Active Downforce Control for Accurate Normal Force Tracking
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Solution Overview
Problem
Existing methods for active downforce control in vehicles lack accurate normal force tracking and reliable downforce estimation, leading to inefficiencies in aerodynamic actuator control.
Innovation Solution
A method using a combined state space model that integrates a half-car state space model and an actuator state space model, developed with a neural network, to determine and control the positions of aerodynamic actuators based on vehicle inputs, eliminating the need for direct downforce calculation and estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional downforce estimation methods are used, then the system is simpler to implement, but the measurement precision of normal force is insufficient
Solution Approach 1:
The system segments the downforce estimation problem into two distinct state space models: a half-car model for vehicle dynamics and an actuator model for aerodynamic component behavior. This segmentation allows each model to specialize in specific aspects, improving overall measurement precision while keeping individual model complexities manageable.
Solution Approach 2:
The patent merges the half-car state space model and the actuator state space model into a unified combined state space model. This integration combines the strengths of both models, enabling accurate normal force tracking by simultaneously considering vehicle dynamics and actuator characteristics, thus resolving the contradiction between precision and complexity.
2Reliability
If direct downforce calculation methods are used, then the computational process is simpler, but the reliability of downforce estimation is poor
Solution Approach 1:
The combined state space model implements a feedback mechanism where the model continuously predicts normal force based on current system states, and this prediction is fed back into the control system. This feedback loop enhances the reliability of downforce estimation by continuously refining the estimates based on actual system behavior, while the model-based approach provides a systematic framework that manages the increased complexity.
3Measurement precision
If advanced neural network models are integrated, then the prediction accuracy improves, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary approach by developing a neural network model that acts as a bridge between the half-car model and the actuator model. This neural network intermediary processes complex nonlinear relationships and provides smooth transitions between the two state space models, improving prediction accuracy while managing the integration complexity through a dedicated intermediate computational layer.
Data Source
AI summary
A method for downforce control includes receiving vehicle inputs. The method includes determining a first normal-force request at the front axle and a second normal-force request at the rear axle using the purality of vehicle inputs and a prediction model. The prediction model is a combined state space model that integrates a half-car state space model and an actuator state space model, the half-car state space model is developed using a half-car model, and the actuator state space model is developed using a neural network model. The method further includes determining a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the first normal-force request and the second normal-force request, respectively.