IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics0360544224034960.html
   My bibliography  Save this article

High gain Bi-directional KY converter for low power EV applications

Author

Listed:
  • Nagabushanam, K Mounika
  • Mahto, Tarkeshwar
  • Tewari, Somesh Vinayak
  • Udumula, Ramanjaneya Reddy

Abstract

In electric vehicles (EVs), the type of electric motor and converter technology have a significant impact on regulating the operational characteristics of the vehicle. Therefore, in this work, the modified bi-directional KY converter (BKYC) is proposed for EV applications. The main contributions of the proposed converter are high step-up/step-down conversion gain, bi-directional power flow, simplified control structure, continuous current, common ground, low volume, and high efficiency. An inductor on either side of the converter ensures continuous current flow and passive components are arranged to operate in series to offer high step-up/step-down conversion. The charging and discharging operations, steady-state analysis, and design process of the proposed converter are discussed in detail and compared with similar bi-directional converter topologies. Further, the efficiency analysis of the proposed converter is presented and found that the efficacy of 95.51 % in charging operation and 96.52 % in discharging operation of operation. The simulations are carried out using MATLAB/Simulink environment. Further, a prototype of a modified bi-directional KY converter is implemented with a TMS320F28335 processor and validated with theoretical and simulation counterparts.

Suggested Citation

  • Nagabushanam, K Mounika & Mahto, Tarkeshwar & Tewari, Somesh Vinayak & Udumula, Ramanjaneya Reddy, 2024. "High gain Bi-directional KY converter for low power EV applications," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224034960
    DOI: 10.1016/j.energy.2024.133718
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224034960
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133718?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. Zhang, LiPeng & Liu, Wei & Qi, BingNan, 2020. "Energy optimization of multi-mode coupling drive plug-in hybrid electric vehicles based on speed prediction," Energy, Elsevier, vol. 206(C).
    2. Julio López Seguel & Seleme I. Seleme & Lenin M. F. Morais, 2022. "Comparative Study of Buck-Boost, SEPIC, Cuk and Zeta DC-DC Converters Using Different MPPT Methods for Photovoltaic Applications," Energies, MDPI, vol. 15(21), pages 1-26, October.
    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. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    2. Víctor Ferreira Gruner & Jefferson William Zanotti & Walbermark Marques Santos & Thiago Antonio Pereira & Lenon Schmitz & Denizar Cruz Martins & Roberto Francisco Coelho, 2023. "Modified Current Sensorless Incremental Conductance Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 16(2), pages 1-16, January.
    3. Shi, Dehua & Xu, Han & Wang, Shaohua & Hu, Jia & Chen, Long & Yin, Chunfang, 2024. "Deep reinforcement learning based adaptive energy management for plug-in hybrid electric vehicle with double deep Q-network," Energy, Elsevier, vol. 305(C).
    4. Hu, Zhiyuan & Yang, Rui & Fang, Liang & Wang, Zhuo & Zhao, Yinghua, 2024. "Research on vehicle speed prediction model based on traffic flow information fusion," Energy, Elsevier, vol. 292(C).
    5. Lin, Xinyou & Wu, Jiayun & Wei, Yimin, 2021. "An ensemble learning velocity prediction-based energy management strategy for a plug-in hybrid electric vehicle considering driving pattern adaptive reference SOC," Energy, Elsevier, vol. 234(C).
    6. Zhou, Yang & Chen, Bo & Xu, Xianfeng & Zhang, Zhen & Ravey, Alexandre & Péra, Marie-Cécile & Ma, Ruiqing, 2024. "Data-driven cost-optimal energy management of postal-delivery fuel cell electric vehicle with intelligent dual-loop battery state-of-charge planner," Energy, Elsevier, vol. 290(C).
    7. Wei, Hongqian & Zhang, Nan & Liang, Jun & Ai, Qiang & Zhao, Wenqiang & Huang, Tianyi & Zhang, Youtong, 2022. "Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance," Energy, Elsevier, vol. 238(PB).
    8. Lijie Feng & Kehui Liu & Jinfeng Wang & Kuo-Yi Lin & Ke Zhang & Luyao Zhang, 2022. "Identifying Promising Technologies of Electric Vehicles from the Perspective of Market and Technical Attributes," Energies, MDPI, vol. 15(20), pages 1-22, October.
    9. Nie, Zhigen & Jia, Yuan & Wang, Wanqiong & Chen, Zheng & Outbib, Rachid, 2022. "Co-optimization of speed planning and energy management for intelligent fuel cell hybrid vehicle considering complex traffic conditions," Energy, Elsevier, vol. 247(C).
    10. Zhang, Junjiang & Feng, Ganghui & Yan, Xianghai & He, Yundong & Liu, Mengnan & Xu, Liyou, 2024. "Cooperative control method considering efficiency and tracking performance for unmanned hybrid tractor based on rotary tillage prediction," Energy, Elsevier, vol. 288(C).
    11. Zhen Zhu & Yanpeng Yang & Dongqing Wang & Yingfeng Cai & Longhui Lai, 2022. "Energy Saving Performance of Agricultural Tractor Equipped with Mechanic-Electronic-Hydraulic Powertrain System," Agriculture, MDPI, vol. 12(3), pages 1-22, March.
    12. Wang, Shuai & Wu, Xiuheng & Zhao, Xueyan & Wang, Shilong & Xie, Bin & Song, Zhenghe & Wang, Dongqing, 2023. "Co-optimization energy management strategy for a novel dual-motor drive system of electric tractor considering efficiency and stability," Energy, Elsevier, vol. 281(C).
    13. Yang, Chao & Wang, Muyao & Wang, Weida & Pu, Zesong & Ma, Mingyue, 2021. "An efficient vehicle-following predictive energy management strategy for PHEV based on improved sequential quadratic programming algorithm," Energy, Elsevier, vol. 219(C).
    14. Dapai Shi & Junjie Guo & Kangjie Liu & Qingling Cai & Zhenghong Wang & Xudong Qu, 2023. "Research on an Improved Rule-Based Energy Management Strategy Enlightened by the DP Optimization Results," Sustainability, MDPI, vol. 15(13), pages 1-13, July.
    15. Tobias Frambach & Ralf Kleisch & Ralf Liedtke & Jochen Schwarzer & Egbert Figgemeier, 2022. "Environmental Impact Assessment and Classification of 48 V Plug-in Hybrids with Real-Driving Use Case Simulations," Energies, MDPI, vol. 15(7), pages 1-21, March.
    16. Lu, Nianci & Pan, Lei & Cui, Guomin & Pedersen, Simon & Shivaie, Mojtaba & Arabkoohsar, Ahmad, 2024. "The energy management strategy of two-by-one combined cycle gas turbine based on dynamic programming," Energy, Elsevier, vol. 313(C).
    17. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    18. Jin, Yue & Yang, Lin & Du, Mao & Qiang, Jiaxi & Li, Jingzhong & Chen, Yuxuan & Tu, Jiayu, 2023. "Two-scale based energy management for connected plug-in hybrid electric vehicles with global optimal energy consumption and state-of-charge trajectory prediction," Energy, Elsevier, vol. 267(C).
    19. Lipeng, Zhang & Xin, Liu & Shuaishuai, Liu & Haoran, Guo & Kaixin, Shi, 2024. "Low energy consumption traction control for centralized and distributed dual-mode coupling drive electric vehicle on split ramps," Energy, Elsevier, vol. 289(C).
    20. Lin, Xinyou & Xia, Yutian & Huang, Wei & Li, Hailin, 2021. "Trip distance adaptive power prediction control strategy optimization for a Plug-in Fuel Cell Electric Vehicle," Energy, Elsevier, vol. 224(C).

    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:energy:v:313:y:2024:i:c:s0360544224034960. 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.journals.elsevier.com/energy .

    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.