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Agnostic Battery Management System Capacity Estimation for Electric Vehicles

Author

Listed:
  • Lisa Calearo

    (Department of Wind and Energy Systems, Technical University of Denmark (DTU), Risø Campus, 2800 Roskilde, Denmark
    Ramboll Danmark A/S, 2300 Copenhagen, Denmark)

  • Charalampos Ziras

    (Department of Wind and Energy Systems, Technical University of Denmark (DTU), Risø Campus, 2800 Roskilde, Denmark)

  • Andreas Thingvad

    (Hybrid Greentech ApS, 4000 Roskilde, Denmark)

  • Mattia Marinelli

    (Department of Wind and Energy Systems, Technical University of Denmark (DTU), Risø Campus, 2800 Roskilde, Denmark)

Abstract

Battery degradation is a main concern for electric vehicle (EV) users, and a reliable capacity estimation is of major importance. Every EV battery management system (BMS) provides a variety of information, including measured current and voltage, and estimated capacity of the battery. However, these estimations are not transparent and are manufacturer-specific, although measurement accuracy is unknown. This article uses extensive measurements from six diverse EVs to compare and assess capacity estimation with three different methods: (1) reading capacity estimation from the BMS through the central area network (CAN)-bus, (2) using an empirical capacity estimation (ECE) method with external current measurements, and (3) using the same method with measurements coming from the BMS. We show that the use of BMS current measurements provides consistent capacity estimation (a difference of approximately 1%) and can circumvent the need for costly experimental equipment and DC chargers. This data can simplify the ECE method only by using an on-board diagnostics port (OBDII) reader and an AC charger, as the car measures the current directly at the battery terminals.

Suggested Citation

  • Lisa Calearo & Charalampos Ziras & Andreas Thingvad & Mattia Marinelli, 2022. "Agnostic Battery Management System Capacity Estimation for Electric Vehicles," Energies, MDPI, vol. 15(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9656-:d:1008476
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    References listed on IDEAS

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    2. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
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