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The Effect of Distributed Parameters on Conducted EMI from DC-Fed Motor Drive Systems in Electric Vehicles

Author

Listed:
  • Li Zhai

    (National Engineering Laboratory for Electric Vehicle, Beijing Institute of Technology, Beijing 100081, China
    Co-Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Liwen Lin

    (National Engineering Laboratory for Electric Vehicle, Beijing Institute of Technology, Beijing 100081, China
    Co-Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Xinyu Zhang

    (Beijing Institute of Radio Metrology and Measurement, Beijing 100854, China)

  • Chao Song

    (National Engineering Laboratory for Electric Vehicle, Beijing Institute of Technology, Beijing 100081, China
    Co-Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

Abstract

The large dv/dt and di/dt outputs of power devices in DC-fed motor drive systems in electric vehicles (EVs) always introduce conducted electromagnetic interference (EMI) emissions and may lead to motor drive system energy transmission losses. The effect of distributed parameters on conducted EMI from the DC-fed high voltage motor drive systems in EVs is studied. A complete test for conducted EMI from the direct current fed(DC-fed) alternating current (AC) motor drive system in an electric vehicle (EV) under load conditions is set up to measure the conducted EMI of high voltage DC cables and the EMI noise peaks due to resonances in a frequency range of 150 kHz–108 MHz. The distributed parameters of the motor can induce bearing currents under low frequency sine wave operation. However the impedance of the distributed parameters of the motor is very high at resonance frequencies of 500 kHz and 30 MHz, and the effect of the bearing current can be ignored, so the research mainly focuses on the distributed parameters in inverters and cables at 500 kHz and 30 MHz, not the effect of distributed parameters of the motor on resonances. The corresponding equivalent circuits for differential mode (DM) and common mode (CM) EMI at resonance frequencies of 500 kHz and 30 MHz are established to determine the EMI propagation paths and analyze the effect of distributed parameters on conducted EMI. The dominant distributed parameters of elements responsible for the appearing resonances at 500 kHz and 30 MHz are determined. The effect of the dominant distributed parameters on conducted EMI are presented and verified by simulation and experiment. The conduced voltage at frequencies from 150 kHz to 108 MHz can be mitigated to below the limit level-3 of CISPR25 by changing the dominant distributed parameters.

Suggested Citation

  • Li Zhai & Liwen Lin & Xinyu Zhang & Chao Song, 2016. "The Effect of Distributed Parameters on Conducted EMI from DC-Fed Motor Drive Systems in Electric Vehicles," Energies, MDPI, vol. 10(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:10:y:2016:i:1:p:1-:d:85898
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    References listed on IDEAS

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    1. Hu, Xiaosong & Murgovski, Nikolce & Johannesson, Lars & Egardt, Bo, 2013. "Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes," Applied Energy, Elsevier, vol. 111(C), pages 1001-1009.
    2. Li Zhai & Xinyu Zhang & Natalia Bondarenko & David Loken & Thomas P. Van Doren & Daryl G. Beetner, 2016. "Mitigation Emission Strategy Based on Resonances from a Power Inverter System in Electric Vehicles," Energies, MDPI, vol. 9(6), pages 1-17, May.
    3. Tommasini, Riccardo & Spertino, Filippo, 1999. "Electric power distribution and environment: interference of power installation magnetic fields on computer systems," Applied Energy, Elsevier, vol. 64(1-4), pages 181-193, September.
    4. Ferrero, Enrico & Alessandrini, Stefano & Balanzino, Alessia, 2016. "Impact of the electric vehicles on the air pollution from a highway," Applied Energy, Elsevier, vol. 169(C), pages 450-459.
    5. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    6. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
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    Cited by:

    1. Li Zhai & Yu Cao & Liwen Lin & Tao Zhang & Steven Kavuma, 2018. "Mitigation Conducted Emission Strategy Based on Transfer Function from a DC-Fed Wireless Charging System for Electric Vehicles," Energies, MDPI, vol. 11(3), pages 1-17, February.
    2. Li Zhai & Tao Zhang & Yu Cao & Sipeng Yang & Steven Kavuma & Huiyuan Feng, 2018. "Conducted EMI Prediction and Mitigation Strategy Based on Transfer Function for a High-Low Voltage DC-DC Converter in Electric Vehicle," Energies, MDPI, vol. 11(5), pages 1-17, April.

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