IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i7p1768-d156391.html
   My bibliography  Save this article

Integrated Sizing and Energy Management for Four-Wheel-Independently-Actuated Electric Vehicles Considering Realistic Constructed Driving Cycles

Author

Listed:
  • Zhenpo Wang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Changhui Qu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Lei Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Jin Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Wen Yu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

Abstract

This paper presents an integrated optimization framework of sizing and energy management for four-wheel-independently-actuated electric vehicles. The optimization framework consists of an inner and an outer layer that are responsible for energy management, i.e., torque allocation, and powertrain parameter optimizations. The optimal torque allocation in the inner layer is achieved via the dynamic programming (DP) method while the desirable powertrain parameters in the outer layer are pursued based on the exhaustive method. In order to verify the proposed optimization framework, two driving cycles are constructed to represent the comprehensive and realistic driving conditions. One cycle is built by combining six typical driving cycles, which cover urban, high-way and rural driving styles to enhance representativeness. The other one is synthesized using the Markov chain method based on a vast quantity of real-time operating data of electric vehicles in Beijing. Simulation results demonstrate that the proposed strategy decreases the power consumption by 15.1% and 13.3%, respectively, in the two driving cycles, compared to the non-optimal, even-torque-allocation strategy.

Suggested Citation

  • Zhenpo Wang & Changhui Qu & Lei Zhang & Jin Zhang & Wen Yu, 2018. "Integrated Sizing and Energy Management for Four-Wheel-Independently-Actuated Electric Vehicles Considering Realistic Constructed Driving Cycles," Energies, MDPI, vol. 11(7), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1768-:d:156391
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/7/1768/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/7/1768/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan Zou & Fengchun Sun & Xiaosong Hu & Lino Guzzella & Huei Peng, 2012. "Combined Optimal Sizing and Control for a Hybrid Tracked Vehicle," Energies, MDPI, vol. 5(11), pages 1-14, November.
    2. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2015. "A combined optimal sizing and energy management approach for hybrid in-wheel motors of EVs," Applied Energy, Elsevier, vol. 139(C), pages 260-271.
    3. Zhenpo Wang & Yachao Wang & Lei Zhang & Mingchun Liu, 2017. "Vehicle Stability Enhancement through Hierarchical Control for a Four-Wheel-Independently-Actuated Electric Vehicle," Energies, MDPI, vol. 10(7), pages 1-18, July.
    4. Amjad, Shaik & Neelakrishnan, S. & Rudramoorthy, R., 2010. "Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1104-1110, April.
    5. Kanghyun Nam & Yoichi Hori & Choonyoung Lee, 2015. "Wheel Slip Control for Improving Traction-Ability and Energy Efficiency of a Personal Electric Vehicle," Energies, MDPI, vol. 8(7), pages 1-21, July.
    6. Sidra Mumtaz & Saima Ali & Saghir Ahmad & Laiq Khan & Syed Zulqadar Hassan & Tariq Kamal, 2017. "Energy Management and Control of Plug-In Hybrid Electric Vehicle Charging Stations in a Grid-Connected Hybrid Power System," Energies, MDPI, vol. 10(11), pages 1-21, November.
    7. Wang, Hewu & Zhang, Xiaobin & Ouyang, Minggao, 2015. "Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing," Applied Energy, Elsevier, vol. 157(C), pages 710-719.
    8. Wang, Hong & Huang, Yanjun & Khajepour, Amir & Song, Qiang, 2016. "Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 182(C), pages 105-114.
    9. M. Hadi Amini & Orkun Karabasoglu, 2018. "Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks," Energies, MDPI, vol. 11(1), pages 1-25, January.
    10. Brady, John & O’Mahony, Margaret, 2016. "Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas," Applied Energy, Elsevier, vol. 177(C), pages 165-178.
    11. Zhang, Lei & Hu, Xiaosong & Wang, Zhenpo & Sun, Fengchun & Dorrell, David G., 2018. "A review of supercapacitor modeling, estimation, and applications: A control/management perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1868-1878.
    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. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng & Li, Xiaoyu & Qu, Changhui, 2019. "Driving cycles construction for electric vehicles considering road environment: A case study in Beijing," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Huang, Yanjun & Wang, Hong & Khajepour, Amir & Li, Bin & Ji, Jie & Zhao, Kegang & Hu, Chuan, 2018. "A review of power management strategies and component sizing methods for hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 132-144.
    3. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2016. "Modelling the multilevel structure and mixed effects of the factors influencing the energy consumption of electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1351-1360.
    5. Naseri, F. & Karimi, S. & Farjah, E. & Schaltz, E., 2022. "Supercapacitor management system: A comprehensive review of modeling, estimation, balancing, and protection techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    6. Cui, Yuepeng & Xu, Hao & Zou, Fumin & Chen, Zhihui & Gong, Kuangmin, 2021. "Optimization based method to develop representative driving cycle for real-world fuel consumption estimation," Energy, Elsevier, vol. 235(C).
    7. Qiao, Qinyu & Zhao, Fuquan & Liu, Zongwei & He, Xin & Hao, Han, 2019. "Life cycle greenhouse gas emissions of Electric Vehicles in China: Combining the vehicle cycle and fuel cycle," Energy, Elsevier, vol. 177(C), pages 222-233.
    8. Liu, Yang & Zhang, Qi & Lyu, Cheng & Liu, Zhiyuan, 2021. "Modelling the energy consumption of electric vehicles under uncertain and small data conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 313-328.
    9. Wu, Ziyang & Wang, Can & Wolfram, Paul & Zhang, Yaxin & Sun, Xin & Hertwich, Edgar, 2019. "Assessing electric vehicle policy with region-specific carbon footprints," Applied Energy, Elsevier, vol. 256(C).
    10. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    11. 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).
    12. Lee, Gwangryeol & Song, Jingeun & Han, Jungwon & Lim, Yunsung & Park, Suhan, 2023. "Study on energy consumption characteristics of passenger electric vehicle according to the regenerative braking stages during real-world driving conditions," Energy, Elsevier, vol. 283(C).
    13. Wang, Yachao & Wen, Yi & Zhu, Qinggong & Luo, Jiaxin & Yang, Zhengjun & Su, Sheng & Wang, Xin & Hao, Lijun & Tan, Jianwei & Yin, Hang & Ge, Yunshan, 2022. "Real driving energy consumption and CO2 & pollutant emission characteristics of a parallel plug-in hybrid electric vehicle under different propulsion modes," Energy, Elsevier, vol. 244(PB).
    14. Gong, Huiming & Zou, Yuan & Yang, Qingkai & Fan, Jie & Sun, Fengchun & Goehlich, Dietmar, 2018. "Generation of a driving cycle for battery electric vehicles：A case study of Beijing," Energy, Elsevier, vol. 150(C), pages 901-912.
    15. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
    16. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    17. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    18. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    19. Shi, Xiao & Pan, Jian & Wang, Hewu & Cai, Hua, 2019. "Battery electric vehicles: What is the minimum range required?," Energy, Elsevier, vol. 166(C), pages 352-358.
    20. Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.

    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:11:y:2018:i:7:p:1768-:d:156391. 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.