# US 12,759,365 B2 · Coordination between active downforce and active suspension controls for maximized tire grip for a vehicle > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,759,365 B2 - Document ID: US12759365B2 - Status: Granted - Kind: B2 - Application number: 18/776,889 - Filed: 2024-07-18 - Published: 2026-01-22 - Granted: 2026-10-13 - Earliest claimed: 2024-07-18 - Assignees: GM Global Technology Operations LLC - Applicants: GM Global Technology Operations LLC - Inventors: Mohammad Pournazeri; Mustafa Hakan Turhan; Arash Hashemi; Naser Mehrabi; Juchirl Park; SeyedAlireza Kasaiezadeh Mahabadi - Web page: https://patentgrep.com/patent/US12759365B2 ## CPC - B60G 17/0195: characterised by the regulation being combined with other vehicle control systems {(conjoint control of vehicle sub-units including control of suspension systems B60W10/22)} - B60G 17/016: characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input {(B60G17/017 takes precedence)} - B60G 2400/252: vertical - B60G 2500/30: Height or ground clearance - B60G: VEHICLE SUSPENSION ARRANGEMENTS (air-cushion vehicles B60V; {cycle suspensions B62K25/00}) ## Abstract Examples described herein provide a method for coordination between active downforce and active suspension controls for a vehicle that includes determining an optimal ride height for the vehicle based on current conditions of the vehicle. The method further includes determining a suspension actuator force to implement the optimal ride height for the vehicle. The method further includes controlling, by an active suspension system of the vehicle, an actuator using the suspension actuator force to achieve the optimal ride height for the vehicle. ## Claims ### Claim 1 A computer-implemented method for coordination between active downforce and active suspension controls for a vehicle, the method comprising: determining an optimal ride height for the vehicle based on current conditions of the vehicle, 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; determining a suspension actuator force to implement the optimal ride height for the vehicle, wherein the suspension actuator force is determined using a model predictive control engine; and controlling, by an active suspension system of the vehicle, an actuator using the suspension actuator force to achieve the optimal ride height for the vehicle. ### Claim 2 (depends on 1) 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. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the optimal ride height is determined using a ride height optimizer engine. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the ride height optimizer engine comprises aerodynamic maps and a neural network. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein the neural network is a shallow fully connected neural network. ### Claim 6 (depends on 4) The computer-implemented method of claim 4, wherein the neural network converts the aerodynamic maps to a non-linear state space model. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the model predictive control engine comprises an aerodynamic model of the vehicle and a suspension model of a suspension system of the vehicle. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the model predictive control engine comprises a suspension prediction model generated by converting a nonlinear neural network to a linear suspension model and combining the linear suspension model with a half-car model. ### Claim 9 A vehicle comprising: an active downforce system for controlling an adjustable aerodynamic surface of the vehicle; an active suspension system for controlling an actuator, the actuator adjusting a ride height of the vehicle; and a processing system communicatively coupled to the active downforce system and the active suspension system, the processing system comprising: a memory comprising computer readable instructions; and 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 controls for the vehicle, the operations comprising: determining an optimal ride height for the vehicle based on current conditions of the vehicle, wherein determining the optimal ride height is based at least in part on a specific aerodynamic position of the adjustable aerodynamic surface of the vehicle and a longitudinal velocity of the vehicle; determining a suspension actuator force to implement the optimal ride height for the vehicle, wherein the suspension actuator force is determined using a model predictive control engine; and causing the active suspension system of the vehicle to control the actuator using the suspension actuator force to achieve the optimal ride height for the vehicle. ### Claim 10 (depends on 9) The vehicle of claim 9, wherein the optimal ride height comprises a front ride height of the vehicle and a rear ride height of the vehicle. ### Claim 11 (depends on 10) The vehicle of claim 10, wherein the optimal ride height is determined using a ride height optimizer engine. ### Claim 12 (depends on 11) The vehicle of claim 11, wherein the ride height optimizer engine comprises a neural network that converts aerodynamic maps to a non-linear state space model. ### Claim 13 (depends on 9) The vehicle of claim 9, wherein the model predictive control engine comprises an aerodynamic model of the vehicle and a suspension model of a suspension system of the vehicle. ### Claim 14 A computer program product comprising 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 comprising: determining an optimal ride height for a vehicle based on current conditions of the vehicle, 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; determining a suspension actuator force to implement the optimal ride height for the vehicle, wherein the suspension actuator force is determined using a model predictive control engine; and controlling, by an active suspension system of the vehicle, an actuator using the suspension actuator force to achieve the optimal ride height for the vehicle. ### Claim 15 (depends on 14) The computer program product of claim 14, wherein the optimal ride height comprises a front ride height of the vehicle and a rear ride height of the vehicle. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the optimal ride height is determined using a ride height optimizer engine. ### Claim 17 (depends on 16) The computer program product of claim 16, wherein the ride height optimizer engine comprises a neural network that converts aerodynamic maps to a non-linear state space model. ### Claim 18 (depends on 12) The vehicle of claim 12, wherein the neural network is a shallow fully connected neural network. ### Claim 19 (depends on 12) The vehicle of claim 12, wherein the neural network converts the aerodynamic maps to a non-linear state space model. ### Claim 20 (depends on 17) The computer program product of claim 17, wherein the neural network is a shallow fully connected neural network, and wherein the neural network converts the aerodynamic maps to a non-linear state space model.