Emb adaptive pressure following control method and system for abs operating condition optimization and application

Abstract

The EMB adaptive pressure following control method and system for ABS working condition optimization and application of the application relate to the field of vehicle control.S1: during each braking request, the q-axis current Iq and angular velocity ω are obtained, and an ABS activation signal, a target wheel cylinder pressure P_target and wheel speed signals of each wheel are received;S2: based on a dynamic model, the dynamic relationship between the q-axis current Iq and the angular velocity ω in a stable braking working condition without ABS is analyzed, and the recursive least squares method is used to estimate the equivalent rotational inertia J_est in the current working condition, the dynamic model being Jdω / dt=KtIq‑Tf‑F_clampr;S3: inertia torque compensation is calculated based on a feedforward model, the feedforward model being T_inertia=J_est(d²θ / dt²)_target;S4: damping torque is calculated based on a virtual damping model, the virtual damping model being T_damp=B_virtω;S5: the inertia torque compensation and the damping torque are superimposed on the PI current loop given to generate a new q-axis current instruction Iq_ref_FF.The application can improve the EMB pressure dynamic response capability in the ABS working condition, and reduce the energy consumption and heat generation of the EMB motor under high-frequency switching.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to an EMB adaptive pressure following control method, system and application optimized for ABS conditions. Background Technology

[0002] In related technologies, due to the rotational inertia of the motor and transmission mechanism, the actual rate of change of braking force in the vehicle's EMB system during the rapid boost / depressurization cycle required by ABS cannot keep up with the target command, resulting in EMB pressure lag under ABS conditions and a decrease in control accuracy. Moreover, the frequent forward and reverse operations of the motor to overcome rotational inertia lead to a sharp increase in motor copper losses and overheating, thus causing energy loss and heat generation problems under high-frequency switching, affecting system reliability and lifespan. Summary of the Invention

[0003] This application proposes an EMB adaptive pressure following control method, system, and application optimized for ABS operating conditions, which improves the dynamic response capability of EMB pressure under ABS operating conditions, reduces the energy consumption and heat generation of EMB motors under high-frequency switching, and effectively overcomes the negative impact of motor rotational inertia.

[0004] The first aspect of this application provides an EMB adaptive pressure following control method optimized for ABS operating conditions, which includes the following steps: S1: During each braking request, acquire the q-axis current Iq and angular velocity ω in real time, and receive the ABS activation signal, target wheel cylinder pressure P_target and wheel speed signals of each wheel; S2: Based on the dynamic model, by analyzing the dynamic relationship between the q-axis current Iq and the angular velocity ω under smooth braking conditions without ABS, the equivalent moment of inertia J_est under the current condition is estimated online using the recursive least squares method. The dynamic model is J_est. dω / dt=Kt Iq-Tf-F_clamp r, where J is the equivalent moment of inertia of the motor and transmission mechanism, Kt is the motor torque constant, Tf is the friction torque, F_clamp is the clamping force, and r is the force-torque conversion coefficient; S3: Calculate the inertia torque compensation based on the feedforward model, where the feedforward model is T_inertia=J_est (d²θ / dt²)_target, where T_inertia is the amount of inertial torque compensation required to achieve the target pressure change rate dP_target / dt, and (d²θ / dt²)_target is obtained by differentiating the target wheel cylinder pressure P_target through the force-displacement-angle conversion relationship; S4: Calculate the damping torque based on the virtual damping model, where the virtual damping model is T_damp=B_virt. ω, where T_damp is the damping torque and B_virt is the virtual damping coefficient adjusted according to the working stage of ABS; S5: By superimposing the inertia torque compensation and the damping torque onto the PI current loop setpoint, a new q-axis current command Iq_ref_FF is generated.

[0005] The EMB adaptive pressure following control method for ABS operating conditions according to the first aspect of this application has at least the following beneficial effects: At each braking request, by obtaining the q-axis current, angular velocity, ABS activation signal, target wheel cylinder pressure, and wheel speed signals, the inertia torque compensation and damping torque are calculated based on the dynamic model, feedforward model, and virtual damping model. The inertia torque compensation (as a feedforward term) and the damping torque (as a damping term) are then superimposed on the PI current loop setpoint to generate a new q-axis current command. This enables the EMB motor to output torque according to the new q-axis current command. Through feedforward compensation, the negative impact of the motor's rotational inertia on ABS high-frequency pressure regulation is actively offset, ensuring that the torque output by the motor more accurately follows the pressure change requirements, solving the pressure following lag problem. Simultaneously, by introducing variable virtual damping, the "electric braking" effect helps the motor decelerate faster, thereby accelerating the system response and suppressing oscillations, optimizing the dynamic process, and helping to reduce unnecessary motor reversal and emergency stops, reducing energy loss and heat generation.

[0006] In some embodiments of this application, the EMB adaptive pressure following control method optimized for ABS operating conditions further includes the following steps: When the ABS transitions from the pressure holding stage to the pressure reduction stage, the virtual damping coefficient B_virt is increased.

[0007] In some embodiments of this application, the EMB adaptive pressure following control method optimized for ABS operating conditions further includes the following steps: The central controller sends an event trigger signal only when the ABS is activated / deactivated; When each wheel EMB controller receives an ABS activation event trigger signal, it executes steps S1 to S5.

[0008] In some embodiments of this application, the EMB adaptive pressure following control method optimized for ABS operating conditions further includes the following steps: The EMB controller reports status information to the central controller only when the EMB pressure deviation exceeds the limit or the motor overheats.

[0009] In some embodiments of this application, the EMB adaptive pressure following control method optimized for ABS operating conditions further includes the following steps: If the pressure following error of the EMB exceeds the threshold within N consecutive ABS cycles, the central controller will mark the corresponding EMB status as an abnormal state of "ABS function limited", where N is a positive integer greater than 1. The central controller controls the EMB in an abnormal state to exit the ABS pressure regulation task and maintain a constant safety pressure. The central controller controls the other EMBs to take over all ABS adjustment tasks and limit the vehicle's maximum deceleration.

[0010] In some embodiments of this application, step 1 includes the following steps: Collect the three-phase current and rotor electrical angle of the motor; The q-axis current Iq is calculated based on the three-phase current of the motor and the rotor electrical angle.

[0011] A second aspect of this application provides an EMB adaptive pressure following control system optimized for ABS operating conditions, comprising: The acquisition module is used to acquire the q-axis current Iq and angular velocity ω in real time during each braking request, and to receive the ABS activation signal, target wheel cylinder pressure P_target and wheel speed signals of each wheel; The online estimation module is used to estimate the equivalent moment of inertia J_est under the current operating condition based on a dynamic model. This estimation is achieved by analyzing the dynamic relationship between the q-axis current Iq and angular velocity ω under smooth braking conditions without ABS, and then using the recursive least squares method. The dynamic model is J_est. dω / dt=Kt Iq-Tf-F_clamp r, where J is the equivalent moment of inertia of the motor and transmission mechanism, Kt is the motor torque constant, Tf is the friction torque, F_clamp is the clamping force, and r is the force-torque conversion coefficient; The first calculation module is used to calculate the inertia torque compensation based on a feedforward model, wherein the feedforward model is T_inertia=J_est (d²θ / dt²)_target, where T_inertia is the amount of inertial torque compensation required to achieve the target pressure change rate dP_target / dt, and (d²θ / dt²)_target is obtained by differentiating the target wheel cylinder pressure P_target through the force-displacement-angle conversion relationship; The second calculation module is used to calculate the damping torque based on a virtual damping model, wherein the virtual damping model is T_damp=B_virt. ω, where T_damp is the damping torque and B_virt is the virtual damping coefficient adjusted according to the working stage of ABS; The instruction generation module is used to generate a new q-axis current instruction Iq_ref_FF by superimposing the inertia torque compensation and the damping torque onto the PI current loop setpoint.

[0012] The EMB adaptive pressure following control system optimized for ABS operating conditions according to the second aspect of this application has at least the following beneficial effects: During each braking request, the q-axis current, angular velocity, ABS activation signal, target wheel cylinder pressure, and wheel speed signals are obtained by the acquisition module. The inertia torque compensation and damping torque are calculated based on the dynamic model, feedforward model, and virtual damping model using the online estimation module, the first calculation module, and the second calculation module. Then, the command generation module superimposes the inertia torque compensation (as a feedforward term) and the damping torque (as a damping term) onto the PI current loop setpoint to generate a new q-axis current command. This allows the EMB motor to output torque based on the new q-axis current command. Through feedforward compensation, the negative impact of the motor's rotational inertia on ABS high-frequency pressure regulation is actively offset, ensuring that the motor's output torque more accurately follows pressure change requirements, solving the pressure following lag problem. Simultaneously, by introducing adjustable virtual damping, the "electric braking" effect helps the motor decelerate faster, thereby accelerating pressure release and suppressing oscillations, optimizing the dynamic process, and helping to reduce unnecessary motor reversal and sudden stops, reducing energy loss and heat generation.

[0013] A third aspect of this application provides a vehicle that includes an EMB adaptive pressure following control system optimized for ABS conditions, as described in the second aspect embodiment.

[0014] The vehicle according to the third aspect of the present application has at least the following beneficial effects: by configuring the above-mentioned EMB adaptive pressure following control system optimized for ABS conditions, the vehicle can effectively counteract the negative impact of the rotational inertia of the EMB motor through predictive compensation and virtual damping control, so that the pressure following characteristics of the EMB approach or even surpass those of the traditional hydraulic system, and can reduce unnecessary motor reversal and emergency stop, thereby reducing copper loss and improving the system's continuous working ability under extreme conditions.

[0015] A fourth aspect of this application provides an electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform an EMB adaptive pressure following control method optimized for ABS operating conditions, as described in the first aspect embodiment.

[0016] A fifth aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the EMB adaptive pressure following control method for ABS operating conditions as described in the first aspect embodiment.

[0017] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the EMB adaptive pressure following control method optimized for ABS operating conditions, provided in the embodiments of this application. Figure 2 This is a flowchart illustrating an EMB adaptive pressure following control method optimized for ABS operating conditions, according to another embodiment of this application. Figure 3 This is a detailed flowchart of step S1 in the EMB adaptive pressure following control method for ABS operating conditions optimized according to the embodiments of this application; Figure 4 This is a newly added flowchart of the EMB adaptive pressure following control method optimized for ABS operating conditions provided in the embodiments of this application; Figure 5 This is a newly added flowchart of an EMB adaptive pressure following control method optimized for ABS operating conditions, provided according to another embodiment of this application. Figure 6 This is a simplified structural diagram of an EMB adaptive pressure following control system optimized for ABS operating conditions, provided in the embodiments of this application. Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0019] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0020] In the description of this application, it should be understood that the use of terms such as "first" and "second" may explicitly or implicitly include one or more of those features. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0021] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0022] In the automotive field, the steerable mechanical brake (also known as electro-mechanical brake, or EMB) system serves as a key actuator in the vehicle's intelligent chassis. It directly drives the brake caliper to clamp the brake disc via an electric motor to achieve braking. It eliminates the need for traditional hydraulic circuits and has significant advantages such as fast response, high integration, and easy deep collaboration with Advanced Driver Assistance Systems (ADAS) / Autonomous Driving (AD) functions.

[0023] However, under the conditions of anti-lock braking systems (ABS) where control precision and dynamic response are extremely demanding, the performance of EMB systems has not completely surpassed that of technologically mature hydraulic braking systems, and even shows disadvantages in some aspects. Related technologies use displacement sensors to directly measure the gap between the brake pads and the brake disc, or use strain gauges to provide feedback on clamping force. However, relying on dedicated sensors increases costs and has not yet resulted in an effective closed-loop control logic to address the unique challenges of ABS operation.

[0024] The core challenge facing EMB (Electronic Braking System) under ABS conditions lies in the pressure following lag caused by the motor's rotational inertia. The essence of ABS control is to rapidly release braking force when the wheels are about to lock up and quickly rebuild braking force after the wheels regain speed, forming a high-frequency pressure "pulse" regulation. For traditional hydraulic systems, pressure regulation is mainly limited by the switching speed of the solenoid valve and the pipeline volume, and the liquid itself can be approximated as incompressible, thus exhibiting good pressure following characteristics. However, for EMB systems, the braking force is generated by a motor driving the actuator, and the motor and its transmission mechanism (such as a ball screw) have a significant rotational inertia. During the rapid decompression phase of ABS, even if the controller immediately issues a reverse rotation command, the motor cannot stop or reverse instantly due to inertia, resulting in the braking force not being released quickly. Similarly, during the pressurization phase, inertia must be overcome to accelerate pressure buildup. This "pressure following lag" effect caused by the motor's rotational inertia severely restricts the pressure regulation frequency and accuracy of EMB-ABS, making it difficult to achieve the control effect of hydraulic-ABS, and may even lead to longer braking distances or decreased vehicle stability.

[0025] Furthermore, multi-round EMB coordination typically employs periodic central scheduling, such as Controller Area Network (CAN) messages synchronizing every 10ms. Under high-frequency ABS switching conditions, communication delays or packet loss can further exacerbate the inconsistency in braking force response across wheels, affecting overall vehicle stability. Moreover, most fault handling solutions in related technologies rely on hardware redundancy, which is inconsistent with the trend towards low-cost EMB industrialization.

[0026] Therefore, there is an urgent need for a control scheme that can effectively overcome the limitation of motor rotational inertia and improve the dynamic pressure following performance of the EMB system under ABS conditions.

[0027] The following is for reference. Figures 1 to 7 This application describes an EMB adaptive pressure following control method, system, and application optimized for ABS operating conditions, according to embodiments of the present application.

[0028] like Figures 1 to 5 As shown, the EMB adaptive pressure following control method optimized for ABS conditions according to the first aspect of this application can be applied to vehicles. It is implemented based on software algorithms, requiring no additional hardware, is low-cost, and easy to deploy on existing EMB platforms. This embodiment can improve the dynamic response capability of EMB pressure under ABS conditions, reduce the energy consumption and heat generation of the EMB motor during high-frequency switching, and effectively overcome the negative impact of pressure following lag caused by motor rotational inertia.

[0029] like Figure 1 As shown, the EMB adaptive pressure following control method optimized for ABS operating conditions includes the following steps: Step S1: During each braking request, acquire the q-axis current Iq and angular velocity ω in real time, and receive the ABS activation signal, target wheel cylinder pressure P_target, and wheel speed signals of each wheel; Step S2: Based on the dynamic model, by analyzing the dynamic relationship between the q-axis current Iq and the angular velocity ω under the smooth braking condition without ABS, the equivalent moment of inertia J_est under the current condition is estimated online using the recursive least squares method. The dynamic model is J_est. dω / dt=Kt Iq-Tf-F_clamp r, where J is the equivalent moment of inertia of the motor and transmission mechanism, Kt is the motor torque constant, Tf is the friction torque, F_clamp is the clamping force, and r is the force-torque conversion coefficient; Step S3: Calculate the inertia torque compensation based on the feedforward model, where the feedforward model is T_inertia=J_est (d²θ / dt²)_target, where T_inertia is the amount of inertial torque compensation required to achieve the target pressure change rate dP_target / dt, and (d²θ / dt²)_target is obtained by differentiating the target wheel cylinder pressure P_target through the force-displacement-angle conversion relationship; Step S4: Calculate the damping torque based on the virtual damping model, where the virtual damping model is T_damp=B_virt. ω, where T_damp is the damping torque and B_virt is the virtual damping coefficient adjusted according to the working stage of ABS; Step S5: Generate a new q-axis current command Iq_ref_FF by superimposing the inertia torque compensation and damping torque onto the PI current loop setpoint.

[0030] Among them, such as Figure 3 As shown, step 1, which involves acquiring the q-axis current Iq and angular velocity ω in real time during each braking request, and receiving the ABS activation signal, target wheel cylinder pressure P_target, and wheel speed signals for each wheel, specifically includes the following steps: Step S11: Collect the three-phase current and rotor electrical angle of the motor; Step S12: Calculate the q-axis current Iq based on the three-phase current of the motor and the rotor electrical angle.

[0031] Understandably, in step S1, it is necessary to collect the existing electrical signals of the EMB motor and the vehicle status. The three-phase current of the EMB motor can be directly obtained through a current sensor, calculated through a two-phase measurement method, or obtained through a sampling resistor. The rotor electrical angle θ can be directly measured by physical sensors such as an encoder or a rotary transformer. After acquiring the motor's three-phase current and rotor electrical angle θ in real time, the q-axis current Iq is obtained through Clark and Park transformations based on the motor's three-phase current and rotor electrical angle θ. The angular velocity ω can be provided by a rotary transformer or encoder. In addition, the bus voltage Udc can be acquired in real time. The bus voltage Udc can constrain the limit range that the q-axis current Iq can reach. The given reference value of the q-axis current Iq can be calculated using the bus voltage Udc, indirectly changing the steady-state operating point of the q-axis current Iq.

[0032] During each braking request, especially when the vehicle's longitudinal deceleration exceeds a preset threshold (e.g., 0.3g, where g is the acceleration due to gravity) or the wheel speed difference enters the ABS activation area, the q-axis current Iq and angular velocity ω are acquired in real time. At the same time, the ABS activation signal, target wheel cylinder pressure P_target, and wheel speed signals of each wheel are received from the vehicle controller (VCU) via the vehicle's CAN bus.

[0033] In step S2, a dynamic model incorporating rotational inertia needs to be constructed, and the key parameter of equivalent rotational inertia needs to be identified. This is achieved by establishing an electromechanical coupling dynamic model of the EMB actuator, namely J... dω / dt=Kt Iq-Tf-F_clamp The equivalent moment of inertia J is considered as a key parameter to be identified. In analyzing the dynamic relationship between the q-axis current Iq and angular velocity ω under non-ABS conditions (such as smooth braking), the equivalent moment of inertia J_est under the current condition is estimated online using the recursive least squares (RLS) method. The friction torque can be estimated based on the friction model or online using a state observer. The clamping force can be directly measured by a force sensor or estimated using a stiffness model or an observer.

[0034] In step S3, a feedforward model is constructed, i.e., T_inertia = J_est (d²θ / dt²)_target, a model-predictive-based feedforward compensation mechanism is designed. Based on the equivalent moment of inertia J_est under the current operating condition obtained in step S2 and the current angular velocity ω, the inertial torque compensation amount T_inertia required to overcome to achieve the target pressure change rate dP_target / dt is calculated. This inertial torque compensation amount T_inertia is then superimposed as a feedforward term onto the basic PI current loop to generate a new q-axis current command. This actively counteracts the influence of the motor's moment of inertia through feedforward compensation, enabling the EMB motor's output torque to more accurately follow pressure change requirements. This embodiment is based on online identification of the moment of inertia J using feedforward compensation control. The moment of inertia J of the EMB is estimated online as the core disturbance term, and a feedforward compensator is designed to counteract its negative impact on ABS high-frequency pressure regulation. This directly counteracts the moment of inertia through feedforward compensation, fundamentally solving the problem of pressure following lag.

[0035] In step S4, B_virt is an adjustable virtual damping coefficient. For ABS operating conditions, by introducing a virtual damping control strategy, oscillations caused by excessive system rigidity are avoided during the pressure holding or fine-tuning phases of ABS, achieving a smooth transition. This is achieved by constructing a virtual damping model, i.e., T_damp = B_virt. A damping torque T_damp, proportional to the angular velocity ω, is calculated. This damping torque T_damp is then superimposed as a virtual damping term onto the PI current loop setpoint to generate a new q-axis current command. By introducing variable virtual damping into EMB control, particularly enhancing virtual damping during the ABS decompression phase, the system response can be accelerated and oscillations suppressed, ultimately simulating the dynamic characteristics of a traditional hydraulic system. This embodiment cleverly utilizes the controllability of the EMB motor itself through a virtual damping strategy to simulate the advantageous characteristics of a hydraulic system and optimize the dynamic process.

[0036] In step S5, after obtaining the inertia torque compensation amount T_inertia and the damping torque T_damp, the inertia torque compensation amount T_inertia and the damping torque T_damp are superimposed on the PI current loop setpoint, where PI represents proportional-integral control, to obtain a new q-axis current command Iq_ref_FF, so that the EMB motor outputs torque according to the new q-axis current command Iq_ref_FF. This embodiment, through predictive compensation and virtual damping control, can effectively offset the negative impact of the motor's rotational inertia, making the pressure following characteristics of the EMB system approach or even exceed those of a hydraulic system. This significantly improves the dynamic response rate of EMB pressure under ABS conditions. Moreover, it can reduce unnecessary motor reversals and emergency stops, thereby reducing motor copper losses, energy consumption, and temperature rise, and improving the continuous working capability of the EMB system under extreme conditions.

[0037] In some embodiments, such as Figure 2 As shown, based on steps S1 to S5, the EMB adaptive pressure following control method optimized for ABS operating conditions further includes the following steps: Step S31: When the ABS transitions from the pressure holding stage to the pressure reduction stage, increase the virtual damping coefficient B_virt.

[0038] It is understandable that ABS has a pressurization phase, a pressure holding phase, and a depressurization phase. During emergency braking of a vehicle, these three phases cycle sequentially. In this embodiment, during the ABS depressurization phase, the virtual damping coefficient B_virt is appropriately increased, and then the damping torque T_damp is calculated. This allows the EMB motor to decelerate more quickly through the "electric braking" effect, simulating a damping effect similar to that of a throttle orifice in a hydraulic system, thereby accelerating pressure release. Outside of the ABS depressurization phase, the virtual damping coefficient B_virt can be appropriately decreased.

[0039] In some embodiments, such as Figure 4 As shown, the EMB adaptive pressure following control method optimized for ABS operating conditions also includes the following steps: Step S6: The central controller sends an event trigger signal only when the ABS is activated / deactivated; Step S7: When each wheel EMB controller receives the ABS activation event trigger signal, it executes steps S1 to S5.

[0040] Furthermore, such as Figure 4 As shown, the EMB adaptive pressure following control method optimized for ABS operating conditions also includes the following steps: Step S8: Only when the EMB pressure deviation exceeds the limit or the motor overheats, the corresponding EMB controller reports the status information to the central controller.

[0041] Understandably, in related technologies, multi-wheel EMB coordination faces communication bottlenecks under ABS conditions. Traditional periodic synchronization mechanisms consume significant bus bandwidth under high-frequency ABS control and are susceptible to interference. In this embodiment, the vehicle's central controller, such as the Electronic Stability Control (ESC) system, only sends event trigger signals when ABS is activated and deactivated, rather than periodically synchronizing. Upon receiving the ABS activation event trigger signal, each wheel EMB controller locally executes the control logic of steps S1 to S5, enabling each wheel EMB to independently complete pressure following. Furthermore, each wheel EMB only actively reports its status to the central controller when its own state undergoes a significant change, such as exceeding pressure deviation limits or motor overheating. This embodiment, through an event-driven architecture and event-driven collaborative control, significantly reduces the communication load under high-frequency ABS conditions, improving system real-time performance and anti-interference capabilities.

[0042] In some embodiments, such as Figure 5 As shown, based on steps S6 to S8, the EMB adaptive pressure following control method optimized for ABS operating conditions further includes the following steps: Step S91: If the pressure following error of the EMB exceeds the threshold in N consecutive ABS cycles, the central controller will mark the status of the corresponding EMB as an abnormal state of "ABS function limited", where N is a positive integer greater than 1. Step S92: The central controller controls the EMB in an abnormal state to exit the ABS pressure regulation task and maintain a constant safety pressure; Step S93: The central controller controls other EMBs to take over all ABS adjustment tasks and limit the vehicle's maximum deceleration.

[0043] Understandably, the specific value of N can be determined based on the actual situation, for example, N = 2. When the pressure following error of the EMB corresponding to certain wheels exceeds the threshold in N consecutive ABS cycles, the central controller will mark these EMBs as "ABS function limited" and instruct them to exit the ABS pressure regulation condition and maintain a safe constant pressure. At this time, the EMBs corresponding to the remaining wheels will take over the ABS regulation task, and the central controller will limit the maximum deceleration of the entire vehicle to ensure vehicle safety. This embodiment, by adopting an event-driven ABS coordination and fault degradation mechanism, not only changes the traditional periodic synchronization mode and manages ABS coordination in an event-triggered manner, significantly reducing communication load, but also achieves intelligent fault safety degradation by combining local performance monitoring, enabling the system to have fault degradation capabilities based on local state awareness, ensuring driving safety, and thus enhancing the robustness and safety of the system.

[0044] The following description uses the example of a passenger vehicle's EMB system triggering ABS during emergency braking on a slippery road surface to illustrate the inventive concept of this application.

[0045] When the vehicle is in normal driving condition, EMB is in standby mode. If the left front wheel experiences a sudden drop in wheel speed and shows signs of locking, the EMB system determines that ABS intervention is necessary. Specifically, if the vehicle's current state is: the rate of change of the left front wheel's wheel speed is dω / dt < 150 rad / s 2 The slip ratio is λ > 20% (typical critical slip ratio for locking), the target slip ratio is set to the optimal value λopt = 15%, and the pressure drop rate triggered by the decompression command is dP / dt = 10 MPa / s.

[0046] In this scenario, the left front wheel EMB controller implements the aforementioned EMB adaptive pressure following control method optimized for ABS conditions. Based on the target wheel cylinder pressure curve, the moment of inertia is estimated online, and the feedforward compensation (i.e., the inertia torque compensation) is calculated and superimposed on the PI current loop setpoint. Upon the arrival of the first decompression command, a large virtual damping is applied to help the EMB motor decelerate rapidly.

[0047] For example, the target cylinder pressure curve is P_target(t)=P0 e t / τ Where P0 = 8 MPa, τ = 0.05 s; the online estimated moment of inertia is J_est = 0.0005 kg / pm. 2 (This is a reasonable value, corresponding to small motors and transmission mechanisms); the inertia torque compensation amount (i.e., inertia compensation) for feedforward compensation is T_inertia = J_est dωm / dt, assuming dωm / dt is 2000 rad / s 2 Then T_inertia≈ 1.0 N\cdotpm; Virtual damping coefficient (enabled during initial decompression) is B_virt=0.02 N\cdotpm\cdotps / rad (significantly higher than the normal value of 0.002, which can be used to quickly suppress motor rebound); The PI current loop superposition command is based on the current I0=10 A, and the peak value after superposition compensation reaches Ipeak=18 A.

[0048] Under coordinated monitoring, if the right rear wheel does not trigger ABS due to normal road surface adhesion, conventional braking is maintained. All EMB nodes report a "Cycle_OK" signal only after completing one ABS pressure cycle. For example, the specific state of the right rear wheel is: road surface adhesion coefficient μ=0.85 (dry asphalt), ABS not triggered, and the corresponding EMB for the right rear wheel maintains a constant clamping force. The reporting mechanism for the "Cycle_OK" signal is as follows: each ABS pressure cycle is T=0.1s (corresponding to 10Hz), and after completing a complete "pressure increase-pressure hold-pressure decrease" sequence, the node sends a Cycle_OK message via the CAN FD bus.

[0049] Under fault degradation, assuming that the EMB corresponding to the right front wheel degrades due to overheating and its pressure following performance decreases by 10% in two consecutive ABS cycles, after receiving the alarm, the EMB system instructs the right front wheel to maintain a safe pressure of 5MPa. At the same time, it distributes all ABS adjustment tasks to the EMB corresponding to the left front wheel and the EMB corresponding to the left rear wheel, and limits the vehicle deceleration to within 0.7g.

[0050] Based on the same inventive concept, corresponding to the EMB adaptive pressure following control method for ABS operating conditions optimized in the first aspect embodiment of this application, the second aspect embodiment of this application provides an EMB adaptive pressure following control system for ABS operating conditions optimized.

[0051] like Figures 1 to 6 As shown, the EMB adaptive pressure following control system optimized for ABS operating conditions according to the second aspect embodiment of this application includes an acquisition module, an online estimation module, a first calculation module, a second calculation module, and an instruction generation module.

[0052] The acquisition module is used to acquire the q-axis current Iq and angular velocity ω in real time during each braking request, and to receive the ABS activation signal, target wheel cylinder pressure P_target, and wheel speed signals for each wheel. Specifically, the acquisition module can execute step S1 in the EMB adaptive pressure following control method optimized for ABS conditions according to the first aspect embodiment.

[0053] The online estimation module is used to estimate the equivalent moment of inertia J_est under the current operating condition based on a dynamic model. This is achieved by analyzing the dynamic relationship between the q-axis current Iq and angular velocity ω under smooth braking conditions without ABS, and then using the recursive least squares method. The dynamic model is J_est. dω / dt=Kt Iq-Tf-F_clamp r, where J is the equivalent moment of inertia of the motor and transmission mechanism, Kt is the motor torque constant, Tf is the friction torque, F_clamp is the clamping force, and r is the force-torque conversion coefficient. Specifically, the online estimation module can execute step S2 in the EMB adaptive pressure following control method optimized for ABS conditions according to the first aspect embodiment.

[0054] The first calculation module is used to calculate the inertia torque compensation based on a feedforward model, where T_inertia = J_est (d²θ / dt²)_target, where T_inertia is the amount of inertial torque compensation required to achieve the target pressure change rate dP_target / dt, and (d²θ / dt²)_target is obtained by differentiating the target wheel cylinder pressure P_target through the force-displacement-angle conversion relationship. Specifically, the first calculation module can execute step S3 in the EMB adaptive pressure following control method optimized for ABS conditions in the first aspect embodiment.

[0055] The second calculation module is used to calculate the damping torque based on a virtual damping model, where T_damp=B_virt. ω, where T_damp is the damping torque and B_virt is the virtual damping coefficient adjusted according to the operating phase of the ABS. Specifically, the second calculation module is capable of executing step S4 in the EMB adaptive pressure following control method optimized for ABS operating conditions according to the first aspect embodiment.

[0056] The function of the instruction generation module is to generate a new q-axis current instruction Iq_ref_FF by superimposing the inertia torque compensation and damping torque onto the PI current loop setpoint. Specifically, the instruction generation module can execute step S5 in the EMB adaptive pressure following control method optimized for ABS conditions in the first aspect embodiment.

[0057] In the EMB adaptive pressure following control system optimized for ABS conditions in this embodiment, when the vehicle confirms each braking request from the driver, the q-axis current, angular velocity, ABS activation signal, target wheel cylinder pressure, and wheel speed signals are obtained through the acquisition module. The inertia torque compensation and damping torque are calculated based on the dynamic model, feedforward model, and virtual damping model through the online estimation module, the first calculation module, and the second calculation module. Then, the command generation module superimposes the inertia torque compensation (as a feedforward term) and the damping torque (as a damping term) onto the PI current loop setpoint to generate a new q-axis current command. This allows the EMB motor to output torque based on the new q-axis current command. Through feedforward compensation, the negative impact of the motor's rotational inertia on ABS high-frequency pressure regulation is actively offset, ensuring that the motor's output torque more accurately follows pressure changes, solving the pressure following lag problem. Simultaneously, by introducing adjustable virtual damping, the "electric braking" effect helps the motor decelerate faster, thereby accelerating pressure release and suppressing oscillations, optimizing the dynamic process, and helping to reduce unnecessary motor reversal and sudden stops, thus reducing energy loss and heat generation.

[0058] like Figures 1 to 6 As shown, a vehicle according to a third aspect embodiment of this application includes an EMB adaptive pressure following control system optimized for ABS conditions, as described in the second aspect embodiment.

[0059] Specifically, the vehicle can be a private car, such as a sedan, SUV, MPV, or pickup truck. The vehicle can also be a commercial vehicle, such as a van, bus, small truck, or large semi-trailer. The vehicle can be a new energy vehicle, such as a hybrid electric vehicle or a pure electric vehicle.

[0060] By configuring the aforementioned EMB adaptive pressure following control system optimized for ABS conditions, the vehicle can effectively counteract the negative impact of the EMB motor's rotational inertia through predictive compensation and virtual damping control. This allows the EMB's pressure following characteristics to approach or even surpass those of traditional hydraulic systems. Furthermore, it can reduce unnecessary motor reversal and emergency stops, thereby reducing copper losses and improving the system's continuous working capability under extreme conditions.

[0061] like Figures 1 to 5 , Figure 7As shown, an electronic device according to a fourth aspect embodiment of this application includes: at least one processor, a memory, an input / output interface, a communication interface, and a bus. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform an EMB adaptive pressure following control method optimized for ABS operating conditions, as described in the first aspect embodiment. The memory, at least one processor, input / output interface, and communication interface are interconnected internally via the bus.

[0062] It is understood that the processor can be implemented using a general-purpose CPU (i.e., central processing unit), microprocessor, or one or more integrated circuits, to execute relevant computer programs in order to implement the EMB adaptive pressure following control method optimized for ABS operating conditions according to the first aspect embodiment.

[0063] The memory primarily comprises a program storage area and a data storage area. The program storage area stores the operating system and at least one application program required for a given function; the data storage area stores data created based on terminal usage. Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some examples, the memory may further include memory remotely located relative to the processor, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0064] Input / output interfaces are used to connect input / output units to enable information input and output. Input / output units can be integrated into the device as components or externally connected to provide corresponding functions. Input units may include touchscreens, microphones, various sensors, etc., while output units may include displays, speakers, vibrators, indicator lights, etc.

[0065] The communication interface is used to connect the communication unit to enable communication and interaction between this device and other devices. The communication unit can perform communication functions via wired or wireless means.

[0066] A bus is a pathway that transmits information between various components of a device, such as processors, memory, input / output interfaces, and communication interfaces.

[0067] like Figures 1 to 5 As shown, a computer-readable storage medium according to a fifth aspect embodiment of the present application stores a computer program thereon, which, when executed by a processor, implements the EMB adaptive pressure following control method optimized for ABS operating conditions as described in the first aspect embodiment.

[0068] The computer-readable storage medium of this application embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, and portable compact disk read-only memory (CD-ROM). ROM, optical storage device, magnetic storage device, or any suitable combination thereof. In embodiments of this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0069] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0070] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0071] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0072] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. An EMB adaptive pressure following control method optimized for ABS operating conditions, characterized in that, The steps include the following: S1: During each braking request, acquire the q-axis current Iq and angular velocity ω in real time, and receive the ABS activation signal, target wheel cylinder pressure P_target and wheel speed signals of each wheel; S2: Based on the dynamic model, by analyzing the dynamic relationship between the q-axis current Iq and the angular velocity ω under smooth braking conditions without ABS, the equivalent moment of inertia J_est under the current condition is estimated online using the recursive least squares method. The dynamic model is J_est. dω / dt=Kt Iq-Tf-F_clamp r, where J is the equivalent moment of inertia of the motor and transmission mechanism, Kt is the motor torque constant, Tf is the friction torque, F_clamp is the clamping force, and r is the force-torque conversion coefficient; S3: Calculate the inertia torque compensation based on the feedforward model, where the feedforward model is T_inertia=J_est (d²θ / dt²)_target, where T_inertia is the amount of inertial torque compensation required to achieve the target pressure change rate dP_target / dt, and (d²θ / dt²)_target is obtained by differentiating the target wheel cylinder pressure P_target through the force-displacement-angle conversion relationship; S4: Calculate the damping torque based on the virtual damping model, where the virtual damping model is T_damp=B_virt. ω, where T_damp is the damping torque and B_virt is the virtual damping coefficient adjusted according to the working stage of ABS; S5: By superimposing the inertia torque compensation and the damping torque onto the PI current loop setpoint, a new q-axis current command Iq_ref_FF is generated.

2. The EMB adaptive pressure following control method optimized for ABS operating conditions according to claim 1, characterized in that, The EMB adaptive pressure following control method optimized for ABS operating conditions also includes the following steps: When the ABS transitions from the pressure holding stage to the pressure reduction stage, the virtual damping coefficient B_virt is increased.

3. The EMB adaptive pressure following control method for ABS operating conditions optimized according to claim 1, characterized in that, The EMB adaptive pressure following control method optimized for ABS operating conditions also includes the following steps: The central controller sends an event trigger signal only when the ABS is activated / deactivated; When each wheel EMB controller receives an ABS activation event trigger signal, it executes steps S1 to S5.

4. The EMB adaptive pressure following control method optimized for ABS operating conditions according to claim 3, characterized in that, The EMB adaptive pressure following control method optimized for ABS operating conditions also includes the following steps: The EMB controller reports status information to the central controller only when the EMB pressure deviation exceeds the limit or the motor overheats.

5. The EMB adaptive pressure following control method for ABS operating conditions optimized according to claim 4, characterized in that, The EMB adaptive pressure following control method optimized for ABS operating conditions also includes the following steps: If the pressure following error of the EMB exceeds the threshold within N consecutive ABS cycles, the central controller will mark the corresponding EMB status as an abnormal state of "ABS function limited", where N is a positive integer greater than 1. The central controller controls the EMB in an abnormal state to exit the ABS pressure regulation task and maintain a constant safety pressure. The central controller controls the other EMBs to take over all ABS adjustment tasks and limit the vehicle's maximum deceleration.

6. The EMB adaptive pressure following control method for ABS operating conditions optimized according to claim 1, characterized in that, Step 1 includes the following steps: Collect the three-phase current and rotor electrical angle of the motor; The q-axis current Iq is calculated based on the three-phase current of the motor and the rotor electrical angle.

7. An EMB adaptive pressure following control system optimized for ABS operating conditions, characterized in that, include: The acquisition module is used to acquire the q-axis current Iq and angular velocity ω in real time during each braking request, and to receive the ABS activation signal, target wheel cylinder pressure P_target and wheel speed signals of each wheel; The online estimation module is used to estimate the equivalent moment of inertia J_est under the current operating condition based on a dynamic model. This estimation is achieved by analyzing the dynamic relationship between the q-axis current Iq and angular velocity ω under smooth braking conditions without ABS, and then using the recursive least squares method. The dynamic model is J_est. dω / dt=Kt Iq-Tf-F_clamp r, where J is the equivalent moment of inertia of the motor and transmission mechanism, Kt is the motor torque constant, Tf is the friction torque, F_clamp is the clamping force, and r is the force-torque conversion coefficient; The first calculation module is used to calculate the inertia torque compensation based on a feedforward model, wherein the feedforward model is T_inertia=J_est (d²θ / dt²)_target, where T_inertia is the amount of inertial torque compensation required to achieve the target pressure change rate dP_target / dt, and (d²θ / dt²)_target is obtained by differentiating the target wheel cylinder pressure P_target through the force-displacement-angle conversion relationship; The second calculation module is used to calculate the damping torque based on a virtual damping model, wherein the virtual damping model is T_damp=B_virt. ω, where T_damp is the damping torque and B_virt is the virtual damping coefficient adjusted according to the working stage of ABS; The instruction generation module is used to generate a new q-axis current instruction Iq_ref_FF by superimposing the inertia torque compensation and the damping torque onto the PI current loop setpoint.

8. A vehicle, characterized in that, This includes the EMB adaptive pressure following control system optimized for ABS operating conditions as described in claim 7.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the EMB adaptive pressure following control method optimized for ABS operating conditions as described in any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the EMB adaptive pressure following control method for ABS operating conditions as described in any one of claims 1 to 6.

Images