Coordination between active downforce and active suspension control to maximize grip

Abstract

Examples described herein provide a method for coordination between active downforce and active suspension control of a vehicle that includes determining an optimal terrain clearance of the vehicle based on a current condition of the vehicle. The method also includes determining a suspension actuator force to achieve an optimal terrain clearance of the vehicle. The method also includes controlling, by an active suspension system of the vehicle, an actuator using a suspension actuator force to achieve an optimal terrain clearance of the vehicle.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to vehicles, and in particular to coordination between active downforce and active suspension control for maximizing tire grip for a vehicle. BACKGROUND

[0002] Modern vehicles (e.g., automobiles, motorcycles, boats, or any other type of automobile) can be equipped with various systems for improving the handling, stability, and overall performance of the vehicle. For example, a vehicle can include an active downforce system that provides for dynamically adjusting aerodynamic elements / surfaces (e.g., gurney flaps, rear wings, and / or the like, including combinations and / or multiples thereof) of the vehicle to increase the downward force exerted on the vehicle to improve the tire grip of the vehicle. As another example, a vehicle can include an active suspension system to control the forces between the wheels of the vehicle and the body of the vehicle to provide ride comfort. SUMMARY

[0003] In one embodiment, a method for coordination between active downforce and active suspension control for a vehicle is provided. The method includes determining an optimal ride height for the vehicle based on current conditions of the vehicle. The method also includes determining a suspension actuator force to achieve the optimal ride height for the vehicle. The method further includes using the suspension actuator force by an active suspension system of the vehicle to control the actuator to achieve the optimal ride height for the vehicle.

[0004] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method can include the optimal ride height including a front ride height of the vehicle and a rear ride height of the vehicle.

[0005] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method can include determining the optimal ride height using a ride height optimizer engine.

[0006] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method can include the ride height optimizer engine including an aerodynamic map and a neural network.

[0007] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method can include the neural network being a shallow fully connected neural network.

[0008] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method can include the neural network converting the aerodynamic map into a non-linear state space model.

[0009] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method can include determining the optimal ride height based at least in part on a specific aerodynamic position of an adjustable aerodynamic surface of the vehicle and a longitudinal velocity of the vehicle.

[0010] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method can include determining the suspension actuator force using a model predictive control engine.

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

[0012] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method can include the model predictive control engine including a suspension prediction model generated by converting a nonlinear neural network into a linear suspension model and combining the linear suspension model with a half-car model.

[0013] In another embodiment, a vehicle is provided. The vehicle includes an active downforce system for controlling an adjustable aerodynamic surface of the vehicle. The vehicle also includes an active suspension system for controlling an actuator that adjusts a ride height of the vehicle. The vehicle further includes a processing system communicatively coupled to the active downforce system and the active suspension system. The processing system includes a memory having computer-readable instructions, a processing device for executing the computer-readable instructions, the computer-readable instructions controlling the processing device to perform operations for coordination between active downforce and active suspension control for the vehicle. The operations include determining an optimal ride height for the vehicle based on current conditions of the vehicle. The operations also include determining a suspension actuator force to achieve the optimal ride height of the vehicle. The operations further include causing the active suspension system of the vehicle to control the actuator using the suspension actuator force to achieve the optimal 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 can include the optimal ride height including a front ride height of the vehicle and a rear ride height of the vehicle.

[0015] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle can include determining the optimal ride height using a ride height optimizer engine.

[0016] In addition to one or more of the features described herein, or as an alternative, additional embodiments of a vehicle can include the ride height optimizer engine including a neural network that converts an aerodynamic map to a nonlinear state space model.

[0017] In addition to one or more of the features described herein, or as an alternative, additional embodiments of a vehicle can include determining the optimal ride height based at least in part on a specific aerodynamic position of an adjustable aerodynamic surface of the vehicle and a longitudinal velocity of the vehicle.

[0018] In another embodiment, a computer program product is provided. The computer program product includes a computer readable storage medium having program instructions embodied therewith, the program instructions executable by at least one processor to cause the at least one processor to perform operations. The operations include determining an optimal ride height of a vehicle based on current conditions of the vehicle. The operations also include determining a suspension actuator force to achieve the optimal ride height of the vehicle. The operations also include controlling, by an active suspension system of the vehicle, an actuator using the suspension actuator force to achieve the optimal ride height of the vehicle.

[0019] In addition to one or more of the features described herein, or as an alternative, additional embodiments of the computer program product can include the optimal ride height including a front ride height of the vehicle and a rear ride height of the vehicle.

[0020] In addition to one or more of the features described herein, or as an alternative, additional embodiments of the computer program product can include determining the optimal ride height using a ride height optimizer engine.

[0021] In addition to one or more of the features described herein, or as an alternative, additional embodiments of the computer program product can include the ride height optimizer engine including a neural network that converts an aerodynamic map to a nonlinear state space model.

[0022] In addition to one or more of the features described herein, or as an alternative, additional embodiments of the computer program product can include determining the optimal ride height based at least in part on a specific aerodynamic position of an adjustable aerodynamic surface of the vehicle and a longitudinal velocity of the vehicle.

[0023] The above features and advantages and other features and advantages of the present disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0024] Other features, aspects, and details are described below with reference to the drawings. The foregoing summary is not un

[0025] Figure 1 is an illustration of a vehicle having a processing system that provides coordination between an active roll system and an active suspension system in accordance with one or more embodiments;

[0026] Figure 2 is a block diagram of a processing system for coordination between an active roll system and an active suspension system in accordance with one or more embodiments; Figure 1

[0027] Figure 3 is a block diagram of an architecture for providing coordination between an active roll system and an active suspension system for a vehicle in accordance with one or more embodiments; Figure 1

[0028] Figure 4 is a block diagram of an architecture for providing coordination between an active roll system and an active suspension system for a vehicle in accordance with one or more embodiments; Figure 1

[0029] Figure 5 is a flow diagram of a method for coordination between active roll and active suspension control for a vehicle in accordance with one or more embodiments;

[0030] Figure 6 is a graph depicting forces on a vehicle of Figure 1

[0031] Figure 7 is an architecture in which a model predictive control engine generates recommended suspension forces for front and rear axles in accordance with one or more embodiments;

[0032] Figure 8 is an architecture for force manipulation on active suspension control in accordance with one or more embodiments;

[0033] Figure 9 is a flow diagram of a method for coordination between active roll and active suspension control for a vehicle in accordance with one or more embodiments; and

[0034] Figure 10 is a block diagram of a processing system for implementing one or more embodiments described herein. DETAILED DESCRIPTION

[0035] ​​​​The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application, or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features. As used herein, the term module refers to processing circuitry, which can include an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality.

[0036] In vehicles, various systems can be used to improve handling, stability, and overall performance of the vehicle. For example, a vehicle can include an active downforce system, an active suspension system, and / or the like, including combinations and / or multiples thereof. Generally, the active downforce system and the active suspension system operate independently and uncoordinated. The ride height of the vehicle, which is primarily controlled by the active suspension system, can affect the downforce on the vehicle. For example, as the front and / or rear ride height changes, the aerodynamic characteristics (e.g., angle of attack) of the vehicle also change. As another example, the maximum achievable downforce at each axle is limited by the front and rear ride heights. Consider the case where the downforce at the front axle depends on the front and rear ride heights, as most of the downforce is generated by the underbody of the vehicle. During a maneuver such as corner exit, the front end of the vehicle moves away from the ground due to load transfer. In this case, the front downforce is significantly reduced.

[0037] One or more embodiments described herein relate to coordination between active downforce and active suspension control to maximize tire grip of a vehicle. For example, one or more embodiments described herein cause the active suspension system to generate some force such that the ride height at the front and rear of the vehicle is adjusted to achieve maximum downforce for the current conditions of the vehicle.

[0038] According to one or more embodiments, a control framework is provided that optimizes the performance of a vehicle equipped with an active downforce system and an active suspension system (e.g., a fully active height control system, etc.). By using the active suspension system to adjust the ride height of the vehicle (which also adjusts the angle of attack), the optimal ride height 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 height at the front and rear axles of the vehicle and recommends suspension forces to achieve the ideal ride height. One or more embodiments take into account the effect of the suspension on tire capacity, thereby ensuring a full calculation of the recommended suspension forces. This integrated approach aims to improve the stability, handling, and overall performance of the vehicle.

[0039] According to one or more embodiments, during highly dynamic maneuvers, such as cornering exits, the changing ride height and angle of attack (ride height) of the vehicle can significantly affect the downforce generated by the aerodynamic elements. This reduction in aerodynamic downforce generation can impact the stability, performance, and consistency of the vehicle.

[0040] According to one or more embodiments, a vehicle equipped with multiple active systems, such as active suspension and active aerodynamics (e.g., active downforce), can face conflicts in their control algorithm objectives. Each system has its own priorities and objectives. For example, the active suspension system can aim to enhance ride comfort, while the active downforce system focuses on providing the necessary grip to maintain vehicle stability. Resolving these conflicts and ensuring coordinated operation of these systems is desirable for optimal vehicle performance.

[0041] One or more embodiments described herein provide a fully integrated solution by modifying existing active downforce and active suspension systems and their associated control algorithms. An independent coordination approach that coordinates between the active downforce and active suspension systems can avoid significant changes to any current control algorithms and, at the same time, maintain modularity of the software.

[0042] Figure 1 FIG. 1 is an illustration of a vehicle 100 having a processing system 102 that provides coordination between an active downforce system 104 and an active suspension system 106, in accordance with one or more embodiments. The vehicle 100 can be a car, truck, van, bus, motorcycle, boat, or any other type of automobile. According to an embodiment, the vehicle 100 includes an internal combustion engine that is fueled by gasoline, diesel, or the like. According to another embodiment, the vehicle 100 is a hybrid electric vehicle that is partially or fully powered by electricity. According to another embodiment, the vehicle 100 is an electric vehicle that is powered by electricity. According to one or more embodiments, the vehicle 100 is an autonomous or semi-autonomous vehicle. An autonomous vehicle is a vehicle that has the capability of driving itself. A semi-autonomous vehicle is a vehicle that has certain autonomous features (e.g., automatic parking, lane keeping, etc.) but lacks full autonomous control.

[0043] According to one or more embodiments, the vehicle 100 includes the processing system 102 described herein, which is shown in greater detail in Figure 2 The vehicle 100 also includes the active downforce system 104 and the active suspension system 106. The active downforce system 104 provides for dynamically adjusting the aerodynamic elements / surfaces of the vehicle 100 to increase the downward force exerted on the vehicle, thereby improving the tire grip of the vehicle. The active suspension system 106 provides for automatically adjusting the suspension settings of the vehicle 100 to maintain optimal contact between the tires of the vehicle and the road surface, thereby providing improved ride and handling characteristics.

[0044] Reference is now made to Figures 2-10 Further features of the processing system 102 are described.

[0045] In particular, Figure 2 is a block diagram of a processing system 102 for coordination between an active roll control system 104 and an active suspension system 106, in accordance with one or more embodiments. The processing system 102 includes a processing device 202, a memory 204, and a roll control and suspension coordination engine 210. It should be appreciated that the processing system 102 can be any device suitable for providing coordination between active roll control and active suspension control to maximize tire grip of a vehicle. For example, the processing system 102 can be a device implemented in or otherwise associated with the vehicle 100. As another example, the processing system 102 can be a smartphone, a tablet computer, a laptop computer, a desktop computer, a wearable computing device, etc., including combinations and / or multiples thereof.

[0046] The processing device 202 is any suitable processing circuitry for processing data and / or instructions. The processing device 202 is an example of one or more processing devices 1021, as described in greater detail herein. Figure 10

[0047] The memory 204 is any suitable device for storing data and / or instructions. The memory 204 is an example of one or more of system memory 1022, random access memory 1023, and / or read only memory 1024, as described in greater detail herein. Figure 10

[0048] The roll control and suspension coordination engine 210 coordinates between the active roll control system 104 and the active suspension system 106, as described in greater detail herein.

[0049] Other aspects and features of the roll control and suspension coordination engine 210 are described herein with reference to Figures 3 to 9 .

[0050] With regard to Figure 2 ​​The various components, modules, engines, etc. (e.g., the downforce and suspension coordination engine 210) described can be implemented as instructions stored on a computer-readable storage medium, implemented as hardware modules, implemented as special-purpose hardware (e.g., application-specific hardware, application-specific integrated circuits (ASICs), application-specific special processors (ASSPs), field-programmable gate arrays (FPGAs), implemented as embedded controllers, hardwired circuitry, etc.), or some combination or combination thereof. According to aspects of the present disclosure, the engines described herein can be a combination of hardware and programming. The programming can be processor-executable instructions stored on a tangible memory, and the hardware can comprise a processing device 202 to execute those instructions. Thus, system memory (e.g., memory 204) can store program instructions that, when executed by the processing device 202, implement the engines described herein. Other engines can also be utilized to include other features and functionality described in other examples herein.

[0051] Turning now to Figure 3 , according to one or more embodiments, an architecture 300 is provided for providing coordination between the active downforce system 104 and the active suspension system 106 for the vehicle 100. The architecture 300 provides for determining a desired ride height to achieve a maximized front downforce and / or rear downforce, and then provides for determining an optimal suspension actuator force to achieve the desired ride height.

[0052] In Figure 3In overview, the vehicle 100 generates an estimate of the suspension actuator forces and transmits the estimate to the active downforce system 104 and the active suspension system 106, as shown. The active downforce system 104 generates position commands for positioning the aerodynamic elements / surfaces of the vehicle 100, which are sent to the vehicle. The position commands are also sent to the downforce and suspension coordination engine 210, which includes a ride height optimizer engine 310 and a model predictive control engine 312. The ride height optimizer engine 310 uses an actuator model to generate a desired ride height (e.g., front ride height and / or rear ride height), which is sent to the model predictive control engine 312. The model predictive control engine 312 uses a half-car model plus 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 includes an aerograph (e.g., a lookup table) obtained through wind tunnel testing. The lookup table model cannot be used for optimization or model predictive control (MPC) prediction models. Thus, the lookup table model is converted into a neural network with a mathematical representation. The non-linear mathematical representation is used directly for optimization, linearized, and combined with the half-car model in the MPC prediction model. It should be understood that the commands / signals described as being sent to the vehicle 100 by the active downforce system 104 and / or the active suspension system 106 are commands / signals to other systems, controllers, devices, components, and / or the like of the vehicle 100, including combinations and / or multiples thereof.

[0053] According to one or more embodiments, the model predictive control engine 312 can successfully track the target pitch and heave of the vehicle 100 due to the optimal ride height. The active downforce and suspension coordination provides an overall increase in tire grip force. For example, as shown in the following table of example results, the tire grip force with coordination exceeds the tire grip force without coordination for both the front and the rear:

[0054]

[0055] It is apparent that the active downforce and suspension coordination provides an overall increase in tire grip force and downforce. For example, in one test, the front of the vehicle 100 experienced an increase of approximately 17% in increased grip force compared to no downforce between the active downforce and the active suspension, while the rear of the vehicle 100 experienced an increase of approximately 5% in increased grip force compared to no downforce between the active downforce and the active suspension.

[0056] Figure 4 A block diagram of an architecture 400 for providing coordination between an active downforce system 104 and an active suspension system 106 for a vehicle 100 is depicted in accordance with one or more embodiments. The architecture 400 showsFigure 3 Additional features and functionality of the architecture 300.

[0057] As Figure 3 illustrated, Figure 4 The architecture 400 includes 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.

[0058] As Figure 4 illustrated, the active downforce system 104 controls one or more aerodynamic elements / surfaces 405 that can 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) that can be connected to and / or integrated into the vehicle 100.

[0059] Also as Figure 3 illustrated, Figure 4 The architecture includes a downforce and suspension coordination engine 210 that includes a ride height optimizer engine 310 and a model predictive control engine 312. The active downforce system 104 feeds current position requests into the ride height optimizer engine 310, which determines an optimal ride height. The optimal ride height is the ride height of the vehicle 100 that results in achieving a desired downforce for the vehicle 100. The optimal ride height is fed into the model predictive control engine 312, which generates suspension actuator force (also referred to as active suspension force) requests that are fed into the active suspension system 106.

[0060] The ride height optimizer engine 310 uses an aerodynamic map (“aero map”) in the form of a neural network to determine the optimal ride height. The aero map is developed during wind tunnel testing of the vehicle 100. Typically, the aero map cannot be used directly for control, so a neural network is used to convert the aero map into a usable form (e.g., converting a lookup table into a neural network for predictive control). The neural network can be any suitable network architecture, such as a shallow fully connected neural network. The ride height optimizer engine 310 provides the vehicle 100 with maximized aerodynamic downforce while maintaining vehicle balance.

[0061] The model predictive control engine 312 determines or calculates the suspension actuator forces sent to the active suspension system 106. This ensures that the vehicle 100 is properly positioned (in terms of ride height / angle of attack) to achieve the desired downforce. The model predictive control engine 312 includes an aerodynamics model (“aero model”) for the vehicle 100 and a suspension model for the suspension system (e.g., one or more actuators 407) of the vehicle 100.

[0062] The downforce and suspension coordination engine 210 improves vehicle dynamics (e.g., tire capacity, yaw control, and lateral speed control) during cornering maneuvers, improves braking, improves vehicle pitch control to maintain vehicle balance, and reduces drag when downforce is not needed (e.g., in straight-line driving situations) to maximize vehicle speed. Reference is made to Figures 5 to 7 The features and functionality of the downforce and suspension coordination engine 210 (including the ride height optimizer engine 310 and the model predictive control engine 312) are described in more detail.

[0063] Figure 5 A flowchart of a method 500 for coordination between active downforce and active suspension control for a vehicle is depicted in accordance with 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 downforce and suspension coordination engine 210), the processing system 1000 of FIG. 10 (e.g., using the downforce and suspension coordination engine 210), etc., including combinations and / or multiples thereof. Reference is now made to the method 500 being described in relation to one or more of the processing system 102, Figure 10 Figures 1-4 The method 500 is described in relation to one or more of the processing system 102, the processing system 1000 of FIG. 10, etc., but is not limited thereto.

[0064] At block 502, the downforce and suspension coordination engine 210 converts the aerodynamics map into a neural network (or multiple neural networks). In particular, the aero map is converted into a neural network alternative, which enables replacing the look-up table with an analytical formula for the aero map. The aero map can determine the front and rear downforce from look-up tables developed during wind tunnel testing of the vehicle 100 as follows:

[0065] Front downforce = lookup (ride height, air speed, actuation position)

[0066] Rear downforce = lookup (ride height, air speed, actuation position).

[0067] To convert the aero map (which takes the form of an actuator model known as an aero map actuator model) into a linear state space model, a neural network is designed and trained to fit the aero map actuator model. The neural network model is then converted to a nonlinear state space model using the following equation:

[0068]

[0069] where​ is the front and rear downforce, is the speed and the ride height, is the actuator command, and is the time. The equation can be extended as follows:

[0070]

[0071] where W is a weight value and B is a bias value.

[0072] With continued reference to Figure 5 , at block 504, the downforce and suspension coordination engine 210 designs the ride height optimizer engine 310 to obtain an optimal ride height that maximizes the downforce given the specific aerodynamic position of the one or more adjustable aerodynamic elements / surfaces of the vehicle 100 and the longitudinal speed of the vehicle 100. In particular, at block 504, the optimal ride height is determined for a given command and a given vehicle speed to maximize the downforce generated at each wing of the vehicle 100 while ensuring that the ratio of the maximized downforce is within an acceptable range that does not affect (or at least maintains within) the vehicle balance.

[0073] Using the neural network mathematical correlation, the front downforce and the rear downforce can be written as a function of the front ride height and the rear ride height as follows:

[0074]

[0075] where is the forward downforce, is the rearward downforce, is the front ride height, is the rear ride height, is the front suspension command, is the rear suspension command, and is a sigmoid activation function. It should be appreciated that any suitable activation function can be used, such as sigmoid, rectified linear unit (ReLU), hyperbolic tangent (tanh), etc., including combinations thereof and / or multiple thereof.

[0076] According to one or more embodiments, an interior point nonlinear optimization technique is used to maximize the front downforce ( ) and the rear downforce ( ). For example, gradient-based methods are designed to solve problems where both the objective function and the constraint function are continuous and have continuous first derivatives. In this maximization problem, the constraints are to ensure that the maximized downforce (e.g., and ) the aerodynamic bias is not affected by exceeding a desired limit, which is represented as follows:

[0077]

[0078] where is a selectable constant, which can be a percentage of force, a quantity of force, and / or the like, including combinations and / or multiples thereof.

[0079] With continued reference to Figure 5 , at block 506, the downforce and suspension coordination engine 210 obtains a computationally efficient suspension prediction model suitable for model-based control. This includes converting a non-linear neural network to a linear suspension model, and then integrating the linear suspension model with a half-car model to develop a final suspension prediction model.

[0080] Reference is now made to Figure 6 The linear suspension model 600 is described in more detail, Figure 6 depicts forces on the vehicle 100 with respect to vehicle parts 602 and suspension parts 604, in accordance with one or more embodiments. In Figure 6 , and are front and rear axle downforce, respectively, and are pitch angle and its rate of change, respectively, and are heave and its rate of change, respectively, and are front and rear axle spring force, respectively, and and are recommended active suspension axle forces. The linear suspension model has two degrees of freedom: heave and pitch. The linear suspension model can be represented by the following equations:

[0081]

[0082]

[0083] where is a gravitational constant, is heave, and is the center of gravity of the vehicle 100 from the front and rear axles of the vehicle 100, respectively, is the total mass of the vehicle 100, is the momentum of the vehicle 100, and and are load transfer portions transferred to the wheels by anti-dive and anti-squat mechanisms. According to one or more embodiments, the axle spring force and The calculation is as follows:

[0084]

[0085]

[0086] Half-car state-space model Can be represented by the following equation:

[0087]

[0088] The term is the spatial state vector, denoted as:

[0089]

[0090] The term is the input, denoted as:

[0091]

[0092] Where the input includes suspension axle forces (e.g., and ) and axle deflections (e.g., and ).

[0093] In an extended form, the half-car state-space model is represented as follows:

[0094]

[0095] The nonlinear state-space model is converted to a linear model using the partial derivatives of the state X with respect to the following inputs, which can be computed analytically using automatic differentiation:

[0096]

[0097] The resulting converted nonlinear state-space model is represented as follows:

[0098]

[0099] The linear state-space representation of the neural network model is expressed as follows:

[0100]

[0101] Where:

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] Using the foregoing, the integrated state space suspension model (e.g., final suspension prediction model) is generated as follows. The state space model in continuous time domain is converted to discrete time domain as follows:

[0112]

[0113]

[0114]

[0115]

[0116] The discrete equation can be rewritten as follows:

[0117]

[0118] The new discrete state can be defined as:

[0119]

[0120] By incorporating the state space model of the downforce, the state space formulation can be rewritten for the new state as follows:

[0121]

[0122] With continued reference to Figure 5 at block 508, the downforce and suspension coordination engine 210 designs the model predictive control engine 312 to obtain the suspension axle forces required to track the optimal ride height. For example, Figure 7Architecture 700 is depicted, in which model predictive control engine 312 generates recommended suspension forces for the front and rear axles. To this end, vehicle 100 generates estimates (as described herein), which are fed into ground clearance optimizer engine 310. Ground clearance optimizer engine 310 generates a target ground clearance. Ground clearance to pitch and undulation conversion can be performed at block 702 to convert the target ground clearance into a converted target ground clearance. Model predictive control engine 312 receives the converted target ground clearance and uses them to generate suspension forces that enable active suspension system 106 of vehicle 100 to control the suspension (e.g., one or more actuators 407) to achieve an optimal ground clearance for vehicle 100.

[0123] Continue to refer to Figure 5 It may also include additional processes, and it should be understood that... Figure 5 The processes depicted are illustrative, and other processes may be added, or existing processes may be removed, modified, or rearranged without departing from the scope of this disclosure. It should also be understood that... Figure 5 The process described herein can be implemented as program instructions stored on a non-transitory computer-readable storage medium, when executed by a computing system (e.g., Figure 1 and Figure 2 Processing system 102 Figure 10 Processing systems 1000, etc., including combinations and / or multiple processors (e.g., Figure 2 Processing equipment 202 Figure 10 When a processor 1021, etc., including combinations and / or multiple thereof, is executed, the processor performs the process described herein.

[0124] Now go to Figure 8 This diagram depicts a block diagram of an architecture 800 for force manipulation in active suspension control according to one or more embodiments. In this example, the operator of vehicle 100 can select a drive mode (e.g., Sport mode, Comfort / Tour mode, Eco mode, etc., including combinations and / or multiples thereof) for operating vehicle 100. It should be understood that the various drive modes have different settings regarding downforce and suspension control. For example, in Sport mode, the suspension is commanded to follow a recommended ground clearance to maximize downforce and tire grip, thereby sacrificing ride comfort. However, in Comfort / Tour mode, the suspension is commanded to achieve maximum ride comfort, and downforce is constrained by front and rear ground clearance, thereby sacrificing performance.

[0125] exist Figure 8In the example of FIG. 8, the driver command interpreter 802 receives the recommended active suspension axle force 801 from the model predictive control engine 312 as described herein. At block 804, the driver command interpreter 802 performs force arbitration based on the recommended active suspension axle force from the model predictive control engine 312 and based on the suspension force request from the suspension driver command interpreter 806.

[0126] The driver command interpreter 802 sends the driver mode from the suspension driver command interpreter 806 and the suspension control force from the force arbitration block 804 to the integrated suspension supervisory controller 810. The integrated suspension supervisory controller 810 sends the corner suspension force to one or more actuators 407 based on the driver mode and the suspension control force. The one or more actuators 407 can send the suspension actuator commands to the hardware controls 812 to achieve the desired suspension force (e.g., the corner suspension force from the integrated suspension supervisory controller 810).

[0127] Figure 9 A flow diagram of a method 900 for coordination between active dive force and active suspension control for a vehicle is depicted in accordance with 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 dive force and suspension coordination engine 210), the processing system 1000 of FIG. 10 (e.g., using the dive force and suspension coordination engine 210), etc., including combinations and / or multiples thereof. The method 900 is now described with reference to one or more of the processing systems 102, 1000, etc., but is not limited thereto. Figure 10 Figures 1-4 The method 900 is now described with reference to one or more of the processing systems 102, 1000, etc., but is not limited thereto.

[0128] At block 902, the dive force and suspension coordination engine 210 determines an optimal ride height for the vehicle 100 using the ride height optimizer engine 310 based on current conditions (e.g., vehicle measurements and / or estimates) of the vehicle as described herein. At block 904, the dive force and suspension coordination engine 210 determines a suspension actuator force to achieve the optimal ride height as described herein using the model predictive control engine 312. At block 906, the active suspension system 106 of the vehicle 100 uses the suspension actuator force to control the actuators (e.g., one or more actuators 407) to achieve the optimal ride height for the vehicle 100.

[0129] Additional processes can also be included, and it should be understood that Figure 9 The processes depicted in FIG. 10 represent illustrations, and that other processes can be added or existing processes can be modified or rearranged without departing from the scope of the present disclosure. It should be understood that Figure 9 The processes depicted in FIG. 10 can be implemented as program instructions stored on a non-transitory computer-readable storage medium, which when executed by a computing system (e.g., the processing system 102 of FIG. 1, the processing system 1000 of FIG. 10, etc.) cause the computing system to perform the processes described herein. Figure 1 and Figure 2 The processing system 102 of FIG. 1, Figure 10 ​processors (e.g., of the processing system 1000, etc., including combinations and / or multiples thereof) to execute instructions and process data such as captured images and / or video. Figure 2 the processing device 202, Figure 10 the processor 1021, etc., including combinations and / or multiples thereof) to execute instructions and process data such as captured images and / or video.

[0130] It should be appreciated that one or more embodiments described herein can be implemented in the context of any other type of computing environment now known or later developed. For example, Figure 10 A block diagram of a processing system 1000 for implementing the techniques described herein is depicted. The processing system 1000 is an example of a cloud computing node in accordance with one or more embodiments described herein. In example, the processing system 1000 has one or more central processing units (also called “processors” or “processing resources” or “processing devices”) 1021a, 1021b, 1021c, etc. (collectively or generically referred to as processor(s) 1021 and / or processing device(s) 1021) in accordance with the one or more embodiments described herein. In aspects of the disclosure, each processor 1021 can include a reduced instruction set computer (RISC) microprocessor. Processors 1021 are coupled to system memory 1022 and / or various other components via a system bus 1033. System memory 1022 can comprise one or more of temporary and / or persistent memory devices such as random access memory (RAM) 1023, read only memory (ROM) 1024, etc., including combinations and / or multiples thereof. System bus 1033 can include a basic input / output system (BIOS), which controls certain basic functions of the processing system 1000.

[0131] Also depicted are input / output (I / O) adapter 1027 and network adapter 1026 coupled to the system bus 1033. I / O adapter 1027 can be a small computer system interface (SCSI) adapter that communicates with a hard disk 1035 and / or storage device 1036, or any other similar component. I / O adapter 1027, hard disk 1035, and storage device 1036 are collectively referred to herein as mass storage device(s) 1034. An operating system 1040 used to

[0132] A display (e.g., a display monitor) 1039 is connected to the system bus 1033 via a display adapter 1032, which can include a graphics adapter to improve the performance of graphics intensive applications and video controllers. In one aspect of the disclosure, the adapters 1026, 1027, and / or 1032 can be connected to one or more I / O buses, which are connected to the system bus 1033 via an intermediate bus bridge (not shown). Suitable I / O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters commonly include common protocols, such as Peripheral Component Interconnect (PCI). Additional input / output devices are shown as connected to the system bus 1033 via user interface adapter 1028 and display adapter 1032. A keyboard 1029, a mouse 1030, and a speaker 1031 can be interconnected to the system bus 1033 via user interface adapter 1028, which can include, for example, a super I / O chip integrated with a single integrated circuit for multiple device adapters.

[0133] In some aspects of the disclosure, the processing system 1000 includes a graphics processing unit (GPU) 1037. The graphics processing unit 1037 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. Generally, the graphics processing unit 1037 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more efficient than general-purpose CPUs for algorithms where the processing of large blocks of data is done in parallel.

[0134] Thus, as configured, the processing system 1000 includes processing capability in the form of processors 1021, storage capability including system memory 1022 and mass storage 1034, input capability in the form of input devices such as keyboard 1025 and mouse 1030, and output capability in the form of output devices such as speaker 1031 and display 1039. In some aspects of the disclosure, portions of the system memory 1022 and mass storage 1034 collectively store an operating system 1040 to coordinate the functions of various components shown in processing system 1000.

[0135] The terms "a" and "an" do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. The term "or" means "and / or" unless clearly indicated otherwise. The reference in this disclosure to "aspect" means that a particular element described in connection with the aspect is included in at least one aspect described herein and can or can not be present in other aspects. Additionally, it should be understood that described elements can be combined in any suitable manner in the various aspects.

[0136] When an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements can also be present. In contrast, when an element is referred to as being "directly on" another element, there are no intervening elements present.

[0137] Unless otherwise indicated herein, all test standards are the most recent standard in effect as of the filing date of this application, or, if priority is claimed, the filing date of the earliest priority application in which the test standard appears.

[0138] Unless otherwise defined, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0139] While the foregoing disclosure has been described in reference to exemplary embodiments, it will be understood by those skilled in the art that various changes can be made and equivalents can be substituted for elements thereof without departing from the scope of the disclosure. In addition, many modifications can be made to adapt a particular situation or material to the teachings of the disclosure without departing from the central scope thereof. Therefore, it is intended that the disclosure not be limited to the particular embodiments disclosed, but will include all embodiments falling within the scope of the disclosure.

Claims

1. A computer-implemented method for coordination between active ride height and active suspension control of a vehicle, the method comprising: determining an optimal ride height of the vehicle based on current conditions of the vehicle; determining a suspension actuator force to achieve the optimal ride height of the vehicle; and controlling, by an active suspension system of the vehicle, an actuator using the suspension actuator force to achieve the optimal ride height of the vehicle.

2. The computer-implemented method of claim 1, wherein the optimal ride height comprises a front ride height of the vehicle and a rear ride height of the vehicle.

3. The computer-implemented method of claim 1, wherein the optimal ride height is determined using a ride height optimizer engine.

4. The computer-implemented method of claim 3, wherein the ride height optimizer engine comprises an aerodynamic map and a neural network.

5. The computer-implemented method of claim 4, wherein the neural network is a shallow fully connected neural network.

6. The computer-implemented method of claim 4, wherein the neural network converts the aerodynamic map into a nonlinear state space model.

7. The computer-implemented method of claim 1, wherein determining the optimal ride height is based at least in part on a specific aerodynamic position of an adjustable aerodynamic surface of the vehicle and a longitudinal velocity of the vehicle.

8. The computer-implemented method of claim 1, wherein the suspension actuator force is determined using a model predictive control engine, wherein the model predictive control engine comprises an aerodynamic model of the vehicle and a suspension model of a suspension system of the vehicle.

9. The computer-implemented method of claim 1, wherein the suspension actuator force is determined using a model predictive control engine, wherein the model predictive control engine comprises a suspension prediction model generated by converting a nonlinear neural network into a linear suspension model and combining the linear suspension model with a half-car model.

10. A vehicle comprising: an active ride height system to control an adjustable aerodynamic surface of the vehicle; an active suspension system to control an actuator that adjusts a ride height of the vehicle; and a processing system communicatively coupled to the active ride height system and the active suspension system, the processing system comprising: a memory comprising computer-readable instructions; and a processing device to execute the computer-readable instructions, the computer-readable instructions controlling the processing device to perform operations for coordination between active ride height and active suspension control of the vehicle, the operations comprising: determining an optimal ride height of the vehicle based on current conditions of the vehicle; determining a suspension actuator force to achieve the optimal ride height of the vehicle; and controlling, by an active suspension system of the vehicle, an actuator using the suspension actuator force to achieve the optimal ride height of the vehicle. ​ ​

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