Technical Paper
Inverse Analysis of Road Contact Force and Contact Location Using Machine Learning with Measured Strain Data
2024-04-09
2024-01-2267
To adapt to Battery Electric Vehicle (BEV) integration, the significance of protective designs for battery packs against vehicle-road interaction is very high, and there is also a keen interest in the feasibility assessment technique using Computer-Aided Engineering (CAE) tools for prototype-free evaluations. However, the challenge lies in obtaining real-world empirical data to verify the accuracy of the predictive CAE. Collecting real-world data using actual battery pack can be time-consuming, costly, and accurately ascertaining the precise location, magnitude, and direction of the input force from the road to the battery pack poses a challenging task. Therefore, in this study, we developed a methodology using machine learning, specifically Gaussian Process Regression (GPR), to perform inverse analysis of the direction, magnitude, and contact location of vehicle-road interaction forces during rough road conditions.