IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v266y2020ics0306261920303196.html
   My bibliography  Save this article

Prototype implementation of advanced electric vehicles drivetrain system: Verification and validation

Author

Listed:
  • Ahmed, Abdelsalam A.
  • Ramadan, Haitham S.

Abstract

The design of high-efficient green means of transportation has become a real worldwide challenge, particularly to cope with the due sustainable development commitments. Accordingly, the realization and the development of a full electric drive system, as a drivetrain, for Electric Vehicles (EVs) has become necessary while considering the proper power and control circuits. Enhancing the efficiency of the energy conversion in EV’s powertrain can be conveniently performed through the proposed advanced control technique. This paper presents the prototype design and modelling of an all-in-one EV for industrial, educational and research activities. The proposed electric drivetrain, with its inherent flexibility advantage, enables verifying hardware and software solutions. The EV prototype consists of a 1.1 kW induction AC drive. The advanced electric drive system (EDS) is implemented as a novel part of the EV. This original EDS consists of power switches IGBT modules, advanced gate drivers, position and phase current sensors, and interface circuits. This EDS is governed by a non-commercial digital control tool TMS320F28335 DSP programmed by C++ in code composer studio (CCS). The advanced gate drivers are used for isolating and amplifying the control signals to the power switches. The advanced indirect field-oriented control (FOC) technique is used for torque and speed control of the AC drive. For adjusting the level of the rotor flux at random load variation circumstances, two control modes are adopted: flux-increased control (FIC) and flux-limited control (FLC). The design of the full EV prototype together with the integrated electric vehicle drivetrain (EVD) are presented. Consequently, experiments and simulations are performed to validate the significance of using such proposed two-mode controller. Through simulation analysis, the new EVD used for the EV set-up is verified. The simulation results demonstrate lower drawn supply currents with the proposed control technique. Thus, the energy conversion process becomes more efficient due to the increased power transmitted from the battery to wheels. The experimental setup for the novel EVD is integrated. The proposed two-mode control technique is experimentally verified considering random accelerator pedal as a reference torque input. The results illustrate the significant performance of using the proposed cascaded FIC and FLC techniques in EVs owing to the smooth and efficient transition between the modes. The different tests and measurements illustrate the usability of the proposed EVD as an ideal alternative to the commercial AC drives in favor of its developmental flexibility, commercialization-independency, and affordability.

Suggested Citation

  • Ahmed, Abdelsalam A. & Ramadan, Haitham S., 2020. "Prototype implementation of advanced electric vehicles drivetrain system: Verification and validation," Applied Energy, Elsevier, vol. 266(C).
    • Handle: RePEc:eee:appene:v:266:y:2020:i:c:s0306261920303196
    DOI: 10.1016/j.apenergy.2020.114807
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920303196
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114807?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. Rechkemmer, Sabrina Kathrin & Zang, Xiaoyun & Zhang, Weimin & Sawodny, Oliver, 2019. "Lifetime optimized charging strategy of Li-ion cells based on daily driving cycle of electric two-wheelers," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Kolhe, M. & Adhikari, S.K. & Muneer, T., 2019. "Parked electric car's cabin heat management using photovoltaic powered ventilation system," Applied Energy, Elsevier, vol. 233, pages 403-411.
    3. Hill, Graeme & Heidrich, Oliver & Creutzig, Felix & Blythe, Phil, 2019. "The role of electric vehicles in near-term mitigation pathways and achieving the UK’s carbon budget," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Keller, Victor & English, Jeffrey & Fernandez, Julian & Wade, Cameron & Fowler, McKenzie & Scholtysik, Sven & Palmer-Wilson, Kevin & Donald, James & Robertson, Bryson & Wild, Peter & Crawford, Curran , 2019. "Electrification of road transportation with utility controlled charging: A case study for British Columbia with a 93% renewable electricity target," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Wu, Di & Radhakrishnan, Nikitha & Huang, Sen, 2019. "A hierarchical charging control of plug-in electric vehicles with simple flexibility model," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. López, I. & Ibarra, E. & Matallana, A. & Andreu, J. & Kortabarria, I., 2019. "Next generation electric drives for HEV/EV propulsion systems: Technology, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    7. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    8. Bellocchi, Sara & Klöckner, Kai & Manno, Michele & Noussan, Michel & Vellini, Michela, 2019. "On the role of electric vehicles towards low-carbon energy systems: Italy and Germany in comparison," Applied Energy, Elsevier, vol. 255(C).
    9. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    10. Du, Jiuyu & Liu, Ye & Mo, Xinying & Li, Yalun & Li, Jianqiu & Wu, Xiaogang & Ouyang, Minggao, 2019. "Impact of high-power charging on the durability and safety of lithium batteries used in long-range battery electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    11. Buonomano, A. & Calise, F. & Cappiello, F.L. & Palombo, A. & Vicidomini, M., 2019. "Dynamic analysis of the integration of electric vehicles in efficient buildings fed by renewables," Applied Energy, Elsevier, vol. 245(C), pages 31-50.
    12. Wenig, Jürgen & Sodenkamp, Mariya & Staake, Thorsten, 2019. "Battery versus infrastructure: Tradeoffs between battery capacity and charging infrastructure for plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    13. Peng, Hui & Wang, Junzheng & Shen, Wei & Shi, Dawei & Huang, Yuan, 2019. "Compound control for energy management of the hybrid ultracapacitor-battery electric drive systems," Energy, Elsevier, vol. 175(C), pages 309-319.
    14. Moon, Saedaseul & Lee, Deok-Joo, 2019. "An optimal electric vehicle investment model for consumers using total cost of ownership: A real option approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Gao, Zhiming & LaClair, Tim & Ou, Shiqi & Huff, Shean & Wu, Guoyuan & Hao, Peng & Boriboonsomsin, Kanok & Barth, Matthew, 2019. "Evaluation of electric vehicle component performance over eco-driving cycles," Energy, Elsevier, vol. 172(C), pages 823-839.
    16. Trianni, Andrea & Cagno, Enrico & Accordini, Davide, 2019. "Energy efficiency measures in electric motors systems: A novel classification highlighting specific implications in their adoption," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    17. Runge, Philipp & Sölch, Christian & Albert, Jakob & Wasserscheid, Peter & Zöttl, Gregor & Grimm, Veronika, 2019. "Economic comparison of different electric fuels for energy scenarios in 2035," Applied Energy, Elsevier, vol. 233, pages 1078-1093.
    18. Morganti, Eleonora & Browne, Michael, 2018. "Technical and operational obstacles to the adoption of electric vans in France and the UK: An operator perspective," Transport Policy, Elsevier, vol. 63(C), pages 90-97.
    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. Olivier Bethoux, 2020. "Hydrogen Fuel Cell Road Vehicles: State of the Art and Perspectives," Energies, MDPI, vol. 13(21), pages 1-28, November.
    2. Lin Li & Serdar Coskun & Jiaze Wang & Youming Fan & Fengqi Zhang & Reza Langari, 2021. "Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples," Energies, MDPI, vol. 14(12), pages 1-30, June.

    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. Abd Alla, Sara & Bianco, Vincenzo & Tagliafico, Luca A. & Scarpa, Federico, 2021. "Pathways to electric mobility integration in the Italian automotive sector," Energy, Elsevier, vol. 221(C).
    2. Ren, Haoshan & Ma, Zhenjun & Fai Norman Tse, Chung & Sun, Yongjun, 2022. "Optimal control of solar-powered electric bus networks with improved renewable energy on-site consumption and reduced grid dependence," Applied Energy, Elsevier, vol. 323(C).
    3. David Borge-Diez & Pedro Miguel Ortega-Cabezas & Antonio Colmenar-Santos & Jorge Juan Blanes-Peiró, 2021. "Contribution of Driving Efficiency to Vehicle-to-Building," Energies, MDPI, vol. 14(12), pages 1-30, June.
    4. Feng, Zhiyan & Zhang, Qingang & Zhang, Yiming & Fei, Liangyu & Jiang, Fei & Zhao, Shengdun, 2024. "Practicability analysis of online deep reinforcement learning towards energy management strategy of 4WD-BEVs driven by dual-motor in-wheel motors," Energy, Elsevier, vol. 290(C).
    5. Saleh Aghajan-Eshkevari & Sasan Azad & Morteza Nazari-Heris & Mohammad Taghi Ameli & Somayeh Asadi, 2022. "Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies," Sustainability, MDPI, vol. 14(4), pages 1-31, February.
    6. Yang, Yang & Yuan, Wei & Zhang, Xiaoqing & Ke, Yuzhi & Qiu, Zhiqiang & Luo, Jian & Tang, Yong & Wang, Chun & Yuan, Yuhang & Huang, Yao, 2020. "A review on structuralized current collectors for high-performance lithium-ion battery anodes," Applied Energy, Elsevier, vol. 276(C).
    7. Jun Li & Bin Yang & Mingke He, 2023. "Capabilities Analysis of Electricity Energy Conservation and Carbon Emissions Reduction in Multi-Level Battery Electric Passenger Vehicle in China," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    8. 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).
    9. Li, Mengyu & Lenzen, Manfred & Wang, Dai & Nansai, Keisuke, 2020. "GIS-based modelling of electric-vehicle–grid integration in a 100% renewable electricity grid," Applied Energy, Elsevier, vol. 262(C).
    10. Hamels, Sam & Himpe, Eline & Laverge, Jelle & Delghust, Marc & Van den Brande, Kjartan & Janssens, Arnold & Albrecht, Johan, 2021. "The use of primary energy factors and CO2 intensities for electricity in the European context - A systematic methodological review and critical evaluation of the contemporary literature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    11. Burd, Joshua Thomas Jameson & Moore, Elizabeth A. & Ezzat, Hesham & Kirchain, Randolph & Roth, Richard, 2021. "Improvements in electric vehicle battery technology influence vehicle lightweighting and material substitution decisions," Applied Energy, Elsevier, vol. 283(C).
    12. Wang, Yong & Wu, Yuankai & Tang, Yingjuan & Li, Qin & He, Hongwen, 2023. "Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 332(C).
    13. Sofiane Bacha & Ramzi Saadi & Mohamed Yacine Ayad & Mohamed Sahraoui & Khaled Laadjal & Antonio J. Marques Cardoso, 2023. "Autonomous Electric-Vehicle Control Using Speed Planning Algorithm and Back-Stepping Approach," Energies, MDPI, vol. 16(5), pages 1-26, March.
    14. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    15. 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.
    16. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    17. Chen, Jie & Wang, Ruochen & Ding, Renkai & Luo, Ding, 2024. "Matching design and numerical optimization of automotive thermoelectric generator system applied to range-extended electric vehicle," Applied Energy, Elsevier, vol. 370(C).
    18. Feiyu Hou & Fei Yao & Zheng Li, 2022. "A Torque-Compensated Fault-Tolerant Control Method for Electric Vehicle Traction Motor with Short-Circuit Fault," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    19. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    20. Matteo Acquarone & Claudio Maino & Daniela Misul & Ezio Spessa & Antonio Mastropietro & Luca Sorrentino & Enrico Busto, 2023. "Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control," Energies, MDPI, vol. 16(6), pages 1-22, March.

    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:266:y:2020:i:c:s0306261920303196. 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.