Load Analysis and Test Bench Load Spectrum Generation for Electric Drive Systems Based on Virtual Proving Ground Technology

The reliability of the EDS (Electric Drive System) in electric vehicles is crucial to overall vehicle performance. This study addresses the challenge of acquiring high-fidelity internal load data in the early development phase due to the absence of prototypes, overcoming the limitations of traditional road tests, which are costly, time-consuming, and unable to measure gear meshing forces. A method based on a VPG (Virtual Proving Ground) is proposed to acquire internal loads of a dual-motor EDS, analyze the impact of typical virtual fatigue durability road conditions on critical components, and generate load spectra for test bench experiments. Through point cloud data-based road modeling and rigid-flexible coupled simulation, dynamic loads are accurately extracted, with pseudo-damage contributions from eight intensified road conditions quantified using pseudo-damage calculations, and equivalent sinusoidal load spectra generated using the rainflow counting method and linear cumulative damage theory. Compared to the limitations of existing VPG methods that rely on simplified models, this study enhances the accuracy of internal load extraction, providing technical support for EDS durability testing. Building on existing research, it focuses on high-fidelity acquisition of EDS loads and load spectrum generation, improving applicability and addressing deficiencies in simulation accuracy. This study represents a novel application of VPG technology in electric drive system development, resolving the issue of insufficient early-stage load spectra. It provides data support for durability optimization and bench testing, with future validation planned using real vehicle data.