- By:
- Gadiyar, Nishanth ; Ribeiro, Pedro Eugenio M; Rallabandi, Vandana P; Chowdhury, Md S; Ozpineci, Burak
- Page Number:
- 1-6
- Book Title:
- 2026 IEEE Transportation Electrification Conference & Expo (ITEC) + IEEE Electric Aircraft Technologies Symposium (EATS)
- Publication Date:
- June 29, 2026
- Publisher Location:
- IEEE, United States of America
- Conference Name:
- 2026 IEEE Transportation Electrification Conference and Electrified Aircraft Technologies Symposium (ITEC+EATS)
- Conference Location:
- Novi, Michigan, United States of America
- Conference Sponsor:
- IEEE
Abstract
Multi‑gear transmission systems are commonly used in electric traction drives for medium and heavy‑duty vehicles, while most passenger‑vehicle electric drivetrains rely on a single fixed ratio to reduce cost, weight, and complexity. Using multiple gear ratios can enable downsizing of the motor and inverter while still meeting performance requirements. Additionally, appropriately chosen ratios allow the motor to operate more frequently in high‑efficiency regions, improving overall energy usage and reducing operating costs over the drive cycle. This paper presents a systematic approach for selecting optimal gear ratios for electric drive systems. A neural‑network model is first developed to represent motor losses across the full torque–speed range using data generated from finite element analysis. This model enables fast, accurate evaluation of motor efficiency under varying operating conditions. A genetic‑algorithm‑based optimization framework is then applied to identify gear ratios that maximize energy cost savings over the drive cycle, with the resulting optimal ratios stored for real‑time implementation.
