Control and Design Optimization of an Electric Vehicle Transmission Using Analytical Modeling Methods

TL;DR

This paper presents an analytical framework for optimizing electric vehicle transmission control and design, integrating component modeling, topology configuration, and global optimization algorithms to improve energy efficiency and performance.

Contribution

It introduces a systematic analytical modeling and optimization approach for EV transmission design, enabling global optimality and validation against benchmark vehicles.

Findings

Two-gear transmission reduces energy consumption by 0.8%.

Framework accurately predicts energy use with 0.2% error.

Method supports gear ratio selection for powertrain optimization.

Abstract

This paper introduces a framework to systematically optimize the control and design of an electric vehicle transmission, connecting powertrain sizing studies to detailed gearbox design methods. To this end, we first create analytical models of individual components: gears, shafts, bearings, clutches, and synchronizers. Second, we construct a transmission by systematically configuring a topology with these components. Third, we place the composed transmission within a powertrain and vehicle model, and compute the minimum-energy control and design, employing solving algorithms that provide global optimality guarantees. Finally, we carry out the control and design optimization of a fixed- and two-gear transmission for a compact family electric vehicle, whereby we observe that a two-gear transmission can improve the energy consumption by 0.8 %, while also achieving requirements on gradeability and performance. We validate our framework by recreating the transmission that is mounted in the benchmark test case vehicle and recomputing the energy consumption over the New European Driving Cycle, where we notice an error in energy consumption of 0.2 %, affirming that our methods are suitable for gear ratio selection in powertrain design optimization.