This paper presents a driving-cycle-aware shape and topology optimization workflow for interior permanent magnet synchronous machines used in traction drives. A k-means clustering approach reduces full driving cycles to representative operating points so that optimization remains computationally feasible while preserving realistic operating behavior. The workflow combines binary topology optimization, Normalized Gaussian Networks (NGnet), and spline-based shape optimization under electromagnetic, mechanical overspeed, and inverter voltage constraints. A Laplace-based mesh deformation strategy enables simultaneous optimization of magnet geometry and flux-barrier topology. Two optimized rotor designs are manufactured and tested experimentally. The central contribution is a validated, constraint-aware optimization pipeline that achieves permanent-magnet reduction of up to 10% while maintaining required torque capability and near-reference full-cycle efficiency.

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