Non–Dominated Sorting Genetic Algorithm Based Determination of Optimal Torque–Split Ratio for a Dual–Motor Electric Vehicle

Multi–motor electric vehicles (MMEVs) have been identified as a solution to the inherent disadvantages of current electric vehicles (EVs) in energy consumption and driving range. These improvements are achieved by operating each motor in its peak efficiency region, as appropriate for the driving scenario. However, an MMEV’s increased efficiency is highly dependent on the sizes of the motors. Existing studies focus mostly on improving overall system efficiency through improved energy management control strategies. The few studies that do seek to size the powertrain, introduce additional components, and alter the power capabilities of the vehicle, often resulting in increased cost and reduced dynamic performance. This paper investigates electric motor sizing methodologies used in these studies and restructures their implementation without adding new components to the existing powertrain. The optimal torque split ratio is determined using a Non–Dominated Sorting Genetic Algorithm (NSGA–II) for achieving improved overall system efficiency while preserving dynamic performance. Furthermore, using the proposed methodology, the optimal torque–split ratio is determined for a 2021 Ford Mustang Mach–E EV case study. Improved system efficiency through the optimization does not require redesign of the Mach–E’s powertrain as no additional components are introduced thus avoiding additional manufacturing costs.