Electric control wet clutch torque vector distribution system based on road conditions and control method

Abstract

The invention relates to an electric control wet clutch torque vector distribution system based on road conditions and a control method. The system comprises a road condition real-time monitoring device which is used for acquiring changes of various parameters of a vehicle so as to indirectly acquire road surface parameter information and transmitting the information to an information processing device; the electric control hydraulic all-wheel drive coupler is located at the rear end of the whole vehicle transmission shaft and is responsible for transmitting power to front wheels; the electronic control anti-skid differential mechanisms are arranged on a front shaft and a rear shaft respectively and are responsible for power distribution of two wheels of the same shaft. And the information processing device is used for receiving and processing the pavement parameter information and transmitting control signals to the electronic control hydraulic all-wheel drive coupler and the electronic control antiskid differential mechanism. The method comprises the following steps: adjusting torque distribution according to an error signal by using PID control; fuzzy control is used for processing uncertainty and nonlinearity to adapt to complex road conditions; model predictive control is utilized to predict future states and optimize torque distribution based on a vehicle dynamics model.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle transmissions, and in particular, to an electronically controlled wet clutch torque vectoring distribution system and a control method based on road conditions. Background Art

[0002] Existing clutch torque vectoring distribution systems have significantly improved vehicle handling, stability, traction, and safety by dynamically adjusting torque distribution. At the same time, fuel efficiency and driving experience have been enhanced, making them suitable for a variety of road conditions and driving conditions.

[0003] CN105172785A discloses a method and system for automotive torque vectoring distribution. The method for automotive torque vectoring distribution includes: a controller obtains a control signal sent by a vehicle main control module. The controller is disposed on the vehicle chassis and can drive a wheel side motor of a vehicle wheel through a universal joint, or is disposed inside a wheel hub of the vehicle wheel and can directly drive the hub motor of the vehicle wheel; according to the control signal, the torque and speed of the vehicle wheel are controlled.

[0004] CN114013271A discloses a torque vectoring distribution electric drive system and a control method thereof. The electric drive system includes: a drive assembly having two connecting shafts and configured to be able to drive at least one of the two connecting shafts to rotate, and further configured to be able to adjust the speed of each connecting shaft; two intermediate shafts, each intermediate shaft being disposed parallel to one of the connecting shafts; two first meshing gear sets, including a first input gear and a first output gear that mesh; two output shafts, each output shaft being disposed parallel to one of the intermediate shafts, a left drive wheel being provided on one output shaft, and a right drive wheel being provided on the other output shaft; two second meshing gear sets, including a second input gear and a second output gear that mesh.

[0005] CN113108041A discloses an electric drive system and a torque vectoring distribution method thereof, including a differential case and a first electric motor meshing with the differential case. The differential case is connected to a left half shaft gear and a right half shaft gear. The differential case is integrally connected with a first sun gear and a third sun gear. The first sun gear meshes with a first planet gear. The first planet gear meshes with a second sun gear. The second sun gear is coaxially connected to a right drive wheel with the right half shaft gear. The third sun gear meshes with a second planet gear. The second planet gear meshes with a fourth sun gear. The fourth sun gear is coaxially connected to a left drive wheel with the left half shaft gear. Planet carriers are provided on both the left drive wheel and the right drive wheel, and the planet carriers are connected to brakes.

[0006] However, existing clutch torque vectoring distribution systems are prone to traffic accidents under occasional mixed road conditions. Therefore, the field requires a more precise torque vectoring distribution scheme, such as torque vectoring distribution based on road conditions.

[0007] In addition, on the one hand, there are differences in the understanding of those skilled in the art; on the other hand, when the applicant made this invention, a large number of documents and patents were studied, but due to space limitations, all details and content were not listed in detail. However, this does not mean that this invention does not possess the features of these prior arts. On the contrary, this invention already possesses all the features of the prior arts, and the applicant reserves the right to add relevant prior arts in the background art. Summary of the Invention

[0008] Aiming at the deficiencies of the prior art, the present invention provides an electronically controlled wet clutch torque vectoring distribution system and control method based on road conditions to improve the stability and safety of vehicles under occasional mixed road conditions, thereby solving at least part of the above technical problems.

[0009] The present invention discloses an electronically controlled wet clutch torque vectoring distribution system and control method based on road conditions, which includes: a real-time road condition monitoring device, an electronically controlled hydraulic all-wheel drive coupler, an electronic control anti-skid differential, and an information processing device. The real-time road condition monitoring device can obtain road surface information and transmit it to the control unit. The electronically controlled hydraulic all-wheel drive coupler can achieve adaptive torque distribution according to the provided road surface information to improve traction distribution, handling stability, fuel economy, etc. The two cooperate with each other to develop a multi-power distribution strategy based on engine torque characteristics, motor torque characteristics, and torque coordination management, and realize the optimization control of the smoothness of the vehicle drive output.

[0010] The real-time road condition monitoring device essentially indirectly obtains road surface parameter information by sensing the changes of parameters such as wheel speed, acceleration, yaw rate, and steering angle, and transmits this information to the information processing device.

[0011] The electronically controlled hydraulic all-wheel drive coupler is located at the rear end of the vehicle drive shaft and is mainly responsible for transmitting power to the front wheels. When driving on a normal and smooth road surface, the electronically controlled hydraulic all-wheel drive coupler does not participate in power distribution; when receiving the road surface information sent by the information processing device, the electronically controlled hydraulic all-wheel drive coupler will enter the engaged state and redistribute the power between the front and rear axles according to the transmitted road surface information to meet the control requirements under different driving conditions.

[0012] An electronic control anti-skid differential is provided for each of the front and rear axles, which is mainly responsible for distributing power between the two wheels of the same axle. When driving on a normal and smooth road surface, the function of the electronic control anti-skid differential is the same as that of an open differential; when receiving the road surface information sent by the information processing device, the electronic control anti-skid differential will redistribute the power between the left and right wheels according to the road surface information to keep the vehicle stable when cornering and on complex road surfaces.

[0013] The information processing device is used to receive and process road surface parameter information, and transmit control signals to the electronically controlled hydraulic all-wheel drive coupler and the electronic control anti-skid differential.

[0014] Preferably, the real-time road condition monitoring device may include a wheel speed sensor, an acceleration sensor, a torque sensor, a tire pressure sensor, a steering angle sensor, a vision sensor, and a road condition recognition sensor.

[0015] The wheel speed sensor can estimate the longitudinal force and slip ratio by monitoring the rotational speed of each wheel and combining it with the vehicle speed.

[0016] The acceleration sensor indirectly calculates the tire force by measuring the longitudinal and lateral accelerations of the vehicle, and can also estimate the slip ratio in combination with the rotational speed change. The torque sensor can directly measure the torque of the drive shaft or hub, calculate the driving force or braking force, and can also calculate the slip ratio in combination with the wheel speed. The tire pressure sensor can monitor the internal pressure of the tire and indirectly reflect the force condition. The steering angle sensor estimates the sideslip angle by measuring the steering wheel angle and combining it with the vehicle speed. The vision sensor identifies the lane lines and vehicle trajectory through a camera and indirectly estimates the sideslip angle. Road condition recognition sensors such as cameras, radars, etc. identify the road surface conditions (such as wet, dry, ice and snow, etc.).

[0017] Preferably, the electronically controlled hydraulic all-wheel drive coupler may include a hydraulic pump, a hydraulic clutch, an electronic control unit, a solenoid valve, a hydraulic oil tank and pipelines, a cooling system, a coupler housing, and a drive shaft connector.

[0018] The hydraulic pump provides the pressure required by the hydraulic system and drives the flow of hydraulic oil; the hydraulic clutch controls the engagement and separation of the clutch through hydraulic pressure to achieve torque transmission; the electronic control unit receives sensor signals and controls the working state of the hydraulic system; the solenoid valve can adjust the flow direction and pressure of the hydraulic oil, and the hydraulic oil tank and pipelines are used to store and transport hydraulic oil; the cooling system can prevent the hydraulic system from overheating; the coupler housing is used to protect the internal components and provide installation support; the drive shaft connector is located at the input and output ends of the coupler and transmits torque from the engine to the front and rear axles.

[0019] According to a preferred embodiment, the system of the present invention can collect data such as wheel speed, acceleration, yaw angular velocity, steering angle, etc. in real time and obtain the current road surface condition through the road condition recognition sensor.

[0020] According to a preferred embodiment, the system of the present invention can estimate the vehicle state (such as sideslip angle, yaw rate, slip ratio, etc.) based on the sensor data in the real-time road condition monitoring device.

[0021] According to a preferred embodiment, the system of the present invention can evenly distribute torque under normal road conditions to ensure vehicle stability and handling; increase the torque of the inner wheels and reduce the torque of the outer wheels on slippery road conditions to prevent skidding; further optimize torque distribution on ice and snow road conditions to ensure maximum traction and stability; and dynamically adjust torque distribution according to the steering angle and yaw rate on curved road conditions to improve cornering stability.

[0022] According to a preferred embodiment, the system of the present invention can use PID control to adjust torque distribution according to error signals (such as yaw rate error); use fuzzy control to handle uncertainties and non-linear situations and adapt to complex road conditions; and use model predictive control (MPC) to predict future states and optimize torque distribution based on the vehicle dynamics model.

[0023] The present invention has the following beneficial technical effects:

[0024] The present invention provides an electronically controlled wet clutch torque vectoring distribution system and control method based on road conditions, including a road condition real-time monitoring device and an electronically controlled hydraulic all-wheel drive coupler; the road condition real-time monitoring device collects vehicle state data and road condition information in real time through various sensors and measurement units, and formulates torque distribution strategies using methods such as PID control, fuzzy control, and model predictive control; the electronically controlled hydraulic all-wheel drive coupler distributes torque under different road conditions according to the obtained torque distribution strategies, evenly distributes torque under normal road conditions to ensure vehicle stability and handling; increases the torque of the inner wheels and reduces the torque of the outer wheels on slippery road conditions to prevent skidding; further optimizes torque distribution on ice and snow road conditions to ensure maximum traction and stability; and dynamically adjusts torque distribution according to the steering angle and yaw rate on curved road conditions to improve cornering stability. Through the above-mentioned electronically controlled wet clutch torque vectoring distribution system and control method based on road conditions, by combining advanced sensor technology, control algorithms, and wet clutch technology, the performance and adaptability of the vehicle can be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the sensor installation position provided by the present invention;

[0026] Figure 2 It is a schematic diagram of the electronically controlled hydraulic all-wheel drive coupler provided by the present invention;

[0027] Figure 3 It is a power transmission diagram when driving on a road with good road conditions provided by the present invention;

[0028] Figure 4 It is a power transmission diagram in four-wheel drive mode provided by the present invention;

[0029] Figure 5Power transmission diagram when driving in off-road environment provided by the present invention;

[0030] Figure 6 Control schematic diagram provided by the present invention.

[0031] List of reference numerals

[0032] 1: Wheel speed sensor; 2: Tire pressure sensor; 3: Longitudinal acceleration sensor; 4: Lateral acceleration sensor; 5: Vision sensor; 6: Torque sensor; 7: Steering angle sensor; 8: Road condition recognition sensor; 9: Housing; 10: Coupler; 11: First gear; 12: Second gear; 13: Third gear; 14: Fourth gear; 15: First shaft; 16: Chain; 17: Fifth gear; 18: Second shaft. Detailed implementation manners

[0033] The following is a detailed description with reference to the drawings.

[0034] Embodiment 1

[0035] The present invention discloses an electronically controlled wet clutch torque vectoring distribution system based on road conditions, which includes a real-time road condition monitoring device, an electronically controlled hydraulic all-wheel drive coupler, an electronic control anti-skid differential, and an information processing device.

[0036] The real-time road condition monitoring device includes various sensors and measurement units, and the installation positions are as Figure 1 shown. The wheel speed sensor 1 is installed in the hub of each wheel and is used to monitor the rotation speed of each wheel, so as to obtain road surface information. The tire pressure sensor 2 is installed inside the tire valve or the hub and is used to monitor the tire pressure. The acceleration sensors are divided into a longitudinal acceleration sensor 3 and a lateral acceleration sensor 4. The longitudinal acceleration sensor 3 is installed near the vehicle body chassis, and the lateral acceleration sensor 4 is installed on the side of the vehicle body. The two are used to measure the longitudinal and lateral accelerations of the vehicle. The vision sensor 5 is installed inside the front windshield and near the rearview mirror, at the position of the rear parking camera, and around the front, rear, left, and right of the vehicle, and is used to monitor the surrounding road conditions. The torque sensor 6 is installed near the engine output shaft and is used to monitor the engine output torque. The steering angle sensor 7 is installed on the steering column or the steering gear and is used to monitor the steering angle and steering speed of the steering wheel. The road condition recognition sensor 8 is installed on the roof, the front bumper or the grille and is used to detect the surrounding environment and identify wet, slippery or icy roads, etc.

[0037] The electronically controlled hydraulic all-wheel drive coupler can distribute torque according to road condition information. The hydraulic pump is usually installed inside or near the housing 9 of the electronically controlled hydraulic all-wheel drive coupler, and adjusts the pressure and flow rate of the hydraulic oil according to the instructions of the electronic control unit to control torque distribution. The hydraulic clutch is located in the core part of the electronically controlled hydraulic all-wheel drive coupler, connects the front and rear drive shafts, and can adjust the torque distribution ratio between the front and rear shafts according to the magnitude of the hydraulic pressure. The electronic control unit can be installed in the engine compartment or the cockpit, and is used to monitor the vehicle state in real time and control the operation of the hydraulic pump and the solenoid valve. The solenoid valve is installed in the hydraulic pipeline and controls the flow rate and pressure of the hydraulic oil according to the instructions of the ECU, thereby adjusting the working state of the hydraulic clutch. The hydraulic oil tank is installed near the electronically controlled hydraulic all-wheel drive coupler. The hydraulic pipeline connects the hydraulic pump, the solenoid valve and the hydraulic clutch, and the two provide hydraulic oil for the hydraulic system to ensure the circulation and cooling of the hydraulic oil. The cooling system reduces the temperature of the hydraulic oil through the radiator or cooler to ensure the stable operation of the system. The housing 9 of the electronically controlled hydraulic all-wheel drive coupler connects the front and rear drive shafts, is used to accommodate components such as the hydraulic pump, the hydraulic clutch, and the solenoid valve, and provides sealing and protection. The drive shaft connector is located at the input end and the output end of the electronically controlled hydraulic all-wheel drive coupler, and is used to transmit torque from the engine to the front and rear shafts.

[0038] The electronically controlled anti-skid differential can be installed on the rear axle or the front axle of the vehicle transmission system. It can improve traction, handling and safety by intelligently distributing power to adapt to various road conditions, thereby enhancing the driving experience. On wet or uneven roads, by restricting the power distribution of the slipping wheels and increasing the power of the wheels with grip, the traction is improved; when turning, the power distribution of the inner and outer wheels is optimized to reduce understeer or oversteer, keep the vehicle stable and reduce the risk of losing control; in case of emergency, it prevents the wheels from slipping, keeps the vehicle stable and reduces the risk of losing control. The electronically controlled anti-skid differential can automatically adjust the power distribution according to the real-time road conditions to adapt to different driving environments.

[0039] The information processing device is used to receive and process the road surface parameter information and transmit control signals to the electronically controlled hydraulic all-wheel drive coupler and the electronically controlled anti-skid differential.

[0040] In practical applications, as Figure 2 shown, the electronically controlled hydraulic all-wheel drive coupler may specifically include: a housing 9, a coupler 10, a gear set, a first shaft 15, a chain 16, a second shaft 18, wherein the gear set may further include a first gear 11, a second gear 12, a third gear 13, a fourth gear 14, a fifth gear 17.

[0041] The first shaft 15 comes from the input shaft of the gearbox and directly leads to the rear axle. There are three sets of gears on it, namely the first gear 11, the second gear 12, and the fourth gear 14. The third gear 13 can connect the first gear 11 and the second gear 12 or connect the second gear 12 and the fourth gear 14. The coupler 10 can distribute power by controlling the degree of gear engagement. The first gear 11 and the fifth gear 17 are connected by a chain 16. The second shaft 18 directly leads to the front axle.

[0042] When driving on a road with good conditions, the power transmission is as Figure 3 shown. At this time, the first gear 11, the second gear 12, the third gear 13, and the fourth gear 14 are not engaged and rotate with the first shaft 15. The power is transmitted to the rear axle through the first shaft 15. At this time, it is in a high-speed two-wheel drive state, which can effectively reduce fuel consumption.

[0043] When in four-wheel drive mode, the power transmission is as Figure 4 shown. At this time, the third gear 13 connects the first gear 11 and the second gear 12 together. When the power is transmitted to the rear axle through the first shaft 15, a part of the power will be transmitted to the second shaft 18 through the coupler 10, the first gear 11, the second gear 12, the third gear 13, the chain 16, and the fifth gear 17, and finally transmitted to the front axle. Since it is transmitted through the coupler 10, the power distribution ratio between the front and rear axles can reach up to 33:67. In addition, the degree of engagement of the coupler 10 can be controlled according to the road conditions, so as to realize power distribution according to the road conditions.

[0044] When driving in off-road environments such as sand and a large amount of snow, the power transmission is as Figure 5 shown. At this time, the third gear 13 connects the first gear 11 and the fourth gear 14 together. When the power is transmitted to the rear axle through the first shaft 15, a part of the power will be transmitted to the second shaft 18 through the first gear 11, the third gear 13, the fourth gear 14, the chain 16, and the fifth gear 17, and finally transmitted to the front axle. Since it is transmitted through the gear set and the chain 16, the power distribution between the front and rear axles can reach 50:50, and it has relatively strong off-road ability.

[0045] The control schematic diagram of the whole system is as Figure 6 shown. The principle for the road condition real-time monitoring device to judge the road surface condition is based on the combined action of the tire pressure sensor 2, the wheel speed sensor 1, and the torque sensor 6. The road surface condition (such as slippery, uneven, adhesion change, etc.) is comprehensively judged through their respective measurement data. The following is the principle of their combined action and the formula representation:

[0046] The tire pressure sensor 2 measures the internal air pressure P of the tire. The change in air pressure can reflect the contact state between the tire and the road surface:

[0047] P = P0 + △P,

[0048] Among them, P is the current tire pressure, P0 is the standard pressure, and △P is the pressure change.

[0049] Furthermore, the pressure change △P may be related to road unevenness or tire deformation.

[0050] The wheel speed sensor 1 can measure the rotational speed ω of each wheel, and determine whether there is wheel slip or road adhesion change by comparing the rotational speed differences of each wheel:

[0051]

[0052] Among them, ω i is the rotational speed of the i-th wheel, v i is the linear speed of the i-th wheel, and r is the wheel radius.

[0053] If the rotational speed ω of a certain wheel i is significantly higher than that of other wheels, it may indicate that the wheel is slipping.

[0054] The torque sensor 6 measures the torque T of the drive shaft or the driving wheel, and the torque change can reflect the road adhesion and the distribution of driving force:

[0055] T = F·r,

[0056] Among them, T is the torque, F is the driving force, and r is the wheel radius.

[0057] If the torque T suddenly drops, it may indicate a loss of driving force (such as a wet road surface).

[0058] Further preferably, by comprehensively analyzing the data of tire pressure, wheel speed, and torque, the road condition can be judged. The following are the steps and formulas for comprehensive analysis:

[0059] (1) The change in tire pressure P can reflect the contact area and deformation between the tire and the road surface:

[0060] For an inflated tire, the contact area A between the tire and the road surface can be approximately expressed as:

[0061]

[0062] Among them, C shape represents the tire cross-section shape coefficient (usually 0.8 - 1.2), which is related to the tire aspect ratio and structural stiffness.

[0063] The relationship between the tire radial deformation δ (sinking amount) and the tire pressure can be simplified by the elastic shell theory:

[0064]

[0065] Among them, Ktire represents the stiffness of the tire carcass itself; represents the rate of change of the contact area with deformation.

[0066] (2) By comparing the rotational speeds ω of each wheel i , calculate the slip ratio S:

[0067]

[0068] where ω 驱动 is the rotational speed of the driving wheel, and ω 非驱动 is the rotational speed of the non - driving wheel.

[0069] If the slip ratio S exceeds the threshold value, it indicates insufficient road surface adhesion.

[0070] (3) Calculate the change in driving force through the torque T:

[0071]

[0072] If the driving force F suddenly drops, it may indicate a wet road surface or tire slip.

[0073] (4) Combine the air pressure P, slip ratio S, and driving force F to comprehensively judge the road surface condition:

[0074] Judging the road surface state (wet, uneven or adhesion change) by combining parameters such as tire air pressure, slip ratio, and driving force is a process of multi - parameter fusion analysis:

[0075] Tire air pressure mainly indirectly reflects the road surface state by affecting the tire contact area and stiffness:

[0076] Low air pressure: The contact area increases, which may enhance the adhesion on a wet road surface (but too high will reduce the grip).

[0077] Abnormal fluctuations: If the air pressure sensor shows frequent changes, it may indicate an uneven road surface.

[0078] The slip ratio is a direct basis for judging adhesion: When the slip ratio is less than 5%, the road surface is dry and the adhesion is high (such as an asphalt road surface); when the slip ratio is higher than 15%, the road surface is wet (such as an ice surface, water accumulation) or the adhesion suddenly drops. Frequent oscillations of the slip ratio may indicate an uneven road surface or alternating adhesion conditions.

[0079] On a dry road surface, the driving force peak is higher and appears between 10% - 20% of the slip ratio. On a wet road surface, the driving force peak is lower, at 5% - 10%. When the driving force drops suddenly after rising with the slip ratio, it indicates breaking through the adhesion limit (such as an ice surface).

[0080] By comprehensively analyzing tire pressure, wheel speed differences, and driving force, it is possible to determine whether the road surface is slippery, uneven, or has insufficient adhesion, thus providing a basis for decision-making for the vehicle control system.

[0081] Based on the combined action of the acceleration sensor and the steering angle sensor 7, the road surface conditions (such as adhesion, slipperiness, etc.) can be indirectly inferred by measuring the vehicle's dynamic response and the driver's operation. The following are the principles and formula representations of their combined action:

[0082] The acceleration sensor measures the longitudinal acceleration a x and the lateral acceleration a y , reflecting the vehicle's motion state. Among them, the longitudinal acceleration a x and the lateral acceleration a y can be calculated respectively by the following formulas:

[0083]

[0084] where a x is the longitudinal acceleration, F 驱动力 is the driving force provided by the engine, F 阻力 is the air resistance, rolling resistance, etc., m is the vehicle mass; a y is the lateral acceleration, v is the vehicle speed, and R is the turning radius.

[0085] The steering angle sensor 7 measures the steering wheel steering angle θ, reflecting the driver's steering intention:

[0086] θ = k·δ,

[0087] where θ is the steering wheel steering angle, k is the steering transmission ratio, and δ is the front wheel steering angle.

[0088] By comprehensively analyzing the data of acceleration and steering angle, it is possible to determine whether the vehicle is traveling along the expected trajectory, thereby inferring the road surface adhesion condition. The following are the steps and formulas for comprehensive analysis:

[0089] According to the steering angle θ and the vehicle speed v, calculate the expected lateral acceleration a y，预期 :

[0090]

[0091] where L is the vehicle wheelbase, and δ is the front wheel steering angle (related to the steering angle θ).

[0092] Compare the actual lateral acceleration a y measured based on the acceleration sensor with the expected lateral acceleration a y，预期 to calculate the deviation Δa y :

[0093] Δay = a y -a y,预期 。

[0094] If the deviation Δa y is large, it may indicate insufficient road surface adhesion (such as a wet or slippery road surface):

[0095] Dynamically update the road surface friction coefficient through the lateral acceleration deviation:

[0096]

[0097] where a y,max is the maximum lateral acceleration that the vehicle can achieve at the current vehicle speed.

[0098] When Δa y ≈ 0 (the actual acceleration is close to the theoretical value) and μ ≈ 1.0, it is a high - adhesion road surface (such as dry asphalt).

[0099] When Δa y > 0.3g (the actual acceleration is significantly lower than the theoretical value) and μ ≈ 0.2 - 0.4, it is a low - adhesion road surface (such as ice or snow).

[0100] Preferably, by combining the longitudinal acceleration and the lateral acceleration, the road surface condition can be further judged: If a x is significantly lower than expected, it may indicate a loss of driving force (such as wheel slip); if a y is significantly lower than expected, it may indicate understeer or a wet road surface.

[0101] The vision sensor 5 captures the image of the road ahead through the camera to identify the road surface type, obstacles, lane lines, traffic signs, etc. The present invention can adjust the engagement force and speed of the clutch according to the road surface condition to ensure smooth power transmission; it can also identify the obstacles ahead and adjust the clutch in advance to avoid sudden acceleration or deceleration, improving driving smoothness; when turning or changing lanes, it can also adjust the clutch response to ensure that the power output is consistent with the driving intention Figure 1 .

[0102] The road condition recognition sensor 8 can detect the vehicle state and road surface conditions, such as slope, bumps, wetness, etc. When going uphill or downhill, the present invention can adjust the clutch engagement strategy to ensure reasonable power output and prevent wheel slip or insufficient power; on bumpy road surfaces, the present invention can adjust the clutch engagement speed to reduce shock and improve comfort; on wet road surfaces, the present invention can adjust the clutch control to prevent wheel slip and ensure vehicle stability.

[0103] In an electronically controlled wet clutch torque vectoring distribution system based on road conditions, the weight distribution of multi-sensor data (wheel speed, tire pressure, acceleration, vision, torque, steering angle) needs to comprehensively consider real-time performance, reliability, operating condition adaptability, and control objectives (such as stability, efficiency, response speed). In the electronically controlled wet clutch torque vectoring distribution system for road conditions, the weight distribution of multi-sensor data (wheel speed, tire pressure, acceleration, vision, torque, steering angle) needs to comprehensively consider real-time performance, reliability, operating condition adaptability, and control objectives (such as stability, efficiency, response speed).

[0104] Use the basic weights under steady-state conditions such as straight-line cruising and medium-grip road surfaces:

[0105] Sensor Base Weight Applicable Scenario Wheel Speed 30％ Core Feedback, Dependent on All Operating Conditions Acceleration 25％ Yaw / Lateral Dynamics Control Torque 15％ Power Demand Matching (e.g., Acceleration / Climbing) Steering Angle 10％ Steering Intention Recognition (e.g., Cornering Torque Distribution) Vision 15％ Preview Road Conditions (Curves, Low-Adhesion Road Surfaces) Tire Pressure 5％ Load Change Compensation

[0106] When driving on curves, low-grip road surfaces, etc., the weights will be dynamically adjusted based on the operating conditions:

[0107]

[0108] If rain / fog is detected, the weight of vision will be reduced to 5% - 10%; if the ABS is activated (slip ratio > 15%), the weight of wheel speed will be reduced to 15% - 20%; if the vehicle vibrates violently under off-road conditions, the weight of acceleration will be increased to 30% - 35%. During short-term control (< 100 ms), the weights of wheel speed, acceleration, and torque will be increased; during long-term prediction (> 1 s), the weights of vision, steering angle, and tire pressure will be increased.

[0109] Through this strategy, the system can achieve high-precision torque distribution under complex road conditions.

[0110] The information processing device processes road surface information and performs PID control. Among them, PID control realizes fast response and precise control of system errors by adjusting three parameters: proportional (P), integral (I), and derivative (D). In the torque vectoring distribution system, the main functions of PID control include:

[0111] (1) Torque distribution optimization: According to the road condition information provided by the road condition real-time monitoring device, the PID controller dynamically adjusts the torque distribution of the left and right wheels to ensure the stability of the vehicle under complex road conditions such as curves and slippery road surfaces;

[0112] (2) Clutch engagement control: The PID controller accurately controls the engagement force and speed of the wet clutch according to the wheel state and road condition information, avoiding impacts or slips during power transmission;

[0113] (3) Dynamic response: The PID controller can quickly respond to road condition changes, adjust the torque distribution and clutch engagement strategy in real time, and improve the controllability and safety of the vehicle.

[0114] In a road condition-based electronically controlled wet clutch torque vectoring distribution system, the implementation of PID control may include the following steps:

[0115] A1. Error calculation: The system calculates the error between the target torque distribution and the actual torque distribution based on the data from the road condition real-time monitoring device.

[0116] A2. The PID controller calculates the control output according to the error value, and the formula is as follows:

[0117]

[0118] where u(t) is the control output (such as the clutch engagement force or torque distribution ratio); e(t) is the error signal, e(t) = r(t) - y(t), r(t) is the desired torque distribution, and y(t) is the actual torque distribution; K p is the proportional gain, used for quickly responding to the error; K i is the integral gain, used for eliminating the steady-state error; K d is the derivative gain, used for suppressing system oscillation.

[0119] A3. Parameter adjustment: The performance of the PID controller depends on the adjustment of the three parameters K p , K i , K d . In the torque vectoring distribution system, these parameters can be determined by the following methods:

[0120] Empirical method: Initially set the parameters according to experimental data or empirical values;

[0121] Self-tuning method: Automatically adjust the parameters using an adaptive algorithm (such as the Ziegler-Nichols method);

[0122] Optimization algorithm: Use intelligent algorithms such as genetic algorithms and particle swarm optimization to optimize the PID parameters.

[0123] A4. Execute control: The output signal of the PID controller is sent to the actuator to adjust the engagement force of the wet clutch and the torque distribution ratio.

[0124] Assume that the vehicle is driving on a wet and slippery road surface, and the system detects a decrease in road adhesion through the road condition real-time monitoring device. The control process of the PID controller is as follows:

[0125] Error monitoring: The system detects wheel slip and calculates the error between the target torque and the actual torque;

[0126] PID calculation: The PID controller calculates the control output according to the error value and reduces the clutch engagement force;

[0127] Execution control: The actuator adjusts the clutch engagement force according to the PID output signal to prevent wheel slip;

[0128] Feedback regulation: The system monitors the wheel state in real time and continuously adjusts the PID parameters to ensure stable vehicle driving.

[0129] Fuzzy control simulates the human decision-making process, converting fuzzy inputs (such as road condition information and vehicle state) into clear control outputs (such as clutch engagement force and torque distribution ratio). The fuzzy controller can process various road condition information and dynamically adjust the torque distribution strategy according to this information. The vehicle dynamics system and the clutch engagement process have non-linear characteristics. Fuzzy control can effectively handle these non-linear problems through a fuzzy rule base and an inference mechanism. Fuzzy control has strong robustness to system parameter changes and external disturbances and can maintain stable control performance under uncertain road conditions. Fuzzy control can consider multiple control objectives simultaneously and achieve multi-objective optimization through fuzzy rules.

[0130] In the electronically controlled wet clutch torque vectoring distribution system based on road conditions, the implementation of fuzzy control can include the following steps:

[0131] B1. Definition of input variables: The input variables of the fuzzy controller usually include road condition information (such as road adhesion, slope, bumpiness, etc.) and vehicle state (such as vehicle speed, acceleration, steering angle, wheel speed difference, etc.). These output variables are obtained through a real-time road condition monitoring device.

[0132] B2. Fuzzification: Convert the exact values of the input variables into fuzzy values. For example, road adhesion can be divided into three fuzzy levels: "low", "medium", and "high"; vehicle speed can be divided into three fuzzy levels: "low speed", "medium speed", and "high speed". Each fuzzy level corresponds to a membership function, which is used to describe the degree to which the variable belongs to this level.

[0133] B3. Fuzzy rule base: Consists of a series of "if-then" rules, which are used to describe the relationship between input variables and output variables. For example, if the road adhesion is "low" and the vehicle speed is "high", then the clutch engagement force is "small"; if the road adhesion is "high" and the steering angle is "large", then the torque distribution of the outer wheel is "large". These rules are designed based on expert experience or experimental data.

[0134] B4. Fuzzy inference: According to the fuzzy values of the input variables and the fuzzy rule base, calculate the fuzzy values of the output variables through a fuzzy inference mechanism. Among them, the inference methods used can include the Mamdani method and the Sugeno method.

[0135] B5. Defuzzification: Convert the fuzzy output value obtained from fuzzy inference into an exact value for controlling the actuator. The defuzzification methods used may include the centroid method, the maximum membership degree method, etc.

[0136] B6. Execution control: Send the exact output value after defuzzification to the actuator to adjust the engagement force and torque distribution ratio of the wet clutch.

[0137] The output of fuzzy control can be expressed as:

[0138]

[0139] where, is the membership degree of the input variable; is the membership degree of the output variable; Defuzzify is the defuzzification operation.

[0140] By simulating the human decision-making process, fuzzy control can effectively handle complex road conditions and non-linear system problems. Its core advantages lie in strong adaptability, good robustness, and easy expansion, which can significantly improve the stability, comfort, and safety of the vehicle. By combining with other control methods such as PID control, the system performance can be further optimized.

[0141] The core of model predictive control is to optimize the target and constraint conditions by establishing a vehicle dynamics model and solve the optimal torque distribution within a future period of time. The following is the formulaic representation of this control principle:

[0142] The vehicle dynamics model can be expressed as a state-space equation:

[0143] x(k + 1) = f(x(k), u(k), w(k)),

[0144] where, x(k) is the system state vector; u(k) is the control input vector; w(k) is the external disturbance vector; f(·) is the non-linear vehicle dynamics model.

[0145] Furthermore, x(k), u(k), and w(k) are respectively expressed as follows:

[0146]

[0147] where, v x 、v y are respectively the longitudinal and lateral speeds, γ、 are respectively the yaw angle / angular velocity, ω fi 、ω fr 、ω ri 、ω rr are respectively the rotational speeds of the left front wheel, right front wheel, left rear wheel, and right rear wheel; T fi 、Tfr , T ri , T rr are the driving / braking torques of the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively, and δ f is the front wheel steering angle; μ, θ are the road surface adhesion coefficient, roll angle, and slope angle respectively.

[0148] Furthermore, the non-linear vehicle dynamics model is as follows:

[0149]

[0150] In model predictive control, based on the current state x(k) and control input u(k), the states for the next N p steps are predicted:

[0151] x(k+i|k) = f(x(k+i-1|k), u(k+i-1|k), w(k+i-1|k)), i = 1, 2, …, N p ,

[0152] where x(k+i|k) represents the state at the (k+i)-th step predicted at the k-th step; u(k+i-1|k) represents the control input at the (k+i-1)-th step predicted at the k-th step; N p is the prediction horizon.

[0153] Model predictive control determines the optimal control input by minimizing the objective function J. The objective function can include: vehicle stability, energy consumption, and comfort. Among them, the objective function can be expressed as:

[0154]

[0155] where x ref (k+i|k) is the reference state; Q, R, S are weight matrices, representing the weights of state error, control input, and input change respectively; N c is the control horizon; Δu(k+i|k) = u(k+i|k) - u(k+i-1|k) represents the change in control input.

[0156] During the optimization process, the following constraint conditions need to be satisfied:

[0157] Control input constraint:

[0158] u min ≤ u(k+i|k) ≤ u max ;

[0159] State constraint:

[0160] x min ≤ x(k+i|k) ≤ x max ;

[0161] Actuator dynamic constraints (such as the torque transmission characteristics of a wet clutch):

[0162] u(k+i│k) = g(T clutch , T max ),

[0163] where T clutch is the torque transmitted by the wet clutch; T max is the maximum torque that the clutch can transmit.

[0164] In each control cycle, model predictive control performs feedback correction based on the difference between the actual measurement value and the predicted values x(k) and x(k|k - 1):

[0165] x(k) = x(k|k - 1) + K·(x meas (k) - x(k|k - 1)),

[0166] where x meas (k) is the actual measurement value; K is the feedback gain matrix.

[0167] In each control cycle, after solving the optimization problem, model predictive control outputs the first value of the optimal control sequence:

[0168] u(k) = u * (k|k),

[0169] where u*(k|k) is the optimal solution of the optimization problem.

[0170] Finally, the optimal control input u(k) output by model predictive control (MPC) is used for torque distribution of the electronically controlled wet clutch:

[0171] T left (k) = u1(k), T right (k) = u2(k),

[0172] where T left (k) and T right (k) are the torque distributions of the left and right wheels respectively; u1(k) and u2(k) are the components of the control input vector.

[0173] Through the above formulas, model predictive control realizes the prediction, optimization, and feedback correction of the vehicle state in the electronically controlled wet clutch torque vector distribution system based on road conditions, thereby dynamically adjusting the torque distribution and improving vehicle performance and driving safety.

[0174] In the electronically controlled wet clutch torque vectoring distribution system, the optimization of key parameters such as PID gains, fuzzy rule bases, and MPC prediction horizons needs to be comprehensively designed in combination with the system's dynamic characteristics, real-time road conditions, and algorithm characteristics. The following are the specific optimization methods:

[0175] Preferably, the PID gain optimization method can include two aspects: parameter initialization and dynamic parameter tuning methods.

[0176] For parameter initialization, based on the transfer function or step response experiment, the proportional (K p ), integral (K i ), and derivative (K d ) gains are initially set using the Ziegler-Nichols method. The typical initial value ranges are:

[0177] K p ∈[0.1, 1.0], K i ∈[0.01, 0.1], K p ∈[0.001, 0.01].

[0178] Preferably, the dynamic parameter tuning method can include three aspects: adaptive PID, genetic algorithm, and reinforcement learning. Adaptive PID can adjust the gains online according to the wheel states (such as yaw rate error, sideslip angle). For example, on high-adhesion roads, increase K p to improve the response, and on low-adhesion roads, decrease K p to prevent oscillation; the genetic algorithm uses ITAE (Integral of Time multiplied by the Absolute Error) as the fitness function to optimize the PID parameters offline; reinforcement learning learns the optimal gain mapping table in the simulation environment through Q-learning or DDPG algorithms. Finally, the step response and anti-interference ability are verified through hardware-in-the-loop (HIL) testing.

[0179] For the fuzzy rule base design, usually the yaw rate error e and the error change rate Δe are selected, and the output is the torque correction amount ΔT. Triangular or Gaussian types are adopted, covering 5 - 7 fuzzy sets such as Negative Big (NB), Zero (ZO), Positive Big (PB), etc. If the input is 7 fuzzy sets for each of the 2 variables, the total number of combined rules is 7×7 = 49.

[0180] Similar rules are merged through clustering analysis (such as fuzzy C-means) to reduce to 20 - 30; key rules are preferentially designed based on typical working conditions (such as snow, slippery roads), and then edge cases are supplemented; the rule base is trained through ANFIS (Adaptive Neuro-Fuzzy Inference System) to reduce redundancy.

[0181] Preferably, the optimization of the MPC prediction horizon can include three aspects: prediction horizon length selection, dynamic adjustment method, and cost function weights.

[0182] Regarding the prediction horizon length selection, the prediction horizon Np : Usually 5 to 20 steps, sampling time T s = 10 to 50 ms (corresponding to a prediction range of 0.5 to 1 s). Control time domain N c : Take 1 / 3 to 1 / 2 of N p to reduce the computational load.

[0183] The dynamic adjustment method can be based on curvature prediction: increase N in a curve p to cover the entire steering process, and decrease N on a straight road p ; It can also calculate resource constraints: adopt receding horizon control (RHC) to adjust N online p to ensure that the solution time is less than T s .

[0184] The weight of the cost function makes the weight of the yaw angle tracking error greater than the weight of the torque change rate.

[0185] In addition, a multi-parameter collaborative optimization framework can also be used, which includes offline optimization and online adjustment. Offline optimization can use the NSGA-II multi-objective optimization algorithm to search for the Pareto front of PID / MPC / fuzzy parameters with tracking error, energy consumption, and comfort as indicators. Online adjustment can switch parameter sets through an upper-layer decision maker according to road condition recognition (such as vision / radar data). For example: reduce the PID gain in snow, and the fuzzy rules focus on anti-skid; increase the MPC prediction time domain in a track environment to improve the aggressiveness of torque distribution.

[0186] Preferably, in the electronically controlled wet clutch torque vectoring system based on road conditions, the priorities and switching conditions of PID control, fuzzy control, and model predictive control (MPC) need to be dynamically adjusted according to real-time working conditions, system response requirements, and computing resources. The preferred priority order is: model predictive control (MPC) > fuzzy control > PID control. As a high-order control strategy, MPC is preferentially used for complex dynamic working conditions (such as extreme cornering, low-adhesion road surfaces) because its multi-variable optimization ability can globally coordinate torque distribution. Fuzzy control is suitable for situations where there is noise in sensor information or the road condition uncertainty is high (such as ice and snow roads, partially wet and slippery roads), and it can respond quickly through empirical rules. PID control, as the underlying basic control, is used for steady-state or simple working conditions (such as straight-line cruising, slight steering) to ensure stability and computing efficiency.

[0187] Preferably, the conditions for the system to switch to MPC include:

[0188] Dynamic condition trigger: Lateral acceleration > 0.4g, yaw rate error > 10%, tire slip rate exceeds the threshold;

[0189] High road condition complexity: The system detects a non-P structured road surface (such as off-road, snow) or a sudden change in adhesion coefficient;

[0190] Predictive demand: When the navigation anticipates a sharp turn or a series of consecutive turns ahead, it is necessary to optimize the torque distribution sequence in advance.

[0191] Preferably, the conditions for the system to switch to fuzzy control include:

[0192] Sensor uncertainty: The wheel speed / yaw rate signals have high noise, or the confidence level of the estimated road surface adhesion coefficient is low;

[0193] Nonlinear working conditions: The vehicle is in medium steering (0.2g < lateral acceleration < 0.4g) and the PID control shows oscillation;

[0194] Rapid response demand: For sudden skidding (such as when a single wheel wades through water), it is necessary to quickly compensate with fuzzy rules.

[0195] Preferably, the conditions for the system to switch to PID control include:

[0196] Steady-state working conditions: Straight-line driving or uniform cornering (lateral acceleration < 0.2g), and the error continuously remains below the threshold (such as the yaw rate error < 5%);

[0197] System resource limitation: If the MPC calculation times out (such as the real-time performance cannot meet the 10ms cycle), it will be degraded to PID;

[0198] Fault recovery: After the fuzzy logic parameters are inaccurate, it will fallback to the conservative PID parameters.

[0199] Embodiment 2

[0200] This embodiment is a further improvement of Embodiment 1, and the repeated content will not be elaborated.

[0201] The present invention also discloses a control method for torque vector distribution of an electronically controlled wet clutch based on road conditions, which uses the torque vector distribution system of Embodiment 1 to achieve torque vector distribution. Among them, the control method includes:

[0202] Using PID control to adjust torque distribution according to the error signal; using fuzzy control to handle uncertainties and nonlinearities to adapt to complex road conditions; using model predictive control based on the vehicle dynamics model to predict future states and optimize torque distribution.

[0203] It should be noted that the above specific embodiments are exemplary. Those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also fall within the scope of the disclosure of the present invention and within the protection scope of the present invention. Those skilled in the art should understand that the description and drawings of the present invention are illustrative and do not constitute a limitation on the claims. The protection scope of the present invention is defined by the claims and their equivalents. The description of the present invention contains multiple inventive concepts. Phrases such as "preferably" or "according to a preferred embodiment" indicate that the corresponding paragraphs disclose an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept. Throughout the text, the features guided by "preferably" are only optional and should not be construed as being required to be provided. Therefore, the applicant reserves the right to waive or delete the relevant preferred features at any time.

Claims

1. An electronically controlled wet clutch torque vectoring distribution system based on road conditions, characterized in that It includes: A real-time road condition monitoring device, which is used to obtain the changes of parameters including wheel speed, acceleration, yaw angular velocity and steering angle of a vehicle, so as to indirectly obtain road surface parameter information and transmit this information to an information processing device; An electronically controlled hydraulic all-wheel drive coupler, which is located at the rear end of the vehicle's drive shaft and is responsible for transmitting power to the front wheels. Among them, when driving on a normal and smooth road surface, the electronically controlled hydraulic all-wheel drive coupler does not participate in power distribution; when receiving the road surface information sent by the information processing device, the electronically controlled hydraulic all-wheel drive coupler can enter the engaged state and redistribute the power between the front and rear axles according to the transmitted road surface information to meet the control requirements under different driving conditions; An electronically controlled anti-skid differential, one is arranged on each of the front and rear axles, and it is responsible for distributing the power between the two wheels on the same axle. Among them, when receiving the road surface information sent by the information processing device, the electronically controlled anti-skid differential can redistribute the power between the left and right wheels according to the road surface information to keep the vehicle stable when cornering and on complex road surfaces; An information processing device, which is used to receive and process road surface parameter information and transmit control signals to the electronically controlled hydraulic all-wheel drive coupler and the electronically controlled anti-skid differential.

2. The system according to claim 1, wherein The real-time road condition monitoring device includes one or more of the following sensors: A wheel speed sensor, which is used to monitor the rotation speed of each wheel to estimate the longitudinal force and slip ratio in combination with the vehicle speed; An acceleration sensor, which is used to measure the longitudinal and lateral accelerations of the vehicle to indirectly calculate the tire force and estimate the slip ratio in combination with the rotation speed change; A torque sensor, which is used to directly measure the torque of the drive shaft or wheel hub, calculate the driving force or braking force, and calculate the slip ratio in combination with the wheel speed; A tire pressure sensor, which is used to monitor the internal pressure of the tire to indirectly reflect the force condition; A steering angle sensor, which is used to measure the steering wheel angle to estimate the sideslip angle in combination with the vehicle speed; A vision sensor, which is used to identify the lane lines and vehicle trajectory through a camera to indirectly estimate the sideslip angle; A road condition recognition sensor, which is used to identify the road surface condition.

3. The system according to claim 1 or 2, characterized in that, The real-time road condition monitoring device obtains data including wheel speed, acceleration, yaw angular velocity and / or steering angle based on the configured sensors and obtains the current road surface condition through the road condition recognition sensor, so that the information processing device can estimate the vehicle state according to the data sent by the real-time road condition monitoring device.

4. The system according to any one of claims 1 to 3, characterized in that The electronically controlled hydraulic all-wheel drive coupler includes: a housing (9), a coupler (10), a gear set, a first shaft (15), a chain (16), a second shaft (18), wherein the gear set includes a first gear (11), a second gear (12), a third gear (13), a fourth gear (14), a fifth gear (17), and the first gear (11) and the fifth gear (17) are connected by a chain (16).

5. The system according to any one of claims 1 to 4, characterized in that, The first shaft (15) comes from the input shaft of the gearbox and directly leads to the rear axle. It is provided with a first gear (11), a second gear (12), and a fourth gear (14); the third gear (13) can connect the first gear (11) and the second gear (12) or connect the second gear (12) and the fourth gear (14); the coupler (10) can distribute power by controlling the degree of gear engagement; the second shaft (18) directly leads to the front axle.

6. The system according to any one of claims 1 to 5, characterized in that When driving on a road surface with good conditions, the first gear (11), the second gear (12), the third gear (13), and the fourth gear (14) are not engaged and rotate with the first shaft (15). The power is transmitted to the rear axle through the first shaft (15).

7. The system according to any one of claims 1 to 6, characterized in that In four-wheel drive mode, the third gear (13) connects the first gear (11) and the second gear (12). When the power is transmitted to the rear axle through the first shaft (15), part of the power is transmitted to the second shaft (18) through the coupler (10), the first gear (11), the second gear (12), the third gear (13), the chain (16), and the fifth gear (17), and finally transmitted to the front axle.

8. The system according to any one of claims 1 to 7, characterized in that, When driving in an off-road environment, the third gear (13) connects the first gear (11) and the fourth gear (14). When the power is transmitted to the rear axle through the first shaft (15), part of the power is transmitted to the second shaft (18) through the first gear (11), the third gear (13), the fourth gear (14), the chain (16), and the fifth gear (17), and finally transmitted to the front axle.

9. The system according to any one of claims 1 to 8, characterized in that The information processing device can process road surface information and redistribute the torque vector by using one or more of PID control, fuzzy control, and model predictive control. Among them, when using PID control, the torque distribution can be adjusted according to the error signal; when using fuzzy control, it can handle uncertainty and nonlinear situations and adapt to complex road conditions; when using model predictive control, it can predict the future state and optimize the torque distribution based on the vehicle dynamics model.

10. A control method for torque vectoring distribution of an electronically controlled wet clutch based on road conditions, characterized in that, It uses the torque vector distribution system as described in any one of claims 1 to 9 to achieve torque vector distribution, wherein the control method includes: Using PID control to adjust the torque distribution according to the error signal; using fuzzy control to handle uncertainty and nonlinear situations and adapt to complex road conditions; using model predictive control to predict the future state and optimize the torque distribution based on the vehicle dynamics model.

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