Drive Cycle-Based Design Optimization of Traction Motor Drives for Battery Electric Vehicles Using Data-Driven Approaches 2024-01-2172

This paper demonstrates a data-driven methodology for the system-level design of high-power traction motor drives in modern battery electric vehicles. With the immense growth of battery electric vehicles in this transformative decade, the expected time to develop and market these powertrain components is becoming significantly shorter than for internal combustion engines. This rising demand is further complicated due to more stringent cost, efficiency and power density targets set by the U.S. Department of Energy. Hence, a system-level perspective is maintained in this data-driven methodology to identify the design requirements for traction motor drives by relying on a dynamic vehicle simulation toolchain and various drive cycles (e.g., EPA MCT, WLTC, US06, etc.). The proposed data-driven approach can be used across different battery electric vehicle platforms including passenger and commercial types. A case study for a future-proof high-voltage architecture is demonstrated here for a C-segment all-wheel-drive mass market battery electric vehicle. Simulation results in this case study, validated against real-world driving data, indicate improvement in the total energy loss for different drive cycles as well as reduction in the mass, volume, and current draw of the designed machine using system-level feedback, thereby enabling higher torque and power density. The simulation results indicate that the total energy losses for five drive cycles were reduced by at least 11% and at most 27%, while increasing the Tip-In (0–100 kph) acceleration time by 3 seconds; a compromise between vehicle performance and driving efficiency is expected. The operating points from various driving scenarios define the overall sizing requirements of the traction motor drive through statistical analysis in order to meet different optimization targets, such as extending the maximum efficiency region. These system-level requirements of the traction motor drive directly affect the co-design framework of multiphysics simulations along with the vehicle dynamics. The proposed data-driven methodology aids in effectively addressing the vehicle-level performance targets while downsizing the traction motor drive components to increase the overall range and reduce the system costs.