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Power Line Communications for Automotive High Voltage Battery Systems: Channel Modeling and Coexistence Study with Battery Monitoring

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  • Thomas F. Landinger

    (Institute for Electrical Energy Storage Technology, Technical University of Munich (TUM), Arcisstr. 21, 80333 Munich, Germany
    Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany)

  • Guenter Schwarzberger

    (Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany)

  • Guenter Hofer

    (Infineon Technologies Austria AG, Development Center Graz, Babenberger Str. 10, 8020 Graz, Austria)

  • Matthias Rose

    (Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany)

  • Andreas Jossen

    (Institute for Electrical Energy Storage Technology, Technical University of Munich (TUM), Arcisstr. 21, 80333 Munich, Germany)

Abstract

As electric vehicles are gaining increasing worldwide interest, advances in driving range and safety become critical. Modern automotive battery management systems (BMS) compete with challenging performance and safety requirements and need to monitor a large amount of battery parameters. In this paper, we propose power line communications (PLC) for high voltage (HV) traction batteries to reduce the BMS wiring effort. By modeling a small-scale battery pack for frequencies up to 300 MHz, we predict the PLC channel transfer characteristics and validate the results using a PLC hardware demonstrator employing a narrowband single-carrier modulation. The results demonstrate that battery PLC is a demanding task due to low access impedances and cell coupling effects, yet transfer characteristics can be improved by optimal impedance matching. PLC for HV BMS not only saves weight and cost, but also improves flexibility in BMS design. PLC enables single-cell monitoring techniques such as online electrochemical impedance spectroscopy (EIS) without additional wiring. Online EIS can be used for in-situ state and temperature estimation saving extra sensors. This work unveils possible coexistence issues between PLC and battery monitoring. In particular, we demonstrate that certain PLC data or packet rates have to be avoided not to interfere with EIS measurements.

Suggested Citation

  • Thomas F. Landinger & Guenter Schwarzberger & Guenter Hofer & Matthias Rose & Andreas Jossen, 2021. "Power Line Communications for Automotive High Voltage Battery Systems: Channel Modeling and Coexistence Study with Battery Monitoring," Energies, MDPI, vol. 14(7), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1851-:d:524939
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    References listed on IDEAS

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    1. Balakumar Balasingam & Mostafa Ahmed & Krishna Pattipati, 2020. "Battery Management Systems—Challenges and Some Solutions," Energies, MDPI, vol. 13(11), pages 1-19, June.
    2. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    3. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    4. Yi Wu & Saurabh Saxena & Yinjiao Xing & Youren Wang & Chuan Li & Winco K. C. Yung & Michael Pecht, 2018. "Analysis of Manufacturing-Induced Defects and Structural Deformations in Lithium-Ion Batteries Using Computed Tomography," Energies, MDPI, vol. 11(4), pages 1-22, April.
    5. Alberto Sendin & Ivan Peña & Pablo Angueira, 2014. "Strategies for Power Line Communications Smart Metering Network Deployment," Energies, MDPI, vol. 7(4), pages 1-44, April.
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