Active Suspension Modal Modeling for Smoother Vehicle Control
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Solution Overview
Problem
Conventional active suspension systems rely on simplified models that are not based on actual vehicle parameters, leading to inaccurate predictions and overreactive responses, which can compromise ride comfort and increase component wear.
Innovation Solution
The use of a modal expansion model that represents the vehicle as a linear combination of natural modes, allowing for the determination of actual vehicle parameters such as mass, damping, and stiffness matrices from sensor data, enabling more accurate predictions and smoother actuator responses over a longer period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simplified models are used for active suspension control, then device complexity is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent transforms the simplified model into a data-driven model by changing the parameters from theoretical assumptions to actual measured vehicle parameters (mass, damping, stiffness) obtained from sensor data and system identification, thereby improving prediction accuracy without proportionally increasing complexity
Solution Approach 2:
The patent implements feedback by using sensor data from the vehicle to continuously update and refine the mathematical model through system identification, allowing the model to adapt to actual vehicle behavior and improve prediction accuracy over time
2Ease of operation
If simplified models are used for active suspension control, then ease of operation is improved, but reliability deteriorates
Solution Approach 1:
The patent uses feedback from sensor data to continuously refine the model parameters through system identification, ensuring the model reflects actual vehicle behavior and improves reliability while maintaining operational simplicity
Solution Approach 2:
The system performs self-service by automatically updating its own model parameters using its own sensor data and system identification algorithms, improving reliability without requiring external intervention or complex manual calibration
3Speed
If conventional models predict over short time windows, then response speed is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent changes the model parameters from fixed theoretical values to adaptive parameters derived from sensor data and system identification, enabling accurate predictions over extended time windows while maintaining fast response through efficient computational algorithms
4Speed
If overreactive responses are generated, then responsiveness is improved, but energy consumption increases
Solution Approach 1:
The patent changes the control parameters from overreactive responses based on simplified models to optimized responses based on accurate data-driven predictions, reducing unnecessary actuator movements and energy consumption while maintaining appropriate responsiveness to actual vehicle dynamics
Data Source
AI summary
An active suspension control system for a vehicle includes a mathematical model based on a modal expansion of the vehicle. Model parameters of the vehicle can be extracted from the modal expansion using sensor data generated on the vehicle, e.g., on demand and/or in real time. The model parameters and the modal expansion can be used to determine a vehicle state, predict future vehicle states, and control aspects of an active suspension system based on the predicted future vehicle states. The model parameters may also be used to update the mathematical model, e.g., to account for component wear over time, and/or to detect anomalies or defects in the active suspension system.