Architecture and model predictive control based method for managing chassis and driveline actuators

Abstract

A system for managing chassis and driveline actuators of a motorized transportation vehicle includes a control module executing program code portions that cause a sensor to obtain transportation vehicle state information, receive driver input and generate a desired dynamic output based on the driver input and the transportation vehicle state information, then estimate actuator actions based on the transportation vehicle state information, generate one or more control action constraints based on the transportation vehicle state information and the estimated actuator actions, generate a reference control action based on the transportation vehicle state information, the estimated actions of the one or more actuators, and the control action constraints, and integrate the transportation vehicle state information, the estimated actuator actions, the desired dynamic output, the reference control action, and the control action constraints to generate an optimal control action that falls within a predefined actuator capacity range and ensures driver control of the transportation vehicle.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to control systems for motorized transportation vehicles, and more specifically, to systems and methods for precisely modeling pneumatic tire characteristics. BACKGROUND

[0002] Static and dynamic motorized transportation vehicle control systems are increasingly used to manage a wide variety of static and dynamic motorized transportation vehicle performance characteristics. This is particularly true for challenging driving scenarios involving tire slip. In many challenging driving scenarios, control actions such as wheel and / or axle torque should be distributed in an optimal manner so that tire capacity is fully utilized in both longitudinal and lateral directions. Typical tire capacity management is performed within an on-board computing platform or controller and sensors, including an Inertial Measurement Unit (IMU) to measure how the motorized transportation vehicle is moving in space, known as vehicle dynamics. The IMU measures the acceleration of the motorized transportation vehicle in three axes: x (forward / backward), y (left / right), and z (up / down). The IMU also measures how fast the motorized transportation vehicle is turning around three axes, known as pitch rate (around y), yaw rate (around z), and roll rate (around x). The on-board computing platform or controller uses the measured data to estimate the forces acting on the motorized transportation vehicle.

[0003] While the current systems and methods of distributing tire forces achieve their intended purpose, there is a need for new and improved tire force distribution systems and methods that allow for enhanced transportation vehicle stability in complex driving scenarios and are capable of generating increased forces at the tire / road interface or ground contact patch while maintaining or reducing cost and complexity, reducing calibration efforts, and improving ease of use, while also providing increased redundancy and robustness. SUMMARY

[0004] According to aspects of the present disclosure, a system for managing chassis and driveline actuators of a motorized transportation vehicle includes one or more sensors disposed on the motorized transportation vehicle, the one or more sensors measuring real-time static and dynamic data about the motorized transportation vehicle. The system further includes one or more actuators disposed on the motorized transportation vehicle, the one or more actuators changing static and dynamic characteristics of the motorized transportation vehicle. The system further includes a control module having a processor, a memory, and an input / output (I / O) port, the processor executing program code portions stored in the memory. The program code portions include a first program code portion causing the one or more sensors to obtain transportation vehicle state information, a second program code portion receiving driver inputs and generating a desired dynamic output based on the driver inputs and the transportation vehicle state information, and a third program code portion estimating actions of the one or more actuators based on the transportation vehicle state information. The program code portions further include a fourth program code portion generating one or more control action constraints based on the first program code portion and the third program code portion, a fifth program code portion generating a reference control action based on the transportation vehicle state information, the estimated actions of the one or more actuators, and the control action constraints, and a sixth program code portion integrating the transportation vehicle state information, the estimated actions of the one or more actuators, the desired dynamic output, the reference control action, and the control action constraints to generate an optimal control action. The optimal control action defines a modified driver desired dynamic output control signal to the one or more actuators of the motorized transportation vehicle, the signal falling within a range of predefined actuator capacities and maximizing driver control of the transportation vehicle in complex driving scenarios.

[0005] In another aspect of the present disclosure, the one or more sensors further include at least one of an inertial measurement unit (IMU) capable of measuring orientation, acceleration, and velocity in three dimensions. The one or more sensors further include a Semi Active Damping Suspension (SADS) sensor capable of measuring orientation, position, velocity, acceleration in both linear and rotational aspects, and a Global Positioning System (GPS) sensor capable of measuring a physical location of the motorized transportation vehicle. The one or more sensors further include a wheel speed sensor, a throttle position sensor, an accelerator position sensor, a steering position sensor, and a tire pressure monitoring sensor.

[0006] In yet another aspect of the present disclosure, the real-time static and dynamic data further includes lateral velocity, longitudinal velocity, yaw rate, wheel angular velocity, and longitudinal, lateral, and normal forces on each tire of the motorized transportation vehicle.

[0007] In another aspect of this disclosure, the second program code portion receives one or more of the following: a torque request from the driver; and a steering input from the driver. Based on measurements from one or more sensors and capacity estimates from one or more actuators, the torque request and steering input from the driver are converted into a desired dynamic output that approximates the vehicle dynamic characteristics indicated by the driver input.

[0008] In another aspect of this disclosure, the third program code portion generates a capacity estimate for one or more actuators based on vehicle status information and a predetermined actuator capacity range.

[0009] In another aspect of this disclosure, the fourth program code portion generates one or more control action constraints based on the vehicle state information and the capacity estimation of one or more actuators, such that the control action constraints limit the control signals to one or more actuators to possible control actions within the physical limits or capacities of the actuators and fall within the grip capacity of the tires of the motor vehicle.

[0010] In another aspect of this disclosure, the reference control actions generated by the fifth program code section further include one or more of the following: output commands to one or more Electronic Limited Slip Differential (eLSD) actuators, output commands to one or more Electronic All-Wheel-Drive (eAWD) actuators, and output commands to one or more active aerodynamic actuators. The output commands to one or more eLSD, eAWD, and active aerodynamic actuators are calculated to achieve linearized specific transient response characteristics of the motorized vehicle.

[0011] In another aspect of this disclosure, the optimal control action generated by the sixth program code portion also includes control signals to one or more actuators of the motor vehicle, which enable, at a given point in time, to increase the stability, maneuverability, mobility, and controllability of the motor vehicle from a first level to a second level greater than the first level based on driver input and vehicle state information.

[0012] In another aspect of this disclosure, the optimal control action is defined as a modified dynamic output control signal from one or more actuators of a motor vehicle that the driver desires to increase the performance of the motor vehicle from a first level to a second level greater than the first level, while reducing or substantially eliminating control interventions such as traction control system (TCS) inputs, stability control system inputs, and anti-lock braking system (ABS) inputs.

[0013] In another aspect of this disclosure, a method for managing chassis and drivetrain actuators of a motor vehicle includes processing driver input via a control module having a processor, memory, and input / output (I / O) ports, wherein the processor executes a portion of program code stored in the memory. The program code portion: obtains vehicle state information from one or more sensors mounted to the motor vehicle, the one or more sensors measuring real-time static and dynamic data about the vehicle; receives driver input and generates a desired dynamic output based on the driver input and the vehicle state information; and estimates the actions of one or more actuators mounted to the motor vehicle based on the vehicle state information, the one or more actuators altering the static and dynamic characteristics of the motor vehicle. The program code portion also generates one or more control action constraints based on the vehicle state information and the estimated actions of the one or more actuators mounted to the motor vehicle; generates a reference control action based on the vehicle state information, the estimated actions of the one or more actuators, and the control action constraints; and integrates the vehicle state information, the estimated actions of the one or more actuators, the desired dynamic output, the reference control action, and the control action constraints to generate an optimal control action. The optimal control action is defined as a modified dynamic output control signal that the driver expects to control the vehicle after modifying one or more actuators of the motor vehicle. This signal falls within the range of predefined actuator capacities and maximizes the driver's control over the vehicle in complex driving scenarios.

[0014] In another aspect of this disclosure, obtaining vehicle state information also includes measuring the orientation of the motor vehicle using an inertial measurement unit (IMU) capable of measuring orientation, acceleration, and velocity in three or more degrees of freedom; measuring the orientation of the motor vehicle's suspension components using a semi-active damped suspension (SADS) sensor capable of measuring orientation, position, velocity, and acceleration in both linear and rotational aspects; and measuring the physical position of the motor vehicle using a global positioning system (GPS) sensor. Obtaining vehicle state information also includes measuring the wheel speed of the motor vehicle using a wheel speed sensor, measuring the throttle position of the motor vehicle using a throttle position sensor, measuring the accelerator pedal position using an accelerator position sensor, measuring steering movement using a steering position sensor, and measuring tire information using a tire pressure monitoring sensor.

[0015] In another aspect of this disclosure, obtaining vehicle status information also includes measuring the lateral velocity of the motor vehicle, measuring the longitudinal velocity of the motor vehicle, measuring the yaw rate of the motor vehicle, measuring the wheel angular velocity of the wheels of the motor vehicle, and measuring the longitudinal force, lateral force and normal force on each tire of the motor vehicle.

[0016] In another aspect of this disclosure, receiving driver input and generating a desired dynamic output based on the driver input and vehicle state information further includes receiving a torque request from the driver, receiving steering input from the driver; and converting the torque request from the driver and the steering input into a desired dynamic output based on measurements from one or more sensors and capacity estimates from one or more actuators, the desired dynamic output approximating the vehicle dynamic characteristics indicated by the driver input.

[0017] In another aspect of this disclosure, the method for managing chassis and drivetrain actuators of a motor vehicle further includes estimating the capacity of one or more actuators based on vehicle status information and a predetermined actuator capacity range.

[0018] In another aspect of this disclosure, the method for managing chassis and drivetrain actuators of a motor vehicle further includes generating one or more control action constraints based on vehicle state information and capacity estimates of one or more actuators, such that the control action constraints limit control signals to one or more actuators to control signals possible within the physical limitations or capacities of the actuators, and such that the control signals fall within the grip capacity of the tires of the motor vehicle.

[0019] In another aspect of this disclosure, generating reference control actions further includes generating output commands to one or more electronic limited-slip differential (eLSD) actuators, generating output commands to one or more electronic all-wheel drive (eAWD) actuators, and generating output commands to one or more active aerodynamic actuators. The output commands to one or more eLSD, eAWD, and active aerodynamic actuators are calculated to achieve linearized specific transient response characteristics of the motor vehicle.

[0020] In another aspect of this disclosure, the method for managing the chassis and drivetrain actuators of a motor vehicle further includes generating optimal control actions, the generated optimal control actions further including generating control signals to one or more actuators of the motor vehicle, the control signals enabling, at a given point in time, to increase the stability, maneuverability, mobility and controllability of the motor vehicle from a first level to a second level greater than the first level in response to driver input and vehicle state information.

[0021] In another aspect of this disclosure, the method for managing the chassis and drivetrain actuators of a motor vehicle further includes generating optimal control actions, and generating modified driver-desired dynamic output control signals to one or more actuators of the motor vehicle, the signals increasing the performance of the motor vehicle from a first level to a second level greater than the first level, and reducing or substantially eliminating control intervention from control systems, including: traction control system (TCS) inputs, stability control system inputs, and anti-lock braking system (ABS) inputs.

[0022] In another aspect of this disclosure, a method for managing chassis and drivetrain actuators of a motor vehicle includes processing driver input via a control module having a processor, memory, and input / output (I / O) ports, wherein the processor executes a portion of program code stored in the memory. The program code portion obtains static and dynamic vehicle state information from one or more sensors mounted to the motor vehicle. The one or more sensors measure the orientation of the motor vehicle using an inertial measurement unit (IMU) capable of measuring orientation, acceleration, and velocity in three or more degrees of freedom, and measure the orientation of suspension components of the motor vehicle using a semi-active damped suspension (SADS) sensor capable of measuring orientation, position, velocity, and acceleration in both linear and rotational aspects. The program code portion uses a Global Positioning System (GPS) sensor to measure the physical position of the motor vehicle and uses wheel speed sensors to measure wheel speeds. The program code portion also uses a throttle position sensor to measure the throttle position of the motor vehicle, an accelerator pedal position sensor to measure the accelerator position, a steering position sensor to measure steering movement, and a tire pressure monitoring sensor to measure tire information. The program code section also receives driver input, including torque requests and steering inputs from the driver. Based on measurements from one or more sensors and capacity estimates of one or more actuators, the program code section translates the torque requests and steering inputs from the driver into a desired dynamic output that approximates the vehicle's dynamic characteristics as indicated by the driver input. The program code section also estimates the actions of one or more actuators fitted to the motor vehicle based on vehicle state information, and estimates the capacity of one or more actuators based on the vehicle state information and a predetermined actuator capacity range, which alter the static and dynamic characteristics of the motor vehicle. The program code section also generates one or more control action constraints based on the vehicle state information and the capacity estimates of one or more actuators, such that the control action constraints limit the control signals to one or more actuators to control signals possible within the physical limitations or capacities of the actuators, and ensure that the control signals fall within the grip capacity of the motor vehicle's tires. The program code also generates one or more control action constraints based on vehicle state information and capacity estimates of one or more actuators. These constraints limit the control signals to one or more actuators to possible control signals within the physical limitations or capacities of the actuators, and ensure that the control signals fall within the grip capacity of the vehicle's tires. The program code also generates reference control actions, including: generating output commands to one or more electronic limited-slip differential (eLSD) actuators, generating output commands to one or more electronic all-wheel drive (eAWD) actuators, and generating output commands to one or more active aerodynamic actuators.The program calculates output commands to one or more eLSD, eAWD, and active aerodynamic actuators to achieve linearized transient response characteristics of a motor vehicle. The code integrates vehicle state information, estimated actions of one or more actuators, desired dynamic output, reference control actions, and control action constraints, and generates optimal control actions based on these. The optimal control action is defined as a modified driver-desired dynamic output control signal for one or more actuators of the motor vehicle, falling within a predefined actuator capacity range and maximizing driver control of the vehicle in complex driving scenarios.

[0023] In another aspect of this disclosure, obtaining vehicle state information also includes measuring the lateral velocity of the motor vehicle, measuring the longitudinal velocity of the motor vehicle, measuring the yaw rate of the motor vehicle, measuring the wheel angular velocity of the wheels of the motor vehicle, and measuring the longitudinal force, lateral force, and normal force on each tire of the motor vehicle. Generating optimal control actions also includes generating control signals to one or more actuators of the motor vehicle, which, at a given point in time, increase the stability, handling, maneuverability, and controllability of the motor vehicle from a first level to a second level greater than the first level based on driver input and vehicle state information. Generating optimal control actions also includes generating modified driver-desired dynamic output control signals to one or more actuators of the motor vehicle, which increase the performance of the motor vehicle from the first level to a second level greater than the first level and reduce or substantially eliminate control intervention from control systems, including: traction control system (TCS) inputs, stability control system inputs, and anti-lock braking system (ABS) inputs.

[0024] Further areas of application will become apparent from the description provided herein. It should be understood that the descriptions and specific examples are intended for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0025] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure in any way.

[0026] Figure 1 This is a schematic diagram of a motor vehicle according to one aspect of the present disclosure, the motor vehicle having an architecture and method for a model predictive control methodology to manage chassis and drivetrain actuators;

[0027] Figure 2 It is for the purpose of one aspect of this disclosure Figure 1 A block diagram of a system and method for managing the chassis and drivetrain actuators of a motor vehicle based on model predictive control;

[0028] Figure 3 This is a perspective side view of the actual motion and control actions of a motor vehicle using a system and method for managing chassis and drivetrain actuators based on model predictive control, according to one aspect of this disclosure.

[0029] Figure 4 The use of one aspect of this disclosure for the architecture and methodology of model-based predictive control for managing Figure 1 A schematic diagram of the transmission system of a motor vehicle, including its chassis and drivetrain actuators; and

[0030] Figure 5 This is a flowchart depicting a method for managing chassis and drivetrain actuators based on model predictive control, according to one aspect of this disclosure. Detailed Implementation

[0031] The following description is merely exemplary in nature and is not intended to limit this disclosure, application, or use.

[0032] refer to Figure 1A system 10 based on model-predictive control (MPC) for managing the chassis and drivetrain actuators of a motor vehicle 12 is shown. System 10 includes the motor vehicle 12 and one or more controllers 14. The motor vehicle 12 is shown as a passenger car; however, it should be understood that, without departing from the scope or intent of this disclosure, the motor vehicle 12 may be a truck, bus, tractor-trailer, semi-trailer, sport-utility vehicle (SUV), all-terrain vehicle (ATV), truck, tricycle, motorcycle, aircraft, amphibious vehicle, or any other such vehicle in contact with the ground. The motor vehicle 12 includes one or more wheels with tires 18 and a drivetrain 20. The drivetrain may include various components, such as an internal combustion engine (ICE) 22 and / or an electric motor 24, and a transmission 26 capable of transmitting power generated by the ICE 22 and / or the electric motor 24 to wheels 27 and ultimately to tires 18 fixed to the wheels 27. In one example, the motor vehicle 12 may include an ICE 22 acting on the rear axle 28 of the motor vehicle 12 and one or more electric motors 24 acting on the front axle 30 of the motor vehicle 12. However, it should be understood that the motor vehicle 12 may use one or more ICE 22s and / or one or more electric motors 24 configured in other ways without departing from the scope or intent of this disclosure. For example, the motor vehicle 12 may have an ICE 22 acting only on the front axle 30, while one or more electric motors 24 acting only on the rear axle 28. In other examples, the ICE 22 may act on both the front axle 30 and the rear axle 28, and the electric motor may act on both the front axle 30 and the rear axle 28.

[0033] In several aspects, the drivetrain 20 includes one or more in-plane actuators 32. The in-plane actuators 32 may include an all-wheel drive (AWD) system (including an electronically controlled or electric AWD (eAWD) 34 system) and a limited-slip differential (LSD) (including an electronically controlled or electric LSD (eLSD) 36 system). The in-plane actuators 32, including the eAWD 34 system and the eLSD 36 system, can generate and / or modify the force generated by the tires 18 on the road contact surface 38 in the X and / or Y directions within a predetermined capacity. The eAWD 34 system can transmit torque from the front to the rear of the motor vehicle 12 and / or from one side of the motor vehicle 12 to the other. Similarly, the eLSD 36 system can transmit torque from one side of the motor vehicle 12 to the other. In some examples, the eAWD 34 and / or eLSD 36 can directly alter or manage the torque transmission from the ICE 22 and / or the electric motor 24, and / or the eAWD 34 and eLSD 36 can act on the braking system 40 to regulate the amount of torque transmitted to each of the tires 18 of the motor vehicle 12.

[0034] In other examples, the motor vehicle 12 may include means for altering the normal force on each of the tires 18 of the motor vehicle 12 via one or more out-of-plane actuators 42 (such as active aerodynamic actuators 44 and / or active suspension actuators 46). The active aerodynamic actuator 44 may actively or passively alter the aerodynamic profile of the motor vehicle via one or more active aerodynamic elements 48 (such as flaps, spoilers, fans or other suction devices, actively managed venturi tunnels, etc.). The active suspension actuator 46 may be, for example, an active damper 50. In several aspects, without departing from the scope or intent of this disclosure, the active damper 50 may be a magnetorheological damper or other such electro-, hydraulic, or pneumatically adjustable damper. For the sake of brevity in the following description, ICE 22, electric motor 24, eAWD 34, eLSD 36, braking system 40, aerodynamic control system, active aerodynamic element 48, active damper 46, etc., will be more broadly referred to as actuator 52.

[0035] The terms “front,” “rear,” “inside,” “inward,” “outside,” “outward,” “above,” and “below” are terms used relative to the orientation of the motor vehicle 12, as shown in the accompanying drawings of this application. Thus, “forward” refers to the direction toward the front of the motor vehicle 12, and “rearward” refers to the direction toward the rear of the motor vehicle 12. “Left” refers to the direction toward the left side of the motor vehicle 12 relative to the front of the motor vehicle 12. Similarly, “right” refers to the direction toward the right side of the motor vehicle 12 relative to the front of the motor vehicle 12. “Inside” and “inward” refer to the direction toward the interior of the motor vehicle 12, and “outside” and “outward” refer to the direction toward the exterior of the motor vehicle 12. “Below” refers to the direction toward the bottom of the motor vehicle 12, and “above” refers to the direction toward the top of the motor vehicle 12. Furthermore, the terms “top,” “top,” “bottom,” “side,” and “above” are terms used relative to the orientation of the actuator 52, and the motor vehicle 12 is shown more broadly in the accompanying drawings of this application. Therefore, although the orientation of actuator 52 or motor vehicle 12 may vary relative to a given purpose, these terms are still intended to apply to the orientation of the components of system 10 and motor vehicle 12 shown in the accompanying drawings.

[0036] Controller 14 is a non-generalized electronic control device having a pre-programmed digital computer or processor 54, a non-transitory computer-readable medium or memory 56 (for storing data, such as control logic, software applications, instructions, computer code, data, lookup tables, etc.), and input / output (I / O) ports 58. The computer-readable medium or memory 56 includes any type of media accessible by a computer, such as read-only memory (ROM), random access memory (RAM), hard disk drive, compact optical disc (CD), digital video optical disc (DVD), or any other type of memory. The "non-transitory" computer-readable memory 56 does not include wired, wireless, optical, or other communication links for transmitting transient electrical or other signals. The non-transitory computer-readable memory 56 includes media that can permanently store data and media that can store data and subsequently rewrite it, such as rewritable optical discs or erasable memory devices. Computer code includes any type of program code, including source code, object code, and executable code. Processor 54 is configured to execute code or instructions. The motor vehicle 12 may have a controller 14, including a dedicated Wi-Fi controller or engine control module, transmission control module, main control module, infotainment control module, etc. The I / O port 58 may be configured to communicate via wired communication, wireless communication via the Wi-Fi protocol under IEEE 802.11x, etc., without departing from the scope or intent of this disclosure.

[0037] Controller 14 also includes one or more applications 60. Application 60 is a software program configured to perform a specific function or set of functions. Application 60 may include one or more computer programs, software components, instruction sets, procedures, functions, objects, classes, instances, associated data, or portions thereof, adapted to be implemented in suitable computer-readable program code. Application 60 may be stored within memory 56, or in additional or separate memory. Examples of applications 60 include audio or video streaming services, games, browsers, social media, etc. In other examples, application 60 is used to manage main control system functions, suspension control system functions, or aerodynamic control system functions in the exemplary motor vehicle 12.

[0038] Now for reference Figure 2 And continue to refer to Figure 1 System 10 utilizes one or more applications 60 stored in memory 56 to manage the chassis and drivetrain actuators 52 of the motor vehicle 12. In several aspects, the applications 60 include computer control code portions that coordinate the actuators 52 to redistribute the forces of the tires 18 on the horizontal plane of the axles and / or wheels 27, and / or adjust the capacity of the tires 18 to allow the generation of increased forces at the tire 18 / road contact surface 38. The computer control code portions operate using a physics-based technique that models the functionality of each actuator 52 and the influence of the actuators 52 on the motion of the motor vehicle 12 through the dynamic characteristics of the body 62 and wheels 27 and through a combined tire 18 slip model. The combined tire 18 slip model calculates the normalized longitudinal and lateral forces formed at the tire 18 / road contact surface 38 due to tire 18 deformation and characteristics. Subsequently, based on the available vertical forces, the forces on the tire 18 in the longitudinal and lateral directions are calculated and correlated with the dynamic characteristics of the wheel 27 and the body 62 to understand the effect of the adjusted forces on the dynamic characteristics of the motor vehicle 12.

[0039] More specifically, at block 100, system 10 receives driver input 102 from driver control interpreter (DCI) 104. DCI 104 reads various driver inputs, such as steering input, throttle input, or brake input, and interprets the driver input before generating the desired dynamic characteristic signal 106 in the form of actuator outputs. In several respects, DCI 104 determines the boundaries of optimization and optimal coordination of actuator 52. In complex driving scenarios at or near the tire 18 adhesion limits, driver input may exceed predefined actuator 52 capacity, tire 18 capacity, etc. Therefore, system 10 utilizes constraint optimization to reallocate sufficient capacity in the X and Y directions in real time, and to reallocate the force of tire 18 within the functional and hardware constraints of each of the actuators 52. Out-of-plane actuator 42 can modify the normal force and alter the force generation in the X and / or Y directions at the tire 18 adhesion limits. The constraint optimization checks whether the current capacity of tire 18 is sufficient to cope with the functional and hardware limitations of actuator 52 to redistribute the force of tire 18 to achieve the desired movement of the motor vehicle 12 using eAWD 34 and / or eLSD 36, or if the driver of motor vehicle 12 requests increased traction or lateral grip, the capacity of tire 18 must be increased via active aerodynamic actuator 44. The constraint optimization is solved in real-time to optimally coordinate control commands from the different actuators 52 to maximize the performance capabilities of motor vehicle 12 and minimize control intervention. That is, the performance capabilities of motor vehicle 12 are increased from a first level to a second level greater than the first level, thereby reducing or substantially eliminating control interventions such as traction control system (TCS) inputs, stability control system inputs, anti-lock braking system (ABS) inputs, etc.

[0040] System 10 includes several control devices, one or more of which may be integrated into a single controller 14, or may be integrated into different, separate controllers 14 that communicate electronically with each other. Controller 14 includes a feedforward controller 108, which commands actuator 52 to implement certain transient response characteristics, provide optimal reference control action, and linearize the control response around the operating point. More specifically, feedforward controller 108 provides an active aerodynamic preemptive control signal, an eLSD 36 preemptive control signal, and an eAWD 34 preemptive control signal. The preemptive control signals adjust the output of actuator 52 to match the control signal estimate from sensor / estimation module 110.

[0041] The sensor / estimation module 110 provides information to the optimized feedforward controller 108 and feedback controller 112. In several aspects, the sensor / estimation module 110 generates estimates 116 for each of the various active chassis and dynamic characteristic systems assembled into the motor vehicle 12. In a particular example, the sensor / estimation module 110 includes an aerodynamic model estimate, an eLSD 36 model estimate, an eAWD 34 model estimate, and a vehicle dynamic characteristic estimate 116. Given current vehicle state information, the aerodynamic model estimate calculates possible downforce and maximum downforce. Similarly, the eLSD 36 model estimate includes a clutch torque estimate and a maximum clutch torque capacity estimate. The eAWD 34 model estimate produces a maximum electric motor 24 torque estimate. Finally, the vehicle dynamic characteristic estimate 116 includes the vehicle state, road surface information, tire 18 force calculations, and road angle.

[0042] The dynamic constraint calculator 114 applies the actual physical constraints, as well as the tire 18 and road grip constraints, to the signals from the sensor / estimation module 110.

[0043] Finally, the feedback controller 112 operates to achieve maximum feasibility, stability, maneuverability, mobility, and controllability of the vehicle 12 using in-plane and out-of-plane actuators 32, 42. The feedback controller 112 receives the desired vehicle 12 dynamics signal 106 from the DCI 104, the reference control action 120 from the feedforward controller 108, the control action constraint 122 from the dynamic constraint calculator 114, and measurements 124 from various sensors 64 assembled to the vehicle 12. The feedback controller 112 then integrates the desired dynamics signal 106, the reference control action 120, the control action constraint 122, and the measurements 124 into a model that takes into account the dynamics of the vehicle body 62 and wheels 27, as well as the in-plane and out-of-plane actuators 32, 42. The feedback controller 112 models the torque of the electric motor 24, the output of eLSD 36, the output of eAWD 34, the slip data of the combined tire 18, and the output of the active aerodynamic actuator 44, as well as the front-to-back and / or left-to-right interactions of various actuators 52 of the motor vehicle.

[0044] Furthermore, a Model Predictive Control (MPC) method is used in the feedback controller 112. The feedback controller 112 receives various state variables of the motor vehicle 12 from sensors 64 assembled to the motor vehicle 12. Sensors 64 can measure and record a wide variety of data from the motor vehicle 12. In several examples, sensors 64 may include an inertial measurement unit (IMU) 66, a suspension control unit such as a semi-active damped suspension (SADS) 68, a global positioning system (GPS) sensor 70, a wheel speed sensor 72, a throttle position sensor 74, an accelerator pedal position sensor 76, a brake pedal position sensor 78, a steering position sensor 80, a tire pressure monitoring sensor 82, an aerodynamic component position sensor 84, etc. The IMU 66 can measure motion, acceleration, etc., in several degrees of freedom. In a particular example, the IMU 66 can measure position, motion, acceleration, etc., in at least three degrees of freedom. Similarly, the SADS 68 sensor can be an IMU 66 capable of measuring in three or more degrees of freedom. In some examples, SADS 68 may be a suspension hub accelerometer, etc. Therefore, the state variables of the motor vehicle 12 may include any of a wide variety of data, including, but not limited to: wheel 27 speed data, SADS and IMU data including attitude, acceleration, etc.

[0045] The MPC control logic or algorithm in the feedback controller 112 generates state predictions based on initial state variables measured or estimated by sensors 64 on the motor vehicle 12. Additional estimations 116 can also be performed to model the effects of different factors on the state variables. In cases where the prediction model is nonlinear, the state variable measurements and / or estimations 116 of the motor vehicle 12, along with reference control actions 120, provide linearized models for specific operating parameters. To generate feasible control commands for the various actuators 52, the capacities of the actuators 52 and the tires 18 should be considered during calculation. That is, a given actuator 52 in the motor vehicle 12 may have a limited range of outputs, including, but not limited to, limitations on a limited range of motion, speed and / or acceleration, actuator torque, etc. Similarly, the capacity of the tires 18 may be limited by tread depth, tire wear, tire pressure, tire compound, tire temperature, the coefficient of friction of the road surface at the contact patch 38, etc. Therefore, the feedback control part of the MPC in the feedback controller 112 includes an offline control logic part and an online optimization control logic part. The offline control logic part includes the formulation of the state variables of the motor vehicle 12 and the design of the control target.

[0046] The predictive model control logic predicts the evolution of the state variable (X) and evaluates the relationship between the control action sequence (U) and the output (Y) within a limited prediction range. The predictive model control logic includes core dynamic characteristics of the motor vehicle, such as the body 62 dynamic characteristic model, which includes the longitudinal, lateral, yaw, bounce, and pitch characteristics of the motor vehicle 12. Similarly, the predictive model control logic includes a wheel 27 dynamic characteristic model, which includes angular velocity and relative velocity data, as well as the longitudinal slip and slip ratio characteristics of each wheel 27. The predictive model control logic also includes a tire 18 mechanics model, which contains a combined slip tire model for each tire 18 of the motor vehicle 12. Finally, the predictive model control logic includes an actuator 52 model, which includes the actuator 52 dynamic characteristics, constraints, and functionality.

[0047] Now for reference Figure 3 And continue to refer to Figure 1 and Figure 2 A diagram showing the dynamic characteristics of the main body 62 is provided. The dynamic characteristics of the planar main body 62 of the motor vehicle can be calculated using the following equation:

[0048]

[0049]

[0050]

[0051] Similarly, the dynamic characteristic model of wheel 27, based on angular velocity or slip ratio, is formulated as follows:

[0052]

[0053]

[0054] The combined slip tire model used in this paper can be based on a so-called "Magic Formula" (MF)-based tire model. However, it should be understood that any tire model capable of appropriately representing the nonlinear and combined slip behavior of the tire can be used alternatively, or in combination with the MF tire model, without departing from the scope or intent of this disclosure. The MF tire model is suitable for a wide variety of tire types, constructions, and operating conditions. In the MF tire model, each tire is characterized by multiple coefficients for each force related to tire performance. In some examples, these multiple coefficients relate to the contact patch, lateral and longitudinal forces, self-aligning torque, etc. These coefficients are used as the best fit between experimentally determined tire performance data and the MF model. These coefficients can then be used to generate equations showing how much force is generated for a specific vertical load on the tire, as well as camber angle, sideslip angle, etc. In one example, for longitudinal force F... x The tire model 18 based on MF can be presented as follows:

[0055] F x =(D x sin[C x tanh -1 {B x κ x -E x (B x κ x -tan -1 (B x κ x ))}]+S Vx )G xa

[0056] Under pure slip conditions, i.e., when the tires 18 of the motor vehicle 12 slip relative to the contact surface 38, the following formula applies:

[0057] κ x =κ+S Hx

[0058]

[0059] D x =μ x F z ξ1

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] Therefore, the combined slip tire 18 model can be represented as:

[0067]

[0068] α s =α F +S Hxα

[0069]

[0070] C xa =r Cx1

[0071] E xa =r Ex1 +R Ex2 df z

[0072] S Hxa =r Hx1

[0073] However, when combined slip in the longitudinal and lateral directions is not used, G xa =1. Similarly, when turning slip is not used in the above formula, ξ1 = 1.

[0074] lateral force F y Alternatively, the calculation can be performed using the MF-based tire 18 model based on the following simplified MF equations.

[0075] F y =G yκ F yp +SV yκ

[0076] Under pure slip conditions, the following modified MF-based tire 18 model equations can be used:

[0077] F yp =D y sin[C y tan -1 {B y α y -E y (B y α y -tan -1 (B y α y ))}+C γtan -1 {B γ γ-E γ (B γ γ-tan -1 (B y γ))}]+SV y

[0078] α y α F +S Hy

[0079] C y (p Cy1 λ Cy

[0080] C γ (p Cy2 λ Cγ

[0081] D y Zμ y F z

[0082]

[0083]

[0084]

[0085] K yγ0 (p. 100) Ky6 +p Kt7 df z )F z λK yγ (1+p py5 dpi)

[0086]

[0087] E y (p Ey1 +p Ey2 γ 2 +(p Ey3 +p Ey4γ )sgn(α y )}λ By

[0088] E γ (p Ey5 λE γ

[0089] S Hy (p. 100) Hy1 +p Hy2 df z )λHy

[0090] S Vy =F z (p Vy1 +p Vy2 df z )λ Vy λ μy ξ2

[0091] S Vyγ =F z (p Vy3 +p Vy4 df z )γλ Kyγ λ μy ξ2

[0092] Under combined slip conditions, the following modified MF-based tire 18 model equations can be used:

[0093] D Vyk =μ y F z (r Vy1 +r Vy2 df z +r Vy3 γ)cos(tan -1 (r Vy4 α F ))ξ2

[0094] S Vyk =D Vyκ sin(r Vy5 tan -1 (r Vy6 κ))λ Vyκ

[0095] Weighting function:

[0096]

[0097] κ s =κ+S Hyκ

[0098] B yκ =(r By1 +r By2 γ 2 )cos{tan -1 [r By2 (α F -r By3 )]}λ yκ

[0099] C yκ =r cy1 Eyκ =r Ey1 +r Ey2 df z S Hyκ =r Hy1 +r Hy2 df z

[0100] When not using combined slip: S VYκ =0, G yκ =1. When not using turning slip: ξ i =1, i=1∶4.

[0101] Now for reference Figure 4 And continue to refer to Figures 1 to 3 The torque dynamic characteristics of both the front axle 30 and the rear axle 28 can be characterized as follows:

[0102]

[0103]

[0104]

[0105] Where, τ f τ r These are the time delays for the front and rear axle actuators, respectively. In some respects, the torque distribution or dynamic solution of the front axle 30 and rear axle 28 based on the clutch torque, plus eLSD 36 or the torque vector from wheel 27 to wheel 28, can be modeled by the following equations:

[0106]

[0107]

[0108]

[0109] Where, τ f τ r τ c These are the front axle, the rear axle, and the clutch time delay.

[0110] Similarly, in a motor vehicle 12 equipped with an active aerodynamic actuator 44, the pressure dynamic characteristics under active aerodynamic characteristics can be modeled by the following formula:

[0111]

[0112]

[0113] in,

[0114]

[0115] Among them, F AntiDive and F AntiSquat The load is transferred to the wheels through anti-dive and anti-recoil mechanisms. and It refers to the front spring force and the rear spring force.

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] Finally, based on the above calculations, the state-space model of the motor vehicle 12 can be represented as follows:

[0122]

[0123] y(t) = g(x(t), u(t))

[0124] Similarly, the linear time-varying (LTV) model that approximates the above equations can be calculated as follows:

[0125]

[0126] And it can be presented as:

[0127]

[0128] y(t)=Cx(t)+Du(t)+V(t)

[0129] Among them, state variables and control actions can be defined as:

[0130]

[0131]

[0132] In one example, the first component of matrix A can be calculated as:

[0133]

[0134] Similar matrix calculations can be performed on each of the components of matrices A, B, C, and D. For example, It can be written as:

[0135]

[0136] The LTV part of the MPC algorithm is designed to solve the constrained optimization problem at each sampling time with the following objective and cost terms:

[0137]

[0138]

[0139] x0 = x(t)

[0140] u min ≤u t+k ≤u max k = 0, ..., N-1

[0141] y min ≤Cx t+k ≤y max k = 1, ..., N

[0142] Among them, y t+k，t and Let u represent the predicted and reference longitudinal velocities, respectively, where u is the predicted and reference longitudinal velocities. t+k，t and These represent the control actions for front, left rear, and right rear torque distribution, respectively. Additionally, Δu t+k，t and These represent the changes in control actions and their references, respectively. It is the torque distribution order of front, left rear, and right rear, and This is the driver's torque request. Using the above equations and variables, the output can be displayed as:

[0143]

[0144] in, The nominal operation point representing the status and command. Nominal status. It is calculated based on solving a nonlinear vehicle / wheel model, and It is calculated outside of the MPC, for example, by feedforward control within the feedforward controller 108.

[0145] Once the modeling of the motor vehicle 12, tires 18, and actuator 52 is complete, and the aforementioned cost function and constraints are determined, the controller 14 executes control logic that acts as a quadratic problem (QP) solver. This solver dynamically in real time solves the quadratic problem to optimize the control signal for the actuator 52 of the motor vehicle 12, thereby achieving maximum grip, stability, etc. Specifically, the aforementioned adaptive cost function is optimized online to find a feasible set of control actions for the actuator 52 that minimizes any potential errors. This is achieved by substituting the cost function... And recalculate:

[0146]

[0147] stGU≤W+Sx(t))

[0148] in, The optimal solution is H > 0, and C, Y, W, and S are matrices of appropriate dimensions. The MPC control algorithm is based on the following iterations: at time t, measure or estimate the current state x(t), and if the future input moves U*(x(t)), then solve the QP problem to obtain the optimal sequence.

[0149]

[0150] For this process, discard the remaining best moves and repeat the process again at time t+1.

[0151] Now for reference Figure 5 And continue to refer to Figures 1 to 4 This paper illustrates a method 200 for implementing MPC-based chassis and drivetrain actuator management in a motor vehicle 12. Method 200 begins at block 202. At block 204, system 10 receives driver control input 102 via I / O port 58 of controller 14, more specifically, I / O port 58 of DCI 104. DCI 104 reads and interprets the driver control input 102 to generate a desired dynamic characteristic signal 106 in the form of actuator output. At block 206, system 10 acquires measurements of motor vehicle 12 state information via multiple sensors 64 mounted to motor vehicle 12. More specifically, at block 206, controller 14 receives motor vehicle 12 state information reported by sensors such as IMU 66, SADS 68, and GPS 70. The motor vehicle state information from the sensors 64 can be obtained continuously, cyclically, or intermittently without departing from the scope or intent of this disclosure.

[0152] At block 208, sensor / estimation module 110 and feedback controller 112 receive and process measurements of the state information of the motor vehicle 12. More specifically, at block 208, sensor / estimation module 110 executes control logic that processes the measurements of the state information of the motor vehicle 12 to generate estimates 116 for each of the various active chassis and dynamic characteristic systems fitted to the motor vehicle 12. In a particular example, sensor / estimation module 110 includes aerodynamic characteristic model estimation, eLSD 36 model estimation, eAWD 34 model estimation, and vehicle dynamic characteristic estimation. Given current vehicle state information, the aerodynamic model estimation calculates possible downforce and maximum downforce. Similarly, the eLSD 36 model estimation includes clutch torque estimation and maximum clutch torque capacity estimation. The eAWD 34 model estimation produces a maximum electric motor 24 torque estimate. Finally, the vehicle dynamic characteristic estimation includes vehicle state, road surface information, tire 18 force calculation, and road angle.

[0153] At block 210, DCI 104 receives and processes estimates 116 from sensor / estimation module 110 for each of the active chassis and dynamic characteristics system. More specifically, at block 210, DCI 104 generates a desired dynamic characteristics signal 106. The desired dynamic characteristics signal 106 defines the desired output of actuator 52 fitted to motor vehicle 12 based on driver input and data from sensor 64 and sensor / estimation module 110.

[0154] At block 212, the dynamic constraint calculator 114 receives estimates 116 from the sensor / estimation module 110 for each of the active chassis and powertrain. The dynamic constraint calculator 114 applies the actual physical constraints, as well as tire 18 and road grip constraints, to the signals from the sensor / estimation module 110. At block 214, the dynamic constraint calculator 114 generates control action constraints 122.

[0155] At block 216, along with control action constraints 122 from the dynamic constraint calculator 114, the feedforward controller 108 receives estimates 116 from the sensor / estimation module 110 for each of the active chassis and dynamic characteristics system. The feedforward controller 108 processes the estimates 116 and control action constraints 122, and commands the actuator 52 to implement certain transient response characteristics, providing an optimal reference control action 120, and linearizing the control response around the operating point for each of the active chassis and powertrain systems fitted to the motor vehicle 12. In one example, the feedforward controller 108 provides an active aerodynamic preemptive control signal, an eLSD 36 preemptive control signal, and an eAWD 34 preemptive control signal. The preemptive control signals adjust the output of the actuator 52 to match the control signal estimates from the sensor / estimation module 110. At block 218, the feedforward controller 108 generates the reference control action 120.

[0156] At block 220, feedback controller 112 receives and processes control action constraints 122, reference control action 120, desired actuator output or desired dynamic characteristic signal 106, and estimates and sensor measurements from sensor / estimation module 110 and sensor 64. More specifically, at block 220, feedback controller 112 executes control logic that integrates desired dynamic characteristic signal 106, reference control action 120, control action constraints 122, and measurements 124 into a model that takes into account the dynamic characteristics of body 62 and wheels 27, as well as in-plane actuators 32 and out-of-plane actuators 42. Feedback controller 112 models motor 24 torque, eLSD 36 output, eAWD 34 output, combined tire 18 slip data, and active aerodynamic actuator 44 output, as well as the front-to-back and / or left-to-right interactions of various actuators 52 of the motor vehicle.

[0157] At block 222, feedback controller 112 generates optimal control action signal 126, which provides the driver of motor vehicle 12 with the desired performance characteristics of motor vehicle 12, including, but not limited to, maximum feasible performance, stability, handling, maneuverability, and controllability. At block 224, method 200 ends and returns to block 202, where method 200 continues to operate while motor vehicle 12 is in use.

[0158] The system 10 and method 200 of this disclosure offer several advantages. These advantages include providing the driver or operator of the motor vehicle 12 with the maximum feasible performance, stability, handling, maneuverability, and controllability of the motor vehicle 12 under a wide variety of conditions, including adverse weather conditions, tire 18 deformation, etc. Furthermore, the system 10 and method 200 can operate on the motor vehicle 12 in complex driving scenarios, including high-performance driving situations where the driver may attempt power coasting or drifting, and the system 10 and method 200 will operate to generate adequate force at the tire 18 / road interface or contact patch 38, while also providing maximum tire 18 / road interface or contact patch 38 adhesion in driving scenarios where maximum grip is desired. All these benefits can be obtained using the system 10 and method 200 described herein, while maintaining or reducing cost and complexity, reducing calibration work, and improving ease of use, while also providing increased redundancy and robustness.

[0159] The description of this disclosure is merely exemplary in nature, and variations thereof without departing from the spirit and scope of this disclosure are intended to be within its scope. Such variations should not be considered as departing from the spirit and scope of this disclosure.

Claims

1. A system for managing chassis and drivetrain actuators of a motor vehicle, the system comprising: One or more sensors are mounted on the motor vehicle, the one or more sensors measuring real-time static and dynamic data about the motor vehicle; One or more actuators are disposed on the motor vehicle, the one or more actuators altering the static and dynamic characteristics of the motor vehicle; a control module has a processor, a memory, and input / output (I / O) ports, the processor executing a portion of program code stored in the memory, the portion of program code including: The first program code portion enables the one or more sensors to obtain vehicle status information; The second program code section receives driver input and generates a desired dynamic output based on the driver input and the vehicle status information. The third program code section estimates the action of the one or more actuators based on the vehicle state information; The fourth program code section generates one or more control action constraints based on the first program code section and the third program code section; The fifth program code section generates a reference control action based on the vehicle state information, the estimated actions of the one or more actuators, and the control action constraints. The reference control action generated by the fifth program code section further includes one or more of the following: output commands to one or more electronic limited-slip differential (eLSD) actuators; output commands to one or more electronic all-wheel drive (eAWD) actuators; and output commands to one or more active aerodynamic actuators. The output commands to the one or more electronic limited-slip differentials, electronic all-wheel drive systems, and active aerodynamic actuators are calculated to achieve linearized specific transient response characteristics of the motor vehicle. The sixth program code section integrates the vehicle state information, the estimated actions of the one or more actuators, the desired dynamic output, the reference control action, and the control action constraints to generate the optimal control action. The optimal control action is defined as a modified driver-expected dynamic output control signal for one or more actuators of the motor vehicle, the modified driver-expected dynamic output control signal falling within a predefined actuator capacity range, and maximizing the driver's control over the vehicle in complex driving scenarios.

2. The system according to claim 1, wherein, The one or more sensors further include at least one of the following: An inertial measurement unit (IMU) can measure orientation, acceleration, and velocity in three dimensions. Semi-active damping suspension (SADS) sensors can measure orientation, position, velocity, and acceleration in both linear and rotational aspects; A Global Positioning System (GPS) sensor is used to measure the physical location of the motor vehicle. Wheel speed sensor; Throttle position sensor; Accelerator position sensor; Steering position sensor; and Tire pressure monitoring sensor.

3. The system according to claim 1, wherein, The real-time static and dynamic data also include: Lateral velocity; Longitudinal velocity; Yaw rate; Wheel angular velocity; and The longitudinal force, lateral force, and normal force on each tire of the motor vehicle.

4. The system according to claim 1, wherein, The second program code section receives one or more of the following: Torque request from the driver; and Steering input from the driver, Specifically, based on measurements from the one or more sensors and estimates of the capacity of the one or more actuators, torque requests and steering inputs from the driver are converted into desired dynamic outputs that approximate the vehicle dynamic characteristics indicated by the driver inputs.

5. The system according to claim 4, wherein, The third program code portion generates an estimate of the capacity of the one or more actuators based on the vehicle status information and a predetermined actuator capacity range.

6. The system according to claim 1, wherein, The fourth program code portion generates the one or more control action constraints based on the vehicle state information and the estimated capacity of the one or more actuators, such that the control action constraints limit the control signals to the one or more actuators to possible control actions within the physical limitations or capacity of the actuators and fall within the grip capacity of the tires of the motor vehicle.

7. The system according to claim 1, wherein, The optimal control actions generated by the sixth program code section also include: Control signals to one or more actuators of the motor vehicle, in fact, at a given point in time, based on driver input and vehicle status information, increase the stability, maneuverability, mobility, and controllability of the motor vehicle from a first level to a second level greater than the first level.

8. The system according to claim 1, wherein, The optimal control action is defined as a modified driver-desired dynamic output control signal for one or more actuators of the motor vehicle, which increases the performance of the motor vehicle from a first level to a second level greater than the first level, while reducing or substantially eliminating control intervention, including: traction control system (TCS) input, stability control system input, and anti-lock braking system (ABS) input.

9. A method for managing the chassis and drivetrain actuators of a motor vehicle, the method comprising: The driver input is processed by a control module having a processor, memory, and input / output (I / O) ports. The processor executes a portion of program code stored in the memory, which includes: Vehicle status information is obtained from one or more sensors assembled to the motor vehicle, the one or more sensors measuring real-time static and dynamic data about the vehicle; Receive the driver input and generate the desired dynamic output based on the driver input and the vehicle status information; Based on the vehicle state information, the action of one or more actuators assembled to the motor vehicle is estimated, and the one or more actuators change the static and dynamic characteristics of the motor vehicle. One or more control action constraints are generated based on the vehicle state information and the estimated actions of one or more actuators assembled to the motor vehicle. Based on the vehicle state information, the estimated actions of the one or more actuators, and the control action constraints, a reference control action is generated, which further includes one or more of the following: an output command to one or more electronic limited-slip differential (eLSD) actuators; an output command to one or more electronic all-wheel drive (eAWD) actuators; and output commands to one or more active aerodynamic actuators, wherein the output commands to the one or more electronic limited-slip differentials, electronic all-wheel drive and active aerodynamic actuators are calculated to achieve linearized specific transient response characteristics of the motor vehicle; The system integrates the vehicle state information, the estimated actions of one or more actuators, the desired dynamic output, the reference control action, and the control action constraints, and generates the optimal control action based on this. The optimal control action is defined as a modified driver-expected dynamic output control signal for one or more actuators of the motor vehicle, wherein the modified driver-expected dynamic output control signal falls within the range of a predefined actuator capacity. And to maximize the driver's control over the vehicle in complex driving scenarios.

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