A vehicle braking control system based on a high-speed permanent magnet synchronous motor

Abstract

The application relates to the technical field of vehicle brake control, and discloses a vehicle brake control system based on a high-speed permanent magnet synchronous motor, which comprises a state sensing module, a brake force calculation module and a torque distribution module; the state sensing module is used for acquiring the battery charging state and temperature in real time; the brake force calculation module is used for calculating a predictable regenerative braking force reference line based on an energy acceptance capability model; and the torque distribution module is used for distributing the total braking torque into a predictable regenerative component and an accurately matched friction component. The application establishes a deterministic regenerative braking force reference line, so that the brake system is changed from a passive response to energy fluctuation to stable output control, linear mapping of pedal stroke and deceleration is realized, road surface bumping degree sensing and model online correction are combined, output stability is maintained under complex working conditions, and the technical problem that energy recovery and control quality cannot be considered in a mixed brake system is solved.

Description

Technical Field

[0001] The invention relates to a vehicle braking control system based on a high-speed permanent magnet synchronous motor, belonging to the technical field of vehicle braking control. Background Art

[0002] Existing technologies usually take maximizing energy recovery efficiency as the core goal, and distribute torque by obtaining the maximum regenerative braking torque that the motor can provide in real time: regenerative braking is used first to meet the demand, and the rest is supplemented by friction braking. Although this strategy can improve energy efficiency under ideal working conditions, its underlying logic puts the system in a state of passive response to energy fluctuations.

[0003] Specifically, when the vehicle is in a high battery state of charge, such as in a continuous braking scenario with SOC>95%, the battery management system's slight adjustment of the charging power will cause a sharp jump in the maximum regenerative braking torque. The unpredictability of the regenerative braking force forces the friction braking system to frequently compensate for the sudden gap, causing braking jerks; and under complex road conditions, the pedal signal noise caused by road bumps and the model drift caused by battery aging further amplify the nonlinear fluctuations of the system output. Although the industry has tried to improve control accuracy through predictive algorithms or adding sensors, it has not touched upon the essential limitations of the energy maximization principle, which not only increases the complexity of the system, but also creates new safety hazards when the sensor fails or the model is inaccurate.

[0004] A thorough analysis reveals three systemic bottlenecks in existing technologies: 1. The strong coupling between regenerative braking output and friction compensation creates a cycle of wave transmission and passive response; 2. Heterogeneous interference sources such as mechanical vibration and electrochemical state drift are transmitted and superimposed through the control link; and 3. Hidden parameters such as friction system degradation and transient battery thermal shock are not monitored in real time. These bottlenecks collectively make it difficult to establish a stable mapping between brake pedal travel and vehicle deceleration, restricting the handling quality and functional safety of high-end electric vehicles. Therefore, the technical problem addressed by this invention is how to reconstruct the underlying control logic of the braking system to eliminate output uncertainty while ensuring energy recovery efficiency and achieve robust coordinated control under conditions of multiple interference sources. Summary of the Invention

[0005] The present invention provides a vehicle braking control system based on a high-speed permanent magnet synchronous motor, the main purpose of which is to solve the problems of output unpredictability of the braking system caused by the energy recovery strategy and the instability of coordinated control under multi-source interference.

[0006] To achieve the above objectives, the present invention provides a vehicle braking control system based on a high-speed permanent magnet synchronous motor, comprising:

[0007] a state sensing module configured to obtain the current operating state of the vehicle in real time, the current operating state including the battery charge state and battery temperature;

[0008] a predictable braking force calculation module configured to calculate a predictable regenerative braking force baseline based on a current operating state and a battery energy acceptance model stored in the controller; the predictable regenerative braking force baseline represents the regenerative braking torque that can be continuously and stably provided by the high-speed permanent magnet synchronous motor within a predetermined time window, and the baseline remains unchanged within the predetermined time window;

[0009] A torque distribution module is configured to: upon receiving a total braking torque requested by a driver, determine a commanded regenerative braking torque as the minimum of the total braking torque and a predictable regenerative braking force baseline; and determine a commanded friction braking torque as the difference between the total braking torque and the commanded regenerative braking torque; wherein the torque distribution module cooperatively controls the high-speed permanent magnet synchronous motor and the vehicle's friction brake mechanism based on the commanded regenerative braking torque and the commanded friction braking torque.

[0010] Preferably, in the predictable braking force calculation module, the battery energy acceptance capacity model stored in the controller is a two-dimensional lookup table, the input of which is the battery charging state and the battery temperature, and the output is a stability coefficient. The stability coefficient is used to determine the predictable regenerative braking force baseline and indicate the stability of the battery's current acceptance of charging energy to ensure that the calculated baseline does not change drastically due to fluctuations in the battery's instantaneous state.

[0011] Preferably, the torque distribution module is further configured to dynamically adjust the predictable regenerative braking force baseline according to the vehicle driving mode; wherein the driving mode includes a comfort mode and a sport mode, and the adjustment is achieved based on a predetermined braking response curve corresponding to the driving mode.

[0012] Preferably, when the system performs torque distribution, the torque distribution module does not rely on a future prediction algorithm of the driver's braking intention.

[0013] Preferably, it also includes an energy acceptance model online correction module, which is configured to: during the regenerative braking operation of the high-speed permanent magnet synchronous motor, calculate the model residual between the commanded regenerative braking torque and the actual accepted regenerative braking torque inferred based on the instantaneous change rate of the battery terminal voltage; and when the absolute value of the model residual exceeds a preset residual threshold, select a compensation coefficient from a stored compensation coefficient lookup table based on the specific characteristics of the instantaneous change rate of the battery terminal voltage, and perform real-time correction on the predictable regenerative braking force baseline.

[0014] Preferably, it also includes a braking intention signal purification module, which is configured to: calculate a real-time road surface roughness index based on the high-frequency fluctuation amplitude of the wheel speed sensor signal in the vehicle's non-braking state; and dynamically adjust the filtering time constant of the filter acting on the driver's brake pedal original signal according to the road surface roughness index; wherein, the larger the road surface roughness index, the longer the filtering time constant.

[0015] Preferably, it also includes a braking decision arbitration module, which is configured to: obtain the total friction braking torque representing the driver's braking request calculated by a vehicle body stability system independent of the braking control system; and when the instantaneous deviation rate between the command friction braking torque output by the torque distribution module and the total friction braking torque exceeds a preset deviation threshold, the arbitration module rejects the command friction braking torque output by the torque distribution module and adopts the total friction braking torque as the final command friction braking torque.

[0016] Preferably, in the predictable braking force calculation module, the predictable regenerative braking force baseline The calculation formula can be expressed as: ,in, It is a slope coefficient calibrated based on battery temperature and battery aging status; is the battery's highest state of charge threshold; is the current charging status of the battery.

[0017] Preferably, it also includes a thermal shock compensation module, which is configured to: estimate a fast-response temperature index based on the real-time resistance of the high-speed permanent magnet synchronous motor winding; when the difference between the change rate of the fast-response temperature index and the change rate of the slow-response battery temperature index obtained from the state perception module exceeds a preset change rate threshold, determine that a thermal shock event has occurred; and when a thermal shock event occurs, the thermal shock compensation module lowers the predictable regenerative braking force baseline through a suppression factor that is inversely proportional to the difference between the fast-response temperature index and the slow-response battery temperature index.

[0018] Preferably, it also includes a friction system health status detection module, which is configured to: instruct the high-speed permanent magnet synchronous motor to output a regenerative braking torque micro-disturbance of a limited size within a low-speed diagnostic window before the vehicle stops braking; measure the actual inertia deceleration response curve of the vehicle generated by the micro-disturbance through an accelerometer configured in the vehicle; and determine the health status index of the friction braking system by comparing the difference between the actual inertia deceleration response curve and a pre-stored health status standard response curve, and perform closed-loop compensation on the commanded friction braking torque based on the health status index.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. By establishing a predictable regenerative braking force baseline based on the battery state, the regenerative braking system transitions from a passive response to energy fluctuations to a torque source that provides stable output. This baseline forms a deterministic synergistic relationship with the friction braking force command, breaking down the total braking force request into a predictable regenerative component and a precisely matched friction component. This mechanism reconstruction eliminates braking force steps caused by sudden changes in battery state, enabling a more direct mapping of the vehicle's deceleration response to the driver's pedal input.

[0021] 2. The road roughness perception module dynamically adjusts the braking signal filtering strength based on high-frequency fluctuations in the wheel speed signal, filtering out false operation commands on bumpy roads. Simultaneously, the energy acceptance model online correction module uses the battery terminal voltage variation characteristics to calibrate the prediction baseline in real time. These two modules form closed-loop verification at the front-end signal input and back-end model execution layers, respectively, enabling the system to maintain output stability despite interference from sensor noise, model drift, and other factors, thereby improving control robustness under complex operating conditions.

[0022] 3. A thermal shock event recognition model is constructed based on the time difference between the rapid temperature change response characteristics of the motor winding resistance and the thermal inertia of the battery. When the difference between the fast and slow temperature change rates exceeds a threshold, the prediction baseline is actively lowered through a suppression factor to avoid sudden changes in regenerative force caused by battery temperature sensing delays. This predictive compensation achieved by utilizing existing electromechanical characteristic differences enables the system to maintain smooth braking in scenarios where the ambient temperature changes suddenly.

[0023] 4. Injecting micro-disturbance of regenerative braking force at the end of vehicle braking allows the vehicle's inertial response curve to be captured using a high-precision accelerometer. This response characteristic is strongly correlated with the mechanical state of the friction system, transforming implicit degradation such as wear and air resistance into a quantifiable health index. Closed-loop compensation is then implemented for the friction braking force command. This method transforms drive components into diagnostic tools, enabling online monitoring of key safety parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is an architecture diagram of a vehicle braking control system based on a high-speed permanent magnet synchronous motor according to the present invention;

[0025] Figure 2 This is a performance data comparison chart of the two control strategies of the present invention under high SOC continuous braking conditions;

[0026] Figure 3 This is a workflow diagram of the online correction module of the energy acceptance model of the present invention.

[0027] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described in detail below; obviously, the described embodiments are only part of the embodiments of the present application, rather than all; based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor should belong to the protection scope of the present application.

[0029] The vehicle brake control system based on the high-speed permanent magnet synchronous motor provided by the present application reconstructs the bottom layer control logic of hybrid brake energy management and torque distribution, aims to change the brake system from the passive response state of energy fluctuation to the active control state of stable output deterministic braking force, the overall architecture of the system includes a state perception module, a predictable braking force calculation module and a torque distribution module, and can further integrate an energy acceptance model online correction module, a brake intention signal purification module, a brake decision arbitration module and a friction system health state detection module, the modules work cooperatively to form a control system with multi-level adaptive and safety redundancy capability; in the practice of vehicle brake control, a core challenge lies in that the energy acceptance capability of the battery fluctuates in real time due to its state of charge and temperature, this uncertainty leads to unpredictable regenerative braking torque, and further causes compensatory jitter of friction braking; to cope with this challenge, the state perception module of the present application is configured to obtain the current operating state of the vehicle in real time without interruption, especially the current state of charge of the battery and the battery temperature, and input this series of state parameters to the predictable braking force calculation module; the module has a battery energy acceptance capability model built in, which can be constructed as a two-dimensional lookup table, taking the obtained state of charge of the battery and the battery temperature as input, and outputting a stability coefficient, which is directly used to determine a key control parameter, i.e. the predictable regenerative braking force reference line, which represents the regenerative braking torque that can be continuously and stably provided by the high-speed permanent magnet synchronous motor without attenuation within a preset time window, the specific calculation procedure follows the formula: In the formula, Cmax is the preset maximum state of charge threshold of the battery, and the slope coefficient is determined by following a rigorous offline calibration process, i.e. by performing charge and discharge cycle tests on battery packs in different aging states at different temperature points, the mapping relationship between the value of and the battery temperature and health state is calibrated and stored, so as to ensure The accuracy of the value in the whole life cycle of the battery; by establishing such a baseline with clear physical meaning and stable expectation, the system has a constant regenerative braking torque reference, so that the distribution of total braking force is no longer subject to the instantaneous fluctuations of the internal electrochemical state of the battery, laying a solid physical foundation for realizing the linear mapping relationship between pedal stroke and vehicle deceleration.

[0030] The battery energy acceptance capability model stored in the controller in the application can be physically constructed as a two-dimensional lookup table, and the establishment process of the two-dimensional lookup table follows a standardized offline calibration procedure. Specifically, first, place the battery pack to be calibrated in a high-precision temperature control test box, define a two-dimensional grid test matrix in the designed full working temperature range and full working state of charge (SOC) range, then drive the state of the battery pack to each grid node in turn, and apply a charging power pulse with a standard waveform to each node, while recording the voltage response curve of the battery at high frequency. By analyzing the dynamic characteristics of the response curve, the instantaneous maximum acceptable charging power of the battery under the specific temperature and SOC combination can be calculated, or a stability coefficient converted to indicate the stability of its charging energy acceptance. The coefficient value is taken as the output and filled into the index position of (temperature, SOC) in the two-dimensional lookup table. By systematically traversing all grid nodes and completing data filling, an energy acceptance capability model that can accurately reflect the electrochemical characteristics of the specific battery pack can be constructed.

[0031] In the actual operation of the vehicle, the predictable braking force calculation module uses the above-mentioned model to calculate the predictable regenerative braking force baseline in the following way : The module first obtains the real-time battery temperature and current state of charge through the state perception module, then takes the real-time temperature and as input, performs table lookup or interpolation operation in the internally stored two-dimensional lookup table of the battery energy acceptance capability model, and obtains a slope coefficient accurately corresponding to the current working condition. This slope coefficient has been calibrated offline and contains the influence of battery temperature and health status on energy acceptance capability. Finally, the module substitutes the obtained coefficient into the following formula to calculate the regenerative braking torque baseline that can be continuously and stably provided in the current predetermined time window: , where is the preset maximum state of charge threshold of the battery. This process tightly couples the abstract model with the specific physical quantity calculation, ensuring that the baseline calculation has a basis and the result is accurate and reliable, which is the physical basis for the stable control of the application.

[0032] Given that the driver's braking intention may be affected by external physical factors such as road roughness, the system activates a braking intention signal purification module before receiving a total braking torque request. This module analyzes the high-frequency components of the wheel speed sensor signals in the non-braking state to calculate a real-time road roughness index that quantitatively represents the current road conditions. When this index exceeds a preset value, the system dynamically increases the filtering time constant of the low-pass filter acting on the driver's brake pedal raw signal, effectively filtering out spurious operation commands caused by mechanical vibration and ensuring that the torque distribution module receives the driver's true braking request. The torque distribution module then operates according to the principle of determinism, setting the commanded regenerative braking torque to the smaller of the driver's requested total braking torque and a predictable regenerative braking force baseline. The commanded friction braking torque is precisely determined as the difference between the total braking torque and the determined commanded regenerative braking torque. Furthermore, the module can also adjust the braking response curve based on the vehicle's currently selected driving mode, such as Comfort or Sport, by invoking a preset braking response curve. The system dynamically adjusts the predictable regenerative braking force baseline to match different driving style requirements. This allocation logic does not rely on any future prediction algorithm of the driver's braking intention, but is based entirely on the current stable output of regenerative braking force, thus achieving stability in the entire chain from intention perception to torque execution. To ensure the long-term robustness and safety of the system, the system also includes a braking decision arbitration module as a safety redundancy layer. This module concurrently obtains a total friction braking torque calculated by the vehicle stability system (which is independent of the braking control system) and also represents the driver's braking request, and compares it with the command friction braking torque output by the system's torque distribution module in real time. If the instantaneous deviation rate between the two exceeds a preset deviation threshold, the arbitration module will determine that the system may have a potential fault or model inaccuracy. At this time, it will exercise its veto power, discarding the calculation result of the torque distribution module and instead using the total friction braking torque calculated by the vehicle stability system as the final command friction braking torque output, thereby improving the reliability of the vehicle's braking function.

[0033] In addition, in order to cope with the parameter drift of the battery model due to aging or sudden changes in ambient temperature, the system also introduces a dual adaptive correction mechanism; one is the energy acceptance model online correction module, which during regenerative braking, reversely calculates the regenerative braking torque actually received by the battery by high-frequency sampling of the instantaneous change rate of the battery terminal voltage, and compares it with the commanded regenerative braking torque to calculate the model residual. Once the absolute value of the residual continues to exceed the preset residual threshold, the setting of this threshold is intended to balance the correction sensitivity and system stability. The system selects the compensation coefficient from the pre-stored compensation coefficient lookup table to perform real-time calibration on the predictable regenerative braking force baseline; the second is the thermal shock compensation module, which uses the time scale difference between the fast-response temperature index estimated by the motor winding resistance and the slow-response temperature index of the battery body. When the difference in the change rate of the two exceeds the preset change rate threshold, it is determined that a thermal shock event has occurred, and the predictable regenerative braking force base is immediately actively lowered through an inhibition factor. Finally, in order to make the implicit parameter of the health status of the friction components in the traditional braking system explicit, the system also includes a friction system health status detection module. This module is configured to actively command the high-speed permanent magnet synchronous motor to output a regenerative braking torque micro-disturbance of a limited amplitude within the low-speed diagnosis window at the end of vehicle braking. At the same time, the vehicle's actual inertia deceleration response curve caused by the disturbance is measured using the on-board high-precision accelerometer. By comparing the actual response curve with the pre-stored health status standard response curve, the system can calculate a quantitative friction system health status index. This index can directly reflect the degradation of friction plate wear and other conditions, and can be further used for closed-loop compensation of the commanded friction braking torque. This realizes the non-sensing diagnosis of key safety execution components and active compensation for performance degradation, significantly improving the reliability of the braking system throughout its life cycle.

[0034] Example 1: In a real test scenario of a vehicle equipped with the braking control system of the present invention, a vehicle is traveling downhill for a long distance in a high-altitude mountainous area. The road section has both continuous curves and irregular undulating roads. The initial battery charge state of the vehicle is At a high level; Under this working condition, the vehicle braking system faces multiple concurrent challenges, namely, continuous energy recovery causes the battery charging state to quickly approach the upper limit, and the uneven road surface introduces high-frequency noise into the driver's brake pedal signal; When the vehicle enters the downhill section, the driver controls the vehicle speed through intermittent and variable-intensity braking operations. At this moment, the braking intention signal purification module starts to operate immediately. It obtains the real-time road bump index by real-time solution of the wheel speed sensor data, identifies the moderate bumps on the current road surface, and dynamically extends the filter time constant acting on the original signal of the brake pedal based on the index. A smoothed total braking torque request that can reflect the driver's true braking intention is therefore stably transmitted to the torque distribution module; At the same time, the predictable braking force calculation module is based on the real-time battery temperature and the continuously rising , through the internal battery energy acceptance model, calculates and outputs a sustainable, numerically gradually decreasing, predictable regenerative braking force baseline under the current state The interaction between these two modules creates a system-level synergy. The front-end signal purification provides high-quality, noise-free input for the rear-end deterministic torque distribution, which is a necessary prerequisite for the latter to achieve precise control. The rear-end distribution logic based on a stable baseline also enables the value of the front-end signal purification to be fully reflected. The combination of the two ensures that the initial request for braking force and the final distribution of the regenerative component are smooth and without mutations.

[0035] As the vehicle continues to go downhill, the contradiction that is common in the industry is intensified in this scenario, that is, the conflict between maximizing energy recovery efficiency and ensuring braking control quality. Under the traditional braking strategy, the system will try to use all available regenerative braking force in pursuit of maximum energy recovery, but as the battery state approaches saturation, its energy acceptance capacity will drop rapidly, resulting in a sudden attenuation of regenerative braking force and a significant intervention of friction braking; the control system of the present invention resolves this contradiction through the core mechanism of predictable regenerative braking force baseline. The system does not aim to maximize energy recovery in real time, but anchors the commanded regenerative braking torque at a level that represents a stable and sustainable level. When the battery's actual energy acceptance capacity fluctuates drastically due to state changes, the baseline only undergoes smooth and predictable adjustments, thereby maintaining a high degree of continuity in the output of regenerative braking torque, while the remaining braking force demand is precisely supplemented by the friction braking system. This mechanism transforms the pursuit of energy recovery from doing one's best to doing what one can, achieving the unity of energy recovery and handling quality within a single architecture.

[0036] In this process, the technical solution of the application also embodies the redefinition of the control problem. The traditional technical path strives to accurately track the maximum regenerative braking force that can be provided through increasingly complex battery models and prediction algorithms, which is a typical high-difficulty state prediction problem. The control system of the application avoids accurate prediction of the maximum value, and changes the problem from predicting an uncertain upper limit to establishing a reliable lower limit. Through output This deterministic torque reference eliminates the need for the system to deal with the dramatic nonlinear changes in battery state, and the system works in a control interval defined by itself and relatively linear. The original complex prediction problem is no longer a necessary condition for stable control in the new control framework, the control logic of the system is simplified, and the robustness is correspondingly enhanced. The vehicle eventually smoothly drives off the mountainous road section. Throughout the process, regardless of the changes in battery state and the interference of road conditions, the deceleration of the vehicle always maintains a linear and predictable mapping relationship with the pedal input of the driver, and there is no perceptible braking jerk. At the same time, the regenerative braking system recovers electric energy within its sustainable working interval. The overall performance of the system proves the effectiveness of a control architecture, that is, by actively managing and outputting determinism, rather than passively responding and adapting to uncertainty, multiple control objectives such as safety, comfort and energy efficiency can be achieved simultaneously under complex dynamic conditions.

[0037] Embodiment 2: To objectively verify the effectiveness of the technical solution of the application in suppressing the fluctuation of braking stability at high state of charge, a hardware-in-the-loop simulation test environment is constructed. The core of the test platform is a vehicle controller entity equipped with the complete algorithm of the brake control system of the application, which is connected to a high-fidelity real-time simulator. The simulator accurately simulates the vehicle dynamics model including high-speed permanent magnet synchronous motor, transmission system, friction brake mechanism, and a battery model that can dynamically reflect the change of energy acceptance capacity with state of charge and temperature. The brake request in the test is generated by a programmable pedal actuator module, which is set to reproduce a typical continuous braking condition that can induce instability of the traditional system.

[0038] The key test procedure of this test is designed as a constant braking deceleration request event, which aims to systematically evaluate the dynamic stability of the control system when the state of charge of the battery passes through its nonlinear response region. In this procedure, the target value of the constant braking deceleration request is set to 0.3g, which simulates moderate continuous braking. The initial condition of the test is that the state of charge of the battery model is 90%, and the test continues until the state of charge of the battery model in the simulation model reaches 80%. ​The upper limit of 98% is reached. The selection of this SOC variation range is based on the consideration of covering the typical range in the prior art where the regenerative braking torque fluctuates violently due to the activation of the battery overcharge protection logic, thereby providing a clear identification window for the performance difference between the two control strategies. The test is divided into a control group and a test group. The control group adopts a traditional control strategy aimed at maximizing energy recovery, that is, the instruction regenerative braking torque tracks the maximum acceptable regenerative braking torque of the transient changes of the battery model output in real time. The test group adopts the control strategy based on the predictable regenerative braking force baseline of the present invention. Under the same constant braking deceleration request, the operating data of the two test groups show significant differences. During the test of the control group, when the battery charging state is After exceeding 95%, the commanded regenerative braking torque showed high-frequency, large-scale irregular drops, which directly led to a violent reverse fluctuation of the commanded friction braking torque in order to compensate for the torque gap. In contrast, the commanded regenerative braking torque of the test group remained smooth throughout the braking process, and its value strictly followed the predictable regenerative braking force baseline calculated by the system. The command friction braking torque also shows a steady linear growth. Table 1 records the results at different Core performance data of the two groups at key nodes.

[0039] Table 1: Performance data comparison of the two control strategies under high SOC continuous braking conditions.

[0040]

[0041] Referring to the data in Table 1, it can be observed that the actual deceleration fluctuation rate of the control group is The reason for the sharp increase after exceeding 95% is that the control logic is directly exposed to the uncertainty of the electrochemical state inside the battery. The data of the test group show that the control strategy adopted by the present invention always limits the command regenerative braking torque to the predictable regenerative braking force baseline through the torque distribution module. Under this stable anchor point, the vehicle's braking execution layer is successfully decoupled from the battery's energy receiving layer, so that the vehicle's final deceleration response is no longer subject to instantaneous fluctuations in the battery's internal state. This hardware-in-the-loop simulation test confirmed that under simulated continuous high-SOC braking conditions, the control system using the technical solution of the present invention suppresses the vehicle's actual deceleration fluctuation rate to a low level, and compared with traditional control strategies, its braking process smoothness and stability are improved. The test results confirm the effectiveness of the solution of the present invention at the data level, that is, by establishing a deterministic regenerative braking force output benchmark, it is possible to solve the problem of reduced braking quality caused by energy recovery strategies in hybrid braking systems.

[0042] Example 3: This example combines Figures 1 to 3 , a vehicle braking control system based on a high-speed permanent magnet synchronous motor is described. Figure 1 As shown, the system receives the driver's braking request through the driver's brake pedal. The signal is first processed by the braking intention signal purification module, and then input into the torque distribution module together with the information received from the state perception module, which obtains the battery status including SOC and temperature in real time to determine the total braking torque. The predictable braking force calculation module calculates the predictable regenerative braking force baseline based on the battery status obtained by the state perception module. The baseline can be affected by the energy acceptance model online correction module and the thermal shock compensation module, wherein the thermal shock compensation module also receives the battery terminal voltage change rate as input. The torque distribution module distributes the total braking torque as a command regenerative braking force. The system generates braking torque and command friction braking torque, and coordinates the control of the high-speed permanent magnet synchronous motor and the friction braking mechanism. The system also includes a braking decision arbitration module, which receives input from the vehicle body stability system (independent) and arbitrates the command friction braking torque output by the torque distribution module when necessary; in addition, the friction system health status detection module detects the health status of the friction braking mechanism and can compensate for the command friction braking torque. The system is designed to achieve linear mapping of pedal stroke and deceleration, and maintain output stability under complex working conditions, solving the technical problem of balancing energy recovery and handling quality in hybrid braking systems.

[0043] like Figure 2 As shown in the figure, the actual deceleration fluctuation rate (%) of the control system of the present invention is compared with that of the traditional control system under continuous braking conditions with a battery state of charge (SOC) ranging from 90% to 98%. It can be seen from the figure that the control system of the present invention always maintains an extremely low actual deceleration fluctuation rate in the entire battery state of charge range (90% to 98%), and the curve is smooth; while the actual deceleration fluctuation rate of the traditional control system begins to increase significantly after the battery state of charge SOC reaches about 94%, reaches a peak at 97%, and then decreases slightly, but the overall fluctuation is violent, indicating that the control system of the present invention has better output stability under complex conditions, and is used to improve the coordination problem between energy recovery and control quality in hybrid braking systems.

[0044] like Figure 3As shown in the figure, during the regenerative braking process of the permanent magnet synchronous motor, the energy acceptance model online correction module will feedback the terminal voltage rate from the battery system and reversely infer the actual accepted braking torque based on this; then, the module calculates the residual with the instruction. If the residual exceeds the threshold, it analyzes the voltage change characteristics and queries the compensation coefficient table, and then sends the correction parameters to the predictable braking force calculation module to update the baseline calculation. If the residual is within the normal range, the module continues to monitor; finally, the module can adapt to the battery aging status in real time, thereby continuously correcting the predictable regenerative braking force baseline to ensure the long-term robustness of the system.

[0045] Example 4: In a vehicle that has been in use for a considerable period and whose battery and friction system have naturally aged and worn, the control system of the present invention demonstrates its adaptive and self-diagnostic capabilities in dealing with parameter drift and physical degradation. When the vehicle brakes, due to the aging characteristics of the battery's internal resistance, there is a continuous deviation between the battery's actual energy acceptance capacity and the model's predicted value. This deviation accumulates under specific operating conditions and causes braking force fluctuations not caused by pedal input. To address this control deviation caused by model inaccuracy, the energy acceptance model online correction module integrated in the system is activated. During each regenerative braking period, this module synchronously obtains the value of the commanded regenerative braking torque and the actual regenerative braking torque calculated by inversely calculating the instantaneous rate of change of the battery terminal voltage at a high sampling rate, and calculates the difference between the two to obtain a real-time model residual. The system calculates the residual signal in The moving average absolute value within a preset time window, when the average value continuously exceeds a preset residual threshold, the correction logic is triggered; the setting of this threshold follows a systematic calibration procedure, and its benchmark is the statistical standard deviation of the residual signal baseline noise measured on a precisely modeled battery during the vehicle's factory calibration phase. The threshold itself is set to three times the standard deviation, so as to ensure the correction sensitivity while avoiding overreaction to the normal electronic control noise of the system; after the correction logic is triggered, the system performs a fast Fourier transform on the model residual sequence in the most recent period, and selects the corresponding compensation coefficient from the pre-stored compensation coefficient lookup table based on the distribution characteristics of the residual energy spectrum; when the residual energy is mainly concentrated in the low frequency band, the system determines that the model has a quasi-static gain or bias error, and selects a set of slope coefficients that focus on adjusting the predictable regenerative braking force baseline calculation formula When the energy is concentrated in a specific high-frequency band, it is determined that there is a certain oscillatory instability, and the system will select another set of compensation coefficients to enhance damping, thereby making real-time corrections to the predictable regenerative braking force baseline.

[0046] At the end of the braking process, when the vehicle speed drops to within the preset low-speed diagnostic window, the system's friction system health status detection module automatically performs a diagnostic process; the module first determines a target amplitude of the regenerative braking torque perturbation based on the vehicle's current estimated total mass and real-time speed; its amplitude The determination of follows a linear relationship, that is, it is proportional to the estimated mass and inversely proportional to the current vehicle speed, so as to maintain a relatively constant target perturbation deceleration under different conditions; after applying the perturbation, the module captures the actual inertial deceleration response curve of the vehicle through the on-board high-precision accelerometer and extracts two characteristic parameters from it, namely the time from the perturbation application to the deceleration reaching the peak value , and the integral value of the response curve within a specific time period after the peak ,in Mainly reflects the response delay of the braking system, The system then compares these two measured characteristic parameters with the corresponding parameters of the health state standard response curve obtained during the initial vehicle calibration phase, and calculates a quantitative health state index according to the following procedure: , , where the weight coefficient and It is determined by regression analysis after testing a large number of friction systems with different wear levels; the health status index It is recorded in the vehicle's diagnostic log and used to perform closed-loop compensation for the commanded friction braking torque in subsequent braking events. Through the collaborative work of the above two modules, the system not only corrects the regenerative braking model drift caused by battery aging online and restores the smoothness of the braking process, but also completes the non-sensing detection and quantitative assessment of the health status of the friction system. This control strategy that combines online adaptive correction with periodic self-diagnosis enables the vehicle to autonomously respond to the performance degradation of key components throughout its life cycle, thereby maintaining the high stability and reliability of the braking system.

[0047] Example 5: In a standardized calibration procedure before the technical solution of the present invention is applied to a specific vehicle model, to accurately set the preset deviation threshold in the braking decision arbitration module, a test vehicle is subjected to a benchmark braking test sequence covering various operating conditions at a professional testing ground. This sequence includes representative subjects such as gentle braking at different initial speeds, moderate braking, and emergency braking that triggers the anti-lock braking system. Throughout the test, the vehicle's braking system is confirmed to be in a healthy and fault-free state. During this period, the arbitration module continuously records the commanded friction braking torque output by the torque distribution module of the present invention and the total friction braking torque calculated by the independent body stability system in a high-frequency mode, and calculates the instantaneous deviation rate between the two in real time, thereby obtaining a baseline deviation database representing inherent calculation differences and communication delays between systems and a non-fault state. The final value of the preset deviation threshold is not a fixed empirical value, but is determined as the sum of the statistical mean and six standard deviations of all data points in the baseline deviation database. This statistically based setting method ensures that the arbitration module can largely ignore normal benign fluctuations between systems and only respond to significant abnormal deviations caused by actual system failures.

[0048] To ensure that the triggering logic of the thermal shock compensation module accurately adapts to the inherent thermodynamic differences between the motors and battery packs used in different vehicle models, a standardized thermal shock response calibration procedure was implemented. This procedure places the test vehicle in an environmental chamber capable of rapid temperature changes and subjects it to a series of extreme ambient temperature step events covering the vehicle's design operating temperature range. During this process, the system continuously records the fast-response temperature indicator estimated from the high-speed permanent magnet synchronous motor winding resistance and the slow-response battery temperature indicator obtained from the state sensing module, and calculates the difference in the rate of change between the two. The preset rate of change threshold in the thermal shock compensation module is ultimately calibrated to 1.2 times the maximum difference in the rate of change between the two temperature indicators observed in all the aforementioned non-fault temperature step tests. This calibration method, based on measured physical boundaries, establishes a sensitive and reliable objective benchmark for determining thermal shock events, thereby mitigating the risk of false or missed triggering due to improper threshold setting.

[0049] Example 6: In a standardized engineering procedure for deploying the braking control system of the present invention to a new vehicle platform, the first step is to construct an offline energy acceptance capability model for the new battery pack carried by the vehicle. This procedure places the battery pack to be calibrated in a high-precision temperature-controlled test chamber and defines a two-dimensional grid test matrix within its full operating temperature range and full operating charging state range. Subsequently, the control system drives the state of the battery pack to each grid node in turn and applies a charging power pulse with a standard waveform to each node, while recording the terminal voltage response curve of the battery with high-frequency sampling. By analyzing the dynamic characteristics of the response curve, the instantaneous maximum acceptable charging power of the battery at this specific temperature and charging state can be calculated. After conversion, this value is filled into the corresponding index position in the two-dimensional lookup table of the battery energy acceptance capability model as the core parameter. By systematically traversing all grid nodes and completing data filling, an energy acceptance capability model that can accurately reflect the electrochemical characteristics of this specific battery pack is established.

[0050] After completing the battery model, the next phase of the process involved integrating the control system into a prototype vehicle and performing targeted calibration of the brake intention signal purification module on a test field encompassing various standard road surfaces. The test vehicle was driven at a series of preset speeds, without braking, over smooth roads, Belgian roads, and other bumpy roads with varying spectral density characteristics. During this process, the system recorded a real-time road roughness index calculated from the high-frequency fluctuations of the wheel speed sensor signals. Simultaneously, a high-precision laser displacement sensor monitored the driver's unintended brake pedal movement caused by road vibration. The core control parameter in the signal purification module, namely the mapping between the bumpiness index and the brake signal filter time constant, is not a preset fixed value but is determined through regression analysis of the data collected during this phase. The optimization objective is to minimize the error between the adaptively filtered pedal signal and the true pedal position signal, measured by the laser sensor, after removing unintended movement. This data-driven calibration method enables the optimal brake intention signal purification logic to be matched to the suspension system and tire characteristics of a specific vehicle model.

[0051] Finally, to ensure that the sensors relied upon by the system's diagnostic modules are in normal working order, a pre-emptive self-test and online fault-tolerance mechanism are integrated into the system startup process. Each time the vehicle is powered on and stationary, the system first reads the longitudinal acceleration reading of the onboard accelerometer and verifies that its output value, within a predetermined time window, remains stable within a very narrow tolerance band centered around zero and determined by the sensor's own noise level. If the reading continuously exceeds this tolerance band, the system marks the accelerometer as unreliable and automatically disables the friction system health status detection module that relies on the sensor signal during the current driving cycle, thereby mitigating the risk of misdiagnosis due to sensor failure. This series of standardized engineering procedures, covering everything from core model construction and application parameter calibration to sensor status monitoring, ensures the performance stability of the brake control system of the present invention across different hardware platforms and usage environments, and is an extended implementation method known to those skilled in the art.

[0052] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A vehicle braking control system based on a high-speed permanent magnet synchronous motor, characterized in that: include: a state sensing module configured to obtain the current operating state of the vehicle in real time, the current operating state including the battery charge state and battery temperature; a predictable braking force calculation module configured to calculate a predictable regenerative braking force baseline based on a current operating state and a battery energy acceptance model stored in the controller; the predictable regenerative braking force baseline represents the regenerative braking torque that can be continuously and stably provided by the high-speed permanent magnet synchronous motor within a predetermined time window, and the baseline remains unchanged within the predetermined time window; a torque distribution module configured to, upon receiving a driver-requested total braking torque, determine a commanded regenerative braking torque as a minimum of the total braking torque and a predictable regenerative braking force baseline; and determining a commanded friction braking torque as a difference between a total braking torque and a commanded regenerative braking torque; wherein the torque distribution module cooperatively controls the high-speed permanent magnet synchronous motor and a friction braking mechanism of the vehicle based on the commanded regenerative braking torque and the commanded friction braking torque; The system also includes an energy acceptance model online correction module configured to: during the regenerative braking operation of the high-speed permanent magnet synchronous motor, calculate the model residual between the commanded regenerative braking torque and the actual received regenerative braking torque inferred based on the instantaneous change rate of the battery terminal voltage; and when the absolute value of the model residual exceeds a preset residual threshold, select a compensation coefficient from a stored compensation coefficient lookup table based on the specific characteristics of the instantaneous change rate of the battery terminal voltage to perform real-time correction on the predictable regenerative braking force baseline; in the predictable braking force calculation module, the predictable regenerative braking force baseline The calculation formula can be expressed as: ,in, It is a slope coefficient calibrated based on battery temperature and battery aging status; is the battery's highest state of charge threshold; is the current charging status of the battery.

2. A vehicle braking control system based on a high-speed permanent magnet synchronous motor according to claim 1, characterized in that: In the predictable braking force calculation module, the battery energy acceptance model stored in the controller is a two-dimensional lookup table. The input of this two-dimensional lookup table is the battery charge state and battery temperature, and its output is the stability coefficient. The stability coefficient is used to determine the predictable regenerative braking force baseline and indicate the stability of the battery's current acceptance of charging energy.

3. The vehicle braking control system based on a high-speed permanent magnet synchronous motor according to claim 1, characterized in that: The torque distribution module is further configured to dynamically adjust a predictable regenerative braking force baseline based on a vehicle driving mode, wherein the driving mode includes a comfort mode and a sport mode, and the adjustment is implemented based on a predetermined braking response curve corresponding to the driving mode.

4. The vehicle braking control system based on a high-speed permanent magnet synchronous motor according to claim 1, characterized in that: It also includes a braking intention signal purification module, which is configured to: calculate a real-time road surface roughness index based on the high-frequency fluctuation amplitude of the wheel speed sensor signal in the vehicle's non-braking state; and dynamically adjust the filtering time constant of the filter acting on the driver's brake pedal original signal according to the road surface roughness index; wherein, the larger the road surface roughness index, the longer the filtering time constant.

5. The vehicle braking control system based on a high-speed permanent magnet synchronous motor according to claim 1, characterized in that: It also includes a braking decision arbitration module, which is configured to: obtain a total friction braking torque representing the driver's braking request calculated by a vehicle body stability system independent of the braking control system; and when the instantaneous deviation rate between the command friction braking torque output by the torque distribution module and the total friction braking torque exceeds a preset deviation threshold, the arbitration module rejects the command friction braking torque output by the torque distribution module and adopts the total friction braking torque as the final command friction braking torque.

6. The vehicle braking control system based on a high-speed permanent magnet synchronous motor according to claim 1, characterized in that: The system also includes a thermal shock compensation module configured to estimate a fast-response temperature indicator based on the real-time resistance of the high-speed permanent magnet synchronous motor windings; and determine that a thermal shock event has occurred when the difference between the rate of change of the fast-response temperature indicator and the rate of change of the slow-response battery temperature indicator obtained from the state sensing module exceeds a preset rate of change threshold. And when a thermal shock event occurs, the thermal shock compensation module lowers the predictable regenerative braking force baseline through a suppression factor that is inversely proportional to the difference between the fast response temperature indicator and the slow response battery temperature indicator.

7. The vehicle braking control system based on a high-speed permanent magnet synchronous motor according to claim 1, characterized in that: It also includes a friction system health status detection module, which is configured to: instruct the high-speed permanent magnet synchronous motor to output a regenerative braking torque micro-disturbance of a limited size within a low-speed diagnostic window before the vehicle stops braking; measure the vehicle's actual inertia deceleration response curve generated by the micro-disturbance through an accelerometer configured in the vehicle; and determine the health status index of the friction braking system by comparing the difference between the actual inertia deceleration response curve and a pre-stored health status standard response curve, and perform closed-loop compensation on the commanded friction braking torque based on the health status index.

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