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 an RTB design from an Internal Combustion Engine (ICE) vehicle platform presents several challenges, one of the challenges is accommodating increased rear vehicle load while minimizing cost, with maintaining existing rear hard points. To address this, we employed an experimental study for Computer-Aided Engineering (CAE) using the Taguchi DOE, which avoids costly physical durability tests. The key design parameters considered were the thickness and material grade of the RTB's components, specifically the cross beam, trailing arms, and reinforcements while preserving their original shapes. L8 Orthogonal array is constructed to design the experiment and identify the influence of the design parameters on durability performance, and the optimal combinations for maximizing durability are identified by using TOPSIS multi objective method. This approach offers significant cost savings by avoiding different iterative physical testing during vehicle development stage. The study found that while changes in the thickness or material of components had mere effect on the rear axle's stiffness, the thickness of the cross beam and trailing arms significantly impacted its durability under various loads.