A multi-objective framework for online KPI-based adaptive PWM selection in six-phase electric vehicle traction inverters

This paper presents a multi-objective framework for online KPI-based adaptive pulse-width modulation (PWM) selection in six-phase electric vehicle traction inverters. Unlike conventional modulation approaches that rely on fixed strategies or heuristic switching rules, the proposed method formulates PWM selection as an online supervisory decision process based on measured inverter performance indicators. At each control interval, multiple PWM candidates are evaluated using measured indicators, including common-mode voltage (CMV), current distortion, circulating currents, and switching losses. These objectives are combined into a normalized cost function to identify the most suitable modulation strategy under varying operating conditions. The proposed framework performs PWM selection directly from measured KPIs without requiring a detailed inverter or load model for the supervisory decision process. In addition, two new PWM schemes are developed to reduce CMV while maintaining acceptable current quality and switching performance. These schemes are integrated into the proposed framework and evaluated alongside conventional modulation techniques. The approach is validated through both simulation and experimental studies on a six-phase inverter prototype. Results demonstrate consistent convergence to suitable PWM strategies under steady-state conditions and effective adaptation under dynamic operation. The proposed PWM schemes achieve reductions in RMS CMV of up to 67% compared with the reference PWM strategy. Furthermore, Proposed PWM1 maintains THD below 0.83% across the investigated operating range and achieves up to 67% lower current distortion at a modulation index of 0.8 compared with a recent low-CMV model predictive control method, while requiring up to 72% lower switching losses and 53% lower execution time. The implementation results further confirm the practical feasibility of the proposed framework, with an execution time of approximately 21.44 μs on a dSPACE MicroLabBox platform. The proposed framework provides a scalable and practical solution for online adaptive PWM selection in multiphase traction systems.