Estimation of Gear Utilization and Durability Test Specifications through Virtual Road Torque Data Collection for Light Commercial Vehicles 2024-26-0257

The automotive world is rapidly moving towards achieving shorter lead time using high-end technological solutions by keeping up with day-to-day advancements in virtual testing domain. With increasing fidelity requirements in test cases and shorter project lead time, the virtual testing is an inevitable solution. This paper illustrates method adopted to achieve best approximation to emulate driver behavior with 1-D (one dimensional) simulation based modeling approach. On one hand, the physical testing needs huge data collection of various parameters using sensors mounted on the vehicle. The vehicle running on road provides the real time data to derive durability test specifications. One such example includes developing duty cycle for powertrain durability testing using Road Torque Data Collection (RTDC) technique. This involves intense physical efforts, higher set-up cost, frequent iterations, vulnerability to manual errors and causing longer test lead-time. Whereas, on the other hand, this entire process can be simulated in virtual environment. This approach involves 1-D simulation of longitudinal vehicle dynamics thus availing to understand intra-system component interactions resulting in simulating overall vehicle behavior.

Conventionally, in physical RTDC, the collected data is analyzed in the software like n-Code alongside the vehicle parameters for finding out percent gear utilization and Joint Probability Distribution (JPD) between speed and torque for each gear. This JPD can be further used in developing durability test specifications and the acceleration factors introduced using S-N curve of different gear materials. This paper envisions to study and establish the replication of entire RTDC process into 1-D simulation environment (called “VRTDC”) using Amesim software in such a way that it provides required driver behavior in a closed loop control in response to demand torque estimated at driver wheels, yielding better correlation with real test cases, simple modeling yet higher flexibility to change parameters, zero set up cost and shorter test time.