Powertrain Design Optimization for a Range-Extended Electric Pickup and Delivery Truck

Abstract

The ongoing electrification and data-intelligence trends in logistics industries enable efficient powertrain design and operation. In this work, the commercial package delivery vehicle powertrain design space is revisited with a specific combination of optimization and control techniques that promise accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pickup and delivery truck. A statistical learning approach is used to refine the search for the most optimal designs. Five hybrid powertrain architectures, namely, two-speed e-axle, three-speed and four-speed automatic transmission (AT) with electric motor (EM), direct-drive, and dual-motor options are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. The modeling and optimization processes are performed on the MATLAB™-Simulink platform. A cross-architecture performance and cost comparison is performed, which shows that two-speed e-axle is the optimal architecture for the selected application.