IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i4p533-d95811.html
   My bibliography  Save this article

Impact of Silicon Carbide Devices on the Powertrain Systems in Electric Vehicles

Author

Listed:
  • Xiaofeng Ding

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China)

  • Jiawei Cheng

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China)

  • Feida Chen

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China)

Abstract

The DC/DC converters and DC/AC inverters based on silicon carbide (SiC) devices as battery interfaces, motor drives, etc., in electric vehicles (EVs) benefit from their low resistances, fast switching speed, high temperature tolerance, etc. Such advantages could improve the power density and efficiency of the converter and inverter systems in EVs. Furthermore, the total powertrain system in EVs is also affected by the converter and inverter system based on SiC, especially the capacity of the battery and the overall system efficiency. Therefore, this paper investigates the impact of SiC on the powertrain systems in EVs. First, the characteristics of SiC are evaluated by a double pulse test (DPT). Then, the power losses of the DC/DC converter, DC/AC inverter, and motor are measured. The measured results are assigned into a powertrain model built in the Advanced Vehicle Simulator (ADVISOR) software in order to explore a direct correlation between the SiC and the performance of the powertrain system in EVs, which are then compared with the conventional powertrain system based on silicon (Si). The test and simulation results demonstrate that the efficiency of the overall powertrain is significantly improved and the capacity of the battery can be remarkably reduced if the Si is replaced by SiC in the powertrain system.

Suggested Citation

  • Xiaofeng Ding & Jiawei Cheng & Feida Chen, 2017. "Impact of Silicon Carbide Devices on the Powertrain Systems in Electric Vehicles," Energies, MDPI, vol. 10(4), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:533-:d:95811
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/4/533/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/4/533/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    2. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    3. Ruan, Jiageng & Walker, Paul & Zhang, Nong, 2016. "A comparative study energy consumption and costs of battery electric vehicle transmissions," Applied Energy, Elsevier, vol. 165(C), pages 119-134.
    4. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Efrén Fernández & Alejandro Paredes & Vicent Sala & Luis Romeral, 2018. "A Simple Method for Reducing THD and Improving the Efficiency in CSI Topology Based on SiC Power Devices," Energies, MDPI, vol. 11(10), pages 1-23, October.
    2. Jelena Loncarski & Vito Giuseppe Monopoli & Riccardo Leuzzi & Leposava Ristic & Francesco Cupertino, 2019. "Analytical and Simulation Fair Comparison of Three Level Si IGBT Based NPC Topologies and Two Level SiC MOSFET Based Topology for High Speed Drives," Energies, MDPI, vol. 12(23), pages 1-16, November.
    3. Seok-Kyoon Kim, 2017. "Proportional-Type Performance Recovery DC-Link Voltage Tracking Algorithm for Permanent Magnet Synchronous Generators," Energies, MDPI, vol. 10(9), pages 1-17, September.
    4. Massimiliano Passalacqua & Mauro Carpita & Serge Gavin & Mario Marchesoni & Matteo Repetto & Luis Vaccaro & Sébastien Wasterlain, 2019. "Supercapacitor Storage Sizing Analysis for a Series Hybrid Vehicle," Energies, MDPI, vol. 12(9), pages 1-15, May.
    5. Fernando Acosta-Cambranis & Jordi Zaragoza & Luis Romeral & Néstor Berbel, 2020. "Comparative Analysis of SVM Techniques for a Five-Phase VSI Based on SiC Devices," Energies, MDPI, vol. 13(24), pages 1-25, December.
    6. Rui Xiong & Hailong Li & Xuan Zhou, 2017. "Advanced Energy Storage Technologies and Their Applications (AESA2017)," Energies, MDPI, vol. 10(9), pages 1-3, September.
    7. Hao Liu & Xianjin Huang & Fei Lin & Zhongping Yang, 2017. "Loss Model and Efficiency Analysis of Tram Auxiliary Converter Based on a SiC Device," Energies, MDPI, vol. 10(12), pages 1-20, December.
    8. Ding, Xiaofeng & Lu, Peng & Shan, Zhenyu, 2021. "A high-accuracy switching loss model of SiC MOSFETs in a motor drive for electric vehicles," Applied Energy, Elsevier, vol. 291(C).
    9. Pedro Costa & Sónia Pinto & José Fernando Silva, 2023. "A Novel Analytical Formulation of SiC-MOSFET Losses to Size High-Efficiency Three-Phase Inverters," Energies, MDPI, vol. 16(2), pages 1-19, January.

    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. Lin, Cheng & Yu, Quanqing & Xiong, Rui & Wang, Le Yi, 2017. "A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 205(C), pages 892-902.
    2. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    3. Li, Liang & Li, Xujian & Wang, Xiangyu & Song, Jian & He, Kai & Li, Chenfeng, 2016. "Analysis of downshift’s improvement to energy efficiency of an electric vehicle during regenerative braking," Applied Energy, Elsevier, vol. 176(C), pages 125-137.
    4. Ragab El-Sehiemy & Mohamed A. Hamida & Ehab Elattar & Abdullah Shaheen & Ahmed Ginidi, 2022. "Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm," Energies, MDPI, vol. 15(13), pages 1-20, June.
    5. Ma, Zeyu & Yang, Ruixin & Wang, Zhenpo, 2019. "A novel data-model fusion state-of-health estimation approach for lithium-ion batteries," Applied Energy, Elsevier, vol. 237(C), pages 836-847.
    6. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    7. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    8. Zheng Chen & Xiaoyu Li & Jiangwei Shen & Wensheng Yan & Renxin Xiao, 2016. "A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles," Energies, MDPI, vol. 9(9), pages 1-15, September.
    9. Tang, Xiaopeng & Liu, Boyang & Lv, Zhou & Gao, Furong, 2017. "Observer based battery SOC estimation: Using multi-gain-switching approach," Applied Energy, Elsevier, vol. 204(C), pages 1275-1283.
    10. Lin, Cheng & Gong, Xinle & Xiong, Rui & Cheng, Xingqun, 2017. "A novel H∞ and EKF joint estimation method for determining the center of gravity position of electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 609-616.
    11. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    12. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    13. Xiaogang Wu & Tianze Wang, 2017. "Optimization of Battery Capacity Decay for Semi-Active Hybrid Energy Storage System Equipped on Electric City Bus," Energies, MDPI, vol. 10(6), pages 1-20, June.
    14. Ye, Min & Guo, Hui & Xiong, Rui & Yu, Quanqing, 2018. "A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries," Energy, Elsevier, vol. 144(C), pages 789-799.
    15. Dafen Chen & Jiuchun Jiang & Xue Li & Zhanguo Wang & Weige Zhang, 2016. "Modeling of a Pouch Lithium Ion Battery Using a Distributed Parameter Equivalent Circuit for Internal Non-Uniformity Analysis," Energies, MDPI, vol. 9(11), pages 1-18, October.
    16. Wen, Jianping & Zhao, Dan & Zhang, Chuanwei, 2020. "An overview of electricity powered vehicles: Lithium-ion battery energy storage density and energy conversion efficiency," Renewable Energy, Elsevier, vol. 162(C), pages 1629-1648.
    17. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    18. Zhu, Rui & Duan, Bin & Zhang, Chenghui & Gong, Sizhao, 2019. "Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    19. Xiaogang Wu & Zhe Chen & Zhiyang Wang, 2017. "Analysis of Low Temperature Preheating Effect Based on Battery Temperature-Rise Model," Energies, MDPI, vol. 10(8), pages 1-15, August.
    20. Mu, Hao & Xiong, Rui & Zheng, Hongfei & Chang, Yuhua & Chen, Zeyu, 2017. "A novel fractional order model based state-of-charge estimation method for lithium-ion battery," Applied Energy, Elsevier, vol. 207(C), pages 384-393.

    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:10:y:2017:i:4:p:533-:d:95811. 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.