Adaptive Vehicle Suspension Damping for Road Roughness Control
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Solution Overview
Problem
Current vehicle suspension systems rely on passive components that operate within a limited range, affecting ride comfort and handling, and lack adaptive control mechanisms to respond to varying road surface conditions effectively.
Innovation Solution
An apparatus and method utilizing real-time road surface roughness estimation from vertical acceleration sensors and a machine learning-based prediction model to adjust the damping force of a variable damper, classifying road surface grades and allocating damping forces accordingly to enhance ride comfort and handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If passive spring and damper components are used in suspension systems, then the structure is simple and reliable, but the adaptability to varying road surface conditions is limited
Solution Approach 1:
The patent applies dynamics by transitioning from fixed passive dampers to actively controllable variable dampers. The damping force is dynamically adjusted in real-time based on road surface conditions detected by sensors and processed through machine learning algorithms, enabling the suspension system to adapt to varying road surfaces while maintaining manageable complexity through electronic control.
Solution Approach 2:
The patent implements parameter changes by modifying the damping force parameter of the shock absorber based on classified road surface grades. The system changes physical parameters (damping coefficients) in response to detected road conditions, allowing the same hardware to perform optimally across different road surfaces without increasing structural complexity.
2Ease of operation
If machine learning-based prediction models and real-time sensors are integrated into the suspension control system, then the adaptability and ride comfort improve, but the device complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline with extensive road surface data before deployment. The classification models and prediction algorithms are prepared in advance, allowing the onboard system to make real-time decisions with minimal computational burden during actual operation, thus improving ride comfort without excessively increasing real-time system complexity.
Solution Approach 2:
The patent uses an intermediary approach by introducing a classification layer that translates complex sensor data into simplified road surface grade categories. This intermediary classification step reduces the complexity of direct real-time control by breaking down the control problem into manageable stages: detection, classification, and response.
3Speed
If real-time road surface roughness estimation and classification are performed, then the responsiveness to road conditions improves, but the processing time and computational resources increase
Solution Approach 1:
The patent implements periodic action by updating the suspension control at optimized intervals rather than continuously processing every sensor signal. The system performs road surface classification and damping adjustments at appropriate periodic intervals, balancing responsive control with efficient use of processing time and computational resources.
Data Source
AI summary
An apparatus and a method for controlling a vehicle suspension, may include a variable damper provided between a vehicle body and a wheel, a sensor that measures a vehicle body vertical acceleration and a wheel vertical acceleration, and a controller that estimates a road surface roughness based on the vehicle body vertical acceleration and the wheel vertical acceleration, predicts a road surface grade based on the estimated road surface roughness, and adjusts damping force of the variable damper corresponding to the predicted road surface grade.