Electric Vehicle Behavioral Modeling Methods for Motor Drive System EMI Design Optimization

Paper #:
  • 2015-01-1204

  • 2015-04-14
Zhang, J., Liao, Z., and Sun, Z., "Electric Vehicle Behavioral Modeling Methods for Motor Drive System EMI Design Optimization," SAE Technical Paper 2015-01-1204, 2015, https://doi.org/10.4271/2015-01-1204.
Electromagnetic interference (EMI) is a common problem in power electronics systems. Pulse-width modulation (PWM) control of semiconductor devices in a power converter circuit creates discontinuity in voltage and current with rich harmonics over a broad frequency range, creating both conducted and radiated noise. The increase in switching speed enabled by new power semiconductor devices helps to reduce converter size and reduce switching losses, but further exacerbates the EMI problem. Complying with regulatory EMI emission limits requires the use of EMI filters in almost all power converter designs, and EMI filters are often the dominant elements for system volume, weight, and cost. Electromagnetic interference (EMI) filtering is a critical driver for volume and weight for many applications, particularly in airborne and other mobile platforms. Because of the lack of the ability to accurately model system EMI behavior, EMI filter design usually cannot start until EMI measurement results have become available. This often leads to costly schedule delay and disruption, and the resulting designs are suboptimal at the best. This paper presents systematic EMI modeling methods that solve this problem and enable optimal system EMI solutions to be developed concurrent with the design of the rest of the system. The proposed methods use linear and piece-wise linear behavioral models that are easy to parameterize and to simulate, thereby avoiding the need for detailed modeling of board-level interconnects and the use of complex physics-based semiconductor device models. Both differential-mode and common-mode EMI are considered. The proposed modeling approach is verified in two motor drive systems for which model predictions and experimental measurements are presented and compared.
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