Braking recovery torque dynamic control method and device based on four-dimensional road condition risk field

Abstract

The application discloses a kind of based on four-dimensional road condition risk field braking recovery torque dynamic control method and device, the method includes: real-time acquisition four-dimensional road condition parameters of vehicle driving section;Four-dimensional road condition parameters are respectively normalized, and the corresponding road surface friction risk sub-coefficient, rain fog visibility risk sub-coefficient, bend curvature risk sub-coefficient and road gradient risk sub-coefficient are obtained;Based on four-dimensional road condition parameters, real-time comprehensive road condition risk coefficient is calculated by weighted summation;According to the comprehensive risk coefficient, the working condition safety level is divided according to preset rule, and the braking recovery torque constraint coefficient of corresponding level is matched in preset database;Combining reference recovery torque and braking recovery torque constraint coefficient, the real-time braking recovery torque upper limit is calculated, and the actual braking recovery torque is not more than real-time braking recovery torque upper limit.This application solves the problem that dynamic balance of braking safety and energy recovery efficiency cannot be realized in prior art.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a method and device for dynamic control of braking recovery torque based on a four-dimensional road condition risk field. Background Technology

[0002] New energy vehicles have achieved a significant increase in driving range thanks to regenerative braking technology. However, in complex conditions such as low-traction roads, rainy and foggy weather, sharp bends, and steep slopes, if the regenerative braking torque is not dynamically adjusted according to real-time road conditions, it can easily lead to instability phenomena such as excessive wheel slippage, body skidding, and fishtailing, which seriously reduces the vehicle's braking safety.

[0003] Most existing regenerative braking control technologies only consider parameters such as road surface adhesion coefficient and vehicle speed, lacking a coupled quantitative assessment of multiple road condition risk factors, and thus failing to accurately characterize the risk level of road conditions across all scenarios. Furthermore, they lack an active constraint mechanism for regenerative braking torque based on comprehensive road condition risks, often resorting to passive remediation through the vehicle stability system after vehicle instability occurs, making it difficult to avoid braking safety risks under extreme conditions at the source. Therefore, developing a risk assessment scheme that integrates multi-dimensional road condition parameters with a coordinated control scheme for regenerative braking torque has become an urgent technical problem to be solved in the field of braking safety control for new energy vehicles. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method and device for dynamic control of regenerative braking torque based on a four-dimensional road condition risk field, which aims to solve the problem that the existing regenerative braking torque control cannot achieve a dynamic balance between braking safety and energy recovery efficiency.

[0005] The embodiments of the present invention are implemented as follows: A dynamic control method for braking recovery torque based on a four-dimensional road condition risk field, the method comprising: Real-time collection of four-dimensional road condition parameters of the vehicle's driving route. The four-dimensional road condition parameters include at least the road surface friction coefficient, rain and fog visibility, curve curvature and road slope. The four-dimensional road condition parameters are normalized to obtain the corresponding road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient. A four-dimensional road condition risk field model is constructed, and the real-time comprehensive road condition risk coefficient is calculated by weighted summation based on the four-dimensional road condition parameters. Based on the comprehensive risk coefficient, the working condition safety level is divided according to the preset rules, and the corresponding braking recovery torque constraint coefficient is matched in the preset database. By combining the baseline recovery torque and the braking recovery torque constraint coefficient, the upper limit of the real-time braking recovery torque is calculated, and the actual braking recovery torque is controlled not to exceed the upper limit of the real-time braking recovery torque.

[0006] Furthermore, in the aforementioned dynamic control method for braking recovery torque based on a four-dimensional road condition risk field, the step of normalizing the four-dimensional road condition parameters to obtain the corresponding road surface friction risk sub-coefficient, rain / fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient includes: ; ; ; ; in, For road surface friction risk coefficient, The coefficient of friction of the road surface. The maximum coefficient of friction for dry asphalt pavement. This is the minimum coefficient of friction for the ice surface; For rain and fog visibility risk sub-coefficient, For visibility in rain and fog, For the safety energy threshold, This represents the threshold for extremely poor visibility. For rain and fog visibility risk sub-coefficient, For the curvature of the curve, This refers to the curvature of the extreme curve. Curvature of a straight road; This is the sub-coefficient for road slope risk. For road slope, This is the extreme road gradient. The slope is for a straight road.

[0007] Furthermore, in the aforementioned dynamic control method for braking recovery torque based on a four-dimensional road condition risk field, the step of constructing a four-dimensional road condition risk field model and calculating the real-time comprehensive road condition risk coefficient based on four-dimensional road condition parameters through weighted summation includes:

[0008] : The risk weight coefficients for the corresponding parameters are calibrated using the analytic hierarchy process (AHP) combined with real-vehicle road tests. , , , These are the road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient.

[0009] Furthermore, in the above-mentioned dynamic control method for braking recovery torque based on a four-dimensional road condition risk field, the step of classifying the operating condition safety level according to a preset rule based on a comprehensive risk coefficient includes: Level I operating conditions The first threshold corresponds to dry road surface, sunny weather, and straight road, with no risk of braking instability. Level II operating conditions First threshold <Second Threshold Low risk of braking instability on wet and slippery roads, in light fog, and on gently curving roads.

[0010] Level III operating conditions Second threshold <Third threshold Risk of brake instability in situations involving snow / water accumulation, moderate fog, and sharp bends.

[0011] Level IV operating conditions Third threshold ≤ Fourth threshold It poses a high risk of braking instability in icy road conditions, dense fog / heavy rain, extreme sharp bends, and steep slopes.

[0012] Furthermore, in the above-mentioned dynamic control method for regenerative braking torque based on a four-dimensional road condition risk field, the step of calculating the upper limit of the real-time regenerative braking torque by combining the reference regenerative braking torque and the regenerative braking torque constraint coefficient includes: ; In the formula: This is the upper limit of the real-time braking recovery torque; This is the constraint coefficient for the regenerative braking torque. The baseline recovery torque is used.

[0013] Furthermore, in the above-mentioned dynamic control method for regenerative braking torque based on a four-dimensional road condition risk field, the step of matching the corresponding level of regenerative braking torque constraint coefficients in a preset database includes: When under Level I operating conditions, the braking recovery torque constraint coefficient is 0.9-1.0; When operating under Level II conditions, the braking recovery torque constraint coefficient is 0.6-0.8. When operating under Level III conditions, the braking recovery torque constraint coefficient is 0.3-0.5. When operating under Level IV conditions, the braking recovery torque constraint coefficient is 0.05-0.2.

[0014] Furthermore, the above-mentioned dynamic control method for braking recovery torque based on a four-dimensional road condition risk field further includes: During the control process, the wheel slip ratio and vehicle posture are monitored in real time. If the wheel slip ratio exceeds the safety threshold, the torque constraint coefficient is immediately reduced.

[0015] Another object of the present invention is to provide a dynamic control device for braking recovery torque based on a four-dimensional road condition risk field, the device comprising: The data acquisition module is used to collect four-dimensional road condition parameters of the road segment in real time. The four-dimensional road condition parameters include at least the road surface friction coefficient, rain and fog visibility, curve curvature and road slope. The calculation module is used to normalize the four-dimensional road condition parameters to obtain the corresponding road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient. The module is used to build a four-dimensional road condition risk field model and calculate the real-time comprehensive road condition risk coefficient based on the four-dimensional road condition parameters through weighted summation. The classification module is used to classify the safety level of the working condition according to the comprehensive risk coefficient and preset rules, and match the corresponding braking recovery torque constraint coefficient in the preset database. The control module is used to calculate the upper limit of the real-time braking recovery torque by combining the reference recovery torque and the braking recovery torque constraint coefficient, and to control the actual braking recovery torque to not exceed the upper limit of the real-time braking recovery torque.

[0016] Another object of the present invention is to provide a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0017] Another object of the present invention is to provide a vehicle including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method described above.

[0018] This invention collects four-dimensional road condition parameters in real time, including road surface friction coefficient, rain / fog visibility, curve curvature, and road slope, comprehensively covering four core road condition elements affecting braking safety: road grip, visibility conditions, road alignment, and road inclination. It no longer relies solely on the road surface adhesion coefficient for judgment. Normalization is performed on the four types of road condition parameters to obtain consistent sub-coefficients for road surface friction, rain / fog visibility, curve curvature, and road slope, eliminating computational interference caused by differences in the dimensions of different physical quantities. A four-dimensional road condition risk field model is constructed based on the weighted summation of these four risk sub-coefficients. This method quantifies and couples multiple road condition risks to obtain a unified real-time comprehensive road condition risk coefficient, enabling synchronous coupling and quantitative assessment of multiple road condition risk factors and accurately distinguishing the overall road condition hazard level under different driving scenarios. It classifies corresponding operating condition safety levels according to the comprehensive road condition risk coefficient and retrieves braking regenerative torque constraint coefficients from a preset database that match each safety level. The constraint coefficients are then linked with the baseline regenerative torque to calculate the upper limit of the real-time braking regenerative torque under the current road condition. This actively limits the actual output of the vehicle's regenerative braking torque to not exceeding this upper limit, reducing the proportion of motor regenerative braking force in high-risk road conditions from the source of braking torque distribution and increasing the proportion of mechanical braking force distribution. This solves the technical problems of existing braking energy recovery control technologies that only consider a few parameters such as road surface adhesion coefficient and vehicle speed, lack a means of coupling and quantifying multiple road condition risks, cannot accurately characterize the road condition risk level in all scenarios, and lack an active constraint mechanism for braking regenerative torque based on comprehensive road condition risk. These technologies can only passively compensate for vehicle instability after signs of instability appear, failing to suppress braking safety hazards in extreme road conditions such as wet, low-visibility, sharp bends, and steep slopes from the source.

[0019] The embodiments of the present invention also have at least the following beneficial effects: 1. This invention innovatively integrates road surface friction coefficient, rain and fog visibility, curve curvature, and road slope to construct a four-dimensional road condition risk field, realizing accurate quantification of road condition risks coupled by multiple factors, comprehensively covering various extreme driving conditions, and the risk assessment accuracy is far higher than that of single parameter assessment methods. 2. Based on the real-time comprehensive risk coefficient, four safety levels are divided, and a graded torque active constraint mechanism is established to dynamically limit the braking recovery torque from the source, completely avoiding wheel slippage and vehicle instability under low adhesion, rain and fog, curves and steep slopes, and greatly improving braking safety assurance capability. 3. Achieve a dynamic balance between braking safety and energy recovery efficiency, maximizing energy recovery under safe conditions and prioritizing driving stability under risky conditions, adapting to all driving scenarios. 4. It adopts a closed-loop control architecture with real-time acquisition, real-time calculation, and real-time control. The system has a fast response speed and high control accuracy. It can be directly integrated into the whole vehicle control system of new energy vehicles, with strong compatibility and practicality. Attached Figure Description

[0020] Figure 1 This is a flowchart of the dynamic control method for braking recovery torque based on a four-dimensional road condition risk field in the first embodiment of the present invention; Figure 2 This is a structural block diagram of the braking recovery torque dynamic control device based on a four-dimensional road condition risk field in the third embodiment of the present invention.

[0021] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0022] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0023] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] Example 1 Please see Figure 1 The figure shows a dynamic control method for braking recovery torque based on a four-dimensional road condition risk field in the first embodiment of the present invention, the method including steps S10 to S14.

[0026] Step S10: Real-time acquisition of four-dimensional road condition parameters of the vehicle's driving route. The four-dimensional road condition parameters include at least the road surface friction coefficient, rain and fog visibility, curve curvature, and road slope.

[0027] After the vehicle is powered on and in normal operation, the vehicle controller continuously sends acquisition commands to various onboard acquisition devices. Different parameters correspond to dedicated acquisition hardware and calculation logic. The road friction coefficient is calculated jointly by wheel speed sensors and the Burckhardt road adhesion identification model. This model is a commonly used identification algorithm in the field of vehicle road surface recognition, which can accurately determine the current road surface adhesion based on changes in wheel speed. Rain and fog visibility is directly collected by the onboard meteorological visibility sensor, which can identify the atmospheric transparency in front of the vehicle and output the corresponding distance value. Curve curvature is calculated by fusing onboard navigation map data and vehicle steering angle sensor data, following the conversion relationship between road turning radius and curvature. Road slope is collected in real time by the onboard tilt sensor, which can sense the tilt angle of the vehicle body relative to the horizontal plane and output the corresponding slope value. All acquired raw signals undergo filtering and noise reduction processing to eliminate abnormal data caused by electromagnetic interference and signal jitter, ensuring the accuracy of input parameters.

[0028] Step S11: Normalize the four-dimensional road condition parameters to obtain the corresponding road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient.

[0029] The four-dimensional road condition parameters were normalized to obtain corresponding sub-coefficients for road surface friction risk, rain / fog visibility risk, curve curvature risk, and road slope risk. Since the physical dimensions of the four types of original road condition parameters are different and their numerical ranges vary significantly, they cannot be directly fused. Therefore, normalization is required to convert all parameters into dimensionless risk sub-coefficients within the range of zero to one. The magnitude of each sub-coefficient directly corresponds to the level of braking safety risk posed by a single road condition factor; a larger value indicates a higher risk.

[0030] For example, the step of normalizing the four-dimensional road condition parameters to obtain the corresponding road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient includes: ; ; ; ; in, For road surface friction risk coefficient, The coefficient of friction of the road surface. This is the maximum friction coefficient for dry asphalt pavement. In practical engineering applications, this parameter is fixed at 0.85. The minimum friction coefficient of ice surface can be fixed at 0.1; the larger the road surface friction coefficient, the stronger the road surface grip, and the smaller the corresponding road surface friction risk coefficient. Conversely, the smoother the road surface, the closer the risk coefficient is to one. For rain and fog visibility risk sub-coefficient, For visibility in rain and fog, For safety, a threshold value of 200 meters is uniformly used in engineering applications. Visibility above this value will not interfere with the driver's visual perception during braking. The threshold for extremely poor visibility is uniformly set at 50 meters. Visibility below this value will severely impair the driver's observation and vehicle prediction abilities. The higher the visibility, the lower the corresponding risk sub-coefficient; the lower the visibility, the higher the risk sub-coefficient. For rain and fog visibility risk sub-coefficient, For the curvature of the curve, This represents the extreme curvature of a curve, fixed at 0.02 per meter. The sharper the curve, the greater the curvature value, and the higher the corresponding curve curvature risk coefficient. On straight roads, this coefficient is always zero. Curvature of a straight road; This is the sub-coefficient for road slope risk. For road slope, The maximum road gradient is set at 30 degrees. The slope of a straight road is fixed at zero degrees.

[0031] Step S12: Construct a four-dimensional road condition risk field model, and calculate the real-time comprehensive road condition risk coefficient based on the four-dimensional road condition parameters by weighted summation. Among them, the real-time comprehensive road condition risk coefficient is calculated by weighted summation based on four-dimensional road condition parameters. The four-dimensional road condition risk field model is the core evaluation model of this invention. This model takes four independent risk sub-coefficients as input items, performs weighted fusion according to preset weights, integrates the scattered single road condition risks into a comprehensive risk coefficient that can characterize the overall road condition hazard level, and realizes the coupled quantitative evaluation of multiple road condition risk factors.

[0032] For example, the step of constructing a four-dimensional road condition risk field model and calculating the real-time comprehensive road condition risk coefficient based on four-dimensional road condition parameters through weighted summation includes:

[0033] : These are the risk weight coefficients for the corresponding parameters, and the sum of the four weight coefficients is always equal to one. The weight coefficients are not randomly assigned, but are obtained through a combination of the analytic hierarchy process (AHP) and extensive real-vehicle road tests. The AHP is a classic method in multi-objective weight calibration, capable of rationally allocating weight ratios based on the impact of different road conditions on braking safety. In typical engineering implementation scenarios, the weight coefficient for road surface friction ranges from 0.3 to 0.4, the weight coefficient for rain / fog visibility ranges from 0.2 to 0.3, the weight coefficient for curve curvature ranges from 0.2 to 0.3, and the weight coefficient for road slope ranges from 0.1 to 0.2. , , , These are the road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient.

[0034] Step S13: Based on the comprehensive risk coefficient, classify the working condition safety level according to the preset rules, and match the corresponding braking recovery torque constraint coefficient in the preset database.

[0035] Specifically, the system classifies operating condition safety levels according to preset rules based on a comprehensive risk coefficient, and matches the corresponding braking regenerative torque constraint coefficient in a preset database. The vehicle controller pre-stores the operating condition level classification rules and the constraint coefficient database corresponding to different levels. After obtaining the comprehensive risk coefficient, it determines the current operating condition safety level of the vehicle according to the interval classification standard, and then retrieves the braking regenerative torque constraint coefficient suitable for the current operating condition based on the level index database. This coefficient is used to limit the maximum output ratio of the braking regenerative torque.

[0036] Step S14: Combine the reference recovery torque and the braking recovery torque constraint coefficient to calculate the upper limit of the real-time braking recovery torque, and control the actual braking recovery torque to not exceed the upper limit of the real-time braking recovery torque.

[0037] The baseline regenerative braking torque is the optimal regenerative braking torque calibrated under normal safe operating conditions, taking into account the motor's operating characteristics and the remaining charge of the power battery. Multiplying the baseline regenerative braking torque by a constraint coefficient yields the maximum allowable regenerative braking torque under the current road conditions. During vehicle operation, the motor controller strictly manages the actual output regenerative braking torque, ensuring it remains below or equal to the calculated upper limit. In high-risk road conditions, it proactively reduces the proportion of regenerative braking torque, increasing the proportion of mechanical braking force to guarantee vehicle braking stability.

[0038] For example, the step of calculating the upper limit of the real-time braking recovery torque by combining the reference recovery torque and the braking recovery torque constraint coefficient includes: ; In the formula: This is the upper limit of the real-time braking recovery torque; This is the braking recovery torque constraint coefficient, which is retrieved from a preset database based on the safety level of the operating condition determined in the previous steps. The baseline recovery torque is calculated by the motor controller in combination with the external characteristic curve of the drive recovery motor, the real-time remaining power of the power battery, and the braking requirements of the vehicle. It is the optimal braking recovery torque that balances energy recovery efficiency and braking performance under standard safe operating conditions.

[0039] The process involves multiplying the retrieved constraint coefficients by the real-time calculated baseline recovery torque to obtain the upper limit value of the torque adapted to the current road conditions. The vehicle controller then sends this value to the motor controller. After the vehicle enters braking mode, the motor controller monitors the braking recovery torque output by the motor in real time to ensure that the actual output value never exceeds the upper limit of the torque, thus achieving dynamic constraint of the braking recovery torque.

[0040] In addition, in some optional embodiments of the present invention, the method further includes: During the control process, the wheel slip ratio and vehicle posture are monitored in real time. If the wheel slip ratio exceeds the safety threshold, the torque constraint coefficient is immediately reduced.

[0041] In addition to basic torque constraint control, a closed-loop monitoring and dynamic correction step is added throughout the entire process of vehicle braking regenerative torque control. During the entire process, the vehicle controller monitors two key parameters in real time: wheel slip ratio and vehicle attitude, using wheel speed sensors and vehicle attitude sensors. Wheel slip ratio directly reflects whether wheel slippage occurs, while vehicle attitude indicates whether the vehicle is prone to instability such as sideslip or fishtailing. A safety threshold for wheel slip ratio is preset within the system; in the preferred embodiment, this threshold is set to 8%. When the wheel slip ratio exceeds the preset safety threshold, or when there are obvious signs of vehicle attitude instability, it indicates that the current road condition risk exceeds the initial assessment level, and the existing torque constraint is insufficient to guarantee driving safety. At this time, the vehicle controller immediately lowers the currently used torque constraint coefficient, further reducing the upper limit of the braking regenerative torque and continuing to reduce the output of the motor's regenerative braking force, thus enhancing the effect of mechanical braking. This closed-loop correction mechanism can cope with sudden changes in road conditions, compensate for the limitations of a single risk assessment, further improve the braking stability of the vehicle under complex and extreme conditions, and form a complete closed-loop control system of perception, assessment, control, and correction.

[0042] In summary, the braking recovery torque dynamic control method based on a four-dimensional road condition risk field in the above embodiments of the present invention, by real-time acquisition of four-dimensional road condition parameters including road surface friction coefficient, rain and fog visibility, curve curvature, and road slope, comprehensively covers four core road condition elements affecting braking safety: road surface grip, visibility conditions, road alignment, and road inclination, rather than relying solely on the road surface adhesion coefficient for judgment. Normalization conversion is performed on the four types of road condition parameters to obtain unified sub-coefficients for road surface friction risk, rain and fog visibility, curve curvature risk, and road slope risk with consistent value ranges, eliminating calculation interference caused by differences in the dimensions of different physical quantities. Based on the four types of risk sub-coefficients... A four-dimensional road condition risk field model is constructed by weighted summation of coefficients. This model quantifies and couples multiple types of road condition risks to obtain a unified real-time comprehensive road condition risk coefficient. This enables synchronous coupling and quantitative assessment of multiple road condition risk factors, accurately distinguishing the overall road condition hazard level under different driving scenarios. Based on the comprehensive road condition risk coefficient, corresponding operating condition safety levels are defined. Braking regenerative torque constraint coefficients that match each safety level are retrieved from a preset database. The constraint coefficients are then linked with the baseline regenerative torque to calculate the upper limit of the real-time braking regenerative torque under the current road condition. This actively limits the actual output of regenerative braking torque to not exceed this upper limit throughout the entire process, reducing the proportion of motor regenerative braking force in high-risk road conditions from the source of braking torque distribution, and increasing the proportion of mechanical braking force distribution. This technology addresses the technical problems of existing regenerative braking control technologies, which only consider a few parameters such as road surface adhesion coefficient and vehicle speed, lack quantitative assessment methods for multiple road condition risks, cannot accurately characterize the risk level of road conditions in all scenarios, and do not have an active constraint mechanism for regenerative braking torque based on comprehensive road condition risks. They can only rely on the vehicle stability system to passively remedy the situation after the vehicle shows signs of instability, and cannot suppress braking safety hazards in extreme road conditions such as wet and slippery roads, low visibility, sharp bends, and steep slopes from the source.

[0043] Example 2 This embodiment also proposes a dynamic control method for regenerative braking torque based on a four-dimensional road condition risk field. The difference between the dynamic control method for regenerative braking torque based on a four-dimensional road condition risk field in this embodiment and the dynamic control method for regenerative braking torque based on a four-dimensional road condition risk field in Embodiment 1 is as follows: The step of classifying the safety level of the working condition according to the comprehensive risk coefficient and preset rules includes: Level I operating conditions The first threshold corresponds to dry road surface, sunny weather, and straight road, with no risk of braking instability. Level II operating conditions First threshold <Second Threshold Low risk of braking instability on wet and slippery roads, in light fog, and on gently curving roads.

[0044] Level III operating conditions Second threshold <Third threshold Risk of brake instability in situations involving snow / water accumulation, moderate fog, and sharp bends.

[0045] Level IV operating conditions Third threshold ≤ Fourth threshold It poses a high risk of braking instability in icy road conditions, dense fog / heavy rain, extreme sharp bends, and steep slopes.

[0046] Four sets of numerical thresholds are pre-set, defined as the first threshold, the second threshold, the third threshold, and the fourth threshold. Combined with the numerical range of the comprehensive risk coefficient, the vehicle driving conditions are divided into four safety levels, with different levels corresponding to different road conditions and braking risk levels.

[0047] When the overall risk coefficient is less than the first threshold, the vehicle is classified as being in Level I operating condition. Typical road conditions for this level include dry roads, clear weather, and straight roads. There is no risk of wheel slippage or vehicle instability during braking, and regenerative braking can be performed according to standard strategies. When the overall risk coefficient is greater than or equal to the first threshold but less than the second threshold, the vehicle is classified as being in Level II operating condition. Typical road conditions for this level include slippery roads, light fog, and gently curving roads. There is a low probability of braking instability, and the regenerative braking torque needs to be appropriately limited. When the overall risk coefficient is greater than or equal to the second threshold but less than the third threshold, the vehicle is classified as being in Level III operating condition. Typical road conditions for this level include snow-covered roads, waterlogged roads, moderate fog, and sharp curves. There is a moderate risk of braking instability, and the output ratio of the regenerative braking torque needs to be significantly reduced. When the comprehensive risk coefficient is greater than or equal to the third threshold and less than or equal to the fourth threshold, the vehicle is determined to be in Level IV operating condition. Typical road conditions corresponding to this level are icy roads, dense fog, heavy rain, sharp bends, and steep slopes. The vehicle is in a high braking risk state and the braking recovery torque needs to be limited to an extremely low level to prioritize vehicle braking safety.

[0048] In a preferred embodiment of the present invention, the first threshold is set to 0.25, the second threshold is set to 0.5, the third threshold is set to 0.75, and the fourth threshold is set to 1. After obtaining the comprehensive risk coefficient, the vehicle controller automatically compares it with the preset threshold range to quickly determine the operating condition safety level and provide a level label for the matching torque constraint coefficient. Furthermore, the step of matching the corresponding level of braking recovery torque constraint coefficient in the preset database includes: When under Level I operating conditions, the braking recovery torque constraint coefficient is 0.9-1.0; When operating under Level II conditions, the braking recovery torque constraint coefficient is 0.6-0.8. When operating under Level III conditions, the braking recovery torque constraint coefficient is 0.3-0.5. When operating under Level IV conditions, the braking recovery torque constraint coefficient is 0.05-0.2.

[0049] This involves calling the corresponding constraint coefficient range pre-defined in the database. The constraint coefficients of different levels decrease step by step according to the risk level, so as to realize the control logic that the higher the risk, the stricter the limit on the recovery torque.

[0050] When the vehicle is in Level I safety condition, the corresponding braking recovery torque constraint coefficient ranges from 0.9 to 1.0. Within this range, the braking recovery torque is close to the reference recovery torque, and the vehicle can maximize the recovery of braking energy.

[0051] When the vehicle is in a relatively safe operating condition (Level II), the corresponding braking recovery torque constraint coefficient ranges from 0.6 to 0.8, which appropriately reduces the output of the braking recovery torque and avoids low-probability braking risks.

[0052] When the vehicle is in a Level III risk condition, the corresponding braking recovery torque constraint coefficient ranges from 0.3 to 0.5, which significantly reduces the proportion of braking recovery torque and increases the output ratio of mechanical braking force.

[0053] When the vehicle is in a Level IV high-risk condition, the corresponding braking regenerative torque constraint coefficient ranges from 0.05 to 0.2, retaining only a very small proportion of the braking regenerative torque, prioritizing vehicle braking safety as the primary control objective. All constraint coefficients have been calibrated through extensive real-vehicle braking tests and are stored in the vehicle controller's built-in database, which can be directly retrieved and used after the level determination is completed.

[0054] In summary, the braking recovery torque dynamic control method based on a four-dimensional road condition risk field in the above embodiments of the present invention, by real-time acquisition of four-dimensional road condition parameters including road surface friction coefficient, rain and fog visibility, curve curvature, and road slope, comprehensively covers four core road condition elements affecting braking safety: road surface grip, visibility conditions, road alignment, and road inclination, rather than relying solely on the road surface adhesion coefficient for judgment. Normalization conversion is performed on the four types of road condition parameters to obtain unified sub-coefficients for road surface friction risk, rain and fog visibility, curve curvature risk, and road slope risk with consistent value ranges, eliminating calculation interference caused by differences in the dimensions of different physical quantities. Based on the four risk sub-systems... A four-dimensional road condition risk field model is constructed by weighted summation of multiple road condition risks. This model quantifies and couples various road condition risks to obtain a unified real-time comprehensive road condition risk coefficient, enabling synchronous coupling and quantitative assessment of multiple road condition risk factors and accurately distinguishing the overall road condition hazard level under different driving scenarios. Based on the comprehensive road condition risk coefficient, corresponding operating condition safety levels are defined, and braking regenerative torque constraint coefficients that match each safety level are retrieved from a preset database. The constraint coefficients are then linked with the baseline regenerative torque to calculate the upper limit of the real-time braking regenerative torque under the current road condition. This actively limits the actual output of regenerative braking torque to not exceed this upper limit throughout the entire process, reducing the proportion of motor regenerative braking force in high-risk road conditions from the source of braking torque distribution and increasing the proportion of mechanical braking force distribution. This technology addresses the technical problems of existing regenerative braking control technologies, which only consider a few parameters such as road surface adhesion coefficient and vehicle speed, lack quantitative assessment methods for multiple road condition risks, cannot accurately characterize the risk level of road conditions in all scenarios, and do not have an active constraint mechanism for regenerative braking torque based on comprehensive road condition risks. They can only rely on the vehicle stability system to passively remedy the situation after the vehicle shows signs of instability, and cannot suppress braking safety hazards in extreme road conditions such as wet and slippery roads, low visibility, sharp bends, and steep slopes from the source.

[0055] Example 3 Please see Figure 2 The figure shows a dynamic control device for braking recovery torque based on a four-dimensional road condition risk field proposed in the third embodiment of the present invention. The device includes: The data acquisition module 100 is used to collect four-dimensional road condition parameters of the road segment in real time. The four-dimensional road condition parameters include at least the road surface friction coefficient, rain and fog visibility, curve curvature and road slope. The calculation module 200 is used to normalize the four-dimensional road condition parameters to obtain the corresponding road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient and road slope risk sub-coefficient. Module 300 is used to construct a four-dimensional road condition risk field model and calculate the real-time comprehensive road condition risk coefficient based on the four-dimensional road condition parameters by weighted summation. The classification module 400 is used to classify the safety level of the working condition according to the comprehensive risk coefficient and preset rules, and match the corresponding level of braking recovery torque constraint coefficient in the preset database. The control module 500 is used to calculate the upper limit of the real-time braking recovery torque by combining the reference recovery torque and the braking recovery torque constraint coefficient, and to control the actual braking recovery torque to not exceed the upper limit of the real-time braking recovery torque.

[0056] The functions or operation steps implemented by the above modules are largely the same as those in the above method embodiments, and will not be repeated here.

[0057] Example 4 In another aspect, the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method described in any one of the above embodiments one to two.

[0058] Example 5 In another aspect, the present invention provides a vehicle, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any one of the methods described in embodiments one to two above.

[0059] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable storage medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0061] More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable storage media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0062] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0063] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "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 the invention. 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.

[0064] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A dynamic control method for braking recovery torque based on a four-dimensional road condition risk field, characterized in that, The method includes: Real-time collection of four-dimensional road condition parameters of the vehicle's driving route. The four-dimensional road condition parameters include at least the road surface friction coefficient, rain and fog visibility, curve curvature and road slope. The four-dimensional road condition parameters are normalized to obtain the corresponding road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient. A four-dimensional road condition risk field model is constructed, and the real-time comprehensive road condition risk coefficient is calculated by weighted summation based on the four-dimensional road condition parameters. Based on the comprehensive risk coefficient, the working condition safety level is divided according to the preset rules, and the corresponding braking recovery torque constraint coefficient is matched in the preset database. By combining the baseline recovery torque and the braking recovery torque constraint coefficient, the upper limit of the real-time braking recovery torque is calculated, and the actual braking recovery torque is controlled not to exceed the upper limit of the real-time braking recovery torque.

2. The dynamic control method for braking recovery torque based on a four-dimensional road condition risk field according to claim 1, characterized in that, The steps of normalizing the four-dimensional road condition parameters to obtain the corresponding road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk coefficient, and road slope risk coefficient include: ； ； ； ； in, For road surface friction risk coefficient, The coefficient of friction of the road surface. The maximum coefficient of friction for dry asphalt pavement. This is the minimum coefficient of friction for the ice surface; For rain and fog visibility risk sub-coefficient, For visibility in rain and fog, For the safety energy threshold, This represents the threshold for extremely poor visibility. For rain and fog visibility risk sub-coefficient, For the curvature of the curve, This refers to the curvature of the extreme curve. Curvature of a straight road; This is the sub-coefficient for road slope risk. For road slope, This is the extreme road gradient. The slope is for a straight road.

3. The dynamic control method for braking recovery torque based on a four-dimensional road condition risk field according to claim 1, characterized in that, The steps of constructing a four-dimensional road condition risk field model and calculating the real-time comprehensive road condition risk coefficient based on four-dimensional road condition parameters through weighted summation include: : The risk weight coefficients for the corresponding parameters are calibrated using the analytic hierarchy process (AHP) combined with real-vehicle road tests. , , , These are the road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient.

4. The dynamic control method for braking recovery torque based on a four-dimensional road condition risk field according to claim 3, characterized in that, The step of classifying the safety level of working conditions according to the comprehensive risk coefficient and preset rules includes: Level I operating conditions The first threshold corresponds to dry road surface, sunny weather, and straight road, with no risk of braking instability. Level II operating conditions First threshold <Second Threshold Low risk of braking instability on wet and slippery roads, in light fog, and on gently curving roads. Level III operating conditions Second threshold <Third threshold Risk of brake instability in situations involving snow / water accumulation, moderate fog, and sharp bends. Level IV operating conditions Third threshold ≤ Fourth threshold It poses a high risk of braking instability in icy road conditions, dense fog / heavy rain, extreme sharp bends, and steep slopes.

5. The dynamic control method for braking recovery torque based on a four-dimensional road condition risk field according to claim 1, characterized in that, The step of calculating the upper limit of the real-time braking recovery torque by combining the reference recovery torque and the braking recovery torque constraint coefficient includes: ； In the formula: This is the upper limit of the real-time braking recovery torque; This is the constraint coefficient for the regenerative braking torque. The baseline recovery torque is used.

6. The dynamic control method for braking recovery torque based on a four-dimensional road condition risk field according to claim 1, characterized in that, The step of matching the corresponding level of braking recovery torque constraint coefficient in the preset database includes: When under Level I operating conditions, the braking recovery torque constraint coefficient is 0.9-1.0; When operating under Level II conditions, the braking recovery torque constraint coefficient is 0.6-0.

8. When operating under Level III conditions, the braking recovery torque constraint coefficient is 0.3-0.

5. When operating under Level IV conditions, the braking recovery torque constraint coefficient is 0.05-0.

2.

7. The dynamic control method for braking recovery torque based on a four-dimensional road condition risk field according to claim 6, characterized in that, The method further includes: During the control process, the wheel slip ratio and vehicle posture are monitored in real time. If the wheel slip ratio exceeds the safety threshold, the torque constraint coefficient is immediately reduced.

8. A dynamic control device for braking recovery torque based on a four-dimensional road condition risk field, characterized in that, The device includes: The data acquisition module is used to collect four-dimensional road condition parameters of the road segment in real time. The four-dimensional road condition parameters include at least the road surface friction coefficient, rain and fog visibility, curve curvature and road slope. The calculation module is used to normalize the four-dimensional road condition parameters to obtain the corresponding road surface friction risk sub-coefficient, rain and fog visibility risk sub-coefficient, curve curvature risk sub-coefficient, and road slope risk sub-coefficient. The module is used to build a four-dimensional road condition risk field model and calculate the real-time comprehensive road condition risk coefficient based on the four-dimensional road condition parameters through weighted summation. The classification module is used to classify the safety level of the working condition according to the comprehensive risk coefficient and preset rules, and match the corresponding braking recovery torque constraint coefficient in the preset database. The control module is used to calculate the upper limit of the real-time braking recovery torque by combining the reference recovery torque and the braking recovery torque constraint coefficient, and to control the actual braking recovery torque to not exceed the upper limit of the real-time braking recovery torque.

9. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 7.

10. A vehicle, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method as described in any one of claims 1 to 7.

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