IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9394-d1168775.html
   My bibliography  Save this article

Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model

Author

Listed:
  • Qian Zhang

    (School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China
    School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Shaopeng Tian

    (School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China)

Abstract

The development of hybrid technology can effectively solve the problems of the high pollution and energy consumption levels of automobiles. Therefore, an energy consumption prediction and control algorithm for hybrid vehicles based on a minimum equivalent fuel consumption model is proposed. The model’s battery power consumption is equivalent to the fuel consumption, and the sum of the engine fuel consumption and the battery equivalent fuel consumption is established as the objective function. By utilizing these factors, an innovative minimum equivalent fuel consumption model was constructed that could be used to measure the energy efficiency of hybrid vehicles. The longitudinal force result of braking force distribution control was obtained, as well as the energy consumption prediction structure of a hybrid electric vehicle. The rolling resistance, air resistance, and climbing resistance of the hybrid electric vehicles were calculated, and the energy consumption control algorithm for hybrid electric vehicles was constructed according to the calculation results. The experimental results indicated that under this research algorithm, the driving energy consumption of hybrid electric vehicles was relatively low and the energy consumption and energy efficiency measurements effectively met the actual demand, and the energy consumption prediction and control results were good.

Suggested Citation

  • Qian Zhang & Shaopeng Tian, 2023. "Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9394-:d:1168775
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9394/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9394/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhou, Quan & Li, Yanfei & Zhao, Dezong & Li, Ji & Williams, Huw & Xu, Hongming & Yan, Fuwu, 2022. "Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression," Applied Energy, Elsevier, vol. 305(C).
    2. Taghavifar, Hadi, 2021. "Fuel cell hybrid range-extender vehicle sizing: Parametric power optimization," Energy, Elsevier, vol. 229(C).
    3. Sarvaiya, Shradhdha & Ganesh, Sachin & Xu, Bin, 2021. "Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life," Energy, Elsevier, vol. 228(C).
    4. Hemmati, S. & Doshi, N. & Hanover, D. & Morgan, C. & Shahbakhti, M., 2021. "Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle," Applied Energy, Elsevier, vol. 283(C).
    5. Hu, Lin & Tian, Qingtao & Zou, Changfu & Huang, Jing & Ye, Yao & Wu, Xianhui, 2022. "A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    6. 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).
    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. Witsarut Achariyaviriya & Wongkot Wongsapai & Kittitat Janpoom & Tossapon Katongtung & Yuttana Mona & Nakorn Tippayawong & Pana Suttakul, 2023. "Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches," Energies, MDPI, vol. 16(17), pages 1-14, September.
    2. Aminu Babangida & Péter Tamás Szemes, 2024. "Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain," Energies, MDPI, vol. 17(18), pages 1-38, September.
    3. Runze Liu & Jianming Cai & Lipeng Hu & Benxiao Lou & Jinjun Tang, 2025. "Electric Bus Battery Energy Consumption Estimation and Influencing Features Analysis Using a Two-Layer Stacking Framework with SHAP-Based Interpretation," Sustainability, MDPI, vol. 17(15), pages 1-27, August.

    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. Wang, Weida & Chen, Yincong & Yang, Chao & Li, Ying & Xu, Bin & Xiang, Changle, 2022. "An enhanced hypotrochoid spiral optimization algorithm based intertwined optimal sizing and control strategy of a hybrid electric air-ground vehicle," Energy, Elsevier, vol. 257(C).
    2. Jin, Rui & Li, Lei & Liang, Xiaoling & Zou, Xiang & Yang, Zeyuan & Ge, Shuzhi Sam & Huang, Haihong, 2024. "Energy-efficient design of the powertrain for mechanical-electro-hydraulic equipment via configuring multidimensional controllable variables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    3. Xiang Tian & Ma Wan & Xinqiang Chen & Yingfeng Cai & Xiaodong Sun & Zhen Zhu, 2025. "Driver-Oriented Adaptive Equivalent Consumption Minimization Strategy for Plug-in Hybrid Electric Buses," Energies, MDPI, vol. 18(18), pages 1-18, September.
    4. Alexander Wahl & Christoph Wellmann & Björn Krautwig & Patrick Manns & Bicheng Chen & Christof Schernus & Jakob Andert, 2022. "Efficiency Increase through Model Predictive Thermal Control of Electric Vehicle Powertrains," Energies, MDPI, vol. 15(4), pages 1-21, February.
    5. Zhang, Yahui & You, Xiongxiong & Song, Yunfeng & Zhao, Yahui & Wei, Zeyi & Jiao, Xiaohong, 2025. "Hierarchical eco-driving of connected hybrid electric vehicles: Integrating predictive cruise control and cost-to-go approximation-guided energy management," Energy, Elsevier, vol. 319(C).
    6. Zhang, Zhiqing & Dong, Rui & Tan, Dongli & Duan, Lin & Jiang, Feng & Yao, Xiaoxue & Yang, Dixin & Hu, Jingyi & Zhang, Jian & Zhong, Weihuang & Zhao, Ziheng, 2023. "Effect of structural parameters on diesel particulate filter trapping performance of heavy-duty diesel engines based on grey correlation analysis," Energy, Elsevier, vol. 271(C).
    7. Yushi Wang & Beining Hu & Xianhai Meng & Runjin Xiao, 2024. "A Comprehensive Review on Technologies for Achieving Zero-Energy Buildings," Sustainability, MDPI, vol. 16(24), pages 1-26, December.
    8. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    9. Kim, Dong-Min & Lee, Soo-Gyung & Kim, Dae-Kee & Park, Min-Ro & Lim, Myung-Seop, 2022. "Sizing and optimization process of hybrid electric propulsion system for heavy-duty vehicle based on Gaussian process modeling considering traction motor characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    10. Zhang, Hao & Lei, Nuo & Chen, Boli & Li, Bingbing & Li, Rulong & Wang, Zhi, 2024. "Modeling and control system optimization for electrified vehicles: A data-driven approach," Energy, Elsevier, vol. 310(C).
    11. Ma, Ying & Yang, Heng & Zuo, Hongyan & Ma, Yi & Zuo, Qingsong & Chen, Ying & He, Xiaoxiang & Wei, Rongrong, 2023. "Three-dimensional EG@MOF matrix composite phase change materials for high efficiency battery cooling," Energy, Elsevier, vol. 278(C).
    12. Zhang, Cetengfei & Zhou, Quan & Hua, Min & Xu, Hongming & Bassett, Mike & Zhang, Fanggang, 2023. "Cuboid equivalent consumption minimization strategy for energy management of multi-mode plug-in hybrid vehicles considering diverse time scale objectives," Applied Energy, Elsevier, vol. 351(C).
    13. Ivan Panfilov & Alexey N. Beskopylny & Besarion Meskhi, 2023. "Numerical Simulation of Heat Transfer and Spread of Virus Particles in the Car Interior," Mathematics, MDPI, vol. 11(3), pages 1-18, February.
    14. Chen, Ruihu & Yang, Chao & Ma, Yue & Wang, Weida & Wang, Muyao & Du, Xuelong, 2022. "Online learning predictive power coordinated control strategy for off-road hybrid electric vehicles considering the dynamic response of engine generator set," Applied Energy, Elsevier, vol. 323(C).
    15. Tang, Wenbin & Jiao, Xiaohong & Zhang, Yahui, 2025. "Hierarchical energy management control for connected hybrid electric vehicles in uncertain traffic scenarios," Energy, Elsevier, vol. 315(C).
    16. Zewen Meng & Tiezhu Zhang & Hongxin Zhang & Qinghai Zhao & Jian Yang, 2021. "Energy Management Strategy for an Electromechanical-Hydraulic Coupled Power Electric Vehicle Considering the Optimal Speed Threshold," Energies, MDPI, vol. 14(17), pages 1-12, August.
    17. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    18. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    19. Francesco Mocera & Aurelio Somà & Salvatore Martelli & Valerio Martini, 2023. "Trends and Future Perspective of Electrification in Agricultural Tractor-Implement Applications," Energies, MDPI, vol. 16(18), pages 1-36, September.
    20. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 238(PC).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:15:y:2023:i:12:p:9394-:d:1168775. 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.