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 (RLD) is unavailable This study presents a methodology for virtually generating and processing road load data (RLD) to assess the durability of a new 3-speed electric axle (eAxle) design before building a physical prototype. Using AVL Route Studio, we simulated a range of driving conditions including urban, highway, and mixed-terrain routes, covering diverse global scenarios. These simulations produced high-frequency torque and speed data representative of real-world operation. Given that the raw dataset contained millions of points, direct use for fatigue assessment was impractical. To address this, the data was imported into Romax, where it was condensed into an accelerated duty cycle while preserving the cumulative fatigue damage patterns from the original dataset. Unlike conventional binning methods, which can misrepresent load severity, our damage-matching approach maintained accurate replication of gear contact, gear bending, and bearing damage characteristics. 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.