IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i5p1028-d142829.html
   My bibliography  Save this article

Conducted EMI Prediction and Mitigation Strategy Based on Transfer Function for a High-Low Voltage DC-DC Converter in Electric Vehicle

Author

Listed:
  • Li Zhai

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

  • Tao Zhang

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

  • Yu Cao

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

  • Sipeng Yang

    (R&D Department for New Energy Automobile, Zhengzhou YuTong Bus Co., Ltd., Zhengzhou 450061, China)

  • Steven Kavuma

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

  • Huiyuan Feng

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

Abstract

The high d v /d t and d i /d t outputs from power devices in a high-low voltage DC-DC converter on electric vehicles (EVs) can always introduce the unwanted conducted electromagnetic interference (EMI) emissions. A conducted EMI prediction and mitigation strategy that is based on transfer function for the high-low voltage DC-DC converter in EVs are proposed. A complete test for the DC-DC converter is conducted to obtain the conducted EMI from DC power cables in the frequency band of 150 kHz-108 MHz. The equivalent circuit with high-frequency parasitic parameters of the DC-DC converter is built`1 based on the measurement results to acquire the characteristics of the conducted EMI of the DC power cables. The common mode (CM) and differential mode (DM) propagation coupling paths are determined, and the corresponding transfer functions of the DM interference and CM interference are established. The simulation results of the conducted EMI can be obtained by software Matlab and Computer Simulation Technology (CST). By analyzing the transfer functions and the simulation results, the dominated interference is the CM interference, which is the main factor of the conducted EMI. A mitigation strategy for the design of the CM interference filter based on the dominated CM interference is proposed. Finally, the mitigation strategy of the conducted EMI is verified by performing the conducted voltage experiment. From the experiment results, the conducted voltage of the DC power cables is decreased, respectively, by 58 dBμV, 55 dBμV, 65 dBμV, 53 dBμV, and 54 dBμV at frequency 200 kHz, 400 kHz, 600 kHz, 1.4 MHz, and 50 MHz. The conduced voltage in the frequency band of 150 kHz–108 MHz can be mitigated by adding the CM interference filters, and the values are lower than the limit level-3 of CISPR25 standard (GB/T 18655-2010).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1028-:d:142829
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/5/1028/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/5/1028/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kyunghwan Choi & Kyung-Soo Kim & Seok-Kyoon Kim, 2019. "Proportional-Type Sensor Fault Diagnosis Algorithm for DC/DC Boost Converters Based on Disturbance Observer," Energies, MDPI, vol. 12(8), pages 1-14, April.
    2. Chaiyan Jettanasen & Atthapol Ngaopitakkul, 2019. "The Conducted Emission Attenuation of Micro-Inverters for Nanogrid Systems," Sustainability, MDPI, vol. 12(1), pages 1-31, December.
    3. Feng Wang & Yutao Luo & Hongluo Li & Xiaotong Xu, 2019. "Switching Characteristics Optimization of Two-Phase Interleaved Bidirectional DC/DC for Electric Vehicles," Energies, MDPI, vol. 12(3), pages 1-14, January.
    4. Seok-Kyoon Kim, 2018. "Passivity-Based Robust Output Voltage Tracking Control of DC/DC Boost Converter for Wind Power Systems," Energies, MDPI, vol. 11(6), pages 1-13, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Mohammed, Abubakar Gambo & Elfeky, Karem Elsayed & Wang, Qiuwang, 2022. "Recent advancement and enhanced battery performance using phase change materials based hybrid battery thermal management for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    4. Hayashida, Sherilyn & La Croix, Sumner & Coffman, Makena, 2021. "Understanding changes in electric vehicle policies in the U.S. states, 2010–2018," Transport Policy, Elsevier, vol. 103(C), pages 211-223.
    5. Morshed, Mohammad Javad & Hmida, Jalel Ben & Fekih, Afef, 2018. "A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems," Applied Energy, Elsevier, vol. 211(C), pages 1136-1149.
    6. Jeon, Deok Hwan & Cho, Jae Yong & Jhun, Jeong Pil & Ahn, Jung Hwan & Jeong, Sinwoo & Jeong, Se Yeong & Kumar, Anuruddh & Ryu, Chul Hee & Hwang, Wonseop & Park, Hansun & Chang, Cheulho & Lee, Hyoungjin, 2021. "A lever-type piezoelectric energy harvester with deformation-guiding mechanism for electric vehicle charging station on smart road," Energy, Elsevier, vol. 218(C).
    7. Anuradha Singh & Jyoti Yadav & Sarahana Shrestha & Aparna S. Varde, 2023. "Linking Alternative Fuel Vehicles Adoption with Socioeconomic Status and Air Quality Index," Papers 2303.08286, arXiv.org.
    8. Stefano Rinaldi & Marco Pasetti & Emiliano Sisinni & Federico Bonafini & Paolo Ferrari & Mattia Rizzi & Alessandra Flammini, 2018. "On the Mobile Communication Requirements for the Demand-Side Management of Electric Vehicles," Energies, MDPI, vol. 11(5), pages 1-27, May.
    9. Kumar, Ravi & Lamba, Kuldeep & Raman, Avinash, 2021. "Role of zero emission vehicles in sustainable transformation of the Indian automobile industry," Research in Transportation Economics, Elsevier, vol. 90(C).
    10. Hu, Wenyu & E, Jiaqiang & Zhang, Feng & Chen, Jingwei & Ma, Yinjie & Leng, Erwei, 2022. "Investigation on cooperative mechanism between convective wind energy harvesting and dust collection during vehicle driving on the highway," Energy, Elsevier, vol. 260(C).
    11. Razeghi, Ghazal & Samuelsen, Scott, 2016. "Impacts of plug-in electric vehicles in a balancing area," Applied Energy, Elsevier, vol. 183(C), pages 1142-1156.
    12. Juan Jesús Castillo Aguilar & Javier Pérez Fernández & Juan María Velasco García & Juan Antonio Cabrera Carrillo, 2017. "Regenerative Intelligent Brake Control for Electric Motorcycles," Energies, MDPI, vol. 10(10), pages 1-16, October.
    13. Shareef, Hussain & Islam, Md. Mainul & Mohamed, Azah, 2016. "A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 403-420.
    14. Patyal, Vishal Singh & Kumar, Ravi & Kushwah, Shiksha, 2021. "Modeling barriers to the adoption of electric vehicles: An Indian perspective," Energy, Elsevier, vol. 237(C).
    15. Marco Pasetti & Stefano Rinaldi & Alessandra Flammini & Michela Longo & Federica Foiadelli, 2019. "Assessment of Electric Vehicle Charging Costs in Presence of Distributed Photovoltaic Generation and Variable Electricity Tariffs," Energies, MDPI, vol. 12(3), pages 1-20, February.
    16. Edgar Sokolovskij & Arkadiusz Małek & Jacek Caban & Agnieszka Dudziak & Jonas Matijošius & Andrzej Marciniak, 2023. "Selection of a Photovoltaic Carport Power for an Electric Vehicle," Energies, MDPI, vol. 16(7), pages 1-16, March.
    17. Lee, Yongseung & Kim, Chongman & Shin, Juneseuk, 2016. "A hybrid electric vehicle market penetration model to identify the best policy mix: A consumer ownership cycle approach," Applied Energy, Elsevier, vol. 184(C), pages 438-449.
    18. Matallana, A. & Ibarra, E. & López, I. & Andreu, J. & Garate, J.I. & Jordà, X. & Rebollo, J., 2019. "Power module electronics in HEV/EV applications: New trends in wide-bandgap semiconductor technologies and design aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    19. Fernández-Dacosta, Cora & Shen, Li & Schakel, Wouter & Ramirez, Andrea & Kramer, Gert Jan, 2019. "Potential and challenges of low-carbon energy options: Comparative assessment of alternative fuels for the transport sector," Applied Energy, Elsevier, vol. 236(C), pages 590-606.
    20. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1028-:d:142829. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.