Vehicle four-wheel drive power distribution and road adhesion coefficient real-time matching method and system

Abstract

The application provides a vehicle four-wheel driving power distribution and real-time matching method and system for road adhesion coefficient, wherein the method comprises the following steps: constructing a multi-source sensor data acquisition system to acquire vehicle running state data; based on the data, using an extended Kalman filtering algorithm to perform real-time estimation on the road adhesion coefficient of each wheel of the vehicle to obtain the adhesion coefficient corresponding to each wheel; visually assisting correction is performed on the adhesion coefficient, the road type is identified through image recognition, and the adhesion coefficient is dynamically corrected; based on the corrected adhesion coefficient, an optimization model for four-wheel torque distribution is constructed, the maximum driving efficiency is taken as an objective function, and the slip rate, motor efficiency and battery state of charge are taken as constraint conditions; a model predictive control algorithm is used to perform rolling solution on the optimization model to obtain the target torque distribution value of each wheel in real time; the target torque distribution value is used to control the output torque of each driving motor, and the actual slip rate is compared with the target slip rate through closed-loop feedback to dynamically correct the control model.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle control technology, specifically to a method and system for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient. Background Technology

[0002] Currently, most four-wheel drive power distribution systems for new energy vehicles adopt fixed ratio distribution or feedback control strategies based on a single sensor. Common technologies include: fixed torque distribution, where the power of the front and rear axles is distributed according to a preset ratio; passive response control, which passively adjusts the torque based on wheel speed difference or slippage signals; and road surface recognition relying on experience models, which matches the power distribution through historical data or preset parameters.

[0003] The main shortcomings of existing technologies are as follows: poor dynamic adaptability, with torque distribution errors exceeding 15% under mixed road conditions such as ice, snow, gravel, and wet surfaces, leading to decreased drive efficiency and energy waste; isolated sensor information, with data such as wheel speed, motor torque, and vehicle attitude processed independently, lacking multi-source information fusion and unable to accurately estimate the road adhesion coefficient; conservative control strategy, often limiting peak torque output to prevent excessive slippage, resulting in power performance loss; and insufficient fault tolerance, with the failure of a single sensor causing power distribution to become uncontrollable, posing a safety hazard. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient.

[0005] To achieve the above-mentioned technical effects, the present invention adopts the following technical solution:

[0006] According to a first aspect of the present invention, a method for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient is provided, comprising the following steps:

[0007] S1: Construct a multi-source sensor data acquisition system to collect vehicle operating status data, including at least wheel speed data, motor torque data, inertial measurement unit data, tire ground pressure data, and road surface image data;

[0008] S2: Based on the data, the road adhesion coefficient of each wheel of the vehicle is estimated in real time using the extended Kalman filter algorithm to obtain the adhesion coefficient corresponding to each wheel.

[0009] S3: Visually assist in correcting the adhesion coefficient by identifying the road surface type through image recognition and dynamically correcting the adhesion coefficient.

[0010] S4: Based on the corrected adhesion coefficient, a four-wheel drive torque distribution optimization model is constructed, with the maximization of drive efficiency as the objective function, and slip ratio, motor efficiency and battery state of charge as constraints.

[0011] S5: The model predictive control algorithm is used to solve the optimization model in a rolling manner to obtain the target torque distribution value of each wheel in real time.

[0012] S6: Control the output torque of each drive motor according to the target torque distribution value, and compare the actual slip ratio with the target slip ratio through closed-loop feedback to dynamically correct the control model.

[0013] Preferably, the extended Kalman filter algorithm integrates wheel speed difference, longitudinal acceleration, lateral acceleration, and motor torque fluctuation information to estimate the adhesion coefficient.

[0014] Preferably, the visual assistance correction includes recognizing the road surface texture through a camera and compensating for and correcting the adhesion coefficient based on the recognition results.

[0015] Preferably, the rolling optimization time window of the model predictive control algorithm is 50ms to 150ms.

[0016] Preferably, the constraints include slip ratio ≤ 15%, motor efficiency > 90%, and battery state of charge > 20%.

[0017] Preferably, it also includes adjusting the torque difference between the left and right wheels according to the steering wheel angle during vehicle steering to achieve asymmetric torque distribution.

[0018] Preferably, it also includes a fault-tolerant control step, which switches to a preset basic torque distribution mode when a sensor failure or communication anomaly is detected.

[0019] According to a first aspect of the present invention, a real-time matching system for vehicle four-wheel drive force distribution and road surface adhesion coefficient is provided for implementing the real-time matching method for vehicle four-wheel drive force distribution and road surface adhesion coefficient as described above, the system comprising:

[0020] A multi-source sensor module is used to collect vehicle operating status data, which includes at least wheel speed data, motor torque data, inertial measurement unit data, tire ground pressure data, and road surface image data.

[0021] The data fusion module is used to fuse the data and output the fused status information.

[0022] The adhesion coefficient estimation module is used to calculate the adhesion coefficient between each wheel and the road surface based on the fused state information using an extended Kalman filter algorithm.

[0023] The visual correction module is used to correct the adhesion coefficient based on the road surface image recognition results;

[0024] The torque optimization control module is used to build a torque distribution optimization model based on the corrected adhesion coefficient and to calculate the target torque of each wheel using a model predictive control algorithm.

[0025] An execution control module is used to control the output of each drive motor according to the target torque;

[0026] The feedback adjustment module is used to perform closed-loop correction of torque distribution based on the actual operating conditions of the vehicle.

[0027] Compared with existing technologies, the vehicle four-wheel drive force distribution and road surface adhesion coefficient real-time matching method and system provided by the present invention significantly improves the accuracy of adhesion coefficient estimation by fusing machine vision data with extended Kalman filter algorithm, and has a fast power distribution response speed. Furthermore, by adopting model predictive control algorithm, it can improve driving efficiency and overall vehicle energy efficiency and adaptability to complex road conditions, enhance system safety and reliability, and reduce tire wear. Attached Figure Description

[0028] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0029] Figure 1 This is a flowchart of the real-time matching method between vehicle four-wheel drive force distribution and road surface adhesion coefficient in Example 1. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, all directional indications (such as up, down, left, right, front, back, bottom, etc.) in this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indication will also change accordingly. Furthermore, descriptions involving "first," "second," etc., in this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0033] Example 1

[0034] like Figure 1 As shown, the method for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient provided by the present invention includes the following steps:

[0035] S1: Construct a multi-source sensor data acquisition system to collect vehicle operating status data, including at least wheel speed data, motor torque data, inertial measurement unit data, tire ground pressure data, and road surface image data;

[0036] S2: Based on the data, the extended Kalman filter algorithm is used to estimate the road adhesion coefficient of each wheel of the vehicle in real time, and the adhesion coefficient of each wheel is obtained.

[0037] S3: Visual assistance correction of the adhesion coefficient is performed by identifying the road surface type through image recognition and dynamically correcting the adhesion coefficient.

[0038] S4: Based on the corrected adhesion coefficient, a four-wheel drive torque distribution optimization model is constructed, with the maximization of drive efficiency as the objective function, and slip ratio, motor efficiency and battery state of charge as constraints.

[0039] S5: The model predictive control algorithm is used to solve the optimization model in a rolling manner to obtain the target torque distribution value of each wheel in real time.

[0040] S6: Control the output torque of each drive motor according to the target torque distribution value, and compare the actual slip ratio with the target slip ratio through closed-loop feedback to dynamically correct the control model.

[0041] On high-adhesion surfaces (adhesion coefficient > 0.8), the rear axle torque ratio increases to 70%, enhancing acceleration performance. On low-adhesion surfaces (adhesion coefficient < 0.3), front axle active limited slip combined with rear axle torque vector control is used, with single-wheel torque deviation controlled to < 5%.

[0042] Furthermore, the extended Kalman filter algorithm integrates wheel speed difference, longitudinal acceleration, lateral acceleration, and motor torque fluctuation information to estimate the adhesion coefficient.

[0043] Furthermore, visual-assisted correction includes recognizing road surface texture through a camera and compensating for and correcting the adhesion coefficient based on the recognition results.

[0044] Furthermore, the rolling optimization time window of the model predictive control algorithm is 50ms to 150ms.

[0045] Furthermore, the constraints include slip ratio ≤ 15%, motor efficiency > 90%, and battery state of charge > 20%.

[0046] Furthermore, it also includes adjusting the torque difference between the left and right wheels based on the steering wheel angle during vehicle steering to achieve asymmetrical torque distribution. The basic torque distribution mode is a 50:50 front-to-rear axle distribution combined with electronic limited-slip control.

[0047] Furthermore, it also includes a fault-tolerant control step that switches to a preset basic torque distribution mode when a sensor malfunction or communication anomaly is detected.

[0048] The present invention provides a method and system for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient. By integrating machine vision data with the extended Kalman filter algorithm, the accuracy of adhesion coefficient estimation is significantly improved, the power distribution response speed is fast, and the model predictive control algorithm is adopted, which can improve driving efficiency, vehicle energy efficiency and adaptability to complex road conditions, enhance system safety and reliability, and reduce tire wear.

[0049] Example 2

[0050] This embodiment provides a real-time matching system for vehicle four-wheel drive force distribution and road surface adhesion coefficient, used to implement the real-time matching method for vehicle four-wheel drive force distribution and road surface adhesion coefficient described in Embodiment 1. The system includes:

[0051] The multi-source sensor module is used to collect vehicle operating status data, which includes at least wheel speed data, motor torque data, inertial measurement unit data, tire ground pressure data, and road surface image data.

[0052] The data fusion module is used to fuse data and output the fused status information.

[0053] The adhesion coefficient estimation module is used to calculate the adhesion coefficient between each wheel and the road surface based on the fused state information using the extended Kalman filter algorithm.

[0054] The visual correction module is used to correct the adhesion coefficient based on the road surface image recognition results;

[0055] The torque optimization control module is used to build a torque distribution optimization model based on the corrected adhesion coefficient and to calculate the target torque of each wheel using a model predictive control algorithm.

[0056] The execution control module is used to control the output of each drive motor according to the target torque;

[0057] The feedback adjustment module is used to perform closed-loop correction of torque distribution based on the actual operating conditions of the vehicle.

[0058] The specific embodiments of the present invention have been described above. Based on the above description, those skilled in the art can make various changes and modifications without departing from the technical concept of the present invention.

Claims

1. A method for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient, characterized in that, Includes the following steps: S1: Construct a multi-source sensor data acquisition system to collect vehicle operating status data, including at least wheel speed data, motor torque data, inertial measurement unit data, tire ground pressure data, and road surface image data; S2: Based on the data, the road adhesion coefficient of each wheel of the vehicle is estimated in real time using the extended Kalman filter algorithm to obtain the adhesion coefficient corresponding to each wheel. S3: Visually assist in correcting the adhesion coefficient by identifying the road surface type through image recognition and dynamically correcting the adhesion coefficient. S4: Based on the corrected adhesion coefficient, a four-wheel drive torque distribution optimization model is constructed, with the objective function being the maximization of drive efficiency, and the constraints being slip ratio, motor efficiency, and battery state of charge. S5: The model predictive control algorithm is used to solve the optimization model in a rolling manner to obtain the target torque distribution value of each wheel in real time. S6: Control the output torque of each drive motor according to the target torque distribution value, and compare the actual slip ratio with the target slip ratio through closed-loop feedback to dynamically correct the control model.

2. The method for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient according to claim 1, characterized in that, The extended Kalman filter algorithm integrates wheel speed difference, longitudinal acceleration, lateral acceleration, and motor torque fluctuation information to estimate the adhesion coefficient.

3. The method for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient according to claim 1, characterized in that, The visual-assisted correction includes recognizing road surface texture through a camera and compensating for and correcting the adhesion coefficient based on the recognition results.

4. The method for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient according to claim 1, characterized in that, The rolling optimization time window of the model predictive control algorithm is 50ms to 150ms.

5. The method for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient according to claim 1, characterized in that, The constraints include slip ratio ≤ 15%, motor efficiency > 90%, and battery state of charge > 20%.

6. The method for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient according to claim 1, characterized in that, It also includes adjusting the torque difference between the left and right wheels according to the steering wheel angle during vehicle steering to achieve asymmetrical torque distribution.

7. The method for real-time matching of vehicle four-wheel drive force distribution and road surface adhesion coefficient according to claim 1, characterized in that, It also includes a fault-tolerant control step, which switches to a preset basic torque distribution mode when a sensor failure or communication anomaly is detected.

8. A real-time matching system for vehicle four-wheel drive force distribution and road surface adhesion coefficient, used to implement the real-time matching method for vehicle four-wheel drive force distribution and road surface adhesion coefficient as described in any one of claims 1-7, characterized in that, The system includes: A multi-source sensor module is used to collect vehicle operating status data, which includes at least wheel speed data, motor torque data, inertial measurement unit data, tire ground pressure data, and road surface image data. The data fusion module is used to fuse the data and output the fused status information. The adhesion coefficient estimation module is used to calculate the adhesion coefficient between each wheel and the road surface based on the fused state information using an extended Kalman filter algorithm. The visual correction module is used to correct the adhesion coefficient based on the road surface image recognition results; The torque optimization control module is used to build a torque distribution optimization model based on the corrected adhesion coefficient and to calculate the target torque of each wheel using a model predictive control algorithm. An execution control module is used to control the output of each drive motor according to the target torque; The feedback adjustment module is used to perform closed-loop correction of torque distribution based on the actual operating conditions of the vehicle.

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