Simulation-Driven Matching and Lightweight Transmission Optimization of the Powertrain for a Single-Motor FSEC Race Car

For single-motor Formula Student Electric China (FSEC) race cars, current powertrain design methodologies commonly suffer from the disconnection among parameter matching, dynamic simulation, and structural optimization: gear ratio selection is mostly based on static theoretical calculations, lightweight design does not incorporate full-vehicle dynamic load spectra constraints, and the simulation toolchain (CarSim 2024, OptimumLap version 5, ANSYS 2022) lacks a standardized data closed-loop, leading to prolonged iteration cycles and unquantifiable reliability. To address these issues, this paper takes the Nanning University electric formula race car E66 as the research object and proposes a three-phase integrated design framework of “requirement-driven, multi-simulation co-validation, and lightweight iteration.” The study includes three core contributions: (1) establishing a powertrain parameter matching method based on power boundary calculations and multi-dimensional selection criteria, achieving the integrated selection of the Emrax 228 motor (power density 9.2 kW/kg, Emrax d.o.o., Kamnik, Slovenia) and the Unitek-D3 controller through comparative analysis with the JJE motor (5.7 kW/kg, Jing-Jin Electric Technologies Co., Ltd., Beijing, China); (2) constructing a co-simulation mechanism combining OptimumLap version 5 and CarSim 2024, completing the closed-loop optimization of the gear ratio from the range of 1.6–4.3 to the optimal value of 3.9 under the Hefei NIO track operating conditions, with a 75 m acceleration simulation result of 4.4 s and an endurance lap time of 86 s; (3) introducing ANSYS 2022 topology optimization technology to perform two-iteration lightweight design on the 7075 aluminum alloy main sprocket, achieving 35% mass reduction and 40% volume reduction while maintaining the maximum principal stress at 73.16 MPa (below yield strength). The expected outcome is a replicable development paradigm for single-motor powertrain systems, transforming drivetrain matching from experience-driven to simulation-driven, providing reliable data boundaries for physical vehicle commissioning, and effectively reducing trial-and-error costs.