Inverse tire model for advanced vehicle motion management

Abstract

A reverse tire model for advanced vehicle motion management is provided, in particular a control unit (130, 140) for controlling a heavy vehicle (100), wherein the control unit is arranged to obtain input data representing a desired wheel force (Fx, Fy) to be generated by at least one wheel (210) of the vehicle (100), and to convert the input data into a corresponding equivalent wheel speed or wheel slip to be maintained by the wheel (210) to generate the desired wheel force (Fx, Fy) based on a reverse tire model (f ‑1 ) of the wheel (210), wherein the control unit (130, 140) is arranged to obtain said reverse tire model in dependence on current operating conditions of the wheel (210), and wherein the control unit (130, 140) is arranged to control the heavy vehicle (100) based on said equivalent wheel speed or wheel slip.

Description

Technical Field

[0001] This disclosure relates to vehicle motion management for heavy vehicles, namely, coordinated control of motion support equipment such as service brakes and propulsion devices.

[0002] This invention can be applied to heavy vehicles, such as trucks, buses, and construction machinery. Although the invention will be described primarily with respect to freight transport vehicles (such as semi-trailers and trucks), it is not limited to this specific type of vehicle and can also be used in other types of vehicles, such as passenger cars. Background Technology

[0003] Vehicles are becoming increasingly complex in terms of mechanics, pneumatics, hydraulics, electronics, and software. Modern heavy-duty vehicles may include a variety of different physical devices, such as internal combustion engines, electric motors, friction brakes, regenerative brakes, shock absorbers, air bellows, and power steering pumps. These physical devices are often referred to as motion support devices (MSDs). MSDs can be individually controlled, for example, allowing friction braking (i.e., negative torque) to be applied at one wheel while another wheel on the vehicle (potentially even on the same axle) is simultaneously used to generate positive torque via an electric motor.

[0004] Recently proposed vehicle motion management (VMM) functions, such as those executed on a central vehicle unit computer (VUC), rely on the coordinated combination of MSDs to operate the vehicle in order to achieve the desired motion effects while maintaining vehicle stability, cost efficiency, and safety. WO2019072379 A1 discloses such an example, in which wheel brakes are selectively used to assist the cornering operation of a heavy vehicle.

[0005] A common approach to controlling various slip differentials (MSDs) is to use torque control at the actuator level, regardless of any wheel slippage. However, this approach is not without performance limitations. For example, if excessive wheel slippage occurs (where one or more wheels slip uncontrollably), safety features such as traction control and anti-lock braking systems intervene and request torque override to bring the slippage back into control. These safety features are typically operated by separate control units. If the primary control of the actuator and the actuator-related slip control functions are assigned to different control units, the latency involved in communication between them can limit slip control performance. Furthermore, inconsistent slip assumptions made by the relevant actuators and in the multiple control units used to implement slip control can lead to suboptimal performance.

[0006] There is a need for improved vehicle control methods to better address wheel slippage. Summary of the Invention

[0007] The purpose of this disclosure is to provide a control unit and method that facilitate vehicle control based on slip or wheel speed requests rather than habitual torque requests, the speed or slip requests being obtained based on an improved tire behavior model. This purpose is achieved at least in part by a control unit for controlling a heavy-duty vehicle. The control unit is arranged to receive input data representing desired wheel forces to be generated by at least one wheel of the vehicle, and to convert the input data into corresponding equivalent wheel speeds or wheel slips to be maintained by the wheels, in order to generate the desired wheel forces based on an inverse tire model of the wheels. The control unit is arranged to obtain the inverse tire model according to the current operating conditions of the wheels, and to control the heavy-duty vehicle based on the equivalent wheel speeds or wheel slips.

[0008] Therefore, instead of requesting torque from different actuators as is customary, the wheel slip request is sent to wheel torque actuators at the wheel ends. These actuators then maintain operation under the requested wheel slip. In this way, MSD control moves closer to the wheel ends, where higher bandwidth control is possible due to reduced control loop delays and faster processing (typically available closer to the wheel ends). Thus, despite variable operating conditions, MSD can react more quickly to changes such as road friction, providing more stable wheel forces. Compared to conventional torque-based control, this MSD control method improves both the startability of heavy vehicles and maneuverability at high speeds. For example, if a wheel temporarily leaves the ground or experiences a significantly reduced vertical force due to road bumps, the wheel will not spin uncontrollably. Instead, MSD control will rapidly reduce the applied torque to maintain the wheel slip at the requested value, ensuring that an appropriate wheel speed is maintained when the wheel re-enters the ground.

[0009] A further advantage lies in adjusting the inverse tire model to accommodate changes in the wheel's current operating conditions, as this improves the accuracy and robustness of the mapping between the desired wheel force and the equivalent wheel speed or wheel slip. Thus, when operating conditions change, the inverse tire model is adjusted to better model the current operating conditions. Consequently, as the operating conditions of a given wheel change, the mapping between wheel force and wheel slip (or wheel speed) also changes to compensate for the change in operating conditions.

[0010] In addition to wheel slip or wheel speed, the control unit can also be configured to assign (i.e., request) a steering angle that will be maintained at one or more steering wheels on the vehicle. This steering angle will affect wheel sideslip. Therefore, it is generally advantageous to handle steering and wheel torque (slip or speed) in combination, as this typically improves overall vehicle control in terms of robustness and efficiency.

[0011] The data representing the desired wheel force, depending on several factors, includes the desired wheel torque and the wheel rolling radius. This means that the inverse tire model interface can be adapted to functions that output requested torque, such as conventional vehicle control functions, which, along with the wheel radius, represent or indicate the desired wheel force.

[0012] Current operating conditions, depending on several factors, include the vehicle or wheel-to-ground velocity vector. By understanding the wheel-to-ground velocity, the wheel rotation speed can be controlled to maintain wheel slip at a desired level. The wheel-to-ground velocity also affects the mapping between wheel forces and wheel slip. For example, the contact area between the ground and the wheel may vary depending on the vehicle speed.

[0013] Current operating conditions, depending on several factors, include the normal load on the wheel or the vertical force acting on the wheel. The normal load on a given wheel, together with the coefficient of friction, determines the maximum achievable wheel force. Therefore, it is preferable to adjust the inverse tire model to account for variations in the normal load. The inverse tire model can be made more accurate by measuring or otherwise determining the normal load.

[0014] Current operating conditions, depending on several factors, include the estimated or otherwise determined tire stiffness of the wheels. Tire stiffness significantly impacts the inverse tire model in the linear region from low to moderate wheel slip. A more accurate inverse tire model can be obtained by considering variations in tire stiffness. Optionally, tire stiffness can be corrected for factors related to the tire on a given wheel, such as wear, aging, temperature, inflation pressure, etc. Tire stiffness can be solely longitudinal slip stiffness (which can be used as the basis for scaling lateral slip stiffness) or a vector comprising both longitudinal and lateral slip stiffness.

[0015] Current operating conditions, based on several factors, include the tire-road friction coefficient associated with the wheels. The tire-road friction coefficient also affects the mapping between wheel forces and wheel slippage, particularly as it influences the maximum achievable tire force. Estimated road friction parameters can be used to tune the tire force curve to limit the permissible peak force and also to modify the peak force slip location in the inverse tire model.

[0016] The current operating conditions include, depending on several factors, the minimum required lateral force of the wheels. This means that operation can be required with the minimum lateral force generation capability of a given wheel. For example, if the vehicle is turning, a certain amount of lateral force may need to be generated to successfully complete the turn. Due to the requirement for lateral force, it may be necessary to limit wheel speed to a wheel slip below the requested wheel slip. Similarly, the current operating conditions may optionally include the maximum permissible lateral slip angle of the wheels. In the case of minimum required lateral force and maximum permissible lateral slip angle, the generated longitudinal slip request is limited to a search space within which the minimum lateral force capability is guaranteed using the maximum permissible lateral slip angle. Although both are optional parameters, they can be advantageously used to request longitudinal force in a safe manner that avoids problems such as yaw instability. The vehicle controller can use the minimum required lateral force parameter to ensure that sufficient lateral force capability is still possible to navigate a given path with specific acceleration and curvature curves. The maximum longitudinal speed of the vehicle throughout the maneuver is typically limited by roll stability and road friction. To understand the range of lateral acceleration that a vehicle unit can support during a turning maneuver, it may be necessary to understand the lateral force capability. Therefore, being able to specify the minimum required lateral force capacity is an advantage.

[0017] The vehicle controller can use the maximum permissible lateral slip angle to ensure that the vehicle's yaw moment balance or sideslip is maintained at an acceptable level consistent with the maneuver to be performed. This feature is particularly useful in automated or functional safety-critical applications where it is desirable to keep the tires operating within their linear combination slip range and thus prevent any traction control or yaw stability interventions that could lead to unpredictable effects.

[0018] Based on several factors, the inverse tire model is configured to provide the wheel's residual lateral force capability. This residual lateral force capability can be used to adjust the limits of requests being sent to the wheel end, or as feedback to the control distributor to adjust its control requests, thereby increasing the wheel's lateral force capability if it becomes too low for the current driving scenario.

[0019] Depending on several factors, the inverse tire model is configured to provide a gradient of the desired wheel force relative to wheel speed or wheel slip at the tire operating point, associated with the desired wheel force and current operating conditions. This output can, for example, be used to customize the gain of the speed controller in the actuator based on the priority of the control distributor. For instance, if the vehicle is cornering and the lateral gradient value is high, this indicates that poor speed control performance degrades lateral cornering performance, and the speed controller gain can be adjusted to mitigate this problem. Understanding these gradients also aids in the analysis of stability and control robustness, which is an advantage.

[0020] According to several aspects, the control unit is arranged to store a predetermined inverse tire model in memory, wherein the inverse tire model is stored in memory as a function of the current operating conditions of the wheel. This means that the control unit can access a series of different models, and the control unit can select a suitable model from this series of models.

[0021] According to several aspects, the control unit is arranged to adapt the inverse tire model based on measured wheel behavior and / or vehicle behavior in response to control of the heavy vehicle based on the equivalent wheel speed or wheel slip. Thus, advantageously, the control unit monitors the actual response of the wheels, and possibly also the actual response of the vehicle, and adjusts the inverse tire model accordingly. This means that the control method becomes less sensitive to assumptions about vehicle performance in different scenarios or the impact of different parameters on vehicle controllability. Furthermore, if operating conditions change unexpectedly, the inverse tire model will adapt to such changes, thereby providing robust control even in scenarios not yet encountered.

[0022] Based on several factors, the inverse tire model is tuned to always remain within predetermined upper and / or lower limits of wheel forces that depend on wheel slip or wheel speed. This means that model tuning of the inverse tire model is permitted, but only within certain predetermined boundaries. Therefore, one or more boundaries represent a safety measure to prevent unforeseen errors from occurring during model adaptation. An example of an adaptive inverse tire model is an artificial neural network that is continuously or at least periodically trained based on control inputs and the actual wheel or vehicle responses to those control inputs.

[0023] This article also discloses computer programs, computer-readable media, computer program products, and vehicles associated with the advantages discussed above.

[0024] Generally, all terms used in the claims should be interpreted according to their ordinary meaning in the technical field, unless otherwise expressly defined herein. All references to “an element, device, component, measure, step, etc.”, unless otherwise expressly stated, are disclosed to refer to at least one instance of an element, device, component, measure, step, etc. Unless expressly stated otherwise, the steps of any method disclosed herein need not be performed in the exact order disclosed. Further features and advantages of the invention will become apparent when examining the appended claims and the following description. Those skilled in the art will recognize that different features of the invention can be combined to produce embodiments other than those described below without departing from the scope of the invention. Attached Figure Description

[0025] Referring to the accompanying drawings, a more detailed description of embodiments of the present invention, cited as examples, follows.

[0026] In these diagrams:

[0027] Figure 1 Example heavy vehicles are shown;

[0028] Figure 2 The layout of the motion support equipment is shown schematically.

[0029] Figure 3 The vehicle control functions are shown;

[0030] Figure 4 It is a graph showing the tire force as a function of wheel slippage;

[0031] Figure 5 This demonstrates the adaptation of the wheel behavior model to the measurement data;

[0032] Figure 6 An example motion support device control system is shown;

[0033] Figure 7 This is a flowchart illustrating the method;

[0034] Figure 8 The control unit is schematically shown; and

[0035] Figure 9 An example computer program product is shown. Detailed Implementation

[0036] The invention will now be described more fully below with reference to the accompanying drawings, in which certain aspects of the invention are illustrated. However, the invention may be embodied in many different forms and should not be construed as limited to the embodiments and aspects set forth herein; rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. Throughout the specification, the same reference numerals refer to the same elements.

[0037] It should be understood that the present invention is not limited to the embodiments described herein and shown in the accompanying drawings; rather, those skilled in the art will recognize that many modifications and variations can be made within the scope of the appended claims.

[0038] Figure 1An example vehicle 100 for freight transport is shown, in which the techniques disclosed herein can be advantageously applied. Vehicle 100 includes a tractor or towing vehicle 110 supported on front wheels 150 and rear wheels 160, wherein at least some of the wheels are drive wheels. Typically (but not necessarily), all wheels on the tractor are brake wheels. The tractor 110 is configured to tow a first trailer unit 120 supported on trailer wheels 170 via a known towing seat connection. Trailer wheels are typically brake wheels, but may also include drive wheels on one or more axles.

[0039] It should be understood that the methods and control units disclosed herein can also be advantageously applied to other types of heavy vehicles, such as trucks, construction equipment, buses, etc., with tow bar connections.

[0040] The tractor unit 110 includes a vehicle unit computer (VUC) 130 for controlling various functions, namely propulsion, braking, and steering. Some trailer units 120 also include a VUC 140 for controlling various functions of the trailer (e.g., braking of the trailer wheels, and sometimes propulsion of the trailer wheels). VUCs 130 and 140 may be centralized or distributed across several processing circuits. Multiple parts of the vehicle control functions may also be executed remotely, for example, on a remote server 190 connected to the vehicle 100 via a wireless link 180 and a wireless access network 185.

[0041] The VUC 130 on the tractor unit 110 (and possibly the VUC 140 on the trailer unit 120) can be configured to perform a vehicle control method organized according to a hierarchical functional architecture in which some functions can be contained in the Traffic Condition Management (TSM) domain at a higher level, while other functions can be contained in the Vehicle Motion Management (VMM) domain residing in a lower functional level.

[0042] Figure 2The diagram schematically illustrates a function 200 for controlling wheel 210 via several example MSDs, including friction brakes 220 (e.g., disc brakes or drum brakes) and propulsion devices 250. Friction brakes 220 and propulsion devices are examples of wheel torque generating devices, which can also be referred to as actuators, and they can be controlled by one or more motion support device control units 230. This control is based on measurement data obtained, for example, from wheel speed sensors 240 and other vehicle state sensors 280 (e.g., radar sensors, lidar sensors), as well as vision-based sensors (e.g., camera sensors and infrared detectors). Other examples of torque generating motion support devices that can be controlled according to the principles discussed herein include engine retarders and power steering devices. The MSD control unit 230 can be arranged to control one or more actuators. For example, it is not uncommon for the MSD control unit 230 to be arranged to control two wheels on an axle.

[0043] TSM function 270 plans driving maneuvers within a time horizon of approximately 10 seconds. This time horizon corresponds, for example, the time it takes for vehicle 100 to navigate a curve. Vehicle maneuvers planned and executed by TSM can be correlated with acceleration and curvature curves, which describe the desired vehicle speed and turning radius for a given maneuver. TSM continuously requests the desired acceleration curve a from VMM function 260, which performs force distribution. req and curvature curve c req This allows the VMM function 260 to meet requests from the TSM in a safe and robust manner. The VMM function 260 continuously feeds back capability information to the TSM function, detailing the vehicle's current capabilities, such as the forces it can generate, maximum speed, and acceleration.

[0044] Acceleration and curvature curves can also be obtained from the driver of a heavy vehicle via conventional control input devices (e.g., steering wheel, accelerator pedal, and brake pedal). The sources of these acceleration and curvature curves are beyond the scope of this disclosure and will therefore not be discussed in further detail herein.

[0045] Also refer to Figure 3 The VMM function 260 operates over a time range of approximately one second and continuously displays the acceleration curve a. req and curvature curve c reqThe commands are translated into control commands for controlling vehicle motion functions actuated by different MSDs 220, 250 of vehicle 100, wherein these MSDs report capabilities back to the VMM, which in turn serve as constraints in vehicle control. VMM function 260 performs vehicle state or motion estimation 305, i.e., VMM function 260 continuously determines the vehicle state, including the position, velocity, acceleration, and articulation angle of different units within the vehicle assembly, by monitoring operation using various sensors 306 arranged on vehicle 100 and frequently, but not always, connected to the MSDs 220, 250.

[0046] The result of motion estimation 305 (i.e., the estimated vehicle state) is input to force generation module 310, which determines the required global force V = [V1, V2] for different vehicle units so that vehicle 100 conforms to the requested acceleration curve a. req and curvature curve c req Movement. The required global force vector V is input to the MSD coordination function 320, which distributes wheel forces and coordinates other MSDs (e.g., steering and suspension). The coordinated MSDs then work together to provide the desired lateral force Fy and longitudinal force Fx on the vehicle unit, as well as the required torque Mz, to achieve the desired motion of the vehicle assembly 100.

[0047] By using, for example, a GPS, vision-based sensors, wheel speed sensors, radar sensors, and / or lidar sensors to determine the motion of a vehicle unit and converting that motion into a local coordinate system for a given wheel 210 (e.g., in terms of longitudinal and lateral velocity components), wheel slip can be estimated accurately in real time by comparing the motion of the vehicle unit in the wheel reference coordinate system with data obtained from wheel speed sensors 240 arranged in connection with the wheel 210.

[0048] The following will be combined Figure 4 The tire model discussed in more detail can be used to calculate the desired longitudinal tire force Fx for a given wheel i. i And the equivalent wheel slip λ of the wheel i The conversion between these terms is as follows. Wheel slip λ involves the difference between the wheel's rotational speed and its speed relative to the ground, and will be discussed in more detail below. Wheel speed ω is the rotational speed of the wheel, given in units such as revolutions per minute (rpm) or angular velocity expressed in radians per second (rad / sec) or degrees per second (deg / sec).

[0049] The tire model here is a model of wheel behavior, describing the wheel forces generated as a function of wheel slip in the longitudinal direction (in the rolling direction) and / or the lateral direction (orthogonal to the longitudinal direction). The fundamentals of the tire model are covered in Hans Pacejka's paper "Tyre and vehicle dynamics" (Elsevier Ltd. 2012, ISBN 978-0-08-097016-5). For example, see Chapter 7, which discusses the relationship between wheel slip and longitudinal forces.

[0050] In summary, VMM function 260 manages force generation and MSD coordination; that is, it determines what forces are needed at the vehicle unit to satisfy the request from TSM function 270, in order to, for example, accelerate the vehicle according to a requested acceleration curve requested by TSM, and / or generate specific curvature motions of the vehicle, also requested by TSM. These forces may include, for example, yaw moment Mz, longitudinal force Fx and lateral force Fy, as well as different types of torque to be applied at different wheels.

[0051] The interface between the VMM and MSD, which transmit torque to the vehicle's wheels, has traditionally focused on torque-based requests from the VMM to each MSD, without considering any wheel slippage. However, this approach has significant performance limitations. In the event of a safety emergency or excessive slippage, relevant safety functions (traction control, anti-lock braking, etc.) operating on separate control units typically intervene and request torque overrun to bring the slippage back into control. The problem with this approach is that the communication delay between the primary control of the actuator and the slip control of the actuator, which are assigned to different electronic control units (ECUs), significantly limits slippage control performance. Furthermore, inconsistencies in the slippage assumptions made by the relevant actuators and in the two ECUs used to implement the actual slippage control can lead to suboptimal performance.

[0052] By instead using wheel speed- or wheel slip-based requests on the interface 265 between the VMM and the MSD controller or multiple controllers 230, and thereby transferring the difficult actuator speed control loop to the MSD controller, which typically operates with a shorter sampling time than the VMM function, significant benefits are gained. Compared to torque-based control interfaces, this architecture provides better disturbance rejection, thus improving the predictability of forces generated at the tire-road contact surface.

[0053] refer to Figure 3 The inverse tire model block 330 will determine the required wheel force Fx for each wheel or for a subset of wheels by the MSD coordination block 320. i 、Fy iConverted to the equivalent wheel speed ω wi Or wheel slip λ i These wheel speeds or slippages are then sent to the corresponding MSD controller 230. The MSD controller reports back the capabilities 231a-231c, which can be used as constraints, for example, in the MSD coordination block 320.

[0054] According to SAE J670 (SAE Vehicle Dynamics Standards Committee, January 24, 2008), longitudinal wheel slip λ can be defined as:

[0055]

[0056] Where R is the effective wheel radius in meters, ω x It is the angular velocity of the wheel, v x λ is the longitudinal velocity of the wheel (in the wheel coordinate system). Therefore, λ lies between -1 and 1 and quantifies the degree to which the wheel is slipping relative to the road surface. Wheel slip is essentially the velocity difference measured between the wheel and the vehicle. It should therefore be understood that the techniques disclosed herein are applicable to any type of wheel slip definition. It should also be understood that, in the wheel coordinate system, given the wheel's velocity relative to the surface, the wheel slip value is equivalent to the wheel velocity value.

[0057] VMM 260 and optionally MSD control unit 230 maintain information about v x (In the wheel's reference frame) information, and wheel speed sensors 240, etc., can be used to determine ω. x (The rotational speed of the wheel).

[0058] For a wheel (or tire) to generate wheel forces, slippage must occur. For small slip values, the relationship between slippage and the generated force is approximately linear, where the proportionality constant is typically expressed as the tire's slip stiffness. Tire 210 is subjected to a longitudinal force F. x lateral force F y and normal force F z Normal force F z It is crucial for determining some important vehicle properties. For example, the normal force largely determines the lateral tire force F that the wheel can achieve. y This is because, generally speaking, F y ≤μF z, where μ is the coefficient of friction associated with road friction conditions. The maximum available lateral force for a given lateral slip can be described by the so-called Magic Formula, as described in Hans Pacejka's paper "Tyre and vehicle dynamics" (Elsevier Ltd. 2012, ISBN 978-0-08-097016-5).

[0059] Figure 4 This is a graph illustrating an example of achievable tire forces as a function of wheel slip. The longitudinal tire force Fx exhibits a nearly linear increase for small wheel slip (part 410), followed by a more non-linear behavior for larger wheel slip (part 420). Even with relatively small longitudinal wheel slip, the available lateral tire force Fy decreases rapidly. It is desirable to maintain vehicle operation in the linear region (part 410), where the available longitudinal force in response to applied braking commands is more predictable, and sufficient lateral tire force can be generated when needed. To ensure operation in this region, a wheel slip limit λ of, for example, about 0.1, can be applied to a given wheel. LIM For larger wheel slips (e.g., exceeding 0.1), a more non-linear region 420 can be observed. Controlling the vehicle in this region can be difficult, and it is often best to avoid it. This might be interesting for off-road conditions such as traction, where a larger slip limit for traction control might be preferred, but this is not suitable for on-road operation.

[0060] The VMM 260 can use this type of tire model to generate a desired tire force at a particular wheel. Instead of requesting the torque corresponding to the desired tire force, the VMM can convert the desired tire force into an equivalent wheel slip (or, equivalently, wheel speed relative to the ground speed) and request that slip instead. The main advantage is that the MSD control device 230 will be able to use the vehicle speed v x and wheel rotation speed ω x Maintain operation with the desired wheel slip while delivering the requested torque with a much higher bandwidth.

[0061] Control units 130 and 140 can be arranged to direct a predetermined inverted tire model f -1The inverse tire model is stored in memory, for example, as a lookup table. This inverse tire model is arranged to be stored in memory as a function of the current operating conditions of wheel 210. This means that the behavior of the inverse tire model is adjusted according to the vehicle's operating conditions, which means that a more accurate model is obtained compared to a model that does not consider operating conditions. The model stored in memory can be determined based on experiments and trials, or based on analytical derivation, or a combination of both. For example, the control unit can be configured to access different sets of models selected according to the current operating conditions. One inverse tire model can be customized for high-load driving with high normal force, another inverse tire model can be customized for wet road conditions with low road friction, and so on. The selection of the model to be used can be based on a pre-determined set of selection rules. The model stored in memory can also be at least partially a function of the operating conditions. Thus, the model can be configured to take, for example, normal force or road friction as input parameters to obtain an inverse tire model based on the current operating conditions of wheel 210. It should be understood that many aspects of the operating conditions can be approximated by default operating condition parameters, while other aspects of the operating conditions can be roughly classified into a smaller number of categories. Therefore, obtaining an inverse tire model that depends on the current operating conditions of wheel 210 does not necessarily mean storing a large number of different models, or complex analytical functions capable of handling fine-grained changes in operating conditions. Instead, selecting two or three different models based on the operating conditions may suffice. For example, one model could be used when the vehicle is heavily loaded, and another under different conditions. In all cases, the mapping between tire force and wheel slip varies in some way depending on the operating conditions, which improves the accuracy of the mapping.

[0062] The inverse tire model can also be implemented, at least partially, as a white-adaptation model, configured to automatically or at least semi-automatically adapt to the vehicle's current operating conditions. This can be achieved by continuously monitoring the response of a given wheel to the wheel forces generated in response to a given wheel slip request and / or monitoring the response of the vehicle 100 in response to a wheel slip request. The white-adaptation model can then be adjusted to more accurately model the wheel forces obtained in response to a given wheel slip request from the wheel.

[0063] Figure 5 A graph 500 shows the inverse tire model mapping longitudinal tire force Fx to wheel slip. Measurements 510 with corresponding numerical pairs (F, λ) of tire force F and wheel slip are also plotted. According to some aspects, the control unit disclosed herein is arranged to: in response to control of the heavy vehicle 100 based on equivalent wheel speed or wheel slip, adapt the inverse tire model f based on measured wheel behavior and / or vehicle behavior. -1One such measurement is the resistance encountered by the motor when attempting to generate a specific wheel speed. This "torque state" output signal of the motor can be directly converted into an equivalent wheel force via the effective wheel radius R. Wheel force samples can also be obtained from the VMM function as part of the force distribution process. For example, if the VMM notices that it consistently receives too little longitudinal force in response to a given requested wheel slip, the model can be adjusted to address this discrepancy, for example, by scaling the model to better match the desired wheel force. In this context, note that the inverse tire model does not need to be correct in the absolute reference frame; that is, the inverse tire model must be able to accurately predict the generated force in Newtons for a given wheel slip. Rather, it is sufficient if the inverse tire model allows for successful vehicle control via VMM function 260. Interestingly, by adjusting the inverse tire model in this way based on the measured wheel forces in response to a wheel slip request, other characteristics of the vehicle will be automatically incorporated into the modeling to more accurately represent the mapping between wheel slip and wheel force.

[0064] In the first example of the adaptive inverse tire model, sample pairs (F,λ) of the generated force F and the current wheel slip λ are continuously obtained. The generated force F (longitudinal force Fx and lateral force Fy) and the yaw moment Mz can be determined based on vehicle behavior, i.e., a relationship of the type of Newton's second law, where both mass m and acceleration a can be measured together with the current wheel slip using basic sensor techniques.

[0065] The inverse tire model is then continuously updated to fit the current measurement results. For example, a Kalman filter can be applied to track the coefficients {c} of the polynomial model. i Then, this polynomial model can be used as an inverse tire model. Alternatively, polynomial fitting can be performed to fit the measurement data 510 to the model, which can then be used as an inverse tire model.

[0066] In the second example, a neural network or other form of AI-based method is applied to continuously update the inverse tire model. For example, the network is trained using sample pairs (F,λ) of the generated force F and the current wheel slip λ. The input to the network can be, for example, vehicle load, tire specifications, and road conditions (e.g., friction aspects). The output can be a set of coefficients from a multinomial model, which can be used as a representation of the inverse tire model.

[0067] It should be understood that the model's adaptation does not need to be performed on-vehicle 100. Instead, measurement data can be uploaded to a remote server 190, which can be tasked with finding a suitable model for controlling the vehicle based on wheel slip rather than torque requests. The model can then handle measurement data from more than one vehicle (possibly a group of vehicles of the same type or operating design domain). This model or set of models can then be fed back from the remote server 190 to the vehicle for controlling vehicle 100.

[0068] The entire inverse tire model can, of course, be implemented as a neural network trained during different types of operating conditions. Then, as the operating conditions of the heavy vehicle change, the inverse tire model also changes, causing the wheel slip corresponding to a given wheel force to vary over time, which is an advantage.

[0069] The inverted tire model f can also be adjusted based on wheel slippage or wheel speed. -1 This ensures that the wheel force remains within predetermined upper and lower limits. These limits can be obtained, for example, as statistical limits derived from measurement data 510. For instance, an upper limit 530 and a lower limit 520 can be set to limit the inverse tire model to within one or two standard deviations from the mean, and so on.

[0070] Safety margins can also be applied to the adaptation itself; that is, constrained adaptation can be performed where the inverse tire model is not allowed to deviate from a fenced area around some nominal model curves. This fenced area can be pre-determined or adjusted based on operating conditions or by using a dynamic driving task (DDT) predefined on a known operating design domain (ODD), which will reduce the amount of validation and verification required.

[0071] Refer again Figure 2 The MSD control unit 230 can be configured to control one or more MSDs associated with the wheel 210. The one or more MSDs may include: at least one service brake 220 arranged to generate negative torque through the wheel 210; and a propulsion unit 250 arranged to generate positive and / or negative torque through the wheel 210, the propulsion unit being, for example, an electric motor and / or an internal combustion engine. Other torque-generating devices that can be controlled by the MSD control unit include engine retarders and power steering devices. The MSD control unit 230 is communicatively coupled to the VMM unit 260 for receiving control commands (including wheel speed and / or wheel slip requests) from the VMM unit 260 to control vehicle movement via the one or more MSDs.

[0072] It should be understood that the MSD control unit discussed herein can also be configured to control one or more MSDs associated with wheels other than wheel 210, such as for controlling the wheels of a given axle or the wheels on one side of a trailer unit or the MSDs of all wheels of a trailer unit. Figure 6 The diagram schematically illustrates a system of MSD control units 230a-230f, which are arranged to control the corresponding wheels 210a-210f based on control signals received from the central VMM unit 260. One or more additional vehicle units, such as one or more trailers 120, that may be connected via a trolley unit can also be controlled in this manner. In this case, there may be more than one VMM function, where one VMM function can be assigned a master role, while other VMM functions can be configured to operate in slave mode.

[0073] In summary, the VMM function 260 distributes forces to meet specific acceleration and / or curvature curves. These forces are converted into equivalent wheel slip (or wheel rotation speed), and instead of a conventional torque request, this slip or speed is sent to the MSD control unit 230. Based on the inverse tire model f -1 () is used to perform the conversion from the desired force to the equivalent slip or wheel rotation speed. This inverse tire model is not only a function of the requested wheel torque or wheel force, but also takes into account the current operating scenario in which vehicle 100 is currently operating. According to an example embodiment, the inverse tire model for controlling vehicle 100 is given by the following equation:

[0074] [ω req F y，rem dF x / dω,dF y / dω]=f -1 (T req v x v y F z，act R w C est μ est F y，min α max )

[0075] in:

[0076] T req -Torque request at the wheels

[0077] v x -Longitudinal ground velocity

[0078] v y - Lateral ground speed

[0079] F z，act - Normal load on the wheel

[0080] R w - Rolling radius of the wheel

[0081] C est - The estimated tire stiffness of the wheel (optionally, the lateral tire stiffness C) est，y and longitudinal tire stiffness C est (any one of x)

[0082] μ est - Estimated tire-road friction at the wheel

[0083] F y，min -Minimum required lateral force capacity

[0084] α max -In order to achieve F y，min Maximum permissible lateral slip

[0085] ω req - Wheel rotation speed request, i.e., the target wheel speed to be controlled.

[0086] F y，rem - Remaining lateral force capacity of a given wheel

[0087] dF x / dω- At the requested tire operating point F x Gradient of wheel speed

[0088] dF y / / dω- at the requested tire operating point F y Gradient of wheel speed

[0089] Tire stiffness C est This can be an estimated tire stiffness, which can be corrected for factors such as tire wear, aging, temperature, and inflation pressure. The estimated tire stiffness can be simply the longitudinal slip stiffness, which can be used as the basis for scaling the lateral slip stiffness of a given tire, or the estimated tire stiffness can be a vector including both the longitudinal and lateral slip stiffness of the tire. Tire stiffness can have a significant impact on the force-slip curve 400. If this parameter is not available, the nominal stiffness of the tire can be used in the tire model.

[0090] The estimated friction μ est This can be used to adjust the tire force curve 400 to limit the allowed peak force and also change the peak force slip position in the model. If this optional input is not available, the nominal dry asphalt tire force curve can be used.

[0091] Minimum required lateral force capacity F y，min and the maximum permissible lateral slip angle limit α max These are optional constraints on the tire model, communicated via an interface 265 between the VMM function 260 and various MSD control units 230. Through these additional inputs, the generated longitudinal slip request is constrained to a vector space, where the maximum lateral slip angle α is used. max To ensure lateral force capacity F y，min Both of these optional parameters can be used to request longitudinal force in a safe manner without causing significant yaw instability, etc. F y，min It can be used to ensure that there is still sufficient lateral force capacity in order to be able to make certain turns or complete some other tasks that require generating lateral force (i.e., F). y ) maneuverability, while α max This feature can be used to ensure that a vehicle's yaw moment balance or sideslip is maintained within reasonable pre-configured or dynamically determined limits. This characteristic may be particularly beneficial in autonomous or functional safety-critical applications where it is desirable to keep the tires operating within their linear combination slip range (e.g., Figure 3 (as shown in the range 410), and thus prevent any traction control or yaw stability intervention.

[0092] On the output side, ω req It's a wheel speed request, which is the main request of the tire model, provided that given μ... est And does not violate F y，min and α max This can be done in certain situations, and should be done within the required T. req This is generated in the middle. It should be understood that the wheel speed requires ω. req Based on the ground velocity v x v y It is continuously updated over time to be equivalent to wheel slip, for example, as defined in the wheel slip equation discussed above. Alternatively, the wheel slip value λ can be conveyed. req Instead of wheel speed values. Given wheel speed relative to the ground, wheel slip, and wheel speed, these are equivalent information variables.

[0093] Remaining lateral force capacity F y，rem It can be used to adjust the boundaries of the requests being sent, or as feedback to the control distributor to adjust its control requests in order to increase F. y，rem (For example, if it gets too close to zero).

[0094] Finally, dF x / dω and dF y / dω represents the longitudinal and lateral forces at the requested operating point with respect to the wheel rotation speed ω. tgtThe gradient. For example, these parameters can be used to customize the gain of the speed controller in the actuator based on the priority of the control distributor. For example, if the vehicle is turning and dF y A high / dω value indicates poor speed control performance, which will reduce lateral turning performance. Therefore, the gain of the speed controller can be adjusted to prevent this.

[0095] Several variations of the aforementioned functional interface are certainly possible. One possibility is to simply remove the rolling radius input and use torque and rotational speed T... req R w If we change these to force and linear velocity respectively, then the inverse tire model becomes:

[0096] [ω req F y，rem dF x / dω,dF y / dω]=f -1 (F x，req v x v y F z，act C est μ est F y，min α max )

[0097] Another alternative is to simply treat all tire parameters as a single structural parameter with a predetermined layout (e.g., p). tyre Send. Any value in a field of this structure can be used to update an existing value, while a default value can be used to replace a non-existent field.

[0098] Other options for the output of this inverse tire model can be to send the actual or maximum lateral tire force F in the y-direction. y，max Or the friction capacity μ of the currently used tire y，util This will produce a model function according to the following formula:

[0099] [ω req F y，act dF x / dω,dF y / dω]=f -1 (F x，req v x v y F z，act p tyre F y，min α max )

[0100] [ω req F y，max dF x / dω,dF y / dω]=f -1 (F x，req v x v y F z，act p tyre F y，min α max )

[0101] [ω req μ yutil dF x / dω,dF y / dω]=f -1 (F x，req v x v y F z，act p tyre F y，min α max )

[0102] As mentioned above, many inputs and outputs of the inverse tire model in the example above are optional. For example, the parameter F can be used. z，act R w C est μ est F y，min α max The default value is used instead of the actual measured value. It should also be understood that for a vehicle controlled with an equivalent wheel speed or wheel slip corresponding to the desired wheel force, the output F... y，act dF x / dω,dF y / dω is not required.

[0103] You can also add a steering angle request δ to this inverted tire model. req Alternatively, a target steering angle value to be maintained can be added. In this case, the input includes the required lateral wheel force F. x，req and longitudinal wheel force F y，req Furthermore, in addition to wheel speed or slippage, the output also includes a target steering angle δ for a given wheel. tgt ,Right now:

[0104] [ω req δ req dF x / dω,dF y / dω]=f -1 (F x，req F y，req v x v y F z，act p tyreF y，min α max )

[0105] Furthermore, it should be understood that the wheel rotation speed ω req (That is, given the rotational speed of wheel 210) can be replaced by wheel slip λ. This is because: for a given wheel velocity relative to the ground v x Wheel speed request ω req and wheel slip request λ req It is directly related to the wheel radius R. In other words, wheel speed and wheel slip are usually equivalent information variables.

[0106] In summary, this paper discloses a control unit 130, 140 for controlling a heavy vehicle 100. The control unit is arranged to receive input data representing desired wheel forces Fx, Fy to be generated by at least one wheel 210 of the vehicle 100, and to convert this input data into corresponding equivalent wheel speeds or wheel slippages maintained by the wheel 210, based on an inverse tire model f of the wheel 210. -1 The desired wheel forces Fx and Fy are generated. This input data, representing the desired wheel forces to be generated, can be obtained, for example, from a force distribution process that determines the necessary forces to make the vehicle follow the desired acceleration curve and / or the desired curvature. The acceleration curve and curvature can be obtained from manual control input from the driver of vehicle 100, or from an autonomous or semi-autonomous control algorithm running on the VUC. The desired wheel forces can also be obtained, at least partially, from a remote server 190 via a wireless link.

[0107] The data representing the desired wheel forces Fx and Fy can include the desired wheel torque Treq and the wheel rolling radius R. By providing this torque and radius, the equivalent desired wheel force can be determined, for example, as Fx = Treq * R.

[0108] Control units 130 and 140 are arranged to obtain a reverse tire model based on the current operating conditions of wheel 210, and are also arranged to control the heavy vehicle 100 based on equivalent wheel speed or wheel slip. This means that the control unit is configured to adapt the reverse tire model to the vehicle's current operating conditions in some way. For example, if the vehicle is carrying a heavy load, the reverse tire model used to control the vehicle will be adjusted to cope with changes in operating conditions. Various types of operating condition parameters can be considered, as will be discussed below. By obtaining a reverse tire model based on the current operating conditions, more precise and robust control can be achieved. Therefore, it should be understood that the reverse tire model considered herein is a dynamic model, which, unlike a constant model, is suitable for fitting the current operating conditions of a heavy vehicle. This improves vehicle performance and safety.

[0109] Current operating conditions may include those with component v x v y The vehicle-to-ground speed or wheel-to-ground speed vector is used. This vehicle-to-ground speed can be used to determine the wheel rotational speed corresponding to a given amount of slip, for example, by calculating the normalized wheel slip difference discussed above. Depending on whether the wheel rotates slowly or quickly, some tires will behave differently. Therefore, some inverse tire models may exhibit differences across an operating speed range, for example, from 0 km / h to 150 km / h. It should be understood that wheel control based on the requested wheel rotational speed requires a relatively fast interface between the VMM function 260 and the MSD control unit 230. This is because the wheel rotational speed required to obtain a given wheel slip depends on the ground speed, which may change relatively quickly over time.

[0110] The current operating conditions may optionally also include the normal load Fz or vertical tire force associated with wheel 210. The normal load can have a significant impact on the inverse tire model, i.e., the mapping between the desired wheel force and wheel speed or wheel slip. For example, the maximum available longitudinal tire force Fx is limited by the normal force and the coefficient of friction. Therefore, by parameterizing the inverse tire model based on the normal load Fz, a more accurate inverse tire model can be obtained, which more closely models the current operating conditions of vehicle 100.

[0111] According to several other factors, the current operating conditions include the estimated tire stiffness Cest of wheel 210. If the tire stiffness is explicitly estimated, a more accurate inverse tire model can be obtained. Tire stiffness can be estimated, for example, based on a feedback system, where measurements of tire forces are mapped to wheel slip, and a linear or semi-linear relationship can be determined. Tire stiffness can also be obtained, for example, from a database maintained in remote server 190 or from a memory connected to the VUC, which can be indexed if the tire can be identified. For example, a tire attached to a given wheel can be identified by embedding a radio frequency identification (RFID) device in the tire or by manual configuration.

[0112] Current operating conditions may also include an estimated tire-road friction coefficient μ for the wheels. This road friction can be estimated in real time using known methods, such as those disclosed in US 9,475,500 B2, US 8,983,749 B1, or EP 1719676 B1. The inverse tire model can then be adapted to match the current road friction.

[0113] The current operating conditions may also include the minimum required lateral force capacity Fy,min and / or the maximum permissible lateral slip α of wheel 210. The minimum lateral force capacity Fy,min and the maximum permissible lateral slip angle limit α are optional constraints of the tire model. If this data is used as input to the inverse tire model function, these parameters can be used as constraints to determine the output. For example, it can be determined that the output wheel speed or wheel slip will not generate insufficient lateral force capacity or lateral slip, which is an advantage.

[0114] Conversely, the inverse tire model f -1 It can also be configured to provide the residual lateral force capability Fy,rem of wheel 210. The residual lateral force capability Fy,rem can be used to adjust the boundaries of the request being sent or as feedback to the control distributor to adapt its control request in order to increase the residual lateral force capability if it becomes too low.

[0115] Inverse tire model f -1 It can also be configured to provide gradients dFx and dFy of the desired wheel force with respect to wheel speed or wheel slip at the tire operating point associated with the desired wheel force of wheel 210 and the current operating conditions. These gradients provide information about the model behavior when the input parameters change slightly and can be advantageously used to adjust control algorithms, such as those in the MSD control unit 230. For example, gradients can be used to adjust the gain of control functions, such as PID controllers.

[0116] Figure 7 This is a flowchart illustrating at least some of the methods discussed above. A method for controlling a heavy vehicle 100, executed in control units 130 and 140, is shown. The method includes: obtaining input data S1 representing desired wheel forces Fx and Fy to be generated by at least one wheel 210 of the vehicle 100; and obtaining S2 an inverse tire model f associated with the wheel 210. -1 The inverse tire model depends on the current operating conditions of the wheel 210. The method also includes: converting the input data S3 into corresponding equivalent wheel speeds or wheel slippages maintained by the wheel 210 to generate desired wheel forces Fx and Fy based on the inverse tire model of the wheel 210; and controlling the heavy vehicle 100 based on the equivalent wheel speeds or wheel slippages S4.

[0117] Figure 8Components of a control unit, such as VUC 130 and 140, are schematically shown in relation to multiple functional units. According to the embodiments discussed herein, this control unit can implement one or more of the functions discussed above, such as TSM 270, VMM 260, and / or control function 230. The control unit is configured to perform at least some of the functions discussed above for controlling the heavy vehicle 100. Processing circuitry 810 is provided using any combination of one or more of a suitable central processing unit (CPU), multiprocessor, microcontroller, digital signal processor (DSP), etc., capable of executing software instructions stored in a computer program product (e.g., in the form of storage medium 1020). Processing circuitry 810 may further be provided as at least one application-specific integrated circuit (ASIC) or field-programmable gate array (FPGA).

[0118] Specifically, the processing circuit 810 is configured to cause the control unit 101 to perform a set of operations or steps, such as combining Figure 7 The method discussed herein. For example, storage medium 820 may store the set of operations, and processing circuitry 810 may be configured to retrieve the set of operations from storage medium 820 to cause control unit 900 to execute the set of operations. The set of operations may be provided as an executable instruction set. Thus, processing circuitry 810 is thereby arranged to perform the method disclosed herein.

[0119] Storage medium 820 may also include a permanent storage device, such as any one or a combination of magnetic storage, optical storage, solid-state storage, or even remotely mounted storage.

[0120] The control unit 900 may also include an interface 830 for communicating with at least one external device. Therefore, the interface 830 may include one or more transmitters and receivers, including analog and digital components, and a suitable number of ports for wired or wireless communication.

[0121] The processing circuit 810 controls the general operation of the control unit 900, for example, by sending data and control signals to the interface 830 and the storage medium 820, by receiving data and reports from the interface 830, and by retrieving data and instructions from the storage medium 820. Other components and related functions of the control node are omitted to avoid confusing the concepts presented herein.

[0122] Figure 9 A computer-readable medium 910 is shown carrying a computer program including a program code component 920, which is configured to execute when the program product is run on a computer. Figure 7 The method shown. The computer-readable medium and code components can together form a computer program product 900.

Claims

1. A control unit (130, 140) for controlling a heavy vehicle (100). in, The control unit is arranged as follows: Obtain input data representing the desired wheel forces (Fx, Fy) to be generated by at least one wheel (210) of the vehicle (100), and The input data is converted into a corresponding equivalent wheel speed or wheel slip maintained by the wheel (210) in order to perform an inverse tire model (f) based on the wheel (210). -1 Generate the desired wheel force (Fx, Fy). Wherein, the equivalent wheel speed or wheel slip corresponds to the desired wheel force, such that the equivalent wheel slip is configured to generate the desired wheel force, and the equivalent wheel slip is related to the difference between the equivalent wheel speed and the ground speed. The control units (130, 140) are arranged to obtain the reverse tire model based on the current operating conditions of the wheel (210), and The control units (130, 140) are arranged to control the heavy vehicle (100) based on the equivalent wheel speed or wheel slip, and controlling the vehicle based on the equivalent wheel speed or wheel slip includes sending a request for the equivalent wheel speed or wheel slip to a motion support device controller.

2. The control unit (130, 140) according to claim 1, wherein, The control unit is also arranged to assign a steering angle to one or more of the steering wheels. ).

3. The control unit (130, 140) according to claim 1 or 2, wherein, The data representing the desired wheel force (Fx, Fy) includes the desired wheel torque (Treq) and wheel rolling radius (R).

4. The control unit (130, 140) according to claim 1 or 2, wherein, The current operating conditions include the vehicle's horizontal force distribution (Fx, Fy, Mz) and / or the wheel-to-ground velocity vector (Vx, Vy).

5. The control unit (130, 140) according to claim 1 or 2, wherein, The current operating conditions include the normal load (Fz) of the wheel (210).

6. The control unit (130, 140) according to claim 1 or 2, wherein, The current operating conditions include the estimated tire stiffness (Cest) of the wheel (210).

7. The control unit (130, 140) according to claim 1 or 2, wherein, The current operating conditions include the tire road friction coefficient associated with the wheel ( ).

8. The control unit (130, 140) according to claim 1 or 2, wherein, The current operating conditions include the minimum required lateral force capacity (Fy,min) of the wheel (210).

9. The control unit (130, 140) according to claim 1 or 2, wherein, The current operating conditions include the maximum permissible lateral slip angle of the wheel (210) ).

10. The control unit (130, 140) according to claim 1 or 2, wherein, The reverse tire model (f) -1 The wheel (210) is configured to provide the remaining lateral force capability (Fy,rem) of the wheel (210).

11. The control unit (130, 140) according to claim 1 or 2, wherein, The reverse tire model (f) -1 The system is configured to provide a gradient (dFx, dFy) of the desired wheel force with respect to wheel speed or wheel slip at a tire operating point associated with the desired wheel force of the wheel (210) and the current operating conditions.

12. The control unit (130, 140) according to claim 1 or 2, wherein, The control unit is arranged to store a predetermined reverse tire model (f) in a memory. -1 The inverse tire model is stored in the memory as a function of the current operating conditions of the wheel (210).

13. The control unit (130, 140) according to claim 1 or 2, wherein, The control unit is configured to: in response to control of the heavy vehicle (100) based on the equivalent wheel speed or wheel slip, adjust the inverse tire model (f) based on measured wheel behavior and / or vehicle behavior. -1 ).

14. The control unit (130, 140) according to claim 13, wherein, The reverse tire model (f) -1 The wheel force is adjusted to always remain within a predetermined upper and / or lower limit, depending on wheel slip or wheel speed.

15. The control unit (130, 140) according to claim 1 or 2, wherein, The control unit is arranged to control the reverse tire model (f) -1 ) represents a lookup table.

16. The control unit (130, 140) according to claim 1 or 2, wherein, The control unit is arranged to control the reverse tire model (f) -1 ) represents a neural network.

17. A vehicle (100) comprising a control unit (130, 140) according to any one of claims 1 to 16.

18. A method for controlling a heavy vehicle (100) executed in a control unit (130, 140), the method comprising: S1: Obtain input data representing the desired wheel forces (Fx, Fy) to be generated by at least one wheel (210) of the vehicle (100); S2: Obtain the inverse tire model (f) associated with the wheel (210). -1 ), wherein the reverse tire model depends on the current operating conditions of the wheel (210); S3: The input data is converted into a corresponding equivalent wheel speed or wheel slip maintained by the wheel (210) to generate the desired wheel force (Fx, Fy) based on an inverse tire model of the wheel (210), wherein the equivalent wheel speed or wheel slip corresponds to the desired wheel force, such that the equivalent wheel slip is configured to generate the desired wheel force, and the equivalent wheel slip is related to the difference between the equivalent wheel speed and the ground speed; and S4: Control the heavy vehicle (100) based on the equivalent wheel speed or wheel slip, wherein Controlling the heavy vehicle (100) based on the equivalent wheel speed or wheel slip includes sending a request for the equivalent wheel speed or wheel slip to a motion support device controller.

19. A computer-readable medium (910) carrying a computer program including a program code component (920) for performing the method according to claim 18 when the computer program is run on a computer or on a processing circuit of a control unit.

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