IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4655-d605888.html
   My bibliography  Save this article

An Estimation of the Lightweight Potential of Battery Electric Vehicles

Author

Listed:
  • Lorenzo Nicoletti

    (Department of Mechanical Engineering, School of Engineering and Design, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany)

  • Andrea Romano

    (Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany)

  • Adrian König

    (Department of Mechanical Engineering, School of Engineering and Design, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany)

  • Peter Köhler

    (Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany)

  • Maximilian Heinrich

    (Audi AG, I-EG/A22 Konzeptauslgung Baukasten/Plattform Elektrifiziert, 85055 Ingolstadt, Germany)

  • Markus Lienkamp

    (Department of Mechanical Engineering, School of Engineering and Design, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany)

Abstract

Although battery electric vehicles (BEVs) are locally emission-free and assist automakers in reducing their carbon footprint, two major disadvantages are their shorter range and higher production costs compared to combustion engines. These drawbacks are primarily due to the battery, which is generally the heaviest and most expensive component of a BEV. Lightweight measures (strategies to decrease vehicle mass, e.g., by changing materials or downsizing components) lower energy consumption and reduce the amount of battery energy required (and in turn battery costs). Careful selection of lightweight measures can result in their costs being balanced out by a commensurate reduction in battery costs. This leads to a higher efficiency vehicle, but without affecting its production and development costs. In this paper, we estimate the lightweight potential of BEVs, i.e., the cost limit below which a lightweight measure is fully compensated by the cost savings it generates. We implement a parametric energy consumption and mass model and apply it to a set of BEVs. Subsequently, we apply the model to quantify the lightweight potential range (in €/kg) of BEVs. The findings of this paper can be used as a reference for the development of cheaper, lighter, and more energy-efficient BEVs.

Suggested Citation

  • Lorenzo Nicoletti & Andrea Romano & Adrian König & Peter Köhler & Maximilian Heinrich & Markus Lienkamp, 2021. "An Estimation of the Lightweight Potential of Battery Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-29, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4655-:d:605888
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4655/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/15/4655/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Björn Nykvist & Måns Nilsson, 2015. "Rapidly falling costs of battery packs for electric vehicles," Nature Climate Change, Nature, vol. 5(4), pages 329-332, April.
    2. Pavlovic, J. & Ciuffo, B. & Fontaras, G. & Valverde, V. & Marotta, A., 2018. "How much difference in type-approval CO2 emissions from passenger cars in Europe can be expected from changing to the new test procedure (NEDC vs. WLTP)?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 136-147.
    Full references (including those not matched with items on IDEAS)

    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. Ettore Bompard & Daniele Grosso & Tao Huang & Francesco Profumo & Xianzhang Lei & Duo Li, 2018. "World Decarbonization through Global Electricity Interconnections," Energies, MDPI, vol. 11(7), pages 1-29, July.
    2. Huang, Qisheng & Xu, Yunjian & Courcoubetis, Costas, 2020. "Stackelberg competition between merchant and regulated storage investment in wholesale electricity markets," Applied Energy, Elsevier, vol. 264(C).
    3. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    4. Carsten Helm & Mathias Mier, 2020. "Steering the Energy Transition in a World of Intermittent Electricity Supply: Optimal Subsidies and Taxes for Renewables Storage," ifo Working Paper Series 330, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    5. Yashraj Tripathy & Andrew McGordon & Anup Barai, 2020. "Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles," Energies, MDPI, vol. 13(8), pages 1-18, April.
    6. Maria Taljegard & Lisa Göransson & Mikael Odenberger & Filip Johnsson, 2021. "To Represent Electric Vehicles in Electricity Systems Modelling—Aggregated Vehicle Representation vs. Individual Driving Profiles," Energies, MDPI, vol. 14(3), pages 1-25, January.
    7. De Vivero-Serrano, Gustavo & Bruninx, Kenneth & Delarue, Erik, 2019. "Implications of bid structures on the offering strategies of merchant energy storage systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Ito, Nobuyuki & Takeuchi, Kenji & Managi, Shunsuke, 2019. "Do battery-switching systems accelerate the adoption of electric vehicles? A stated preference study," Economic Analysis and Policy, Elsevier, vol. 61(C), pages 85-92.
    9. Tharsis Teoh & Oliver Kunze & Chee-Chong Teo & Yiik Diew Wong, 2018. "Decarbonisation of Urban Freight Transport Using Electric Vehicles and Opportunity Charging," Sustainability, MDPI, vol. 10(9), pages 1-20, September.
    10. Sun, Li & Sun, Wen & You, Fengqi, 2020. "Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias," Applied Energy, Elsevier, vol. 271(C).
    11. Biswas, D.B. & Bose, S. & Dalvi, V.H. & Deshmukh, S.P. & Shenoy, N.V. & Panse, S.V. & Joshi, J.B., 2020. "A techno-economic comparison between piston steam engines as dispatchable power generation systems for renewable energy with concentrated solar harvesting and thermal storage against solar photovoltai," Energy, Elsevier, vol. 213(C).
    12. Ivan Mareev & Dirk Uwe Sauer, 2018. "Energy Consumption and Life Cycle Costs of Overhead Catenary Heavy-Duty Trucks for Long-Haul Transportation," Energies, MDPI, vol. 11(12), pages 1-18, December.
    13. Toktarova, Alla & Walter, Viktor & Göransson, Lisa & Johnsson, Filip, 2022. "Interaction between electrified steel production and the north European electricity system," Applied Energy, Elsevier, vol. 310(C).
    14. Verdolini, Elena & Vona, Francesco & Popp, David, 2018. "Bridging the gap: Do fast-reacting fossil technologies facilitate renewable energy diffusion?," Energy Policy, Elsevier, vol. 116(C), pages 242-256.
    15. Yazdanie, Mashael & Densing, Martin & Wokaun, Alexander, 2017. "Cost optimal urban energy systems planning in the context of national energy policies: A case study for the city of Basel," Energy Policy, Elsevier, vol. 110(C), pages 176-190.
    16. Ruffini, Eleonora & Wei, Max, 2018. "Future costs of fuel cell electric vehicles in California using a learning rate approach," Energy, Elsevier, vol. 150(C), pages 329-341.
    17. Helm, Carsten & Mier, Mathias, 2021. "Steering the energy transition in a world of intermittent electricity supply: Optimal subsidies and taxes for renewables and storage," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    18. Baek, Seungju & Lee, Hyeonjik & Lee, Kihyung, 2021. "Fuel efficiency and exhaust characteristics of turbocharged diesel engine equipped with an electric supercharger," Energy, Elsevier, vol. 214(C).
    19. Sofia Dahlgren & Jonas Ammenberg, 2021. "Sustainability Assessment of Public Transport, Part II—Applying a Multi-Criteria Assessment Method to Compare Different Bus Technologies," Sustainability, MDPI, vol. 13(3), pages 1-30, January.
    20. Sturm, J. & Ennifar, H. & Erhard, S.V. & Rheinfeld, A. & Kosch, S. & Jossen, A., 2018. "State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter," Applied Energy, Elsevier, vol. 223(C), pages 103-123.

    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:14:y:2021:i:15:p:4655-:d:605888. 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.