Efficient Multi-Parameter Optimization of a Twist Beam Rear Axle for Electric Vehicles using TOPSIS method for Durability

This study investigates the parameter optimization of a Rear Twist Beam (RTB) for an electric vehicle (EV) during the early stages of product development. Adapting the RTB design from a current Internal Combustion Engine (ICE) vehicle platform presents a complex challenge: accommodating increased rear vehicle load while minimizing cost and maintaining existing rear hard points. To address this, a Computer-Aided Engineering (CAE)-driven optimization using the Taguchi method was employed, avoiding costly physical durability tests. The key design parameters considered were the thickness and material grade of the RTB's child parts: the cross beam, trailing arms, and reinforcements, while preserving their original shapes. An L8 orthogonal array was utilized to efficiently evaluate the influence of these parameters on durability performance, and the optimal combinations for maximizing durability were identified. Furthermore, an Analysis of Variance (ANOVA) was conducted to quantify the contribution of each design variable. Throughout this study, the desired ride and handling characteristics for the EV's rear suspension were achieved by tuning other suspension components, such as springs and dampers, while keeping the reference RTB shape constant. This approach offers significant cost savings by avoiding major tooling modifications. Although changes in thickness or material grade slightly altered the rear axle stiffness compared to the reference, the results demonstrated that the thickness parameters of the cross beam and trailing arms exhibit high sensitivity regarding durability performance under various load cases.