Vehicle Axle Acceleration Prediction: An Interpolation Approach

This article explores the development of vehicle axle acceleration prediction models utilizing interpolation techniques for 4-poster device testing in the automotive industry. The main objective is to estimate acceleration data for a specific speed based on data collected at other speeds, employing different interpolation techniques. To enhance the precision of predicted data, a refinement model is introduced, allowing for the fine-tuning of predicted acceleration magnitudes. The research methodology involves data collection through simulation in MSC ADAMS software, and data processing, algorithm development, and result comparisons using MATLAB software. The accuracy of these models is assessed through statistical evaluations, incorporating metrics like Root Mean Square (RMS) and Absolute Mean for insights into predictive performance. The research emphasizes the quest for precision in predicting acceleration data, and the outcomes demonstrate substantial improvements in accuracy.