Computer-implemented method for coordinating active downforce and active suspension control elements for maximized tire grip for a vehicle

Abstract

Examples described herein provide a method for coordination between active downforce and active suspension control elements for a vehicle, including determining an optimal ride height for the vehicle based on current conditions of the vehicle. The method further includes determining a suspension actuator force to implement the optimal ride height for the vehicle. The method further includes, by an active suspension system of the vehicle, controlling an actuator using the suspension actuator force to achieve the optimal ride height for the vehicle.

Description

[0001] The present invention relates to vehicles and, in particular, to a computer-implemented method for coordinating active downforce and active suspension control elements for a vehicle, as is known essentially from DE 10 2018 117 897 A1. Further prior art is described in DE 10 2010 026 432 A1, DE 10 2019 117 228 A1, DE 11 2022 001 793 T5, DE 10 2021 101 716 A1 and DE 10 2022 122 643 A1.

[0002] Modern vehicles (e.g., a passenger car, motorcycle, boat, or any other type of automobile) can be equipped with various systems to improve handling, stability, and overall performance. For example, a vehicle may include an active downforce system that dynamically adjusts aerodynamic elements / surfaces (e.g., Gurney flaps, rear wings, and / or the like, including combinations and / or multiple elements) to increase the downward force exerted on the vehicle, thereby improving tire grip. As another example, a vehicle may include an active suspension system to manage forces between the vehicle's wheels and body to provide ride comfort. SUMMARY

[0003] According to the invention, a method for coordinating between active downforce and active suspension control elements for a vehicle is presented, characterized by the features of claim 1.

[0004] The method involves determining an optimal ride height for the vehicle based on its current states. It further involves determining a suspension actuator force to implement this optimal ride height. The method also includes controlling an actuator using this suspension actuator force via the vehicle's active suspension system to achieve the optimal ride height. The suspension actuator force is determined using a model predictive control engine, which incorporates a suspension prediction model generated by converting a nonlinear neural network into a linear suspension model and combining this linear suspension model with a half-car model.

[0005] In addition to one or more of the features described herein, or as an alternative, embodiments of the method may further include the optimal ride height comprising a front ride height of the vehicle and a rear ride height of the vehicle.

[0006] In addition to one or more of the features described herein, or as an alternative, embodiments of the method may further include determining the optimal ride height using a ride height optimization machine.

[0007] In addition to one or more of the features described herein, or as an alternative, embodiments of the method may further include the ride height optimization machine comprising aerodynamic maps and a neural network.

[0008] In addition to one or more of the features described herein, or as an alternative, embodiments of the method may further include the neural network being a flat, fully connected neural network.

[0009] In addition to one or more of the features described here, or as an alternative, embodiments of the method may further include the neural network converting the aerodynamic maps into a non-linear state-space model.

[0010] In addition to one or more of the features described herein, or as an alternative, embodiments of the method may further include determining the optimal ride height at least partially based on a specific aerodynamic position of an adjustable aerodynamic surface of the vehicle and a longitudinal speed of the vehicle.

[0011] In addition to one or more of the features described herein, or as an alternative, embodiments of the method may further include the model predictive control machine comprising an aerodynamic model of the vehicle and a suspension model of a suspension system of the vehicle.

[0012] In a further embodiment, a vehicle is provided. The vehicle includes an active downforce system for controlling an adjustable aerodynamic surface of the vehicle. The vehicle further includes an active suspension system for controlling an actuator, wherein the actuator sets a ride height of the vehicle. The vehicle further includes a processing system that is communicatively coupled to the active downforce system and the active suspension system. The processing system includes a memory containing computer-readable instructions and a processing device for executing the computer-readable instructions, wherein the computer-readable instructions control the processing device to perform operations for coordinating between the active downforce and active suspension control elements for the vehicle. The operations include those of claim 1.

[0013] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include such that the optimal ride height comprises a front ride height of the vehicle and a rear ride height of the vehicle.

[0014] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include the determination of the optimal ride height using a ride height optimization machine.

[0015] In addition to one or more of the features described here, or as an alternative, further embodiments of the vehicle may include the ride height optimization machine comprising a neural network that translates aerodynamic maps into a non-linear state space model.

[0016] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include the determination of the optimal ride height being based at least partially on a specific aerodynamic position of the adjustable aerodynamic surface of the vehicle and a longitudinal speed of the vehicle.

[0017] Ferber describes a computer program product. The computer program product includes a computer-readable storage medium containing program instructions embodied therein, wherein the program instructions are executable by at least one processor to cause the at least one processor to perform operations. The operations include determining an optimal ride height for a vehicle based on the vehicle's current states. The operations further include determining a suspension actuator force to implement the optimal ride height for the vehicle. The operations further include controlling an actuator by an active suspension system of the vehicle using the suspension actuator force to achieve the optimal ride height for the vehicle.

[0018] In addition to one or more of the features described herein, or as an alternative, further embodiments of the computer program product may include that the optimal ride height comprises a front ride height of the vehicle and a rear ride height of the vehicle.

[0019] In addition to one or more of the features described herein, or as an alternative, further embodiments of the computer program product may include determining the optimal ride height using a ride height optimization machine.

[0020] In addition to one or more of the features described herein, or as an alternative, further embodiments of the computer program product may include the fact that the ride height optimization machine incorporates a neural network that translates aerodynamic maps into a non-linear state-space model.

[0021] In addition to one or more of the features described herein, or as an alternative, further embodiments of the computer program product may include the determination of the optimal ride height being based at least partially on a specific aerodynamic position of an adjustable aerodynamic surface of the vehicle and a longitudinal speed of the vehicle.

[0022] The features and advantages described above, and further features and advantages of the invention, will become apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Further features, advantages and details appear only as examples in the following detailed description, which refers to the drawings; they show: Fig. 1 an illustration of a vehicle comprising a processing system which provides coordination between an active downforce system and an active suspension system, according to one or more embodiments; Fig. 2 a block diagram of the processing system of Fig. 1 for coordination between the active output force system and the active suspension system according to one or more embodiments; Fig. 3. A block diagram of an architecture for providing coordination between the active downforce system and the active suspension system for the vehicle of Fig. 1 according to one or more embodiments; Fig. 4 A block diagram of an architecture for providing coordination between the active downforce system and the active suspension system for the vehicle of Fig. 1 according to one or more embodiments; Fig. 5 a flowchart of a method for coordinating between active downforce and active suspension control elements for a vehicle according to one or more embodiments; Fig. 6 a diagram showing forces on the vehicle from Fig. 1 according to one or more embodiments; Fig. 7 an architecture in which the model predictive control engine generates recommended suspension forces for a front axle and a rear axle, according to one or more embodiments; Fig. 8 an architecture for force handling on active suspension control elements according to one or more embodiments; Fig. 9 a flowchart of a method for coordinating between active downforce and active suspension controls for a vehicle according to one or more embodiments; and Fig. 10 a block diagram of a processing system for implementing one or more embodiments described herein. DETAILED DESCRIPTION

[0024] The following description is for illustrative purposes only. It should be understood that throughout the drawings, corresponding reference numerals denote similar or corresponding sections and features. As used here, the term "module" refers to a processing circuit arrangement that may include an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or grouped) with memory executing one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality.

[0025] Various systems can be used in vehicles to improve handling, stability, and overall performance. For example, a vehicle may include an active downforce system, an active suspension system, and / or combinations thereof. Often, active downforce systems and active suspension systems operate independently and without coordination. A vehicle's ride height, primarily controlled by the active suspension system, can affect downforce forces. For instance, changes in the front and / or rear ride height will also alter the vehicle's aerodynamic properties (such as angle of attack). As another example, the maximum achievable downforce at each axle is limited by the front and / or rear ride height.Consider the following: The downforce at the front axle depends on the front and rear ride heights, since the majority of the downforce is generated by the vehicle's underbody. During maneuvers such as exiting a curve, the front of the vehicle moves away from the ground due to load transfer. In this case, the front downforce is significantly reduced.

[0026] One or more embodiments described herein relate to a coordination between active downforce and active suspension controls for maximized tire grip for a vehicle. For example, one or more embodiments described herein cause the active suspension system to generate certain forces such that the ride height at the front and rear of the vehicle is adjusted to achieve maximum downforce for the current state of the vehicle.

[0027] According to one or more embodiments, a basic control structure is provided that optimizes the performance of vehicles equipped with active downforce systems and active suspension systems (e.g., fully active, height control systems, etc.). By adjusting the ride height of a vehicle (which also sets an angle of attack) using the active suspension system, optimal ride heights can be achieved to maximize aerodynamic forces and improve tire grip. According to one or more embodiments, an interface mechanism is provided that determines the ideal ride heights at the front and rear axles of the vehicle and suggests suspension forces that enable the achievement of these ideal ride heights.One or more embodiments take into account the effect of a suspension on tire capacity, ensuring a comprehensive calculation of the proposed suspension forces. This integrated solution aims to improve vehicle stability, handling, and overall performance.

[0028] According to one or more embodiments, during highly dynamic maneuvers such as corner exits, changes in the vehicle's ride height and angle of attack (the ride heights) can significantly affect the generation of downforce by the aerodynamic elements. This reduction in aerodynamic downforce generation can affect the vehicle's stability, performance, and consistency.

[0029] According to one or more embodiments, vehicles equipped with multiple active systems, such as active suspension and active aerodynamics (e.g., active downforce), may face conflicting tasks in their control algorithms. Each system has its own priorities and tasks. For example, the active suspension system may aim to improve ride comfort, while the active downforce system focuses on providing the necessary grip to maintain vehicle stability. Resolving these conflicts and ensuring harmonious operation of these systems is desirable for optimal vehicle performance.

[0030] One or more embodiments described herein provide a fully integrated solution by modifying existing active output force and active suspension systems and their associated control algorithms. An independent coordination solution approach that coordinates between active output force and active suspension systems can avoid substantial changes to any current control algorithms while maintaining software modularity.

[0031] Fig. Figure 1 is an illustration of a vehicle 100 comprising a processing system 102 that provides coordination between an active drive force system 104 and an active suspension system 106, according to one or more embodiments. The vehicle 100 may be a passenger car, a truck, a van, a bus, a motorcycle, a boat, or any other type of motor vehicle. According to one embodiment, the vehicle 100 includes an internal combustion engine powered by gasoline, diesel, or the like. According to another embodiment, the vehicle 100 is a hybrid electric vehicle powered partially or entirely by electrical power. According to yet another embodiment, the vehicle 100 is an electric vehicle powered by electrical power. According to one or more embodiments, the vehicle 100 is an autonomous or semi-autonomous vehicle.An autonomous vehicle is a vehicle that possesses self-driving capabilities. A semi-autonomous vehicle is a vehicle that has certain autonomous features (e.g., self-parking, lane keeping, etc.) but does not have fully autonomous control.

[0032] According to one or more embodiments, the vehicle 100 includes the processing system 102, which is located in Fig. 2 is shown in more detail and described here. The vehicle 100 also includes the active downforce system 104 and the active suspension system 106. The active downforce system 104 provides dynamic adjustment of aerodynamic elements / surfaces of the vehicle 100 to increase the downward force exerted on the vehicle, thereby improving tire grip. The active suspension system 106 provides automatic adjustment of suspension settings of the vehicle 100 to maintain optimal contact between the vehicle's tires and a road surface, thus providing improved driving and handling characteristics.

[0033] Further features of the processing system 102 will now be described with reference to Fig. 2- Fig. 10 described.

[0034] In particular, Fig. Figure 2 shows a block diagram of the processing system 102 for coordinating the active downforce system 104 and the active suspension system 106 according to one or more embodiments. The processing system 102 comprises a processing device 202, a memory 204, and a downforce and suspension coordination machine 210. It should be noted that the processing system 102 can be any device suitable for providing coordination between active downforce and active suspension controls for maximized tire grip for a vehicle. For example, the processing system 102 can be a device implemented in the vehicle 100 or otherwise associated with it.As another example, the processing system 102 could be a smartphone, a tablet computer, a laptop computer, a desktop computer, a portable computing device and / or the like, which includes combinations and / or several of these.

[0035] The processing device 202 is any suitable processing circuit arrangement for processing data and / or instructions. The processing device 202 is an example of one or more of the processing devices 1021 of Fig. 10, as described in more detail here.

[0036] Memory 204 is any suitable device for storing data and / or instructions. Memory 204 is an example of system memory 1022 and / or read / write memory 1023 and / or read-only memory 1024. Fig. 10, as described in more detail here.

[0037] The output force and suspension coordination machine 210 coordinates between the active output force system 104 and the active suspension system 106, as described in more detail here.

[0038] Further aspects and features of the output force and suspension coordination machine 210 are discussed here in relation to Fig. 3- Fig. 9 described.

[0039] The various components, modules, machines, etc., which with regard to Fig. The machines described in Section 2 (e.g., the output force and suspension coordination machine 210) can be implemented as instructions stored in a computer-readable storage medium, as hardware modules, as special-purpose hardware (e.g., application-specific hardware, application-specific integrated circuits (ASICs), application-specific special-purpose processors (ASSPs), field-programmable gate arrays (FPGAs), as embedded controllers, a hard-wired circuit arrangement, etc.), or as one or more combinations thereof. According to aspects of the present invention, the one or more machines described herein can be a combination of hardware and programming. The programming can be processor-executable instructions stored in physical memory, and the hardware can include the processing device 202 for executing these instructions. Thus, a system memory (e.g.,The memory (204) stores program instructions which, when executed by the processing device (202), implement the machines described herein. Additional machines can also be used to incorporate further features and functionalities, which are described in further examples herein.

[0040] With reference to Fig. 3 An architecture 300 is provided for coordinating the active downforce system 104 and the active suspension system 106 for the vehicle 100 according to one or more embodiments. The architecture 300 provides a determination of a desired ride height to achieve maximized front and / or rear downforces and then provides a determination of optimal suspension actuator forces to achieve the desired ride height.

[0041] In Fig. The vehicle 100 generates estimates for suspension actuator forces and sends these estimates to the active downforce system 104 and the active suspension system 106, as shown. The active downforce system 104 generates positioning instructions for the vehicle 100's aerodynamic elements / surfaces, which are sent to the vehicle. The positioning instructions are also sent to the downforce and suspension coordination machine 210, which includes a ride height optimization machine 310 and a model predictive control machine 312. The ride height optimization machine 310 uses an actuator model to generate a desired ride height (e.g., a front ride height and / or a rear ride height), which is sent to the model predictive control machine 312.The model predictive control machine 312 uses a half-car model with a downforce actuator model to generate recommended suspension actuator forces, which are then sent to the active suspension system 106 and back to the vehicle 100, as shown. The downforce actuator model contains aerodynamic maps (e.g., lookup tables) obtained through wind tunnel testing. The lookup table model cannot be used in optimization or in a predictive model for model predictive control (MPC predictive model). Therefore, this lookup table model is implemented in a neural network with a mathematical representation. This non-linear mathematical representation is used directly in the optimization, is linearized, and is combined with the half-car model in the MPC predictive model.It is to be acknowledged that instructions / signals described as being sent to the vehicle 100 by the active downforce system 104 and / or the active suspension system 106 are instructions / signals that have been sent to other systems, controllers, devices, components and / or the like, which includes combinations and / or multiples thereof, of the vehicle 100.

[0042] According to one or more embodiments, the model predictive control machine 312 can successfully track the pitching and lifting of the vehicle 100 as a result of the optimal ride heights. Active downforce and suspension coordination provides an overall increase in tire grip forces. For example, as shown in the following table of example results, the tire grip with coordination exceeds the tire grip without coordination for both the front and the rear: Sum(Tire Grip(N)2) without coordination Total (tire grip (N) 2 )with coordination Front 792690 818810 Heck 1326000 1339200

[0043] It is evident that active downforce and suspension coordination provides an overall increase in tire grip and downforce forces. For example, in one test, the front of vehicle 100 experienced an essentially 17% increase in added grip compared to no downforce between active downforce and active suspension, while the rear of vehicle 100 experienced an essentially 5% increase in added grip compared to no downforce between active downforce and active suspension.

[0044] Fig. Figure 4 presents a block diagram of an architecture 400 for providing coordination between the active downforce system 104 and the active suspension system 106 for the vehicle 100, which is provided according to one or more embodiments. Architecture 400 shows further features and functions of architecture 300. Fig. 3.

[0045] As in Fig. As described in section 3, the architecture contains 400 of Fig. 4 the active downforce system 104 and the active suspension system 106 for the vehicle 100. In this example, the architecture 400 includes a vehicle dynamics estimator 402 to generate vehicle measurements and estimates (e.g., estimates of suspension actuator forces). The vehicle measurements and estimates are sent from the vehicle dynamics estimator 402 to the active downforce system 104 and the active suspension system 106.

[0046] As in Fig. As shown in Figure 4, the active downforce system 104 controls one or more aerodynamic elements / surfaces 405, which may be connected to and / or integrated into the vehicle 100. The active suspension system 106 controls one or more actuators 407 (and / or other components), which may be connected to and / or integrated into the vehicle 100.

[0047] As also in Fig. As described in section 3, the architecture of Fig. 4 The output force and suspension coordination machine 210, which includes the ride height optimization machine 310 and the model predictive control machine 312. The active output force system 104 feeds a current position request into the ride height optimization machine 310, which determines an optimal ride height. The optimal ride height is the ride height for the vehicle 100 that causes a desired output force for the vehicle 100 to be realized. The optimal ride height is fed into the model predictive control machine 312, which generates a request for a suspension actuator force (also referred to as an active suspension force), which is fed into the active suspension system 106.

[0048] The Ride Height Optimization Machine 310 uses aerodynamic maps (“aero maps”) in the form of a neural network to determine the optimal ride height. The aero maps are developed during wind tunnel testing for the vehicle 100. Typically, the aero maps cannot be used directly for control, so the neural network is used to translate the aero maps into a usable form (e.g., converting lookup tables to a neural network for predictive control). The neural network can be any suitable network architecture, such as a flat, fully connected neural network. The Ride Height Optimization Machine 310 provides a maximization of aerodynamic downforce for the vehicle 100 while maintaining vehicle equilibrium.

[0049] The model predictive control machine 312 determines or calculates a suspension actuator force that is sent to the active suspension system 106. This ensures that the vehicle 100 is correctly positioned (with respect to ride height / angle of attack) to achieve the desired downforces. The model predictive control machine 312 includes an aerodynamic model (“aero model”) for the vehicle 100 and a suspension model for a suspension system (e.g., the one or more actuators 407) of the vehicle 100.

[0050] The downforce and suspension coordination machine 210 improves vehicle dynamics during cornering maneuvers (e.g., tire capacity, yaw control, and lateral speed control), improves braking, enhances roll control to maintain vehicle balance, and reduces drag when no downforce is required (e.g., in straight-line driving situations) to maximize vehicle speed. Features and functionalities of the downforce and suspension coordination machine 210, which includes the ride height optimization machine 310 and the model predictive control machine 312, are described with reference to Fig. 5- Fig. 7 described in more detail.

[0051] Fig. Figure 5 presents a flowchart of a method 500 for coordinating active output force and active suspension controls for a vehicle according to one or more embodiments. The method 500 can be implemented by any suitable system or device, such as the processing system 102 (e.g., using the output force and suspension coordination machine 210), the processing system 1000 of Fig. 10 and / or the like, which contains combinations and / or several thereof, must be implemented. Procedure 500 is now implemented with reference to one or more of Fig. 1- Fig. 4 described.

[0052] In Block 502, the downforce and suspension coordination machine 210 translates aerodynamic maps into a neural network (or multiple neural networks). Specifically, aerodynamic maps are translated into alternative neural networks, enabling the replacement of lookup tables with analytical formulations for the aerodynamic maps. The aerodynamic maps can determine a front and / or a rear downforce from a lookup table developed during wind tunnel testing for vehicle 100, as follows: Front downforce = Backlash (Aviation, Airspeed, Act, Positions) Rear downforce = (Above ground, airspeed, current settings).

[0053] To convert the aerodynamic maps (which take the form of an actuator model called an aerodynamic map actuator model) into a linear state-space model, a neural network is designed and trained to fit the aerodynamic map actuator models. The neural network model is then converted into a non-linear state-space model using the following equation: Xk+1=F(Xk,Uk,Dm,k), where x is a front and / or a rear thrust force, D m.k where U is the speed and U the height, U is an actuator instruction, and k is the time. This equation can be extended as follows: Xk+1=F(Xk,Uk,Dm,k)=W31σ(W21tanh(W11Dm,k+W12Uk+W13Xk+B1)+B2)+ B3, where W is a weight value and B is a preload value.

[0054] With continued reference to Fig. Block 504 designs the downforce and suspension coordination machine 210 and the ride height optimization machine 310 to obtain the optimal ride height that maximizes downforce given a specific aerodynamic position of one or more adjustable aerodynamic elements / surfaces of the vehicle 100 and a longitudinal speed for the vehicle 100. Specifically, Block 504 determines the optimal ride heights for given instructions and a given vehicle speed to maximize downforce generated at each wing of the vehicle 100, while ensuring that the ratio of the maximized downforces is within an acceptable range that does not affect vehicle equilibrium (or at least maintains vehicle equilibrium to an acceptable degree).

[0055] Using a mathematical correlation via a neural network, the front and / or rear downforce can be written as a function of the front and / or rear ride height as follows: [Fdf,frnt(RHfrnt,RHrear)Fdf,rear(RHfrnt,RHrear)]=W31σ(W21tanh(W11[VxRHfrntRHrear]+W12[cmdfrntcmdrear]+ B1)+B2)+B3, where F df,frnt a front downward force, F df,rear a posterior downward force, RH frnt a front ride height is, RH rear a rear ride height is, cmd frnt A front suspension instruction is cmd rear a rear suspension instruction and σ a sigmoid activation function. It is acknowledged that any suitable activation function, such as a sigmoid, a rectified linear unit (ReLU), a hyperbolic tangent (tanh), and / or the like, including combinations and / or multiples thereof, may be used.

[0056] According to one or more embodiments, a non-linear interior point optimization technique is used to determine the front thrust force (F). df,frnt ) and the rear downforce (F df,rear to maximize. For example, a gradient-based solution approach is designed to work on problems where the task and constraint functions are both continuous, and first derivatives are continuous. In this maximization problem, the constraint is to ensure that with the maximized downward forces (e.g., F) df,rear,opt and F df,frnt,opt ) the aerodynamic preload is not impaired beyond a desirable limit, which is expressed as follows: AD-dictated aerodynamic preload−ε≤Fdf,frnt,optFdf,rear,opt+Fdf,frnt,opt≤ AD-dictated aerodynamic preload+ε, where ε is a selectable constant that can be a percentage of a force, an amount of a force and / or the like, which includes combinations and / or several of these.

[0057] With continued reference to Fig. Block 506 provides the downforce and suspension coordination machine 210 with a computationally efficient suspension prediction model suitable for model-based control. This involves converting a nonlinear neural network to a linear suspension model, which is then integrated with a half-car model to develop a final suspension prediction model.

[0058] The linear suspension model 600 is now described with reference to Fig. 6 describes in more detail the forces on the vehicle 100 according to one or more embodiments with respect to a vehicle section 602 and a suspension section 604. In Fig. 6 are Fdff and Fdfr a front and a rear downforce, respectively; θ and θ̇ are the angle of inclination and their rate of change, respectively; z and ż are a lifting force and its rate of change, respectively. Fzsf and Fdfr the front and rear axle spring forces and are F ctrl,f and F ctrl,r The recommended active suspension axis forces. The linear suspension model has two degrees of freedom: lift and pitch. The linear suspension model can be expressed by the following equations: M(z¨+g)=(Fzsf+FAnti Dive)+(Fzsr+FAnti Squat)−Fdff−Fdfr+Fctrl,f+Fctrl,r Iyy θ¨=Lf(Fzsf+FAnti Dive)−Lr(Fzsr+FAnti Squat)−LDFfFdff+LDFrFdfr−Mhpc,cgax+ Fctrl,fLf−Fctrl,rLr, where g is the gravitational constant, z is the lifting, L f and L rwhere m is the total mass of vehicle 100, M is the momentum of vehicle 100, and F is the respective distances between a center of gravity C of vehicle 100 and the front and rear axles of vehicle 100, ... momentum of vehicle 100. Anti Dive and F Anti Squat The section of a load transmission system is transferred to wheels via a submersion and squat protection mechanism. According to one or more embodiments, axle spring forces are Fzsf and Fzsr calculated as follows: Fzsf=Ff0+Kfzf+Cfz˙f Fzsr=Fr0+Krzr+Crz˙r.

[0059] The half-car state space model Ẋ h can be expressed by the following equation: X˙h=Ah Xh+Bh Uh+Dh

[0060] The expression X h is a spatial state vector, which is represented as follows: Xh=[zz˙θθ˙].

[0061] The expression U hare inputs that are represented as follows: Uh=[Fctrl,frnt+Fdf,frntFctrl,rear+Fdf, rear]

[0062] Where the inputs are suspension axis forces (e.g. F) ctrl , front and F ctrl , rear) and axle drive forces (e.g. F df , front and F df , rear) included.

[0063] In an extended form, the half-car state space model is expressed as follows: X: KfLf2−KrLr2IyyCfLf2−CrLr2Iyy︸Ah]Xh+[00−1 / M−1 / M00−LfDFIyy−LrDFIyy︸Bh]Uh +[0Ff0+Fr0+Fantidive+FantisquatM−g0LfFf0−LrFr0+LfFantidive−LrFantisquat−MhaxIyy︸Dh]

[0064] The non-linear state-space model is transformed into a linear model using partial derivatives of the state X with respect to the following inputs, which can be computed analytically using autodifferentiation: ∂xk+1∂Xk,∂Xk+1∂Uk,∂Xk+1∂Dm,k

[0065] The resulting implemented non-linear state space model is expressed as follows: Xk+1=A Xk+BUk+Ds.

[0066] A linear state-space representation of the model using a neural network is expressed as follows: [Fdf,frnt(RHfrnt,RHrear)Fdf,rear(RHfrnt,RHrear)]=(BNN([cmdfrntcmdrear]−[cmdfrntcmdrear]p)+Ds ,NN[Vx−Vxp]+[Fdf,frnt(RHfrnt,RHrear)Fdf,rear(RHfrnt,RHrear)]p−Ds,NN[RHfRHr]p)DxDs,NN[RHfRHr], where BNN=∂Xk+1∂Uk|2×2=W31 M4 W21(I10×10−M5)W12BsNN=∂Xk+1∂Dm,k|2×3=W31 M4 W21(I10×10−M5)W11 M110×1=σ(M2) M210×1=W21tanh(M3)+B2 M310×1=W11Dm,k+W12U^k+W13X^k+B1 M410×10=diag(σ(M2))−diag(σ2(M2)) M510×10=diag(tanh2(M3)) tanh(x)=2 / (1+exp(−2x))−1 σ(x)=1 / (1+exp(−x)).

[0067] Using the foregoing, an integrated state-space suspension model (e.g., the final suspension prediction model) is generated as follows. The state-space model in the continuous-time domain is mapped to the discrete-time domain as follows: Xh(k+1)=Ah,dXh(k)+Bh,dUh(k)+Dh,d Ah,d=eAhTs Bh,d=Ah−1(Ah,d−I)Bh Dh,d=DhTs.

[0068] This discrete equation can be rewritten as follows: Xh(k+1) = Ah, d

[0069] New discrete states can be defined as follows: Xnew=[FdffFdfrzz˙θθ˙]

[0070] The state-space formulation can be rewritten for the new state by including the state-space model for the downward force, as follows: [FdffFdfrzz˙θθ˙]k+1=[02×2Ds,NN[c10Lfc10c30−Lrc30]Bh,dAh,d︸Anew][FdffFdfrzz˙θθ˙]k+Bh,d︸Bnew[Fctrl, frntFctrl, rear]︸Unew +Dh,d+Ds,NN[c2c4]+Dx︸Dnew

[0071] With continued reference to Fig. In block 508, the output force and suspension coordination machine 210 and the model predictive control machine 312 are designed such that they obtain the suspension axis forces required to track the optimal ride height. For example, Fig. Figure 7 represents an architecture 700 in which the model predictive control machine 312 generates recommended suspension forces for a front axle and a rear axle. For this purpose, the vehicle 100 generates an estimate (as described here) which is fed into the ride height optimization machine 310. The ride height optimization machine 310 generates target ride heights. The conversion of ride height to pitch and roll can be performed in block 702 to convert the target ride heights into a converted target ride. The model predictive control machine 312 receives the converted target ride heights and uses them to generate the suspension forces, which are used to cause the active suspension system 106 of the vehicle 100 to control the suspension (e.g., one or more actuators 407) to achieve the optimal ride height for the vehicle 100.

[0072] With continued reference to Fig. 5. Additional processes can also be included, and it is understood that the processes that are in Fig. The processes shown in Figure 5 are for illustrative purposes only and demonstrate that further processes can be added, or existing processes can be removed, modified, or rearranged. It should also be understood that the processes shown in Figure 5 are not intended to be interpreted in this way. Fig. 5 are shown, can be implemented as programming instructions stored in a non-transient, computer-readable storage medium, which, when read by a processor (e.g., the processing device 202 of Fig. 2, the one or more processors 1021 of Fig. 10 and / or the like, which includes combinations and / or several thereof) of a computing system (e.g., the processing system 102 of Fig. 1 and Fig. 2, of the processing system 1000 of Fig. 10 and / or the like, which includes combinations and / or several thereof) are executed, causing the processor to perform the processes described herein.

[0073] With reference to Fig. Figure 8 shows a block diagram of an architecture 800 for force handling at active suspension controls according to one or more embodiments. In this example, an operator of the vehicle 100 can select a driving mode (e.g., a sport mode, a comfort / tour mode, an economy mode, and / or the like, including combinations and / or multiples thereof) to operate the vehicle 100. It should be noted that the different driving modes have different settings with respect to downforce and suspension control. For example, in a sport mode, the suspension is instructed to follow the recommended ride height to maximize downforce and tire grip, sacrificing ride comfort. However, in a comfort / tour mode, the suspension is instructed for maximum ride comfort, and downforce is limited by a front and rear ride height, sacrificing performance.

[0074] In the example of Fig. Block 8 receives a driver instruction interpretation device 802 recommended active suspension axis forces 801 from the model predictive control machine 312, as described herein. The driver instruction interpretation device 802 performs force transmission in block 804 based on the active suspension axis forces recommended by the model predictive control machine 312 and on the basis of a suspension force request from a suspension driver instruction interpretation device 806.

[0075] The driver instruction interpretation device 802 sends the driver mode from the suspension driver instruction interpretation device 806 and suspension control forces from the force transmission block 804 to an integrated suspension monitoring controller 810. The integrated suspension monitoring controller 810 sends corner suspension forces to one or more actuators 407 based on the driver mode and the suspension control forces. The one or more actuators 407 can send suspension actuator instructions to hardware controls 812 to implement the desired suspension forces (e.g., the corner suspension forces from the integrated suspension monitoring controller 810).

[0076] Fig. Figure 9 presents a flowchart of a method 900 for coordinating active output force and active suspension controls for a vehicle according to one or more embodiments. The method 900 can be implemented by any suitable system or device, such as the processing system 102 (e.g., using the output force and suspension coordination machine 210), the processing system 1000 of Fig. 10 and / or the like, which contains combinations and / or several thereof, must be implemented. Procedure 900 is now implemented with reference to one or more of Fig. 1- Fig. 4 described.

[0077] In block 902, the downforce and suspension coordination machine 210, using the ride height optimization machine 310, determines an optimal ride height for the vehicle 100 based on current states (e.g., vehicle measurements and / or estimates) of the vehicle, as described herein. In block 904, the downforce and suspension coordination machine 210, using the model predictive control machine 312, determines a suspension actuator force to implement the optimal ride height, as described herein. In block 906, the active suspension system 106 of the vehicle 100 controls an actuator (e.g., one or more actuators 407) using the suspension actuator force to achieve the optimal ride height for the vehicle 100.

[0078] Additional processes may also be included, and it should be understood that the processes that are in Fig. The processes shown in Figure 9 are for illustrative purposes only and demonstrate that further processes can be added, or existing processes can be removed, modified, or rearranged. It should also be understood that the processes shown in Figure 9 are not intended to be interpreted in this way. Fig. 9 are shown, can be implemented as programming instructions stored in a non-transient, computer-readable storage medium and which, when executed by a processor (e.g., the processing device 202 of Fig. 2, the one or more processors 1021 of Fig. 10 and / or the like, which includes combinations and / or several thereof) of a computing system (e.g., the processing system 102 of Fig. 1 and Fig. 2, the processing system 1000 of Fig. 10 and / or the like, which includes combinations and / or several thereof) are executed, causing the processor to perform the processes described herein.

[0079] It is understood that one or more of the embodiments described here can be implemented in conjunction with any other type of computing environment, whether known now or developed later. For example, Fig.Figure 10 shows a block diagram of a processing system 1000 for implementing the techniques described herein. According to one or more embodiments described herein, the processing system 1000 is an example of a cloud computing node in a cloud computing environment. In examples, the processing system 1000 comprises one or more central processing units (also referred to as "processors" or "processing equipment" or "processing devices") 1021a, 1021b, 1021c, etc. (collectively or generically referred to as one or more processors 1021 and / or one or more processing devices 1021). In aspects of the present invention, each processor 1021 may include a reduced instruction set (RISC) computer microprocessor. Processors 1021 are coupled to a system memory 1022 and / or various other components via a system bus 1033.The system memory 1022 can contain one or more temporary and / or persistent storage devices, such as a read / write memory (RAM) 1023, a read-only memory (ROM) 1024, and / or the like, including combinations and / or multiples thereof. The system bus 1033 can contain a basic input / output system (BIOS) that controls certain basic functions of the processing system 1000.

[0080] Furthermore, an input / output adapter (I / O adapter) 1027 and a network adapter 1026 are shown, which are connected to a system bus 1033. The input / output adapter 1027 can be a small computer systems interface (SCSI) adapter, which communicates with a hard disk 1035 and / or a storage device 1036 or other similar component. The input / output adapter 1027, the hard disk 1035, and the storage device 1036 are collectively referred to here as mass storage 1034. The operating system 1040 for execution in the processing system 1000 can be stored in a mass storage device 1034. The network adapter 1026 connects a system bus 1033 to an external network 1038, which enables the processing system 1000 to communicate with other such systems.

[0081] A display device (e.g., a display monitor) 1039 is connected to the system bus 1033 by a display adapter 1032, which may include a graphics adapter to improve the performance of graphics-intensive applications and a video controller. In one aspect of the present invention, adapters 1026, 1027, and / or 1032 may be connected to one or more input / output buses that are connected to the system bus 1033 by means of an intermediate bus bridge (not shown). Suitable input / output buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols such as the Peripheral Component Connection (PCI). Additional input / output devices are shown as being connected to the system bus 1033 by means of a user interface adapter 1028 and a display adapter 1032.A keyboard 1029, a mouse 1030 and a loudspeaker 1031 can be connected to the system bus 1033 by means of a user interface adapter 1028, which may contain, for example, a super input / output chip that integrates several device adapters into a single integrated circuit.

[0082] In certain aspects of the present invention, a processing system 1000 includes a graphics processing unit (GPU) 1037. The graphics processing unit 1037 is a specialized electronic circuit designed to manipulate and modify memory to accelerate the generation of images in a frame buffer intended for output to a display device. In general, the graphics processing unit 1037 is very efficient in manipulating computer graphics and image processing and has a highly parallel structure, which makes it more effective than general-purpose CPUs for algorithms where the processing of large blocks of data is performed in parallel.

[0083] Thus, the processing system 1000, as configured here, includes processing power in the form of processors 1021, storage capacity comprising system memory 1022 and mass storage 1034, input means such as a keyboard 1025 and a mouse 1030, and output power comprising a loudspeaker 1031 and a display device 1039. In certain aspects of the present invention, a portion of the system memory 1022 and the mass storage 1034 jointly store the operating system 1040 in order to coordinate the functions of the various components shown in the processing system 1000.

Claims

[1] Computer-implemented method for coordinating between active downforce and active suspension controls for a vehicle (100), wherein the method comprises: Determining an optimal ride height for the vehicle (100) based on the current state of the vehicle; Determining a suspension actuator force to implement the optimal ride height for the vehicle (100); and Control by an active suspension system (106) of the vehicle (100) of an actuator (407) using the suspension actuator force to achieve the optimal ride height for the vehicle (100); characterized by , that the suspension actuator force is determined using a model predictive control machine (312) and the model predictive control machine (312) comprises a suspension prediction model generated by converting a non-linear neural network to a linear suspension model and combining the linear suspension model with a half-car model. [2] Computer-implemented method according to claim 1, wherein the optimal ride height comprises a front ride height of the vehicle (100) and a rear ride height of the vehicle (100). [3] Computer-implemented method according to claim 1, wherein the optimal ride height is determined using a ride height optimization machine (310). [4] Computer-implemented method according to claim 3, wherein the ride height optimization machine (310) comprises aerodynamic maps and a neural network. [5] Computer-implemented method according to claim 4, wherein the neural network is a flat fully connected neural network. [6] Computer-implemented method according to claim 4, wherein the neural network translates the aerodynamic characteristics into a non-linear state-space model. [7] Computer-implemented method according to claim 1, wherein the determination of the optimal ride height is carried out at least partially on the basis of a specific aerodynamic position of an adjustable aerodynamic surface of the vehicle (100) and a longitudinal speed of the vehicle (100). [8] Computer-implemented method according to claim 1, wherein the model predictive control machine (312) comprises an aerodynamic model of the vehicle (100) and a suspension model of a suspension system (106) of the vehicle (100). [9] Vehicle (100) comprising: an active downforce system (104) for controlling an adjustable aerodynamic surface of the vehicle (100); an active suspension system (106) for controlling an actuator (407), wherein the actuator (407) sets a ride height of the vehicle (100); and a processing system (1000) that is communicatively coupled to the active output force system (104) and the active suspension system (106), wherein the processing system (1000) includes: a memory (1022) containing computer-readable instructions, and a processing device (1021) for executing the computer-readable commands, wherein the computer-readable commands control the processing device (1021) to perform operations for coordinating between active downforce and active suspension control elements for the vehicle (100), wherein the operations include those of claim 1.

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