Efficiency-Oriented Gear Selection Strategy for Twin Permanent Magnet Synchronous Machines in a Shared Drivetrain Architecture

This article presents a gear selection methodology for electric vehicle powertrains employing two identical Permanent Magnet Synchronous Machines (PMSMs) arranged in a twin-drive configuration. Both machines are coupled through a shared output shaft and operate with coordinated torque–speed characteristics, enabling efficient utilization of the available gear stages. The proposed approach establishes a control-oriented drivetrain framework that incorporates mechanical dynamics together with real-time thermal states and loss mechanisms. Unlike conventional strategies, which rely mainly on static or speed-based shifting rules, the method integrates detailed thermal and electromagnetic loss modeling directly into the gear-shifting logic. By accounting for the dynamic thermal behavior of PMSMs under variable load conditions, the strategy aims to reduce cumulative drivetrain losses, including electromagnetic, thermal, and mechanical, while maintaining high efficiency. The methodology is implemented in a MATLAB/Simulink R2024a and LabVIEW 2024Q2 co-simulation environment, where thermal feedback and instantaneous efficiency metrics dynamically guide gear selection. Simulation results demonstrate measurable improvements in energy utilization, particularly under transient operating conditions. The resulting efficiency maps are broader and flatter, as the motors’ operating points are continuously shifted toward zones of optimal performance through adaptive gear ratio control. The novelty of this work lies in combining real-time loss modeling, thermal feedback, and coordinated gear management in a twin-motor system, validated through experimentally motivated efficiency maps. The findings highlight a scalable and dynamic control framework suitable for advanced electric vehicle architectures, supporting intelligent efficiency-oriented drivetrain strategies that enhance sustainability, thermal management, and system performance across diverse operating conditions.