IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v4y2011i5p758-779d12227.html
   My bibliography  Save this article

Large-Scale Battery System Development and User-Specific Driving Behavior Analysis for Emerging Electric-Drive Vehicles

Author

Listed:
  • Jie Wu

    (Institute of Microelectronics, Tsinghua University, Beijing 10084, China)

  • Kun Li

    (Department of Electrical, Computer and Energy Engineering, University of Colorado at Boulder,)

  • Yifei Jiang

    (Department of Computer Science, University of Colorado at Boulder, Boulder, CO 80309, USA)

  • Qin Lv

    (Department of Computer Science, University of Colorado at Boulder, Boulder, CO 80309, USA)

  • Li Shang

    (Department of Electrical, Computer and Energy Engineering, University of Colorado at Boulder,)

  • Yihe Sun

    (Institute of Microelectronics, Tsinghua University, Beijing 10084, China)

Abstract

Emerging green-energy transportation, such as hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs), has a great potential for reduction of fuel consumption and greenhouse emissions. The lithium-ion battery system used in these vehicles, however, is bulky, expensive and unreliable, and has been the primary roadblock for transportation electrification. Meanwhile, few studies have considered user-specific driving behavior and its significant impact on (P)HEV fuel efficiency, battery system lifetime, and the environment. This paper presents a detailed investigation of battery system modeling and real-world user-specific driving behavior analysis for emerging electric-drive vehicles. The proposed model is fast to compute and accurate for analyzing battery system run-time and long-term cycle life with a focus on temperature dependent battery system capacity fading and variation. The proposed solution is validated against physical measurement using real-world user driving studies, and has been adopted to facilitate battery system design and optimization. Using the collected real-world hybrid vehicle and run-time driving data, we have also conducted detailed analytical studies of users’ specific driving patterns and their impacts on hybrid vehicle electric energy and fuel efficiency. This work provides a solid foundation for future energy control with emerging electric-drive applications.

Suggested Citation

  • Jie Wu & Kun Li & Yifei Jiang & Qin Lv & Li Shang & Yihe Sun, 2011. "Large-Scale Battery System Development and User-Specific Driving Behavior Analysis for Emerging Electric-Drive Vehicles," Energies, MDPI, vol. 4(5), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:5:p:758-779:d:12227
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/4/5/758/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/4/5/758/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qu Zhao, 2018. "Electromobility research in Germany and China: structural differences," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 473-493, October.
    2. Vepsäläinen, Jari & Otto, Kevin & Lajunen, Antti & Tammi, Kari, 2019. "Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions," Energy, Elsevier, vol. 169(C), pages 433-443.
    3. Susanne Rothgang & Matthias Rogge & Jan Becker & Dirk Uwe Sauer, 2015. "Battery Design for Successful Electrification in Public Transport," Energies, MDPI, vol. 8(7), pages 1-23, June.
    4. Jari Vepsäläinen & Antti Ritari & Antti Lajunen & Klaus Kivekäs & Kari Tammi, 2018. "Energy Uncertainty Analysis of Electric Buses," Energies, MDPI, vol. 11(12), pages 1-29, November.

    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:4:y:2011:i:5:p:758-779:d:12227. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.