap is small, braking drag will occur. For EMB systems without clamping force sensors, the nominal zero point deviation will cause the braking force control to be unstable, resulting in poor vehicle stability when the four-wheel braking force is coordinated, posing a fatal risk to driving safety. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide an EMB position self-learning method for road risk identification, aiming to solve the problems raised in the above background technology.

[0006] The embodiment of the present invention is implemented as follows: a method for self-learning EMB position for road risk identification, comprising the following steps: Step 1: After the driver completes a braking system operation, the vehicle's onboard environmental perception system determines that the vehicle is in a safe longitudinal driving environment, that is, there are no vehicles or other traffic participants within the preset safe time range ahead. The vehicle is controlled by the driver or the intelligent driving system, and the EMB position self-learning program can be activated at this time.

[0007] Step 2: Control the EMB motor to push the piston to clamp the caliper at a preset speed and monitor the motor current signal. When the motor current reaches the set threshold current, record the current position. Then, according to the set disc gap value, retract the piston to the working zero point and reset the motor movement position.

[0008] Step 3: The driver or the intelligent driving system takes over the braking system.

[0009] According to a further technical solution, the environmental perception system includes a visual camera or a millimeter-wave radar or a combination of the two sensors.

[0010] A further technical solution is that in step 2, the current observation of the motor is realized by a three-resistance sampling method, sampling resistors are connected in series to the three lower bridge arms of the full-bridge inverter circuit respectively, and the current information is directly obtained by measuring the three-phase current; The motor current uses the first-order low-pass filtering method and the software filtering method to realize current signal processing.

[0011] According to a further technical solution, in step 2, the brake system records the offset before and after the position is reset during the self-learning action execution; According to the design size of brake shoe and brake disc, the driver is reminded to check and maintain the EMB brake when the critical value is reached.

[0012] A further technical solution is that in step 2, during the position self-learning process, when the driver temporarily operates the brake system, the self-learning program is jumped out, and the driver's braking request is executed according to the last self-learning state; Among them, the driver's temporary operation is observed through the brake pedal opening and the brake pedal potential energy signal.

[0013] Another object of the embodiment of the present invention is to provide an EMB embedded controller for road risk identification, based on the above-mentioned EMB position self-learning method for road risk identification, including an EMB position self-learning framework, wherein the EMB position self-learning framework includes a signal conditioning module, a position self-learning function module, a braking force closed-loop control function module, a motor three-closed-loop control algorithm module, a motor drive interface module, and a brake disc maintenance prompt module; The signal conditioning module is used to filter the internal and external signals of the EMB embedded controller, retain valid signals, and process the signal data type; The position self-learning function module is used to implement the aforementioned method flow; The braking force closed-loop control function module is used to convert the external braking force request into the motor control target. The external braking force request information is the brake pedal signal or the intelligent driving brake command signal output by the signal conditioning module; The motor three-loop closed-loop control algorithm module is used to drive the motor, and is implemented using the control strategy of the position loop, speed loop, and current loop; The motor drive interface module is used to apply the PWM modulation signal output by the motor three-loop control algorithm module to the drive chip hardware to send drive current to the motor; The brake disc maintenance reminder module is used to record the position adjustment results after the position self-learning function module is completed, and compare them with the initial thickness of the brake disc and brake shoe. When it is lower than the preset deviation value, an alarm message is issued to remind the driver to perform vehicle maintenance.

[0014] A further technical solution also includes external signals, which include: the position of a front obstacle, an intelligent driving braking command, a motor position sensor, a brake pedal signal, an accelerator pedal signal and a vehicle speed signal.

[0015] A further technical solution also includes an EMB drive motor, which is an execution unit of the EMB system. A spindle angle sensor is provided on the spindle of the EMB drive motor for measuring the rotation angle of the motor.

[0016] A further technical solution also includes an HMI, which is a human-machine interaction interface of the vehicle and is used to display alarm information to prompt the driver to maintain the vehicle brake disc.

[0017] The embodiment of the present invention provides an EMB position self-learning method for road risk identification. The method can monitor the contact position between the brake shoe and the brake disc through the physical signal state of the EMB motor current and other physical signals while the vehicle is moving, and accurately respond to the expected braking force request command according to the brake stiffness fitting model. By completing it in real time while the vehicle is moving, the initial position of the piston can be calibrated before the next braking, which can save the manufacturing cost of the force sensor and related connection accessories, and can judge the wear of the brake shoe and the brake disc in advance, and can provide an estimation basis for system safety redundancy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Flow chart of an EMB position self-learning method for road risk identification provided by an embodiment of the present invention Figure 2 A schematic diagram of the structure of an EMB embedded controller for road risk identification provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the principle of current identification position.

[0019] In the attached figure: EMB position self-learning framework K1; signal conditioning module K11; position self-learning function module K12; braking force closed-loop control function module K13; motor three-closed-loop control algorithm module K14; motor drive interface module K15; brake disc maintenance reminder module K16; external signal K2; front obstacle position K21; intelligent driving braking command K22; motor position sensor K23; brake pedal signal K24; accelerator pedal signal K25; vehicle speed signal K26; EMB drive motor K3; HMI K4. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0022] like Figure 1 As shown, an EMB position self-learning method for road risk identification provided by an embodiment of the present invention includes the following steps: Step 1: After the driver completes a braking system operation, the vehicle's onboard environmental perception system (visual camera or millimeter wave radar or a combination of the two sensors) determines that the vehicle is in a safe longitudinal driving environment, that is, there are no vehicles or other traffic participants within the preset safe time range ahead. The vehicle is controlled by the driver or the intelligent driving system, and the EMB position self-learning program can be activated at this time.

[0023] Step 2: Control the EMB motor to push the piston to clamp the caliper at a preset speed and monitor the motor current signal. When the motor current reaches the set threshold current, record the current position. Then, according to the set disc gap value, retract the piston to the working zero point and reset the motor movement position.

[0024] Step 3: The driver or the intelligent driving system takes over the braking system.

[0025] As a preferred embodiment of the present invention, in step 2, the current observation of the motor is realized by a three-resistance sampling method, that is, sampling resistors are connected in series to the three lower bridge arms of the full-bridge inverter circuit respectively, and more accurate current information is directly obtained by measuring the three-phase current. The motor current adopts a first-order low-pass filtering method and a software filtering method to realize current signal processing, thereby improving the accuracy of monitoring current.

[0026] As a preferred embodiment of the present invention, in step 2, the brake system records the offset before and after the position is reset during the self-learning action. According to the design dimensions of the brake shoe and the brake disc, the driver is reminded to check and maintain the EMB brake when a critical value is reached.

[0027] As a preferred embodiment of the present invention, in step 2, during the position self-learning process, when the driver temporarily operates the brake system, the self-learning program is jumped out, and the driver's braking request is executed according to the last self-learning state. The driver's temporary operation is observed through the brake pedal opening and the brake pedal potential energy signal.

[0028] like Figure 2 As shown, an EMB embedded controller for road risk identification provided by an embodiment of the present invention, based on the EMB position self-learning method for road risk identification, includes an EMB position self-learning framework K1, wherein the EMB position self-learning framework K1 includes a signal conditioning module K11, a position self-learning function module K12, a braking force closed-loop control function module K13, a motor three-closed-loop control algorithm module K14, a motor drive interface module K15, and a brake disc maintenance prompt module K16; The signal conditioning module K11 is used to filter the internal and external signals of the EMB embedded controller, retain valid signals, and process the signal data type, so that the embedded system can process data efficiently; The position self-learning function module K12 is used to implement the aforementioned method flow. Step 1 is the external signal input. According to the external signal provided by the intelligent driving system, it is judged whether the conditions for activating the position self-learning are met; Step 2 is the activation position self-learning process; First, the EMB motor is controlled to push the piston to clamp at a constant speed at the preset target speed; Then, the motor drive current value is used as the observation signal. When the preset target threshold current is reached, it is judged that the brake disc is in contact, that is, the nominal zero point is reached, and the current position information is recorded at this time; Finally, the motor is controlled to retreat and reach the preset target position, that is, the working zero point, and the motor position is set to zero. This process requires the position error of this adjustment to be accumulated for the brake disc maintenance reminder function. Step 3 is to jump out of the program after the self-learning position is completed, and the driver or the intelligent driving system takes over the braking system until the next program is activated. When the driver operates the brake pedal or the intelligent driving system issues a brake request command during the operation of the position self-learning program, the program will jump out directly and wait for the next program activation.

[0029] The braking force closed-loop control function module K13 is used to convert the external braking force request into a motor control target. The external braking force request information can come from the brake pedal signal output by the signal conditioning module K11 or the intelligent driving braking command signal.

[0030] The motor three-loop closed-loop control algorithm module K14 is used to drive the motor and is implemented using the control strategy of the position loop, speed loop and current loop.

[0031] The motor drive interface module K15 is used to apply the PWM modulation signal output by the motor three-closed-loop control algorithm module K14 to the drive chip hardware to send a drive current to the motor K3; The brake disc maintenance reminder module K16 is used to record the position adjustment results after the position self-learning function module K12 is completed, and compare it with the initial thickness of the brake disc and brake shoe. When it is lower than the preset deviation value, an alarm message is issued to remind the driver to perform vehicle maintenance.

[0032] The external signal K2 mainly includes: the position of the obstacle in front K21, the intelligent driving braking command K22, the motor position sensor K23, the brake pedal signal K24, the accelerator pedal signal K25 and the vehicle speed signal K26.

[0033] Among them, the front obstacle position K21 and the intelligent driving braking command K22 come from the vehicle intelligent driving system CAN bus 1; the motor position sensor K23 comes from the spindle angle sensor K31 inside the EMB motor, and the signal is transmitted by wire connection; the brake pedal signal K24, the accelerator pedal signal K25 and the vehicle speed signal K26 come from the vehicle controller CAN bus 2.

[0034] The EMB drive motor K3 is the execution unit of the EMB system. A spindle angle sensor K31 is provided on the spindle of the EMB drive motor K3 to measure the rotation angle of the motor.

[0035] HMI K4 is the vehicle's human-machine interface, which is used to display alarm information and remind the driver to maintain the vehicle's brake disc.

[0036] As a preferred embodiment of the present invention, the principle of the position self-learning function module K12 is as follows Figure 3 As shown, the motor drive current Iq is used as the observation current. When the motor slowly pushes the piston forward, the current is relatively low < A1. When the disc contacts, the motor enters the load working stage, and the observation current rises rapidly. The preset current A1 is the identification current. When the observation current ≥ A1, the motor stops and the current stable position is recorded. Then the motor is retracted to the working zero point according to the preset target position L1 to wait for the next braking request. L2 is the error of this position self-learning calibration, which can be positive or negative.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A self-learning method for EMB position for road risk identification, characterized in that: The following steps are involved: Step 1: After the driver completes a braking system operation, the vehicle's environmental perception system determines that the vehicle is in a safe longitudinal driving environment, that is, there are no vehicles or other traffic participants within the preset safe time range ahead; the vehicle is controlled by the driver or the intelligent driving system, and the EMB position self-learning program is activated at this time; Step 2: Control the EMB motor to push the piston to clamp the caliper at a preset speed, and monitor the motor current signal; When the motor current reaches the set threshold current, the current position is recorded; then according to the set disc gap value, the piston is retracted to the working zero point and the motor movement position is reset; Step 3: The driver or the intelligent driving system takes over the braking system.

2. The EMB position self-learning method for road risk identification according to claim 1 is characterized in that: The environmental perception system includes a visual camera or a millimeter-wave radar or a combination of the two sensors.

3. The EMB position self-learning method for road risk identification according to claim 2 is characterized in that: In step 2, the current observation of the motor is realized by a three-resistance sampling method, where sampling resistors are connected in series to the three lower bridge arms of the full-bridge inverter circuit respectively, and current information is directly obtained by measuring the three-phase current; The motor current uses the first-order low-pass filtering method and the software filtering method to realize current signal processing.

4. The EMB position self-learning method for road risk identification according to claim 1 is characterized in that: In step 2, the brake system records the offset before and after the position is reset during the self-learning action execution; According to the design size of brake shoe and brake disc, the driver is reminded to check and maintain the EMB brake when the critical value is reached.

5. The EMB position self-learning method for road risk identification according to claim 1 is characterized in that: In step 2, during the position self-learning process, when the driver temporarily operates the brake system, the self-learning program is jumped out, and the driver's braking request is executed according to the last self-learning state; Among them, the driver's temporary operation is observed through the brake pedal opening and the brake pedal potential energy signal.

6. An EMB embedded controller for road risk identification, based on the EMB position self-learning method for road risk identification described in claims 1-5, characterized in that: It includes an EMB position self-learning framework, which includes a signal conditioning module, a position self-learning function module, a braking force closed-loop control function module, a motor three-closed-loop control algorithm module, a motor drive interface module, and a brake disc maintenance prompt module; The signal conditioning module is used to filter the internal and external signals of the EMB embedded controller, retain valid signals, and process the signal data type; The position self-learning function module is used to implement the aforementioned method flow; The braking force closed-loop control function module is used to convert the external braking force request into the motor control target. The external braking force request information is the brake pedal signal or the intelligent driving brake command signal output by the signal conditioning module; The motor three-loop closed-loop control algorithm module is used to drive the motor, and is implemented using the control strategy of the position loop, speed loop, and current loop; The motor drive interface module is used to apply the PWM modulation signal output by the motor three-loop control algorithm module to the drive chip hardware to send drive current to the motor; The brake disc maintenance reminder module is used to record the position adjustment results after the position self-learning function module is completed, and compare them with the initial thickness of the brake disc and brake shoe. When it is lower than the preset deviation value, an alarm message is issued to remind the driver to perform vehicle maintenance.

7. The EMB embedded controller for road risk identification according to claim 6, characterized in that: It also includes external signals, which include: the position of the obstacle in front, the intelligent driving braking command, the motor position sensor, the brake pedal signal, the accelerator pedal signal and the vehicle speed signal.

8. The EMB embedded controller for road risk identification according to claim 6, characterized in that: It also includes an EMB drive motor, which is an execution unit of the EMB system. A spindle angle sensor is arranged on the spindle of the EMB drive motor for measuring the rotation angle of the motor.

9. The EMB embedded controller for road risk identification according to claim 6, characterized in that: It also includes an HMI, which is a human-machine interaction interface of the vehicle and is used to display alarm information to prompt the driver to maintain the vehicle brake disc.

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