article · Engineering Science and Technology an International Journal
A multi-objective framework enables online adaptive pulse-width modulation selection in six-phase electric vehicle traction inverters. Rather than relying on fixed strategies or heuristic switching rules, the system treats modulation selection as an online supervisory decision process using real-time measured key performance indicators. These indicators include common-mode voltage, current distortion, circulating currents, and switching losses, which are combined into a normalised cost function without needing a detailed inverter or load model. Two novel modulation schemes were integrated into the framework to suppress common-mode voltage whilst maintaining current quality and switching performance. Validated through simulations and hardware testing on a six-phase prototype using a dSPACE platform, the framework consistently converges to appropriate modulation strategies during steady and dynamic operation. It reduces common-mode voltage significantly while cutting current distortion and switching losses compared to existing methods.
Electric vehicles increasingly explore multiphase motors for better power density and reliability. However, managing electrical stress, energy losses, and current distortion typically requires complex calculations or compromises. An automated, real-time control method that chooses the best switching strategy on the fly can reduce energy waste and hardware wear in multiphase electric vehicle drivetrains without heavy computational demands.
This work applies directly to multiphase electric vehicle drivetrains and traction power systems. Target users include automotive power electronics developers, motor drive manufacturers, and electric traction engineers. Having been validated through experimental testing on a six-phase inverter prototype, the technology sits at an applied research stage that is ready for further engineering towards integration into commercial traction control units.
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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.
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DOI: 10.1016/j.jestch.2026.102495
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