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Longitudinal Dynamics Simulation Tool for Hybrid APU and Full Electric Vehicle

Author

Listed:
  • Giulia Sandrini

    (Department of Mechanical and Industrial Engineering, University of Brescia, I-25123 Brescia, Italy)

  • Marco Gadola

    (Department of Mechanical and Industrial Engineering, University of Brescia, I-25123 Brescia, Italy)

  • Daniel Chindamo

    (Department of Mechanical and Industrial Engineering, University of Brescia, I-25123 Brescia, Italy)

Abstract

Due to problems related to environmental pollution and fossil fuels consumption that have not infinite availability, the automotive sector is increasingly moving towards electric powertrains. The most limiting aspect of this category of vehicles is certainly the battery pack, regarding the difficulty in obtaining high range with good performance and low weights. The aim of this work is to provide a simulation tool, which allows for the analysis of the performance of different types of electric and hybrid powertrains, concerning both mechanical and electrical aspects. Through this model it is possible to test different vehicle configurations before prototype realization or to investigate the impact that subsystems’ modifications may have on a vehicle under development. This will allow to speed-up the model-based design process typical for fully electric and hybrid vehicles. The model aims to be at the same time complete but simple enough to lower the simulation time and computational burden so that it can be used in real-time applications, such as driving simulators. All this reduces the time and costs of vehicle design. Validation is also provided, based on a real vehicle and comparison with another consolidated simulation tool. Maximum error on mechanical quantities is proved to be within 5% while on electrical quantities it is always lower than 10%.

Suggested Citation

  • Giulia Sandrini & Marco Gadola & Daniel Chindamo, 2021. "Longitudinal Dynamics Simulation Tool for Hybrid APU and Full Electric Vehicle," Energies, MDPI, vol. 14(4), pages 1-35, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1207-:d:504416
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    References listed on IDEAS

    as
    1. Zhou, Wei & Chen, Yaoqi & Zhai, Haoran & Zhang, Weigang, 2021. "Predictive energy management for a plug-in hybrid electric vehicle using driving profile segmentation and energy-based analytical SoC planning," Energy, Elsevier, vol. 220(C).
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    Cited by:

    1. Giulia Sandrini & Daniel Chindamo & Marco Gadola, 2022. "Regenerative Braking Logic That Maximizes Energy Recovery Ensuring the Vehicle Stability," Energies, MDPI, vol. 15(16), pages 1-43, August.
    2. Laura Zecchi & Giulia Sandrini & Marco Gadola & Daniel Chindamo, 2022. "Modeling of a Hybrid Fuel Cell Powertrain with Power Split Logic for Onboard Energy Management Using a Longitudinal Dynamics Simulation Tool," Energies, MDPI, vol. 15(17), pages 1-18, August.
    3. Giulia Sandrini & Marco Gadola & Daniel Chindamo & Andrea Candela & Paolo Magri, 2023. "Exploring the Impact of Vehicle Lightweighting in Terms of Energy Consumption: Analysis and Simulation," Energies, MDPI, vol. 16(13), pages 1-31, July.
    4. Giulia Sandrini & Marco Gadola & Daniel Chindamo & Laura Zecchi, 2023. "Model of a Hybrid Electric Vehicle Equipped with Solid Oxide Fuel Cells Powered by Biomethane," Energies, MDPI, vol. 16(13), pages 1-23, June.
    5. Alberto Broatch & Pablo Olmeda & Pau Bares & Sebastián Aceros, 2022. "Integral Thermal Management Studies in Winter Conditions with a Global Model of a Battery-Powered Electric Bus," Energies, MDPI, vol. 16(1), pages 1-24, December.

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