This paper presents a duty-cycle-driven design and optimization methodology for an Interior Permanent Magnet Synchronous Motor (IPMSM) intended for off-highway vehicle applications, based on real-world maneuver analysis. Off-highway machines such as wheel loaders operate under highly dynamic and repetitive load conditions that significantly differ from standardized road vehicle drive cycles. To accurately capture these operating characteristics, representative wheel-loader duty cycles are analyzed to extract realistic torque–speed–time operating envelopes. These envelopes are subsequently used to define application-specific performance targets and constraints, ensuring proper motor sizing and electromagnetic design aligned with real operating conditions.

A preliminary IPMSM design is first established using analytical sizing rules and electromagnetic design guidelines. This baseline design is then validated through high-fidelity finite-element analysis (FEA) using ANSYS Motor-CAD, enabling detailed evaluation of electromagnetic performance, losses, and efficiency under both steady-state and transient conditions. To systematically improve the motor design, a sensitivity analysis based on Design of Experiments (DoE) is conducted using ANSYS optiSLang. This step identifies the most influential geometric and magnetic design parameters affecting key performance indicators such as torque capability, efficiency, torque ripple, cogging torque, and motor mass.

Building on the sensitivity analysis results, a surrogate-assisted multi-objective optimization employing Particle Swarm Optimization (PSO) is performed. The optimization aims to enhance efficiency, reduce torque ripple and cogging torque, and minimize motor mass while satisfying strict electromagnetic, mechanical, and application-driven constraints. Comparative electromagnetic evaluation between preliminary and optimized designs demonstrates significant improvements in torque smoothness, harmonic content, and efficiency, confirming the effectiveness of the proposed optimization framework for off-highway electric powertrain applications.