Vehicle Design Optimization Using AI/ML Methods

Abstract

Energy efficiency and performance are important attributes when developing and designing vehicles. With the transition of the automotive industry towards energy efficiency and sustainability, it is ever more important to save computational resources. Traditional vehicle design optimization heavily relies on computationally expensive simulations. The simulations carried out in this project particularly focus on the powertrain of vehicles. This work focuses on developing a surrogate-assisted, multiobjective optimization framework that efficiently finds the optimal values for the given variables. Surrogate modeling is an engineering method used when the outcome of an experiment cannot be easily computed, so a mathematical approximation is applied. In this case, we use machine learning models to predict the outcome of expensive simulations. These trained model(s) is then used for optimization instead of running optimization on the simulation model directly. First, we generate 4096 Sobol-sampled configurations spanning different parameters like gear ratios and electric motors. We train and compare different surrogate models like Random Forest, XGBoost, and LightGBM on these data, achieving test R2 scores up to 0.96 with Random Forest. Next, we employ the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to explore trade-offs among various conflicting objectives, extracting a Pareto front of optimal designs. A weighted-sum post-processing step or a constrained method later selects a single best-trade-off configuration, which full simulation validates. This framework slashes computational cost and empowers rapid, data-driven vehicle powertrain design.