Intelligent design optimization for traction and steering motors of an autonomous electric shuttle under driving scenarios

Abstract

Electrified autonomous vehicles have become quite popular and have a wide range of applications. The traction and steering motors to be used on an electrified autonomous vehicle are designed considering the lateral and longitudinal forces in the environment where the vehicle operates, and they are selected with extra safety margins and "over-engineering" features. This causes wastage of rare earth elements, along with both cost and energy inefficiencies. For autonomous shuttle vehicles, traction and steering performances can be analyzed based on driving scenarios. The reference speed and steering signals for the selected driving scenarios were run on a dynamic vehicle model and the minimum performance requirements for the traction and steering motors were determined. Then, the determined design parameters by DoE (Design of Experiments) were trained in two different ANN (Artificial Neural Networks) models created for motor models. The trained ANN models were run according to the minimum performance criteria and predicted motor models with new design parameters for the traction and steering motors. The performance results of the predicted traction and steering motor models showed a significant improvement in terms of the minimum performance requirements.