Optimization and reliability analysis of multi-purpose electric truck chassis under payload positioning variation

Abstract

A multi-purpose electric vehicle (EV) truck is a versatile platform designed to accommodate diverse payload configurations in terms of position and mass. Prevailing lightweight design optimization approaches for multi-purpose EV truck chassis predominantly consider a singular assumed payload position, neglecting the multiplicity of potential payload configurations encountered during operation. This oversimplification can culminate in an overfitted design that fails to satisfy structural integrity requirements under certain critical payload distributions. The present investigation addresses this deficiency by optimizing the baseline multi-purpose EV truck chassis for both the assumed and worst-case payload positions through shape and size optimization leveraging the Global Response Surface algorithm. Compared to the baseline model, the optimized chassis designs exhibit 20.6% and 18.4% mass reductions, respectively. Crucially, while the structure optimized under the assumed payload position exhibits an over-fitting phenomenon, attaining a mere 0.33 reliability score when subjected to diverse payload positions, the chassis optimized for the worst-case scenario achieves a 1.00 reliability score, meaning no failure occurs across potential payload distributions. This study underscores the importance of accounting for payload position variability throughout the design process to ensure the structural reliability of multi-purpose EV trucks under diverse operational regimes, constituting a novel contribution to lightweight chassis design methodologies.