IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v205y2017icp880-891.html
   My bibliography  Save this article

A new strategy of efficiency enhancement for traction systems in electric vehicles

Author

Listed:
  • Ding, Xiaofeng
  • Guo, Hong
  • Xiong, Rui
  • Chen, Feida
  • Zhang, Donghuai
  • Gerada, Chris

Abstract

The inverter-motor drive system is the main traction force in electric vehicles (EVs). The overall efficiency of inverter-motor will directly determine the energy consumption of EVs. In this paper, aiming at improving the overall efficiency of inverter-motor, a novel methodology is proposed. Firstly, the iron loss, copper loss and stray loss of motor, as well as the devices’ conduction loss and switching loss in inverter are modeled. Afterwards, based on previous loss model strategy and gold section search strategy, a novel hybrid efficiency-optimization control strategy is proposed. The proposed method combines each benefit in loss-model and gold section search, and can realize high efficiency operation of the inverter-motor system in large power range. Additionally, the proposed method manifests faster search speed and better accuracy compared to conventional methods. Experiment results validated the effectiveness of the proposed hybrid control strategy. Meanwhile, the impact of the efficiency improvement on the driving cycle is further investigated through Advanced Vehicle Simulator (ADVISOR) simulations.

Suggested Citation

  • Ding, Xiaofeng & Guo, Hong & Xiong, Rui & Chen, Feida & Zhang, Donghuai & Gerada, Chris, 2017. "A new strategy of efficiency enhancement for traction systems in electric vehicles," Applied Energy, Elsevier, vol. 205(C), pages 880-891.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:880-891
    DOI: 10.1016/j.apenergy.2017.08.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917310644
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.08.051?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Chun & Xiong, Rui & He, Hongwen & Ding, Xiaofeng & Shen, Weixiang, 2016. "Efficiency analysis of a bidirectional DC/DC converter in a hybrid energy storage system for plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 612-622.
    2. Li, Yunhua & Liu, Mingsheng & Lau, Josephine & Zhang, Bei, 2015. "A novel method to determine the motor efficiency under variable speed operations and partial load conditions," Applied Energy, Elsevier, vol. 144(C), pages 234-240.
    3. 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.
    4. Ding, Xiaofeng & Du, Min & Zhou, Tong & Guo, Hong & Zhang, Chengming, 2017. "Comprehensive comparison between silicon carbide MOSFETs and silicon IGBTs based traction systems for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 626-634.
    5. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    6. Ding, Xiaofeng & Chen, Feida & Du, Min & Guo, Hong & Ren, Suping, 2017. "Effects of silicon carbide MOSFETs on the efficiency and power quality of a microgrid-connected inverter," Applied Energy, Elsevier, vol. 201(C), pages 270-283.
    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. Peikun Sun & Annika Stensson Trigell & Lars Drugge & Jenny Jerrelind, 2020. "Energy-Efficient Direct Yaw Moment Control for In-Wheel Motor Electric Vehicles Utilising Motor Efficiency Maps," Energies, MDPI, vol. 13(3), pages 1-25, January.
    2. Liu, Qin & Zhang, Wencan & Zhang, Zhongbo & Qin, Qichao, 2022. "A drive system global control strategy for electric vehicle based on optimized acceleration curve," Energy, Elsevier, vol. 248(C).
    3. Trancho, E. & Ibarra, E. & Arias, A. & Kortabarria, I. & Prieto, P. & Martínez de Alegría, I. & Andreu, J. & López, I., 2018. "Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles," Applied Energy, Elsevier, vol. 224(C), pages 647-658.
    4. Hung, Nguyen Ba & Sung, Jaewon & Lim, Ocktaeck, 2018. "A simulation and experimental study of operating performance of an electric bicycle integrated with a semi-automatic transmission," Applied Energy, Elsevier, vol. 221(C), pages 319-333.
    5. Hung, Nguyen Ba & Lim, Ocktaeck, 2020. "A review of history, development, design and research of electric bicycles," Applied Energy, Elsevier, vol. 260(C).
    6. Cha, Kyoung-Soo & Kim, Dong-Min & Jung, Young-Hoon & Lim, Myung-Seop, 2020. "Wound field synchronous motor with hybrid circuit for neighborhood electric vehicle traction improving fuel economy," Applied Energy, Elsevier, vol. 263(C).
    7. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.
    8. Hu, Jianjun & Deng, Chenghao & Yang, Dianzhao & Yang, Ying & Jia, Meixia, 2022. "Optimal energy consumption and torque fluctuation control of integrated electric drive system based on mechanical-electromagnetic-thermal coupling characteristics," Energy, Elsevier, vol. 247(C).

    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. Guo, Qingbo & Zhang, Chengming & Li, Liyi & Gerada, David & Zhang, Jiangpeng & Wang, Mingyi, 2017. "Design and implementation of a loss optimization control for electric vehicle in-wheel permanent-magnet synchronous motor direct drive system," Applied Energy, Elsevier, vol. 204(C), pages 1317-1332.
    2. Han, Feng & Guo, Hong & Ding, Xiaofeng, 2021. "Design and optimization of a liquid cooled heat sink for a motor inverter in electric vehicles," Applied Energy, Elsevier, vol. 291(C).
    3. 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.
    4. Chengning Zhang & Xin Jin & Junqiu Li, 2017. "PTC Self-Heating Experiments and Thermal Modeling of Lithium-Ion Battery Pack in Electric Vehicles," Energies, MDPI, vol. 10(4), pages 1-21, April.
    5. Xiaogang Wu & Wenwen Shi & Jiuyu Du, 2017. "Multi-Objective Optimal Charging Method for Lithium-Ion Batteries," Energies, MDPI, vol. 10(9), pages 1-18, August.
    6. 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.
    7. He, Hongwen & Guo, Xiaoguang, 2018. "Multi-objective optimization research on the start condition for a parallel hybrid electric vehicle," Applied Energy, Elsevier, vol. 227(C), pages 294-303.
    8. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
    9. Mingchun Liu & Feihong Gu & Juhua Huang & Changjiang Wang & Ming Cao, 2017. "Integration Design and Optimization Control of a Dynamic Vibration Absorber for Electric Wheels with In-Wheel Motor," Energies, MDPI, vol. 10(12), pages 1-23, December.
    10. Lin, Cheng & Tang, Aihua & Xing, Jilei, 2017. "Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 394-404.
    11. Edemar O. Prado & Pedro C. Bolsi & Hamiltom C. Sartori & José R. Pinheiro, 2022. "An Overview about Si, Superjunction, SiC and GaN Power MOSFET Technologies in Power Electronics Applications," Energies, MDPI, vol. 15(14), pages 1-17, July.
    12. Li, Zhirun & Xiong, Rui & Mu, Hao & He, Hongwen & Wang, Chun, 2017. "A novel parameter and state-of-charge determining method of lithium-ion battery for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 363-371.
    13. Li, Jianwei & Xiong, Rui & Mu, Hao & Cornélusse, Bertrand & Vanderbemden, Philippe & Ernst, Damien & Yuan, Weijia, 2018. "Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime," Applied Energy, Elsevier, vol. 218(C), pages 470-478.
    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. 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.
    16. Ding, Xiaofeng & Chen, Feida & Du, Min & Guo, Hong & Ren, Suping, 2017. "Effects of silicon carbide MOSFETs on the efficiency and power quality of a microgrid-connected inverter," Applied Energy, Elsevier, vol. 201(C), pages 270-283.
    17. Zhang, Jun & Du, Xiong & Qian, Cheng, 2021. "Lifetime improvement for wind power generation system based on optimal effectiveness of thermal management," Applied Energy, Elsevier, vol. 286(C).
    18. Jichao Hong & Zhenpo Wang & Peng Liu, 2017. "Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-16, July.
    19. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    20. 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.

    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:eee:appene:v:205:y:2017:i:c:p:880-891. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.