A control method and system for a hybrid transmission

Abstract

A control method and system for a hybrid transmission, relating to the field of vehicle control, is disclosed. This method, applied in a TCU (Transmission Control Unit), includes: acquiring a road image ahead of the vehicle; evaluating the quality of the road image to obtain an image quality score; when the image quality score is greater than or equal to a preset image quality score threshold, obtaining a road adhesion coefficient curve based on the road image; determining the road surface risk level based on the road adhesion coefficient curve, including low-risk, medium-risk, and high-risk levels; determining the hybrid transmission's shifting strategy based on the road surface risk level; and upon receiving a shift request, controlling the hybrid transmission to shift gears according to the shifting strategy. Implementing the technical solution provided in this application solves the problem that after replacing a vehicle with a third-party battery, the delay deviation in the actual output torque of the motor may cause the tire grip to exceed its limit, resulting in momentary wheel spin and slippage.

Description

Technical Field

[0001] This application relates to the technical field of vehicle control, specifically to a control method and system for a hybrid transmission. Background Technology

[0002] Hybrid vehicles have become an important direction for the development of the automotive industry due to their excellent fuel economy and emission performance. The core of the powertrain control of hybrid vehicles lies in the coordinated distribution and smooth coupling of torque between the two power sources, the engine and the electric motor. This task is mainly accomplished by the hybrid transmission and its control unit (TCU). Therefore, the control strategy of the hybrid transmission directly determines the vehicle's power performance, economy, and smoothness.

[0003] Currently, the mainstream control strategy is that the TCU receives the drive torque request from the vehicle controller and decomposes the drive torque request into engine target torque and motor target torque according to the preset shift rules. Then, based on the engine target torque and motor target torque, the torque output of the engine and motor are controlled respectively, thereby controlling the hybrid transmission to shift gears. Among them, the motor target torque needs to be used as compensation torque to offset the impact caused by the asynchronous speed of the engine and the input shaft, so as to ensure the smoothness of shifting.

[0004] As vehicles age, the motor battery gradually deteriorates, requiring replacement to ensure smooth gear shifting. However, replacing original factory batteries is very expensive and often unavailable. Therefore, many users choose to replace them with third-party batteries with specifications similar to the original factory batteries to meet their daily driving needs.

[0005] However, when a vehicle replaces its battery with a third-party battery, although its nominal parameters may be similar to those of the original battery, its inherent characteristics will change. This will cause a delay between the torque command issued by the TCU and the actual torque output by the motor. This delay will not have a significant impact on the vehicle on normal roads, but on low-traction roads (such as icy or rainy roads), the torque response tolerance will drop significantly due to the reduced tire grip. In this case, the delay in the actual torque output by the motor may cause the tire grip to exceed its limit, resulting in the wheels spinning instantly and slipping, thus posing a safety risk. Summary of the Invention

[0006] In response to the problem that the delay in the actual output torque of the motor after replacing the battery with a third-party battery may cause the tire grip to exceed its limit, resulting in the wheel spinning instantly and causing slippage, this application provides a control method and system for a hybrid transmission.

[0007] In a first aspect, this application provides a control method for a hybrid transmission, applied in a TCU, to acquire road images in front of the vehicle;

[0008] The road image is subjected to a quality assessment to obtain an image quality score. The quality assessment includes a sharpness assessment and a road occlusion area assessment.

[0009] When the image quality score is greater than or equal to a preset image quality score threshold, the road adhesion coefficient curve is obtained based on the road image.

[0010] Based on the road adhesion coefficient curve, the risk level of the road surface is determined, including low risk level, medium risk level and high risk level;

[0011] Based on the risk level of the road surface, the shifting strategy of the hybrid transmission is determined. The control strategy includes a speedometer at gear position and shifting time points. The speedometer at gear position includes the permissible speed for shifting multiple gears.

[0012] Upon receiving a shift request, the hybrid transmission is controlled to shift gears according to its shift strategy.

[0013] Optionally, obtaining the road adhesion coefficient curve based on the road image specifically involves:

[0014] The road image is divided into multiple road segment images along the road direction;

[0015] Extract road surface features from the first road segment image. The road surface features include texture roughness features and road surface reflectivity features. The first road segment image is any one of the multiple road segment images.

[0016] The road surface features of the first road segment image are input into a pre-trained road surface type classification model, and the probability distribution of the road surface type is output. The road surface type includes at least one of dry asphalt, wet asphalt, snow, and ice.

[0017] Based on the probability distribution of the road surface type, a preset road surface type-adhesion coefficient mapping table is queried to obtain multiple basic adhesion coefficients, wherein one basic adhesion coefficient corresponds to one road surface type.

[0018] Get the current ambient temperature;

[0019] Based on the current ambient temperature, the road adhesion coefficient of the first road segment image is determined from a plurality of the base adhesion coefficients;

[0020] The adhesion coefficients of multiple road segment images are used to construct the road adhesion coefficient curve.

[0021] Optionally, determining the shifting strategy of the hybrid transmission based on the risk level of the road surface specifically includes:

[0022] Obtain the motor delay response time obtained from the test;

[0023] Once the hybrid transmission receives a shift request, it obtains the current time point;

[0024] Based on the current time point and the motor delay response time, the shift time points for multiple risk levels are obtained.

[0025] Optionally, determining the shifting strategy of the hybrid transmission based on the risk level of the road surface further includes:

[0026] If the road surface risk level is low risk, then the preset standard gear position speedometer will be used as the gear position speedometer for the low risk level.

[0027] Optionally, determining the shifting strategy of the hybrid transmission based on the risk level of the road surface further includes:

[0028] If the risk level of the road surface is medium risk, then the maximum wheel speed difference of the vehicle is calculated based on the road adhesion coefficient curve.

[0029] The vehicle speed adjustment coefficient for the first gear position is determined based on the maximum wheel speed difference of the vehicle.

[0030] Based on the speed adjustment coefficient of the first gear position, the preset standard gear position speedometer is adjusted to obtain the gear position speedometer of the medium risk level.

[0031] Optionally, determining the shifting strategy of the hybrid transmission based on the risk level of the road surface further includes:

[0032] If the risk level of the road surface is high risk, then the maximum yaw rate of the vehicle is calculated based on the road adhesion coefficient curve.

[0033] The vehicle speed adjustment coefficient for the second gear position is determined based on the vehicle's maximum yaw rate.

[0034] Based on the second gear position speed adjustment coefficient, the speedometer for the medium-risk gear position is adjusted to obtain the speedometer for the high-risk gear position.

[0035] Optionally, after performing a quality assessment on the road image to obtain an image quality score, the method further includes:

[0036] If the image quality score is less than a preset image quality score threshold, then the road surface data, map data, and environmental data of the vehicle's current driving road are obtained.

[0037] The road surface data, map data, and environmental data are input into the road surface condition prediction model, and the road surface condition prediction data is output.

[0038] The road condition prediction data is matched with a preset road surface condition-adhesion coefficient mapping table to generate a road adhesion coefficient prediction curve for the current driving road.

[0039] Secondly, this application provides a control system for a hybrid transmission, the system being a TCU, the TCU including an acquisition module (1), a processing module (2), and a control module (3), wherein:

[0040] The acquisition module (1) is used to acquire an image of the road in front of the vehicle;

[0041] The processing module (2) is used to perform quality assessment on the road image to obtain an image quality score. The quality assessment includes sharpness assessment and road occlusion area assessment. When the image quality score is greater than or equal to a preset image quality score threshold, a road adhesion coefficient curve is obtained based on the road image. Based on the road adhesion coefficient curve, the risk level of the road surface is determined. The risk level includes low risk level, medium risk level and high risk level. Based on the risk level of the road surface, the shifting strategy of the hybrid transmission is determined. The control strategy includes a gear position speedometer and shifting time point. The gear position speedometer includes the permissible speed for shifting multiple gears.

[0042] The control module (3) is used to control the hybrid transmission to shift gears according to the shift strategy of the hybrid transmission when a shift request is received.

[0043] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0044] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0045] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0046] This application assesses the road surface adhesion of the road ahead of the vehicle based on the road image, obtaining a road surface adhesion system curve for the road segment ahead. Then, based on the road surface adhesion coefficient curve, it determines the road surface risk level, which includes low, medium, and high risk levels. A low risk level corresponds to a high road surface adhesion coefficient, where gear shifting is prone to jerking. A medium risk level corresponds to a low road surface adhesion coefficient, where gear shifting is prone to slippage. A high risk level corresponds to an extremely low road surface adhesion coefficient, where gear shifting is prone to fishtailing, drifting, and other instability accidents. Therefore, when the vehicle requests a gear shift, the application adjusts the shift time and shift point speedometer of the hybrid transmission for each risk level. This compensates for the delay in the actual output torque of the motor after replacing the battery with a third-party battery, improving shift smoothness. Furthermore, it suppresses the risk of drive wheel slippage or vehicle instability caused by sudden torque changes during gear shifting, thereby significantly improving driving safety and stability under extreme road conditions. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating a control method for a hybrid transmission provided in an embodiment of this application.

[0048] Figure 2 This is a schematic diagram of the control system of a hybrid transmission provided in an embodiment of this application.

[0049] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0050] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 2. Processing module; 3. Control module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0052] This application provides a control method for a hybrid transmission, which is applied to a TCU, such as... Figure 1 As shown, the method includes steps S101 to S106, which are as follows:

[0053] S101. Obtain an image of the road in front of the vehicle.

[0054] In the above steps, the vehicle's front-facing camera captures real-time images of the road ahead and transmits them to the TCU. The TCU performs preprocessing operations such as distortion correction, noise filtering, and format standardization on the road images to improve the accuracy of subsequent quality assessment of the road images.

[0055] S102. Perform quality assessment on the road images to obtain image quality scores. The quality assessment includes sharpness assessment and road occlusion area assessment.

[0056] S103. When the image quality score is greater than or equal to the preset image quality score threshold, the road adhesion coefficient curve is obtained based on the road image.

[0057] In steps S102 to S103 above, when the vehicle-mounted front camera captures images of the road ahead of the vehicle, if the vehicle is in traffic, the following distance between the vehicle and the vehicle in front is relatively close. In this case, the road ahead of the vehicle will be obscured by the vehicle in front, resulting in the captured road image not fully reflecting the road conditions. In addition, if the vehicle-mounted front camera is aged or in low visibility environments such as at night or in heavy fog, the captured road image cannot accurately describe the detailed features of the road. Therefore, before extracting road surface features from the road image, the road image needs to be quality evaluated to obtain an image quality score. Only when the image quality score is greater than or equal to a preset image quality score threshold is the road adhesion coefficient curve obtained based on the road image analysis, thereby ensuring the accuracy of the road adhesion coefficient curve.

[0058] Specifically, the road images are quality assessed to obtain an image quality score.

[0059] This application evaluates the image from two dimensions: sharpness and road occlusion area. For the sharpness dimension, the Laplacian variance method based on gradient features can be used. The road image is first converted into a gradient image, and then the Laplacian operator is convolved on the road gradient image. The Laplacian variance of the convolution result is calculated. It should be noted that since the Laplacian operator can highlight image edges and details, the variance value is larger for sharp images with sharp edge gradient changes and smaller for blurry images with gentle edge gradient changes. Finally, the Laplacian variance value is normalized to obtain the sharpness score of the road image.

[0060] For the road occlusion area dimension, edge detection algorithms can be used to extract the contours of the road surface and the occluder. Then, the occlusion area of ​​the occluder and the road surface area are calculated. Finally, the proportion of the occlusion area in the total area (the sum of the occlusion area and the road surface area) is calculated, and the calculation results are normalized to obtain the road occlusion area score of the road image.

[0061] Finally, the image quality score is calculated by weighting the sharpness score and the road occlusion area score according to preset weights.

[0062] In one example, the road adhesion coefficient curve is obtained based on a road image, specifically:

[0063] First, the road image is divided into multiple road segment images along the road direction. Then, for any first road segment image, the road surface features of the first road segment image are extracted. The road surface features include texture roughness features and road surface reflectivity features. Both are low-level visual features that are strongly correlated with the physical meaning of the adhesion coefficient. Texture roughness features correspond to the features of the road surface itself, while road surface reflectivity features correspond to features such as water accumulation and ice layer on the road surface. Then, the road surface features of the first road segment image are input into a pre-trained road surface type classification model, and the output is the probability distribution of road surface type. The road surface type classification model is trained from the road surface features and the probability distribution of road surface type. Road surface types include, but are not limited to, dry asphalt, wet asphalt, snow, and ice.

[0064] Then, based on the probability distribution corresponding to the first road segment image, the basic adhesion coefficients of various road surface types that the first road segment image may belong to are obtained from the preset road surface type-adhesion coefficient mapping table.

[0065] In real-world driving environments, the first road segment image may present visual ambiguity issues. For example, the most common phenomenon is "black ice." Ice and wet asphalt may appear similar in texture, but their adhesion coefficients differ significantly. Therefore, to accurately distinguish the adhesion coefficient of the first road segment, this application obtains the current ambient temperature and then determines the road adhesion coefficient of the first road segment image from multiple base adhesion coefficients corresponding to the first road segment image based on the current ambient temperature. It is understandable that the state of the covering medium on the ground differs at different temperatures. For example, at normal temperatures, it is impossible for the road surface to freeze. Therefore, when there is an ice layer on the road surface in the probability distribution of the road segment image, even if its probability value is high, it violates the laws of physics, so its corresponding base adhesion coefficient will not be selected as the adhesion coefficient of the current road segment.

[0066] Finally, after obtaining the adhesion coefficients of multiple road segment images according to the above scheme, the adhesion coefficients of multiple road segment images are constructed into a road adhesion coefficient curve based on road continuity. The road adhesion coefficient curve is a two-dimensional coordinate curve of distance-adhesion coefficient.

[0067] S104. Based on the road adhesion coefficient curve, determine the risk level of the road surface, including low risk level, medium risk level and high risk level.

[0068] In the above steps, the risk level of the road surface section to be driven ahead of the vehicle is determined based on the preset first adhesion coefficient threshold and second adhesion coefficient threshold. The first adhesion coefficient threshold is greater than the second adhesion coefficient threshold. Specifically, the average of multiple adhesion coefficients in the road adhesion coefficient curve is calculated. When the average adhesion coefficient is greater than or equal to the first adhesion coefficient threshold, it is a low-risk level; when the average adhesion coefficient is greater than or equal to the second adhesion coefficient threshold and less than the first adhesion coefficient threshold, it is a medium-risk level; and when the average adhesion coefficient is less than the first adhesion coefficient threshold, it is a high-risk level.

[0069] It needs to be explained that during gear shifting, the hybrid transmission gradually transfers the torque carried by the currently transmitting clutch to the next clutch that is about to engage. This process requires the engine speed to be synchronized with the hybrid transmission speed, but in reality, there will be a certain difference between the two. This difference is compensated by the electric motor. When there is a delay in the actual output torque of the electric motor, the compensation is not timely. At this time, the hybrid transmission has already shifted to the speed of the next gear, but the engine speed cannot catch up with the transmission speed in time, forming a speed difference. As a result, a torque shock is generated at the moment of clutch engagement due to the speed difference, causing a shift jerking phenomenon.

[0070] When the road surface risk level is low, the main problem encountered by the vehicle during driving is shifting jerking due to the high road surface adhesion coefficient. When the road surface risk level is medium, the shifting jerking due to the low road surface adhesion coefficient can cause the wheels to spin briefly, resulting in a slight risk of slippage. When the road surface risk level is high, the shifting jerking due to the extremely low road surface adhesion coefficient can cause severe slippage and amplify the impact of torque, thereby triggering the risk of vehicle drifting and rollover.

[0071] S105. Based on the risk level of the road surface, determine the shifting strategy of the hybrid transmission. The control strategy includes the gear position speedometer and the shifting time point. The gear position speedometer includes the permissible speed for shifting between multiple gears.

[0072] In the above steps, since different road surface risk levels present different risks, the shifting strategy needs to be tailored accordingly. When the road surface risk level is low, the focus is on addressing the driving experience issues caused by shifting jerks. When the road surface risk level is medium, the focus is on addressing the safety risks associated with slight slippage. When the road surface risk level is high, the focus is on addressing the safety risks of vehicle drifting and rollover caused by severe slippage and torque surges. Specifically:

[0073] This application first obtains the motor delay response time for all risk levels through testing. Then, after the hybrid transmission receives a shift request, it obtains the current time point and adds the current time point to the motor delay response time to obtain the shift time point of the hybrid transmission. The shift time point can be understood as the clutch engagement time point of the next gear, thereby offsetting the delay of the actual output torque of the motor and reducing the impact of shift jerking.

[0074] In actual gear shifting, due to the inherent characteristics of the third-party battery, its charging and discharging characteristics are not perfectly compatible with the vehicle's overall transmission system. This results in unstable torque output from the motor, and even with adjustments to the shift timing, a slight jerk still occurs. This slight jerk increases in impact on driving safety as the road surface adhesion coefficient decreases. Therefore, this application adjusts the speedometer at different gear shift points under different road risk levels. The speedometer includes permissible speeds for multiple gear shifts, reconstructing the safe speeds for each gear. This allows the unstable motor torque to be buffered within the new gear speed range, thereby reducing jerkiness. Specifically:

[0075] For low-risk road surfaces, since the road surface adhesion coefficient is high, the wheels are not prone to slippage due to fluctuations in transmission torque. Therefore, it is only necessary to improve the jerking phenomenon when shifting gears. There is no need to adjust the speedometer at the gear position. The preset standard speedometer at the gear position can be directly used as the speedometer at the low-risk level.

[0076] For road surfaces of medium risk level, due to their low road adhesion coefficient, the wheels are prone to slipping. In this case, the wheel speed difference curve of the vehicle on the road ahead should be predicted based on the road adhesion coefficient curve. Specifically, the following formula can be used for calculation:

[0077]

[0078] in, This represents the wheel speed difference corresponding to the i-th data point in the road adhesion coefficient curve. Let represent the wheel speed difference corresponding to the (i-1)th data point in the road adhesion coefficient curve, T represent the shifting impact torque, and R represent the wheel radius. Let F be the road adhesion coefficient corresponding to the i-th data point in the road adhesion coefficient curve, where F is the axle load and k is the torque conversion coefficient.

[0079] In the above formula, starting from the first point on the road adhesion coefficient curve (the vehicle's current position), the wheel speeds of all tires are acquired in real time. The difference between the maximum and minimum wheel speeds among all tires is then used as the wheel speed difference corresponding to the first data point. The wheel speed difference variation curve is then iteratively calculated based on the changes in the road adhesion coefficient on the curve. Here, the wheel radius R, axle load F, torque conversion coefficient k, and shift impact torque T are all known calibration parameters. The axle load represents the load on the drive axle, the torque conversion coefficient represents the torque conversion ratio from the transmission to the wheels, and the shift impact torque represents the torque fluctuation of the transmission output shaft (derived from the pre-measured difference between the maximum and minimum torques).

[0080]

[0081] The numerator represents the force used by the vehicle to overcome the wheel's grip, while the denominator represents the wheel's grip. The comparison between the two can be understood as the degree of slippage. The smaller the road adhesion coefficient, the more severe the slippage and the greater the wheel speed difference.

[0082] Then, the maximum wheel speed difference in the wheel speed difference change curve is identified. The maximum wheel speed difference represents the most severe slippage situation of the vehicle. Therefore, the maximum wheel speed difference is compared with the safe wheel speed difference threshold. If the ratio is greater than 1, it indicates that the vehicle has a relatively serious risk of slippage. In this case, the ratio is used as the speed adjustment coefficient for the first gear position. The preset standard gear position speedometer is adjusted to obtain a medium-risk level gear position speedometer. Specifically, the speed adjustment coefficient for the first gear position is multiplied by the permissible speed for gear shifting at each gear position, so that the vehicle delays gear shifting. After the torque output by the motor is stably transmitted to the hybrid transmission, the gear shift is performed to ensure stability during the gear shifting process. If the ratio is less than 1, it indicates that the risk of slippage of the vehicle is within a controllable range. In this case, the speed adjustment coefficient for the first gear position is set to 1.

[0083] For high-risk road surfaces, due to their extremely low road adhesion coefficient, the wheels are prone to slipping, and the drive axle may drift under torque impact. If the vehicle speed is too high at this time, even more serious rollovers may occur. Therefore, this application predicts the yaw rate change curve of the vehicle on the road ahead based on the road adhesion coefficient curve. Specifically, this application uses a two-degree-of-freedom vehicle dynamics model as the theoretical model, and first calculates the front wheel slip angle and the rear wheel slip angle. The specific calculation method is as follows:

[0084]

[0085]

[0086] in, The front wheel slip angle, Rear wheel slip angle, This is the maximum steering angle of the front wheels. For the vehicle's lateral speed, For the longitudinal speed of the vehicle, The distance from the vehicle's center of gravity to the front axle. The distance from the vehicle's center of gravity to the rear axle. ω represents the yaw rate.

[0087] Then, based on the front wheel slip angle and front wheel slip stiffness, the front wheel slip force is calculated; based on the rear wheel slip angle and rear wheel slip stiffness, the rear wheel slip force is calculated, using the following formulas:

[0088]

[0089] Where F is the lateral force and C is the lateral stiffness. It is the sideslip angle.

[0090] Since the two-degree-of-freedom vehicle dynamics model does not consider the influence of road adhesion on lateral force, when road adhesion is low, the steering sensitivity of the wheels will increase, resulting in a larger slip angle and a correspondingly larger lateral force. Therefore, the lateral forces of the front and rear wheels need to be adjusted as follows:

[0091]

[0092] in, This is the road adhesion coefficient.

[0093] Finally, the front wheel lateral force and rear wheel lateral deviation are substituted into the force balance equation of the two-degree-of-freedom vehicle dynamics model for solution to obtain the yaw rate. The force balance equation of the two-degree-of-freedom vehicle dynamics model includes the lateral force balance equation and the lateral moment balance equation. The expression of these equations is well known to those skilled in the art and will not be described in detail here.

[0094] Then, based on the change in road adhesion coefficient in the road adhesion coefficient curve, the yaw rate change curve is calculated.

[0095] Then, the maximum yaw rate in the yaw rate change curve is identified. Finally, the maximum yaw rate is compared with the safe yaw rate threshold. If the ratio is greater than 1, it indicates that the vehicle has a risk of drifting or rolling over. In this case, the ratio is used as the second gear speed adjustment coefficient to further adjust the speedometer for medium-risk gear positions, resulting in a speedometer for high-risk gear positions. Specifically, the second gear speed adjustment coefficient is multiplied by the permissible gear shifting speed for each gear position in the medium-risk gear position speedometer, thereby further increasing the gear shifting speed to minimize or even avoid gear shifting on extremely low-traction surfaces, reducing the possibility of risk from the source. Similarly, if the ratio is less than 1, it indicates that the vehicle's drifting or rolling over risk is within a controllable range, and the second gear speed adjustment coefficient is set to 1.

[0096] S106. Upon receiving a shift request, perform shift control on the hybrid transmission according to the hybrid transmission's shift strategy.

[0097] In the above steps, if the vehicle receives a shift request triggered by the user during subsequent driving, it will obtain the shift strategy of the hybrid transmission based on preset analysis and control the shift of the hybrid transmission to reduce jerking and improve driving safety.

[0098] In one possible implementation, when the image quality score is less than a preset image quality score threshold, an accurate road surface adhesion coefficient curve cannot be obtained from the road image. In this case, the road surface adhesion coefficient can be predicted based on auxiliary data. Specifically, this involves: first, acquiring road surface data, map data, and environmental data of the road the vehicle is currently traveling on. The road surface data is used to determine the road surface type, the map data is used to determine the road trend of the current road surface type, and the environmental data is used to determine the road surface condition. The road surface data includes, but is not limited to, smoothness and vibration data; the map data includes, but is not limited to, road slope, curvature, length, and historical accident location information; and the environmental data includes, but is not limited to, ambient temperature, humidity, and precipitation probability. Subsequently, the road surface data, map data, and environmental data are input into a pre-trained road surface condition prediction model. This model is built based on machine learning algorithms and trained using historical data from the road surface data, map data, and environmental data. It outputs predicted data on the road surface condition, such as the probability distribution of "snow accumulation" or "icing." Finally, the predicted data is matched with a preset road surface condition-adhesion coefficient mapping table to generate a road adhesion coefficient prediction curve for a future journey. This curve replaces the visually perceived road adhesion coefficient curve and is used for subsequent risk level assessment, thereby improving the accuracy of risk level assessment when road image quality is low.

[0099] Reference Figure 2This application also provides a control system for a hybrid transmission, the system being a TCU, which includes an acquisition module 1, a processing module 2, and a control module 3, wherein:

[0100] Module 1 is used to acquire images of the road in front of the vehicle;

[0101] Processing module 2 is used to perform quality assessment on road images to obtain image quality scores. The quality assessment includes sharpness assessment and road occlusion area assessment. When the image quality score is greater than or equal to a preset image quality score threshold, a road adhesion coefficient curve is obtained based on the road image. Based on the road adhesion coefficient curve, the risk level of the road surface is determined, including low risk level, medium risk level, and high risk level. Based on the risk level of the road surface, the shifting strategy of the hybrid transmission is determined. The control strategy includes a gear position speedometer and shifting time points. The gear position speedometer contains the permissible speed for shifting multiple gears.

[0102] Control module 3 is used to control the hybrid transmission to shift gears according to the hybrid transmission's shift strategy after receiving a shift request.

[0103] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0104] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0105] The communication bus 302 is used to enable communication between these components.

[0106] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0107] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0108] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0109] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a hybrid transmission control method.

[0110] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a hybrid transmission control method. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0111] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0112] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0114] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0115] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0116] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0117] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A control method for a hybrid transmission, characterized in that, When applied in a TCU, the method includes: Acquire an image of the road in front of the vehicle; The road image is subjected to a quality assessment to obtain an image quality score. The quality assessment includes a sharpness assessment and a road occlusion area assessment. When the image quality score is greater than or equal to a preset image quality score threshold, the road adhesion coefficient curve is obtained based on the road image. Based on the road adhesion coefficient curve, the risk level of the road surface is determined, including low risk level, medium risk level and high risk level; Based on the risk level of the road surface, a shifting strategy for the hybrid transmission is determined. This shifting strategy includes a speedometer at gear positions and shift times. The speedometer at gear positions contains permissible speeds for shifting between multiple gears, specifically including: Obtain the motor delay response time obtained from the test; Once the hybrid transmission receives a shift request, it obtains the current time point; Based on the current time point and the motor delay response time, multiple shift time points for the risk levels are obtained; Upon receiving a shift request, the hybrid transmission is controlled to shift gears according to its shift strategy.

2. The method according to claim 1, characterized in that, The process of obtaining the road adhesion coefficient curve based on the road image is as follows: The road image is divided into multiple road segment images along the road direction; Extract road surface features from the first road segment image, the road surface features including texture roughness features and road surface reflectivity features, the first road segment image being any one of the plurality of road segment images; The road surface features of the first road segment image are input into a pre-trained road surface type classification model, and the probability distribution of the road surface type is output. The road surface type includes at least one of dry asphalt, wet asphalt, snow, and ice. Based on the probability distribution of the road surface type, a preset road surface type-adhesion coefficient mapping table is queried to obtain multiple basic adhesion coefficients, wherein one basic adhesion coefficient corresponds to one road surface type. Get the current ambient temperature; Based on the current ambient temperature, the road adhesion coefficient of the first road segment image is determined from a plurality of the base adhesion coefficients; The adhesion coefficients of multiple road segment images are used to construct the road adhesion coefficient curve.

3. The method according to claim 1, characterized in that, The step of determining the shifting strategy of the hybrid transmission based on the risk level of the road surface specifically includes: If the road surface risk level is low risk, then the preset standard gear position speedometer will be used as the gear position speedometer for the low risk level.

4. The method according to claim 3, characterized in that, The step of determining the shifting strategy of the hybrid transmission based on the risk level of the road surface specifically includes: If the risk level of the road surface is medium risk, then the maximum wheel speed difference of the vehicle is calculated based on the road adhesion coefficient curve. The vehicle speed adjustment coefficient for the first gear position is determined based on the maximum wheel speed difference of the vehicle. Based on the speed adjustment coefficient of the first gear position, the preset standard gear position speedometer is adjusted to obtain the gear position speedometer of the medium-risk level.

5. The method according to claim 4, characterized in that, The step of determining the shifting strategy of the hybrid transmission based on the risk level of the road surface specifically includes: If the risk level of the road surface is high risk, then the maximum yaw rate of the vehicle is calculated based on the road adhesion coefficient curve. The vehicle speed adjustment coefficient for the second gear position is determined based on the vehicle's maximum yaw rate. Based on the second gear position speed adjustment coefficient, the speedometer for the medium-risk gear positions is adjusted to obtain the speedometer for the high-risk gear positions.

6. The method according to claim 1, characterized in that, After performing a quality assessment on the road image and obtaining an image quality score, the process further includes: If the image quality score is less than a preset image quality score threshold, then the road surface data, map data, and environmental data of the vehicle's current driving road are obtained. The road surface data, map data, and environmental data are input into the road surface condition prediction model, and the road surface condition prediction data is output. The road surface condition prediction data is matched with a preset road surface condition-adhesion coefficient mapping table to generate a road adhesion coefficient prediction curve for the current driving road.

7. A control system for a hybrid transmission, characterized in that, The system is used to execute a control method for a hybrid transmission as described in any one of claims 1-6, wherein the system is a TCU, and the TCU includes an acquisition module (1), a processing module (2), and a control module (3), wherein: The acquisition module (1) is used to acquire an image of the road in front of the vehicle; The processing module (2) is used to perform quality assessment on the road image to obtain an image quality score. The quality assessment includes sharpness assessment and road occlusion area assessment. When the image quality score is greater than or equal to a preset image quality score threshold, a road adhesion coefficient curve is obtained based on the road image. Based on the road adhesion coefficient curve, the risk level of the road surface is determined. The risk level includes low risk level, medium risk level and high risk level. Based on the risk level of the road surface, the shifting strategy of the hybrid transmission is determined. The shifting strategy includes a gear position speedometer and shifting time points. The gear position speedometer includes the permissible speed for shifting multiple gears. The control module (3) is used to control the hybrid transmission to shift gears according to the shift strategy of the hybrid transmission when a shift request is received.

8. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 6.

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