A robust error sensing strategy for global active noise control in car cabins.

Area of Science:

• Automotive Engineering
• Acoustics
• Control Systems

Background:

• Global active noise control (ANC) systems offer potential for comprehensive cabin noise reduction but face implementation challenges.
• Existing ANC systems often struggle with robustness against head movement and real-world application.

Purpose of the Study:

• To propose and validate a robust error sensing strategy for a feasible global ANC system in electric vehicles.
• To demonstrate the effectiveness of the proposed strategy through simulations and real-time experiments.

Main Methods:

• Development of a robust error sensing strategy using evenly distributed error sensors.
• Simulations using measured road noise data to analyze strategy performance across different car speeds.
• Real-time ANC experiments conducted in a 5-seat A-class sedan utilizing standard car audio loudspeakers.

Main Results:

• Simulations confirmed that evenly distributed error sensors provide a robust strategy for various car speeds.
• The upper limit frequency for 3 dB global control was found to be inversely proportional to the sensor spacing.
• Real-time experiments achieved an average noise reduction of 3.8 dB between 50 and 250 Hz.

Conclusions:

• The proposed global ANC system with a robust error sensing strategy is effective for electric car cabins.
• Findings establish a benchmark for in-cabin noise control and suggest integration of virtual sensing for enhanced practicality.
• The study provides a foundation for future optimization and adaptive algorithm implementation in automotive ANC.