Simulation Driven Virtual Road Load and Accelerated Duty Cycle Generation for 3-Speed eAxle Systems

The transition from Internal Combustion Engine (ICE) vehicles to Battery Electric Vehicles (BEVs) introduces significant challenges in drivetrain development, particularly when historical road load data (RLDA) is unavailable. This research proposes a methodology for virtually generating and processing RLDA to evaluate the durability of a novel 3-speed electric axle (eAxle) system before physical prototype construction. By employing AVL Route Studio, we simulated diverse global driving conditions across urban, highway, and mixed terrains to generate high-frequency torque and speed data. Since the resulting millions of data points are impractical for direct fatigue assessment, we utilised Romax software to condense the virtual RLDA into an accelerated duty cycle while maintaining the original fatigue damage characteristics. Unlike traditional binning techniques that often mischaracterise load severity, our damage-matching approach ensures the condensed cycle accurately replicates gear contact, gear bending, and bearing damage patterns of the comprehensive dataset. This methodology enables early-stage durability validation of eAxle designs without dependence on physical testing or historical data. Our findings suggest a correlation between condensed and original damage profiles for transmission components, indicating that this virtual approach may be useful. The framework offers a potential method for virtual RLDA work that could help with design verification and optimisation for electric drivetrains.