MATLAB Code Implementation for Calculating Weighted Acceleration

In the field of vehicle engineering, the Weighted Root Mean Square Acceleration is a crucial metric for evaluating vehicle ride comfort. MATLAB serves as a powerful numerical computation tool that can efficiently accomplish this calculation task. The computation of weighted acceleration typically involves frequency weighting processing, with main steps including: Data Acquisition: Obtain time-domain vibration acceleration data from vehicle axes (such as vertical, lateral, and longitudinal) through acceleration sensors. The sampling frequency must satisfy the Nyquist criterion to avoid aliasing. Frequency Weighting: Apply frequency weighting filters (such as Wk or Wd weighting networks) to the original acceleration signals according to standards like ISO 2631, emphasizing the impact of frequency bands sensitive to human perception (typically 0.5-80Hz). In MATLAB implementation, this can be achieved using built-in functions like fft for frequency-domain filtering or designing IIR filters with filter design tools for real-time weighting. RMS Calculation: Segment the weighted time-domain signal using time windows and compute the Root Mean Square (RMS) value for each segment to reflect vibration energy. MATLAB's movmean or custom moving window algorithms can optimize computational efficiency for this step. Directional Synthesis: For comprehensive evaluation, combine the weighted RMS values from different axes using specific formulas (such as square root of the sum of squares) to obtain the total weighted value. This can be implemented using MATLAB's vector operations and mathematical functions like sqrt and sum. When implementing in MATLAB, built-in functions can be utilized for FFT-based frequency-domain filtering or IIR filter design for real-time weighting, combined with moving window algorithms to optimize computational efficiency. The final results can be used to compare ride comfort differences between various vehicle models or road conditions.