IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i20p7774-d948584.html
   My bibliography  Save this article

Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast

Author

Listed:
  • Weiyi Lin

    (School of Automobile and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
    National and Local Joint Engineering Research Center for Automotive Technology and Equipment, Hefei University of Technology, Hefei 230009, China)

  • Han Zhao

    (National and Local Joint Engineering Research Center for Automotive Technology and Equipment, Hefei University of Technology, Hefei 230009, China
    School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China)

  • Bingzhan Zhang

    (School of Automobile and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
    National and Local Joint Engineering Research Center for Automotive Technology and Equipment, Hefei University of Technology, Hefei 230009, China)

  • Ye Wang

    (Intelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, China)

  • Yan Xiao

    (Intelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, China)

  • Kang Xu

    (Intelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, China)

  • Rui Zhao

    (Intelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, China)

Abstract

Range-extended Electric Vehicles (REVs) have become popular due to their lack of emissions while driving in urban areas, and the elimination of range anxiety when traveling long distances with a combustion engine as the power source. The fuel consumption performance of REVs depends greatly on the energy management strategy (EMS). This article proposes a practical energy management solution for REVs based on an Adaptive Equivalent Fuel Consumption Minimization Strategy (A-ECMS), wherein the equivalent factor is dynamically optimized by the battery’s State of Charge (SoC) and traffic information provided by Intelligent Transportation Systems (ITS). Furthermore, a penalty function is incorporated with the A-ECMS strategy to achieve the quasi-optimal start–stop control of the range extender. The penalty function is designed based on more precise vehicle velocity forecasting through a nonlinear autoregressive network with exogeneous input (NARX). A model of the studied REV is established in the AVL Cruise environment and the proposed energy management strategy is set up in Matlab/Simulink. Lastly, the performance of the proposed strategy is evaluated over multiple Worldwide Light-duty Test Cycles (WLTC) and real-world driving cycles through model simulation. The simulation conditions are preset such that the range extender must be switched on to finish the planned route. Compared with the basic Charge-Depleting and Charge-Sustaining (CD-CS) strategy, the proposed A-ECMS strategy achieves a fuel-consumption benefit of up to 9%. With the implementation of range extender start–stop optimization, which is based on velocity forecasting, the fuel saving rate can be further improved by 6.7% to 18.2% compared to the base A-ECMS. The proposed strategy is energy efficient, with a simple structure, and it is intended to be implemented on the studied vehicle, which will be available on the market at the end of October 2022.

Suggested Citation

  • Weiyi Lin & Han Zhao & Bingzhan Zhang & Ye Wang & Yan Xiao & Kang Xu & Rui Zhao, 2022. "Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast," Energies, MDPI, vol. 15(20), pages 1-27, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7774-:d:948584
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/20/7774/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/20/7774/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aiyun Gao & Xiaozhong Deng & Mingzhu Zhang & Zhumu Fu, 2017. "Design and Validation of Real-Time Optimal Control with ECMS to Minimize Energy Consumption for Parallel Hybrid Electric Vehicles," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-13, January.
    2. Li, Ji & Zhou, Quan & He, Yinglong & Shuai, Bin & Li, Ziyang & Williams, Huw & Xu, Hongming, 2019. "Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Piotr Wróblewski & Jerzy Kupiec & Wojciech Drożdż & Wojciech Lewicki & Jarosław Jaworski, 2021. "The Economic Aspect of Using Different Plug-In Hybrid Driving Techniques in Urban Conditions," Energies, MDPI, vol. 14(12), pages 1-17, June.
    4. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    5. Zhenzhen Lei & Dong Cheng & Yonggang Liu & Datong Qin & Yi Zhang & Qingbo Xie, 2017. "A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition," Energies, MDPI, vol. 10(1), pages 1-20, January.
    6. Piotr Wróblewski & Wojciech Drożdż & Wojciech Lewicki & Paweł Miązek, 2021. "Methodology for Assessing the Impact of Aperiodic Phenomena on the Energy Balance of Propulsion Engines in Vehicle Electromobility Systems for Given Areas," Energies, MDPI, vol. 14(8), pages 1-24, April.
    7. Yu Miao & Patrick Hynan & Annette von Jouanne & Alexandre Yokochi, 2019. "Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements," Energies, MDPI, vol. 12(6), pages 1-20, March.
    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. Mariusz Niekurzak & Jerzy Mikulik, 2021. "Modeling of Energy Consumption and Reduction of Pollutant Emissions in a Walking Beam Furnace Using the Expert Method—Case Study," Energies, MDPI, vol. 14(23), pages 1-22, December.
    2. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
    3. Geng, Wenran & Lou, Diming & Wang, Chen & Zhang, Tong, 2020. "A cascaded energy management optimization method of multimode power-split hybrid electric vehicles," Energy, Elsevier, vol. 199(C).
    4. Ahmed M. Ali & Dirk Söffker, 2018. "Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions," Energies, MDPI, vol. 11(3), pages 1-24, February.
    5. Elżbieta Macioszek & Maria Cieśla & Anna Granà, 2023. "Future Development of an Energy-Efficient Electric Scooter Sharing System Based on a Stakeholder Analysis Method," Energies, MDPI, vol. 16(1), pages 1-24, January.
    6. 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).
    7. Yongbing Xiang & Xiaomin Yang, 2021. "An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-21, February.
    8. Desreveaux, A. & Bouscayrol, A. & Trigui, R. & Hittinger, E. & Castex, E. & Sirbu, G.M., 2023. "Accurate energy consumption for comparison of climate change impact of thermal and electric vehicles," Energy, Elsevier, vol. 268(C).
    9. Hegde, Bharatkumar & Ahmed, Qadeer & Rizzoni, Giorgio, 2020. "Velocity and energy trajectory prediction of electrified powertrain for look ahead control," Applied Energy, Elsevier, vol. 279(C).
    10. Anisa Surya Wijareni & Hendri Widiyandari & Agus Purwanto & Aditya Farhan Arif & Mohammad Zaki Mubarok, 2022. "Morphology and Particle Size of a Synthesized NMC 811 Cathode Precursor with Mixed Hydroxide Precipitate and Nickel Sulfate as Nickel Sources and Comparison of Their Electrochemical Performances in an," Energies, MDPI, vol. 15(16), pages 1-15, August.
    11. Alexandru Ciocan & Cosmin Ungureanu & Alin Chitu & Elena Carcadea & George Darie, 2020. "Electrical Longboard for Everyday Urban Commuting," Sustainability, MDPI, vol. 12(19), pages 1-14, September.
    12. Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    13. Piotr Krawczyk & Anna Śliwińska, 2020. "Eco-Efficiency Assessment of the Application of Large-Scale Rechargeable Batteries in a Coal-Fired Power Plant," Energies, MDPI, vol. 13(6), pages 1-16, March.
    14. Jack E. N. Swallow & Michael W. Fraser & Nis-Julian H. Kneusels & Jodie F. Charlton & Christopher G. Sole & Conor M. E. Phelan & Erik Björklund & Peter Bencok & Carlos Escudero & Virginia Pérez-Dieste, 2022. "Revealing solid electrolyte interphase formation through interface-sensitive Operando X-ray absorption spectroscopy," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    15. González, L.G. & Cordero-Moreno, Daniel & Espinoza, J.L., 2021. "Public transportation with electric traction: Experiences and challenges in an Andean city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    16. Harika Dasari & Eric Eisenbraun, 2021. "Predicting Capacity Fade in Silicon Anode-Based Li-Ion Batteries," Energies, MDPI, vol. 14(5), pages 1-16, March.
    17. Sewon Kim & Ju-Sik Kim & Lincoln Miara & Yan Wang & Sung-Kyun Jung & Seong Yong Park & Zhen Song & Hyungsub Kim & Michael Badding & JaeMyung Chang & Victor Roev & Gabin Yoon & Ryounghee Kim & Jung-Hwa, 2022. "High-energy and durable lithium metal batteries using garnet-type solid electrolytes with tailored lithium-metal compatibility," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    18. Zhang, Bo & Zhang, Jiangyan & Shen, Tielong, 2022. "Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode," Applied Energy, Elsevier, vol. 305(C).
    19. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    20. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(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:gam:jeners:v:15:y:2022:i:20:p:7774-:d:948584. 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.