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Implementation and Analyses of an Eco-Driving Algorithm for Different Battery Electric Powertrain Topologies Based on a Split Loss Integration Approach

Author

Listed:
  • Alexander Koch

    (Institute of Automotive Technology, Department of Mobility Systems Engineering, School of Engineering & Design, Technical University of Munich (TUM), 85748 Garching bei München, Germany)

  • Lorenzo Nicoletti

    (Institute of Automotive Technology, Department of Mobility Systems Engineering, School of Engineering & Design, Technical University of Munich (TUM), 85748 Garching bei München, Germany)

  • Thomas Herrmann

    (Institute of Automotive Technology, Department of Mobility Systems Engineering, School of Engineering & Design, Technical University of Munich (TUM), 85748 Garching bei München, Germany)

  • Markus Lienkamp

    (Institute of Automotive Technology, Department of Mobility Systems Engineering, School of Engineering & Design, Technical University of Munich (TUM), 85748 Garching bei München, Germany)

Abstract

Eco-driving algorithms optimize the speed profile to reduce the energy consumption of a vehicle. This paper presents an eco-driving algorithm for battery electric powertrains that applies a split loss integration approach to incorporate the component losses. The algorithm consistently uses loss models to overcome the drawbacks of efficiency maps, which cannot represent no-load losses at zero torque. The use of loss models is crucial since the optimal solution includes gliding, during which there are no-load losses. An analysis shows, that state-of-the-art nonlinear programming algorithms cannot represent these no-load losses at zero torque with a small modeling error. To effectively compute the powertrain losses with only a small error in comparison to the measurement data, we introduce a tailored combination of nonlinear inequality constraints that interleave two polynomial fits. This approach can properly represent reality. We parameterize the algorithm and validate the vehicle model used with real-world measurement data. Furthermore, we investigate the influence of the proposed interleaved fits by comparing them to a single continuous high-order polynomial fit and to the state of the art. The algorithm is published open source.

Suggested Citation

  • Alexander Koch & Lorenzo Nicoletti & Thomas Herrmann & Markus Lienkamp, 2022. "Implementation and Analyses of an Eco-Driving Algorithm for Different Battery Electric Powertrain Topologies Based on a Split Loss Integration Approach," Energies, MDPI, vol. 15(15), pages 1-29, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5396-:d:872050
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    References listed on IDEAS

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    1. Caiyang Wei & Theo Hofman & Esin Ilhan Caarls, 2021. "Co-Design of CVT-Based Electric Vehicles," Energies, MDPI, vol. 14(7), pages 1-33, March.
    2. Fotouhi, Abbas & Auger, Daniel J. & Propp, Karsten & Longo, Stefano & Wild, Mark, 2016. "A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1008-1021.
    3. Alexander Koch & Olaf Teichert & Svenja Kalt & Aybike Ongel & Markus Lienkamp, 2020. "Powertrain Optimization for Electric Buses under Optimal Energy-Efficient Driving," Energies, MDPI, vol. 13(23), pages 1-19, December.
    4. Liao, Peng & Tang, Tie-Qiao & Liu, Ronghui & Huang, Hai-Jun, 2021. "An eco-driving strategy for electric vehicle based on the powertrain," Applied Energy, Elsevier, vol. 302(C).
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