Vehicle control method, device and equipment

Abstract

The embodiment of the invention relates to the technical field of vehicles, and discloses a vehicle control method, device and equipment, and the method comprises the steps: obtaining the initial maximum adhesive force of wheels of a vehicle in multiple dimensions; wherein the initial maximum adhesive force of multiple dimensions comprises at least two of the following: a first maximum adhesive force, a second maximum adhesive force and a third maximum adhesive force; the first maximum adhesive force is the maximum adhesive force determined based on the slipping state of the wheel, the second maximum adhesive force is the maximum adhesive force determined based on the road surface image corresponding to the wheel, and the third maximum adhesive force is the maximum adhesive force determined based on the vertical load of the wheel; according to the initial maximum adhesive force of the multiple dimensions, the target maximum adhesive force of the wheel is determined; and controlling the wheels of the vehicle according to the target maximum adhesive force of each wheel. By means of the technical scheme, the problem that in the prior art, reliability of driving force distribution is poor can be solved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, specifically to a vehicle control method, device, and equipment. Background Technology

[0002] Distributed drive vehicles, with their independent wheel drive technology, can achieve precise and individual control of the driving torque of each wheel. When driving on low-traction complex surfaces such as ice, snow, sand, and mud, if some wheels slip due to insufficient road surface traction, the system does not need to adjust the power of all wheels uniformly. Instead, it can selectively reduce the driving torque of the slipping wheels, avoiding power waste and the risk of getting stuck due to excessive slippage.

[0003] Currently, the drive force distribution strategy for distributed drive vehicles mostly estimates the maximum adhesion of each wheel based on the slip ratio, and then distributes the drive force to each wheel based on the maximum adhesion of each wheel. However, this method cannot accurately obtain the slip ratio when the wheels are not slipping, which leads to inaccurate maximum adhesion and a lack of reliable basis for drive force distribution. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a vehicle control method, apparatus and device to solve the problem of poor reliability of driving force distribution in the prior art.

[0005] According to one aspect of the present invention, a vehicle control method is provided, the method comprising:

[0006] The initial maximum adhesion of the vehicle's wheels in multiple dimensions is obtained; wherein the initial maximum adhesion in multiple dimensions includes at least two of the following: a first maximum adhesion, a second maximum adhesion, and a third maximum adhesion; the first maximum adhesion is the maximum adhesion determined based on the wheel's slippage state, the second maximum adhesion is the maximum adhesion determined based on the road surface image corresponding to the wheel, and the third maximum adhesion is the maximum adhesion determined based on the wheel's vertical load.

[0007] The target maximum adhesion of the wheel is determined based on the initial maximum adhesion of the multiple dimensions.

[0008] The vehicle's wheels are controlled based on the target maximum adhesion for each wheel.

[0009] According to another aspect of the present invention, a vehicle control device is provided, comprising:

[0010] The acquisition module is used to acquire the initial maximum adhesion of the vehicle's wheels in multiple dimensions; wherein the initial maximum adhesion in multiple dimensions includes at least two of the following: a first maximum adhesion, a second maximum adhesion, and a third maximum adhesion; the first maximum adhesion is the maximum adhesion determined based on the wheel's slippage state, the second maximum adhesion is the maximum adhesion determined based on the road surface image corresponding to the wheel, and the third maximum adhesion is the maximum adhesion determined based on the wheel's vertical load;

[0011] The determination module is used to determine the target maximum adhesion of the wheel based on the initial maximum adhesion of the multiple dimensions;

[0012] A control module is used to control the wheels of the vehicle based on the target maximum adhesion of each wheel.

[0013] According to another aspect of the present invention, an electronic device is provided, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0014] The memory is used to store at least one executable instruction that causes the processor to perform the operation of the vehicle control method described above.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction that causes an electronic device / vehicle control device to perform the following operations:

[0016] The initial maximum adhesion of the vehicle's wheels in multiple dimensions is obtained; wherein the initial maximum adhesion in multiple dimensions includes at least two of the following: a first maximum adhesion, a second maximum adhesion, and a third maximum adhesion; the first maximum adhesion is the maximum adhesion determined based on the wheel's slippage state, the second maximum adhesion is the maximum adhesion determined based on the road surface image corresponding to the wheel, and the third maximum adhesion is the maximum adhesion determined based on the wheel's vertical load.

[0017] The target maximum adhesion of the wheel is determined based on the initial maximum adhesion of the multiple dimensions.

[0018] The vehicle's wheels are controlled based on the target maximum adhesion for each wheel.

[0019] This invention can obtain the initial maximum adhesion force of each wheel in multiple dimensions, fuse them to obtain a more accurate target maximum adhesion force for each wheel, and then allocate driving torque to each wheel based on the target maximum adhesion force of each wheel, thereby achieving fine control of the vehicle wheels. It can effectively solve the technical pain points of relying on a single slip ratio to estimate the maximum adhesion force in related technologies, such as inaccurate estimation when the wheel is not slipping and poor adaptability to all working conditions. Through the complementary fusion of multi-dimensional data, the accuracy of the target maximum adhesion force under different road conditions and different wheel slip states is ensured, providing a reliable basis for driving force allocation. At the same time, it fully leverages the advantages of independent control of each wheel in distributed drive vehicles, and adjusts the torque of slipping and non-slipping wheels in a targeted manner, avoiding power waste and the risk of getting stuck due to excessive slippage, and significantly improving the vehicle's ability to get out of trouble and driving stability on complex low-adhesion roads such as ice, snow, and sand.

[0020] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0021] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0022] Figure 1 A flowchart of a first embodiment of the vehicle control method provided by the present invention is shown;

[0023] Figure 2 A flowchart of a second embodiment of the vehicle control method provided by the present invention is shown;

[0024] Figure 3 A schematic diagram of a drive torque distribution method provided by the present invention is shown;

[0025] Figure 4 A schematic diagram of an embodiment of the vehicle control device provided by the present invention is shown;

[0026] Figure 5 A schematic diagram of an embodiment of the electronic device provided by the present invention is shown. Detailed Implementation

[0027] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0028] In related technologies, the method of estimating the maximum adhesion of wheels and distributing driving force based on slip ratio has a key limitation: the accurate acquisition of slip ratio depends on the wheel slipping state. When the wheels are not slipping, the slip ratio cannot be accurately captured, leading to distorted estimations of maximum adhesion. This, in turn, makes driving force distribution lack a reliable basis, hindering the full utilization of the advantages of independent wheel control in distributed drive vehicles. Based on this, the inventors further considered that if relying solely on single-dimensional slip ratio data cannot cover the accurate calculation requirements of maximum adhesion under all working conditions, then integrating multi-dimensional maximum adhesion data and utilizing the complementarity of different dimensions for fusion decision-making can overcome the limitations of a single dimension. Therefore, a scheme is proposed that integrates the multi-dimensional maximum adhesion of each wheel to obtain a final accurate maximum adhesion, and then distributes driving force based on this final result. This effectively solves the problem of inaccurate estimation of maximum adhesion when the wheels are not slipping, providing reliable support for driving force distribution in distributed drive vehicles and fully leveraging their advantages in traction and driving stability.

[0029] The execution subject of this invention can be an electronic device with processing capabilities in a vehicle, such as an existing controller in the vehicle or a separately set controller. The controller can be an electronic control unit (ECU), a microcontroller unit (MCU), etc., and this invention does not limit the scope of the invention.

[0030] Figure 1 A flowchart of a first embodiment of the vehicle control method provided by the present invention is shown. Figure 1 As shown, the execution subject of this method can be a controller, and the method includes the following steps:

[0031] Step 110: Obtain the initial maximum adhesion of the vehicle's wheels in multiple dimensions.

[0032] For example, maximum adhesion refers to the ultimate grip that the current road surface can provide to the wheel; in other words, if the grip exceeds this maximum, the wheel will slip. Multiple-dimensional initial maximum adhesion refers to the maximum adhesion obtained from different data sources and based on different calculation logics, including at least two of the following: a first maximum adhesion, a second maximum adhesion, and a third maximum adhesion. Specifically, the first maximum adhesion is the maximum adhesion determined based on the wheel's slippage state; the second maximum adhesion is the maximum adhesion determined based on the road surface image corresponding to the wheel; and the third maximum adhesion is the maximum adhesion determined based on the wheel's vertical load.

[0033] In one example, the controller can communicate with other controllers to directly obtain the initial maximum adhesion force across multiple dimensions. Alternatively, the controller can acquire the wheel speeds of each wheel and the vehicle speed, then calculate the slip ratio of each wheel based on these speeds, and further calculate the first maximum adhesion force based on preset processing logic. Simultaneously, it acquires road surface images captured by cameras at each wheel, performs image processing to obtain the second maximum adhesion force, and acquires the vertical load on each wheel, calculating the third maximum adhesion force based on the vertical load and a preset road surface adhesion coefficient. The road surface adhesion coefficient represents the static friction coefficient between the tire and the road surface, and is related to the type of road surface.

[0034] Specifically, the controller can determine the wheel slip ratio based on the wheel speed and vehicle speed; determine the first road surface adhesion coefficient based on the wheel slip ratio; and determine the first maximum adhesion force based on the first road surface adhesion coefficient. The wheel slip ratio characterizes the wheel's slipping state. For example, the controller can first determine the slip ratio of each wheel based on the mapping relationship between wheel speed, vehicle speed, and slip ratio. Then, based on a preset tire mechanics model, it can determine the first road surface adhesion coefficient of each wheel according to the slip ratio, vertical load, and current driving torque of each wheel. Finally, the product of the first road surface adhesion coefficient and the vertical load of the wheel can be used as the first maximum adhesion force of the wheel. The tire mechanics model describes the mapping relationship between the road surface adhesion coefficient, the vertical load of the wheel, the slip ratio, and the driving torque of the wheel.

[0035] The controller can acquire road surface images corresponding to each wheel captured by cameras on the vehicle; perform recognition processing on the road surface images to obtain the second road surface adhesion coefficient of the wheel; and determine the second maximum adhesion force of the wheel based on the second road surface adhesion coefficient. For example, the controller can perform recognition processing on the road surface images based on a preset image recognition model to determine the road surface type on which the wheel is traveling, and then determine the second road surface adhesion coefficient of the wheel based on the mapping relationship between the road surface type and the adhesion coefficient, and take the product of the second road surface adhesion coefficient and the vertical load of the wheel as the second maximum adhesion force of the wheel. The image recognition model can be a neural network model, and the type of image recognition model is not limited in this embodiment of the invention.

[0036] The controller determines the third maximum adhesion force of the wheel based on the vertical load on the wheel and a preset road surface adhesion coefficient. This preset road surface adhesion coefficient is a fixed value, which can be set according to actual needs.

[0037] Optionally, this step is executed after receiving the instruction to enter the traction control mode. The traction control mode is a specialized drive control mode preset for low-adhesion road surfaces such as sand, ice, snow, and mud, enabling the vehicle to smoothly escape traction using the vehicle control method provided in this embodiment of the invention. The instruction refers to the electronic control signal used to trigger the vehicle to enter the traction control mode.

[0038] The aforementioned instructions may be generated in response to a user's activation of the traction control mode; wherein the activation operation may be at least one of the following: a physical button, a traction mode icon on a touch-screen vehicle display, or a voice command to activate the traction control mode.

[0039] The command can also be generated when the controller determines that the road surface adhesion coefficient is less than a preset adhesion coefficient threshold. As mentioned above, the controller can obtain the road surface adhesion coefficient based on the wheel slip ratio, road surface image, or wheel vertical load, and then automatically generate a command when it determines that the road surface adhesion coefficient is less than the preset adhesion coefficient threshold.

[0040] Step 120: Determine the target maximum adhesion of the wheel based on the initial maximum adhesion in multiple dimensions.

[0041] For example, the target maximum adhesion force refers to the maximum adhesion force of the wheel under the current working conditions, which is finally determined after multi-dimensional data fusion.

[0042] In one example, the controller can determine the current wheel slippage state based on the slip ratio calculated in the aforementioned steps. Based on the slippage state, it determines the weight set corresponding to that wheel. For example, when the slippage state is slipping, the corresponding weight set is weight set 1; when the slippage state is not slipping, the corresponding weight set is weight set 2. Then, based on the weight values ​​in the weight set corresponding to that wheel, the initial maximum adhesion forces across multiple dimensions are weighted and summed to obtain the target maximum adhesion force for that wheel. It should be noted that the weight values ​​in the weight set corresponding to that wheel correspond one-to-one with the initial maximum adhesion forces across multiple dimensions.

[0043] Step 130: Control the vehicle's wheels based on the target maximum adhesion for each wheel.

[0044] For example, the core of controlling the vehicle's wheels is to dynamically distribute the driving torque of each wheel based on the target maximum adhesion of each wheel, ensuring that the driving force of the wheel does not exceed its limit grip capability, while making full use of the adhesion potential of non-slipping wheels and reducing power waste.

[0045] In one example, the controller can first calculate the total torque demand of the vehicle based on the user's throttle opening, and obtain the original drive torque of each wheel according to a preset distribution ratio. Then, based on the target maximum adhesion of each wheel, it calculates the maximum drive torque of that wheel, where the maximum drive torque represents the upper limit of torque for the wheel to not slip. If a wheel is slipping, it means that the original drive torque of that wheel is greater than the maximum drive torque. The drive torque of the slipping wheel is then adjusted to the maximum drive torque, and the torque difference between the original drive torque and the maximum drive torque is determined. Subsequently, the torque margin of all non-slipping wheels can be determined, that is, the difference between the original drive torque of the non-slipping wheel and the maximum drive torque of that wheel. Based on the torque margin of the non-slipping wheels, the torque difference of the slipping wheels is distributed to the non-slipping wheels to ensure that the drive torque of each wheel after distribution is less than or equal to its own maximum drive torque. It should be noted that the embodiments of the present invention do not limit the method of how to allocate the torque difference of the slipping wheel to the non-slipping wheel. For example, if the torque margin of a wheel is greater than or equal to the torque difference, the entire torque difference can be allocated to that wheel. Alternatively, the torque difference of the slipping wheel can be allocated to the non-slipping wheel according to the proportion of the torque margin of each non-slipping wheel.

[0046] In this embodiment, the controller can acquire the initial maximum adhesion force of each wheel in multiple dimensions, fuse them to obtain a more accurate target maximum adhesion force for each wheel, and then allocate driving torque to each wheel based on the target maximum adhesion force of each wheel, thereby achieving fine-grained control of the vehicle wheels. This method effectively solves the technical pain points of related technologies that rely on a single slip ratio to estimate maximum adhesion force, resulting in inaccurate estimation when the wheels are not slipping and poor adaptability across all working conditions. Through the complementary fusion of multi-dimensional data, the accuracy of the target maximum adhesion force is ensured under different road conditions and different wheel slip states, providing a reliable basis for driving force allocation. At the same time, it fully leverages the advantages of independent control of each wheel in distributed drive vehicles, specifically adjusting the torque of slipping and non-slipping wheels, avoiding power waste and the risk of getting stuck due to excessive slippage, and significantly improving the vehicle's ability to get out of trouble and its driving stability on complex low-adhesion surfaces such as ice, snow, and sand.

[0047] Figure 2 A flowchart of a second embodiment of the vehicle control method provided by the present invention is shown. Figure 2 As shown, the method includes the following steps:

[0048] Step 210: Obtain the initial maximum adhesion of the vehicle's wheels in multiple dimensions.

[0049] For example, the controller can obtain the first maximum adhesion, the second maximum adhesion, and the third maximum adhesion as the initial maximum adhesion in multiple dimensions.

[0050] Step 220: For each wheel, obtain the wheel slippage status and current drive torque.

[0051] For example, slippage refers to the degree of relative sliding between the wheel and the road surface, such as slipping or not slipping. This slippage state can be determined by the wheel's slip ratio; when the slip ratio is greater than a preset threshold, it is determined to be slipping, and when it is less than or equal to the threshold, it is determined to be not slipping. The current drive torque refers to the torque value output by the drive motor to the wheel at the current moment, which reflects the wheel's current actual power output level and can be obtained through communication with the drive motor controller.

[0052] Step 230: Determine the weight set corresponding to the slip state based on the slip state.

[0053] The weight set includes at least two of the following weights: a first weight corresponding to the first maximum adhesion force, a second weight corresponding to the second maximum adhesion force, and a third weight corresponding to the third maximum adhesion force.

[0054] For example, the weight set refers to the combination of weight coefficients assigned to the initial maximum adhesion for each dimension, with the sum of the weights being 1, used to characterize the degree of confidence in the initial maximum adhesion for each dimension. It can be understood that the allocation of weight coefficients is related to the wheel slippage state. When slipping, the first maximum adhesion can be calculated based on the slip ratio with higher accuracy; therefore, a higher weight coefficient can be assigned to the first maximum adhesion. When not slipping, the first maximum adhesion calculated based on the slip ratio is inaccurate, and a smaller weight coefficient can be assigned to the first maximum adhesion. The second maximum adhesion, based on road surface image recognition, better reflects the inherent adhesion characteristics of the road surface; therefore, a higher weight coefficient is assigned to the second maximum adhesion. Thus, the weight set differs for different slippage states.

[0055] In some possible implementations, the controller can pre-define a mapping relationship between slippage states and weight sets, thereby determining the weight set corresponding to the slippage state based on the wheel's slippage state. For example, the weight set when the slippage state is slipping is the weight coefficient a1 of the first maximum adhesion force, the weight coefficient a2 of the second maximum adhesion force, and the weight coefficient a3 of the third maximum adhesion force; the weight set when the slippage state is not slipping is the weight coefficient b1 of the first maximum adhesion force, the weight coefficient b2 of the second maximum adhesion force, and the weight coefficient b3 of the third maximum adhesion force, where a1+a2+a3=1, a1 is greater than a2; b1+b2+b3=1, b2 is greater than b1; the values ​​of a3 and b3 can be fixed values, that is, the third maximum adhesion force is used as a fallback item and assigned a fixed proportion of weight to ensure that there is still reliable data reference in extreme cases.

[0056] By clearly defining the priority rule that the weight coefficient of the first maximum adhesion force is higher than that of the second maximum adhesion force under slipping conditions, and the weight coefficient of the second maximum adhesion force is higher than that of the first maximum adhesion force under non-slipping conditions, the weight allocation can accurately match the reliability characteristics of data in each dimension under different working conditions. At the same time, the weight coefficient of the third maximum adhesion force is set to a fixed value as a fallback, which effectively avoids the distortion of the fusion result when a single dimension of data fails, and ensures the stability of the control logic.

[0057] In some possible implementations, the confidence levels corresponding to the initial maximum adhesion in multiple dimensions are obtained; based on the slippage state, the initial weight set corresponding to the slippage state is determined; based on the confidence levels, the initial weight set is adjusted to obtain the weight set corresponding to the slippage state.

[0058] The confidence level is an indicator that quantifies the reliability of the initial maximum adhesion for each dimension. Its value ranges from 0 to 1; a value closer to 1 indicates higher reliability, while a value closer to 0 indicates lower reliability. The confidence level calculation logic for different dimensions is adapted to the characteristics of their data sources. For example, the confidence level of the first maximum adhesion can be determined based on the stability of the road surface adhesion coefficient calculated within a preset time period. Stability is measured by the fluctuation value of the road surface adhesion coefficient within a continuous preset time period (e.g., 50ms). The confidence level of the second maximum adhesion can be determined based on the probability value of the road surface type identified by an image recognition model. The confidence level of the third maximum adhesion can be a preset fixed value. The initial weight set is a combination of weight coefficients for the initial maximum adhesion of each dimension based on the slippage state.

[0059] Specifically, the controller can preset weight adjustment ratios based on confidence level ranges. For example, when the maximum confidence level is greater than a third preset threshold (e.g., 0.85), the weight coefficient increases by 0.05; when the minimum confidence level is less than a fourth preset threshold (e.g., 0.7), the weight coefficient decreases by 0.05. Furthermore, after obtaining the confidence levels corresponding to the initial maximum adhesion across multiple dimensions and the initial weight set corresponding to the slippage state, the controller determines the maximum and minimum confidence levels. Then, according to the preset weight adjustment ratios, if the maximum confidence level is determined to be greater than the third preset threshold, the weight coefficient corresponding to the maximum confidence level is increased by the preset ratio, and the weight coefficients of lower confidence levels are decreased based on the increased weight. Similarly, if the minimum confidence level is determined to be less than the fourth preset threshold, the weight coefficient corresponding to the minimum confidence level is decreased by the preset ratio, and the weight coefficients of higher confidence levels are increased based on the decreased weight. Moreover, the adjusted weight coefficients satisfy the conditions of each weight coefficient in the initial weight set mentioned in the previous example.

[0060] Taking a confidence level of 0.9 for the first maximum adhesion, 0.95 for the second maximum adhesion, and 0.85 for the third maximum adhesion, and the weight coefficients of 0.6, 0.3, and 0.1 for the first maximum adhesion in the initial weight set when the slippage state is defined as slippage, as an example, the confidence level of the second maximum adhesion (0.95) is the maximum value and is higher than the third preset threshold of 0.85. Therefore, the weight coefficient of the second maximum adhesion is increased from 0.3 to 0.35. Thus, the increased weight can be entirely deducted from the weight coefficient of the first maximum adhesion or entirely deducted from the weight coefficient of the third maximum adhesion, or the increased weight can be deducted proportionally or according to the weight coefficient ratio from the weight coefficients of the first and third maximum adhesion, to satisfy the condition of each weight coefficient in the initial weight set when the slippage state is defined as slippage, i.e., a1 + a2 + a3 = 1, where a1 is greater than a2. Finally, the weight set corresponding to the slippage state is obtained.

[0061] The allocation logic when the minimum confidence level is less than the fourth preset threshold is similar to the above principle and will not be repeated here.

[0062] By introducing confidence levels for the initial maximum adhesion force across various dimensions and dynamically adjusting the initial weight set based on the slippage state, the weight allocation aligns with operational priorities and adapts in real-time to changes in the reliability of data across dimensions. This significantly improves the accuracy and robustness of the target maximum adhesion force estimation. Increasing the weight of high-confidence dimensions and decreasing the weight of low-confidence dimensions effectively highlights the role of reliable data and avoids interference from unreliable data. For example, when the confidence level of the second maximum adhesion force obtained from a camera-captured road surface image decreases due to camera occlusion, the weight of the second maximum adhesion force is correspondingly reduced. This avoids the impact of misjudging road surface type on the fusion result and ensures the accuracy of estimation under dynamic conditions. This dynamic adjustment logic frees the weight allocation from fixed rules, enabling it to adapt to complex scenarios such as sudden road surface changes and temporary sensor noise. This makes the maximum adhesion force fusion result more closely reflect actual operating conditions, providing a more reliable basis for subsequent torque allocation, thereby improving the vehicle's ability to escape from difficult situations and its driving stability on complex low-adhesion surfaces.

[0063] Step 240: Based on the weights in the weight set, perform a weighted summation of the initial maximum adhesion forces across multiple dimensions to obtain the target maximum adhesion force of the wheel.

[0064] For example, the controller can calculate the target maximum adhesion force by weighting and summing the initial maximum adhesion force of each dimension in the weight set.

[0065] Step 250: Determine the maximum driving torque of the wheel based on the target maximum adhesion of the wheel.

[0066] For example, the maximum driving torque refers to the maximum power output torque that the wheel can withstand without exceeding the target maximum adhesion and without slipping. The maximum driving torque is equal to the target maximum adhesion multiplied by the wheel radius.

[0067] Step 260: Based on the maximum driving torque of the wheel that is slipping, adjust the current driving torque of each wheel to obtain the adjusted driving torque of each wheel.

[0068] For example, the adjusted drive torque refers to the final output torque value to the wheels. The purpose of this step is to limit the torque of slipping wheels and supplement the torque of non-slipping wheels, so as to ensure that the torque output of all wheels does not exceed their own maximum drive torque, while making full use of the adhesion potential of non-slipping wheels. For example, for wheels in a slipping state, their adjusted drive torque is directly set to the maximum drive torque to avoid excessive slippage caused by continuous exceeding the threshold; for wheels in a non-slipping state, the excess torque of the slipping wheels is first calculated, and then the excess torque is allocated according to the torque margin of the non-slipping wheels, ensuring that the torque of the non-slipping wheels after allocation still does not exceed their own maximum drive torque, thus obtaining the adjusted drive torque of each wheel.

[0069] Specifically, for a wheel that is slipping, the maximum driving torque of that wheel is determined as the adjusted driving torque of that wheel, and the torque difference between the current driving torque of that wheel and the maximum driving torque of that wheel is determined; based on the torque difference of the wheels that are slipping, the current driving torque of the wheels that are not slipping is adjusted to obtain the adjusted driving torque of the wheels that are not slipping; wherein, the adjusted driving torque of the wheel is less than or equal to the maximum driving torque of that wheel.

[0070] Torque difference refers to the difference between the current driving torque and the maximum driving torque of a slipping wheel. It reflects the power redundancy of that wheel beyond its adhesion limit, which needs to be transferred to the non-slipping wheel to avoid wasting power. For example, the controller can adjust the current driving torque of the non-slipping wheel based on the torque difference of the slipping wheel according to a preset drive distribution strategy, thus obtaining the adjusted driving torque for the non-slipping wheel. Figure 3 A schematic diagram of a drive torque distribution method provided by the present invention is shown.

[0071] When there is a slipping wheel, refer to Figure 3 As shown in (a), (b), and (c), the driver allocation strategy can include the following:

[0072] (1) This drive distribution strategy can be to distribute the entire torque difference of the slipping wheel to the non-slipping wheel on the same side as the slipping wheel, thus obtaining the adjusted drive torque of the non-slipping wheel. For example, in (a), the left rear wheel slips, and the torque difference is... Then All driving force is applied to the non-slipping wheel on the left front of the same side. By adjusting the driving force distribution on the same side, the redundant power of the slipping wheel is concentrated and transferred to the non-slipping wheel on the same side, making full use of the similar road surface adhesion characteristics of the wheels on the same side, avoiding power dispersion, and improving the efficiency of getting out of trouble on one side.

[0073] (2) This drive distribution strategy can also be to reduce the drive torque of the non-slipping wheel on the same axle as the slipping wheel by the torque difference, thereby obtaining the adjusted drive torque of that wheel, and then apply this torque difference to the non-slipping wheels on opposite axles to obtain the adjusted drive torque of the wheels that are slipping but not slipping. For example, in (b), the left rear wheel slips, and the torque difference is... This reduces the driving torque of the non-slipping wheel on the right rear of the coaxial axis. The adjusted drive torque of the wheel was then obtained, and applied to both the non-slipping left and right front wheels on opposite axles. This yields the adjusted drive torque. By actively reducing the torque of the non-slipping wheels on the same axle, the risk of slippage due to insufficient traction can be avoided in advance, preventing both wheels on the same axle from slipping simultaneously (slippage on both sides of the rear axle can easily lead to vehicle fishtailing and loss of control, while slippage on both sides of the front axle can easily cause the vehicle to veer off course), thus improving driving stability on low-traction surfaces.

[0074] (3) The drive distribution strategy can also be to preset the distribution ratio, and based on the distribution ratio, distribute the torque difference to the wheels that are not slipping, to obtain the adjusted drive torque. For example, in (c), when the left rear wheel slips, the distribution ratio is same-side wheel: same-axle wheel: opposite-axle opposite-side wheel = 0.5:0.25:0.25. Then, based on the distribution ratio, the torque difference can be distributed to the wheels that are not slipping. The adjusted drive torque is applied to the non-slipping wheels. By using a preset distribution ratio, the distribution ratio of each wheel can be balanced, eliminating the need to precisely determine the torque margin of each wheel. The preset distribution ratio balances power utilization and driving stability, and can adapt to scenarios where the torque margin of each non-slipping wheel is different but not enough to individually handle the entire torque difference.

[0075] In the case of a single wheel slipping, the controller can preset the priority of the drive allocation strategy, such as (1), (2), and (3) in descending order of priority. If the preceding strategy does not meet the conditions, the subsequent drive allocation strategy will be used in sequence. Alternatively, one of the drive allocation strategies can be preset.

[0076] When there are two slipping wheels, refer to Figure 3 As shown in (d), (e), and (f), the drive distribution strategy can be as follows: if it is determined that the two slipping wheels are on different sides, then the torque difference of the slipping wheels is distributed to the non-slipping wheels on the same side as the slipping wheels, resulting in the adjusted drive torque of the non-slipping wheels. For example, in (d), both the left rear and right rear wheels are slipping, and the torque differences are respectively... and Then the torque difference Torque difference applied to the left front wheel on the same left side. The torque is applied to the right front wheel on the same right side. Scenario (e) is similar to (d), and will not be repeated here. The torque difference between the slipping wheels is transferred to the non-slipping wheels on the same side, maintaining torque symmetry between the left and right axles, avoiding vehicle deviation caused by concentrated torque on one side, and balancing extrication efficiency and straight-line driving stability.

[0077] If it is determined that the two slipping wheels are on the same side, the torque difference of the slipping wheel is distributed to the non-slipping wheels on the same axle as the slipping wheel, resulting in the adjusted drive torque for the non-slipping wheels. For example, in (f), both wheels on the left are slipping, and the torque differences are respectively... and Then the torque difference can be Applying torque difference to the right rear wheel on the same axle. Power is applied to the right front wheel on the same axle. This method avoids continuing to distribute power to the same side (which could easily cause vehicle roll), and instead transfers the torque difference to the opposite wheel on the same axle, utilizing the high adhesion potential of the opposite side to supplement power, while maintaining torque balance between the front and rear axles and reducing the risk of loss of control on one side.

[0078] When there are three slipping wheels, refer to Figure 3 As shown in (g), this drive distribution strategy can be to apply the sum of the torque differences of all slipping wheels to the non-slipping wheels to obtain the adjusted drive torque. For example, in (g), the torque difference... , , All torque is applied to the non-slipping right front wheel. When three wheels slip, the only non-slipping wheel is the core of the vehicle's power output, concentrating all torque differences to that wheel to maximize its traction potential.

[0079] When all wheels slip, refer to Figure 3As shown in (h), this drive distribution strategy can be to determine the adjusted drive torque of each wheel as its own maximum drive torque. In other words, each wheel reduces its own torque difference. All-wheel slippage means that the overall road surface adhesion is extremely low. At this time, the limit torque is adjusted to the maximum drive torque of each wheel, so that each wheel operates within the adhesion limit and avoids excessive slippage (excessive slippage can easily lead to vehicle fishtailing and rollover); at the same time, the maximum possible effective driving force is preserved, rather than completely cutting off the power.

[0080] It should be noted that the aforementioned drive distribution strategy requires that the adjusted drive torque of the wheel be less than or equal to the maximum drive torque of that wheel.

[0081] Step 270: Control the wheels based on the adjusted drive torque for each wheel.

[0082] For example, controlling the wheels based on the adjusted drive torque means generating a torque command from the adjusted drive torque and sending it to the drive motor corresponding to each wheel. The drive motor then executes the torque output to control the wheels.

[0083] Optionally, for a wheel in a slipping state, after controlling the wheel based on the adjusted drive torque of the wheel, if it is determined that the wheel meets the preset conditions, the target drive torque of the wheel is determined based on the throttle opening of the vehicle, and the wheel is controlled based on the target drive torque and the adjusted drive torque of the wheel; wherein, the preset conditions include the wheel slipping state being non-slipping, the vehicle throttle opening being increased and the duration being greater than a first preset threshold, and the vehicle traveling distance being greater than a second preset threshold within the duration.

[0084] For example, this step involves controlling the torque-limiting function of the slipping wheel and then gradually restoring power based on the improvement in road surface adhesion and the user's power demand. The preset conditions are a composite condition for determining whether road surface adhesion has truly improved and whether the user has a continuous power demand. Three sub-conditions must be met simultaneously: first, the wheel slippage changes to non-slippage, indicating that the road surface provides sufficient grip; second, the vehicle's throttle opening increases and lasts for a duration greater than a first preset threshold (the first preset threshold is a time standard to exclude temporary throttle fluctuations), reflecting a clear user demand for increased power; and third, the vehicle travels a distance greater than a second preset threshold during the duration (the second preset threshold is a distance standard to exclude wheel spin and confirm effective vehicle travel), ensuring that the improved adhesion is a genuine change in road conditions rather than temporary sensor noise. The target drive torque is the torque value corresponding to the user's demand, calculated based on the current throttle opening. After determining that the preset conditions are met, the controller can gradually approach the target drive torque through a gradient transition to control the wheels and ensure a smooth power recovery.

[0085] In this embodiment, the controller acquires the initial maximum adhesion force of the wheel in multiple dimensions, the wheel slippage state, and the current driving torque. Based on the slippage state, it allocates a dynamic weight set, weights and fuses the initial maximum adhesion forces of each dimension to obtain the target maximum adhesion force, and then determines the maximum driving torque based on the target maximum adhesion force. The controller then adjusts the driving torque of the slipping wheel and the non-slipping wheel respectively to control the wheel operation. This method effectively solves the problem of inaccurate estimation of maximum adhesion due to poor adaptability to working conditions, which leads to inaccurate torque distribution when relying on a single dimension to estimate the maximum adhesion in related technologies. By adapting the weights according to the slippage state and fusing multi-dimensional data, the method ensures the estimation accuracy of the target maximum adhesion under different road conditions (low adhesion, high-low adhesion switching) and wheel states (slippage, no slippage), providing a reliable basis for torque adjustment. At the same time, through the precise adjustment strategy of limiting torque on slipping wheels and supplementing torque on non-slipping wheels, the method fully leverages the advantages of independent control of each wheel in distributed drive vehicles, avoiding power waste and the risk of getting stuck due to excessive slippage of slipping wheels, and maximizing the adhesion potential of non-slipping wheels. This significantly improves the vehicle's ability to get out of trouble and its driving stability on complex low-adhesion roads such as ice, snow, and sand.

[0086] Figure 4 A schematic diagram of an embodiment of the vehicle control device provided by the present invention is shown. Figure 4 As shown, the vehicle control device 300 includes: an acquisition module 310, a determination module 320, and a control module 330.

[0087] The acquisition module 310 is used to acquire the initial maximum adhesion of the vehicle's wheels in multiple dimensions; wherein the initial maximum adhesion in multiple dimensions includes at least two of the following: a first maximum adhesion, a second maximum adhesion, and a third maximum adhesion; the first maximum adhesion is the maximum adhesion determined based on the wheel's slippage state, the second maximum adhesion is the maximum adhesion determined based on the road surface image corresponding to the wheel, and the third maximum adhesion is the maximum adhesion determined based on the wheel's vertical load;

[0088] The determination module 320 is used to determine the target maximum adhesion of the wheel based on the initial maximum adhesion in multiple dimensions;

[0089] The control module 330 is used to control the wheels of the vehicle based on the target maximum adhesion of each wheel.

[0090] In one alternative approach, module 320 is defined for:

[0091] Obtain the wheel slippage status;

[0092] Based on the slippage state, determine the weight set corresponding to the slippage state; wherein, the weight set includes at least two of the following weights: a first weight corresponding to the first maximum adhesion force, a second weight corresponding to the second maximum adhesion force, and a third weight corresponding to the third maximum adhesion force;

[0093] The target maximum adhesion force of the wheel is obtained by weighted summation of the initial maximum adhesion forces across multiple dimensions based on the weights in the weight set.

[0094] In one alternative approach, module 320 is defined for:

[0095] Obtain the confidence level corresponding to the initial maximum adhesion force in multiple dimensions;

[0096] Based on the slippage state, determine the initial weight set corresponding to the slippage state;

[0097] Based on the confidence level, the initial weight set is adjusted to obtain the weight set corresponding to the slippage state.

[0098] In one alternative embodiment, the control module 330 is used for:

[0099] For each wheel, obtain the wheel slippage state and current drive torque;

[0100] Determine the maximum driving torque of the wheel based on the target maximum adhesion of the wheel;

[0101] Based on the maximum driving torque of the wheel that is slipping, the current driving torque of each wheel is adjusted to obtain the adjusted driving torque of each wheel.

[0102] The wheels are controlled based on the adjusted drive torque for each wheel.

[0103] In one alternative embodiment, the control module 330 is used for:

[0104] For a wheel that is slipping, the maximum driving torque of that wheel is determined as the adjusted driving torque of that wheel, and the torque difference between the current driving torque of that wheel and the maximum driving torque of that wheel is determined.

[0105] Based on the torque difference of the wheels in a slipping state, the current driving torque of the wheels in a non-slipping state is adjusted to obtain the adjusted driving torque of the wheels in a non-slipping state.

[0106] Among them, the adjusted driving torque of the wheel is less than or equal to the maximum driving torque of that wheel.

[0107] In one alternative embodiment, the control module 330 is used for:

[0108] If the wheel in the slipping state is determined to meet the preset conditions, the target driving torque of the wheel is determined based on the vehicle's throttle opening, and the wheel is controlled based on the target driving torque and the adjusted driving torque of the wheel. The preset conditions include the wheel's slipping state being non-slipping, the vehicle's throttle opening being increased and lasting for a duration greater than a first preset threshold, and the vehicle's travel distance being greater than a second preset threshold within the duration.

[0109] In one alternative approach, module 310 is used for:

[0110] The third maximum adhesion force of the wheel is determined based on the vertical load borne by the wheel and the preset road surface adhesion coefficient.

[0111] In one alternative approach, module 310 is used for:

[0112] Receive an instruction to enter the traction control mode; wherein the instruction is generated in response to the user's operation to activate the traction control mode, or is generated when it is determined that the road surface adhesion coefficient is less than a preset adhesion coefficient threshold.

[0113] As can be seen from the above, the vehicle control device provided in this embodiment of the invention can effectively solve the technical pain points of relying on a single slip ratio to estimate the maximum adhesion force, which results in inaccurate estimation when the wheels are not slipping and poor adaptability under all working conditions. By complementaring and integrating multi-dimensional data, it ensures the accuracy of the target maximum adhesion force under different road conditions and different wheel slip states, providing a reliable basis for drive force distribution. At the same time, it fully leverages the advantages of independent control of a single wheel in a distributed drive vehicle, and adjusts the torque of slipping and non-slipping wheels in a targeted manner, avoiding power waste and the risk of getting stuck due to excessive slippage, and significantly improving the vehicle's ability to get out of trouble and driving stability on complex low-adhesion roads such as ice, snow, and sand.

[0114] Figure 5 The diagram shows a structural schematic of an embodiment of the electronic device provided by the present invention. The specific embodiments of the present invention do not limit the specific implementation of the electronic device.

[0115] like Figure 5 As shown, the electronic device may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408. The electronic device may be the aforementioned controller.

[0116] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. Communication interface 404 is used to communicate with other network elements such as clients or other servers. The processor 402 executes program 410, specifically performing the relevant steps described above in the vehicle control method embodiment.

[0117] Specifically, program 410 may include program code, which includes computer-executable instructions.

[0118] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The electronic device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0119] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0120] Specifically, program 410 can be called by processor 402 to cause the electronic device to perform the following operations:

[0121] The initial maximum adhesion of the vehicle's wheels in multiple dimensions is obtained; wherein the initial maximum adhesion in multiple dimensions includes at least two of the following: a first maximum adhesion, a second maximum adhesion, and a third maximum adhesion; the first maximum adhesion is the maximum adhesion determined based on the wheel's slippage state, the second maximum adhesion is the maximum adhesion determined based on the road surface image corresponding to the wheel, and the third maximum adhesion is the maximum adhesion determined based on the wheel's vertical load.

[0122] The target maximum adhesion of the wheel is determined based on the initial maximum adhesion from multiple dimensions.

[0123] Control the vehicle's wheels based on the target maximum adhesion for each wheel.

[0124] In one alternative approach, the target maximum adhesion of the wheel is determined based on multiple dimensions of initial maximum adhesion, including:

[0125] Obtain the wheel slippage status;

[0126] Based on the slippage state, determine the weight set corresponding to the slippage state; wherein, the weight set includes at least two of the following weights: a first weight corresponding to the first maximum adhesion force, a second weight corresponding to the second maximum adhesion force, and a third weight corresponding to the third maximum adhesion force;

[0127] The target maximum adhesion force of the wheel is obtained by weighted summation of the initial maximum adhesion forces across multiple dimensions based on the weights in the weight set.

[0128] In one alternative approach, a set of weights corresponding to the slippage state is determined based on the slippage state, including:

[0129] Obtain the confidence level corresponding to the initial maximum adhesion force in multiple dimensions;

[0130] Based on the slippage state, determine the initial weight set corresponding to the slippage state;

[0131] Based on the confidence level, the initial weight set is adjusted to obtain the weight set corresponding to the slippage state.

[0132] In one alternative approach, the vehicle's wheels are controlled based on the target maximum adhesion for each wheel, including:

[0133] For each wheel, obtain the wheel slippage state and current drive torque;

[0134] Determine the maximum driving torque of the wheel based on the target maximum adhesion of the wheel;

[0135] Based on the maximum driving torque of the wheel that is slipping, the current driving torque of each wheel is adjusted to obtain the adjusted driving torque of each wheel.

[0136] The wheels are controlled based on the adjusted drive torque for each wheel.

[0137] In one alternative approach, the current drive torque of each wheel is adjusted based on the maximum drive torque of the wheel experiencing slippage, resulting in the adjusted drive torque for each wheel, including:

[0138] For a wheel that is slipping, the maximum driving torque of that wheel is determined as the adjusted driving torque of that wheel, and the torque difference between the current driving torque of that wheel and the maximum driving torque of that wheel is determined.

[0139] Based on the torque difference of the wheels in a slipping state, the current driving torque of the wheels in a non-slipping state is adjusted to obtain the adjusted driving torque of the wheels in a non-slipping state.

[0140] Among them, the adjusted driving torque of the wheel is less than or equal to the maximum driving torque of that wheel.

[0141] In one alternative approach, after controlling the wheels based on the adjusted drive torque of the wheels, the following steps are included:

[0142] If the wheel in the slipping state is determined to meet the preset conditions, the target driving torque of the wheel is determined based on the vehicle's throttle opening, and the wheel is controlled based on the target driving torque and the adjusted driving torque of the wheel. The preset conditions include the wheel's slipping state being non-slipping, the vehicle's throttle opening being increased and lasting for a duration greater than a first preset threshold, and the vehicle's travel distance being greater than a second preset threshold within the duration.

[0143] In one alternative approach, obtaining the third maximum adhesion force of the wheel includes:

[0144] The third maximum adhesion force of the wheel is determined based on the vertical load borne by the wheel and the preset road surface adhesion coefficient.

[0145] In one alternative approach, prior to obtaining the initial maximum adhesion of the vehicle's wheels in multiple dimensions, the following is included:

[0146] Receive an instruction to enter the traction control mode; wherein the instruction is generated in response to the user's operation to activate the traction control mode, or is generated when it is determined that the road surface adhesion coefficient is less than a preset adhesion coefficient threshold.

[0147] As can be seen from the above, the electronic device provided by the embodiments of the present invention can effectively solve the technical pain points of relying on a single slip ratio to estimate the maximum adhesion force, which results in inaccurate estimation when the wheel is not slipping and poor adaptability under all working conditions. Through the complementary fusion of multi-dimensional data, the accuracy of the target maximum adhesion force under different road conditions and different wheel slip states is ensured, providing a reliable basis for drive force distribution. At the same time, it fully leverages the advantages of independent control of a single wheel in a distributed drive vehicle, and adjusts the torque of the slipping wheel and the non-slipping wheel in a targeted manner, avoiding power waste and the risk of getting stuck due to excessive slippage, and significantly improving the vehicle's ability to get out of trouble and driving stability on complex low-adhesion roads such as ice, snow and sand.

[0148] This invention provides a computer-readable storage medium storing at least one executable instruction that, when executed on an electronic device / vehicle control device, causes the electronic device / vehicle control device to perform the vehicle control method in any of the above-described method embodiments.

[0149] Specifically, executable instructions can be used to cause electronic devices / vehicle control units to perform the following operations:

[0150] The initial maximum adhesion of the vehicle's wheels in multiple dimensions is obtained; wherein the initial maximum adhesion in multiple dimensions includes at least two of the following: a first maximum adhesion, a second maximum adhesion, and a third maximum adhesion; the first maximum adhesion is the maximum adhesion determined based on the wheel's slippage state, the second maximum adhesion is the maximum adhesion determined based on the road surface image corresponding to the wheel, and the third maximum adhesion is the maximum adhesion determined based on the wheel's vertical load.

[0151] The target maximum adhesion of the wheel is determined based on the initial maximum adhesion from multiple dimensions.

[0152] Control the vehicle's wheels based on the target maximum adhesion for each wheel.

[0153] In one alternative approach, the target maximum adhesion of the wheel is determined based on multiple dimensions of initial maximum adhesion, including:

[0154] Obtain the wheel slippage status;

[0155] Based on the slippage state, determine the weight set corresponding to the slippage state; wherein, the weight set includes at least two of the following weights: a first weight corresponding to the first maximum adhesion force, a second weight corresponding to the second maximum adhesion force, and a third weight corresponding to the third maximum adhesion force;

[0156] The target maximum adhesion force of the wheel is obtained by weighted summation of the initial maximum adhesion forces across multiple dimensions based on the weights in the weight set.

[0157] In one alternative approach, a set of weights corresponding to the slippage state is determined based on the slippage state, including:

[0158] Obtain the confidence level corresponding to the initial maximum adhesion force in multiple dimensions;

[0159] Based on the slippage state, determine the initial weight set corresponding to the slippage state;

[0160] Based on the confidence level, the initial weight set is adjusted to obtain the weight set corresponding to the slippage state.

[0161] In one alternative approach, the vehicle's wheels are controlled based on the target maximum adhesion for each wheel, including:

[0162] For each wheel, obtain the wheel slippage state and current drive torque;

[0163] Determine the maximum driving torque of the wheel based on the target maximum adhesion of the wheel;

[0164] Based on the maximum driving torque of the wheel that is slipping, the current driving torque of each wheel is adjusted to obtain the adjusted driving torque of each wheel.

[0165] The wheels are controlled based on the adjusted drive torque for each wheel.

[0166] In one alternative approach, the current drive torque of each wheel is adjusted based on the maximum drive torque of the wheel experiencing slippage, resulting in the adjusted drive torque for each wheel, including:

[0167] For a wheel that is slipping, the maximum driving torque of that wheel is determined as the adjusted driving torque of that wheel, and the torque difference between the current driving torque of that wheel and the maximum driving torque of that wheel is determined.

[0168] Based on the torque difference of the wheels in a slipping state, the current driving torque of the wheels in a non-slipping state is adjusted to obtain the adjusted driving torque of the wheels in a non-slipping state.

[0169] Among them, the adjusted driving torque of the wheel is less than or equal to the maximum driving torque of that wheel.

[0170] In one alternative approach, after controlling the wheels based on the adjusted drive torque of the wheels, the following steps are included:

[0171] If the wheel in the slipping state is determined to meet the preset conditions, the target driving torque of the wheel is determined based on the vehicle's throttle opening, and the wheel is controlled based on the target driving torque and the adjusted driving torque of the wheel. The preset conditions include the wheel's slipping state being non-slipping, the vehicle's throttle opening being increased and lasting for a duration greater than a first preset threshold, and the vehicle's travel distance being greater than a second preset threshold within the duration.

[0172] In one alternative approach, obtaining the third maximum adhesion force of the wheel includes:

[0173] The third maximum adhesion force of the wheel is determined based on the vertical load borne by the wheel and the preset road surface adhesion coefficient.

[0174] In one alternative approach, prior to obtaining the initial maximum adhesion of the vehicle's wheels in multiple dimensions, the following is included:

[0175] Receive an instruction to enter the traction control mode; wherein the instruction is generated in response to the user's operation to activate the traction control mode, or is generated when it is determined that the road surface adhesion coefficient is less than a preset adhesion coefficient threshold.

[0176] As can be seen from the above, the computer-readable storage medium provided in the embodiments of the present invention stores at least one executable instruction. When the executable instruction runs on the electronic device / vehicle control device, it can effectively solve the technical pain points in the related technology where the estimation of maximum adhesion is inaccurate when the wheel is not slipping and the adaptability to all working conditions is poor when relying on a single slip ratio to estimate the maximum adhesion. Through the complementary fusion of multi-dimensional data, the accuracy of the target maximum adhesion is ensured under different road conditions and different wheel slip states, providing a reliable basis for the distribution of driving force. At the same time, it fully leverages the advantages of independent control of single wheels in distributed drive vehicles, and adjusts the torque of slipping wheels and non-slipping wheels in a targeted manner, avoiding power waste and the risk of getting stuck due to excessive slippage, and significantly improving the vehicle's ability to get out of trouble and driving stability on complex low-adhesion roads such as ice, snow, and sand.

[0177] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments of this invention are not directed to any particular programming language.

[0178] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. Similarly, for the sake of brevity and to aid in understanding one or more aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0179] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0180] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A vehicle control method, characterized in that, include: The initial maximum adhesion of the vehicle's wheels in multiple dimensions is obtained; wherein the initial maximum adhesion in multiple dimensions includes at least two of the following: a first maximum adhesion, a second maximum adhesion, and a third maximum adhesion; the first maximum adhesion is the maximum adhesion determined based on the wheel's slippage state, the second maximum adhesion is the maximum adhesion determined based on the road surface image corresponding to the wheel, and the third maximum adhesion is the maximum adhesion determined based on the wheel's vertical load. The target maximum adhesion of the wheel is determined based on the initial maximum adhesion of the multiple dimensions. The vehicle's wheels are controlled based on the target maximum adhesion for each wheel.

2. The method according to claim 1, characterized in that, Determining the target maximum adhesion of the wheel based on the initial maximum adhesion of the multiple dimensions includes: Obtain the slippage state of the wheel; Based on the slippage state, a weight set corresponding to the slippage state is determined; wherein, the weight set includes at least two of the following weights: a first weight corresponding to the first maximum adhesion force, a second weight corresponding to the second maximum adhesion force, and a third weight corresponding to the third maximum adhesion force; The initial maximum adhesion of the multiple dimensions is weighted and summed according to the weights in the weight set to obtain the target maximum adhesion of the wheel.

3. The method according to claim 2, characterized in that, The step of determining the weight set corresponding to the slippage state includes: Obtain the confidence level corresponding to the initial maximum adhesion force in the multiple dimensions; Based on the slippage state, determine the initial weight set corresponding to the slippage state; Based on the confidence level, the initial weight set is adjusted to obtain a weight set corresponding to the slippage state.

4. The method according to claim 1, characterized in that, The step of controlling the vehicle wheels based on the target maximum adhesion of each wheel includes: For each wheel, obtain the wheel's slippage state and current drive torque; The maximum driving torque of the wheel is determined based on the target maximum adhesion of the wheel; Based on the maximum driving torque of the wheel that is slipping, the current driving torque of each wheel is adjusted to obtain the adjusted driving torque of each wheel. The wheels are controlled based on the adjusted drive torque for each wheel.

5. The method according to claim 4, characterized in that, The step of adjusting the current drive torque of each wheel based on the maximum drive torque of the wheel in the slipping state to obtain the adjusted drive torque of each wheel includes: For a wheel that is slipping, the maximum driving torque of that wheel is determined as the adjusted driving torque of that wheel, and the torque difference between the current driving torque of that wheel and the maximum driving torque of that wheel is determined. Based on the torque difference of the wheels in a slipping state, the current driving torque of the wheels in a non-slipping state is adjusted to obtain the adjusted driving torque of the wheels in a non-slipping state. Wherein, the adjusted driving torque of the wheel is less than or equal to the maximum driving torque of the wheel.

6. The method according to claim 5, characterized in that, After controlling the wheels based on the adjusted drive torque for each wheel, the following is included: If it is determined that the slipping state of the wheel meets the preset conditions, then the target driving torque of the wheel is determined based on the throttle opening of the vehicle, and the wheel is controlled based on the target driving torque and the adjusted driving torque of the wheel; wherein, the preset conditions include the wheel slipping state being non-slipping, the throttle opening of the vehicle being increased and the duration being greater than a first preset threshold, and the vehicle traveling a distance greater than a second preset threshold within the duration.

7. The method according to any one of claims 1-6, characterized in that, Obtaining the third maximum adhesion force of the wheel includes: The third maximum adhesion force of the wheel is determined based on the vertical load borne by the wheel and the preset road surface adhesion coefficient.

8. The method according to any one of claims 1-6, characterized in that, Before obtaining the initial maximum adhesion of the vehicle's wheels in multiple dimensions, the following is included: Receive an instruction to enter the traction control mode; wherein the instruction is generated in response to the user's operation to activate the traction control mode, or is generated when it is determined that the road surface adhesion coefficient is less than a preset adhesion coefficient threshold.

9. A vehicle control device, characterized in that, The device includes: The acquisition module is used to acquire the initial maximum adhesion of the vehicle's wheels in multiple dimensions; wherein the initial maximum adhesion in multiple dimensions includes at least two of the following: a first maximum adhesion, a second maximum adhesion, and a third maximum adhesion; the first maximum adhesion is the maximum adhesion determined based on the wheel's slippage state, the second maximum adhesion is the maximum adhesion determined based on the road surface image corresponding to the wheel, and the third maximum adhesion is the maximum adhesion determined based on the wheel's vertical load; The determination module is used to determine the target maximum adhesion of the wheel based on the initial maximum adhesion of the multiple dimensions; A control module is used to control the wheels of the vehicle based on the target maximum adhesion of each wheel.

10. An electronic device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the vehicle control method as described in any one of claims 1-8.

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