Motor Vehicle Noise Control via Self-Learning Parameter Adaptation
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Solution Overview
Problem
Existing methods for reducing disruptive noises and vibrations in motor vehicles often require increased material and manufacturing costs, weight, and are not effective in addressing component-specific variations and changes over time, leading to unforeseeable acoustic excesses.
Innovation Solution
A self-learning noise optimization method using a machine learning or artificial intelligence device to detect and adapt operating parameters in real-time, minimizing acoustic excesses by varying system settings while maintaining overall performance and avoiding natural frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If additional damping material or solid design is used to counteract noise and vibrations, then noise reduction is improved, but vehicle weight and manufacturing costs increase
Solution Approach 1:
The patent implements dynamic adjustment of operating parameters (such as rotational speed, torque, or power output) in real-time to avoid resonant frequencies and minimize noise generation. The system continuously monitors acoustic signals and adapts operational characteristics dynamically, replacing static structural solutions with adaptive control strategies that reduce noise without adding physical mass.
Solution Approach 2:
The invention changes operational parameters (speed, load, frequency) of existing components to optimize acoustic behavior. By adjusting these parameters based on detected noise levels and resonant frequency analysis, the system achieves noise reduction through operational optimization rather than physical modification, avoiding the need for additional damping materials.
2Object-affected harmful factors
If additional damping material or solid design is used to counteract noise and vibrations, then noise reduction is improved, but manufacturing costs increase
Solution Approach 1:
The patent replaces mechanical/structural noise reduction solutions (damping materials, solid design modifications) with an electronic control system that uses sensors, processors, and actuators to actively manage noise. This substitution of mechanical approaches with electronic control enables noise reduction through software-based parameter optimization, reducing manufacturing complexity and costs.
Solution Approach 2:
The system performs self-diagnosis and self-optimization by continuously monitoring its own acoustic emissions and automatically adjusting operating parameters to minimize noise. This self-service capability eliminates the need for complex external control systems and reduces manufacturing costs by enabling the system to adapt and optimize itself during operation.
3Productivity
If standard design is used during vehicle development, then development time is reduced, but component-specific variations and changes over time lead to unforeseeable acoustic excesses
Solution Approach 1:
The patent implements a feedback mechanism where acoustic signals are continuously monitored and fed back to the control system. This feedback loop enables real-time detection of noise sources and automatic adjustment of operating parameters to maintain optimal acoustic performance. The system adapts to component variations and changes over time by continuously learning from acoustic feedback, ensuring consistent noise reduction performance despite standard design limitations.
Solution Approach 2:
The system performs preliminary identification of resonant frequencies and noise sources during initial operation and continues to monitor and adjust throughout the vehicle lifecycle. By establishing baseline acoustic characteristics early and maintaining continuous optimization, the system proactively prevents acoustic excesses rather than reacting to them, ensuring reliable noise control from the outset.
Data Source
AI summary
A method for optimizing noise in a motor vehicle includes automatically, during operation of the motor vehicle, capturing and analyzing acoustic signals for acoustic excess. In response to detecting acoustic excess, a current load requirement and a current operating parameter value for a motor vehicle system that complies with the load requirement are captured. The operating parameter is varied, with continued compliance with the load requirement, by a self-learning adaptation device to reduce the acoustic excess. In response to the reduction of the acoustic excess, a corresponding new operating parameter is set as a new operating strategy for the system for complying with the respective load requirement. The new strategy is set to be used in response to the occurrence of the respective load requirement. The noise optimization is performed in a predefined optimization operating mode of the motor vehicle in which different predefined load requirements are run through in a predefined program.