IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p7970-d1296752.html
   My bibliography  Save this article

Determination of Energy Savings via Fuel Consumption Estimation with Machine Learning Methods and Rule-Based Control Methods Developed for Experimental Data of Hybrid Electric Vehicles

Author

Listed:
  • Yılmaz Seryar Arıkuşu

    (Department of Electrical Engineering, Manisa Celal Bayar University, 45140 Manisa, Turkey)

  • Nevra Bayhan

    (Department of Electrical and Electronics Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey)

  • Hasan Tiryaki

    (Department of Electrical and Electronics Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey)

Abstract

In this study, a parallel hybrid electric vehicle produced within the scope of our project titled “Development of Fuel Efficiency Enhancing and Innovative Technologies for Internal Combustion Engine Vehicles” has been modeled. Firstly, a new rule-based control method is proposed to minimize fuel consumption and carbon emission values in driving cycles in the experimental model of the parallel hybrid electric vehicle produced within the scope of this project. The proposed control method ensures that the internal combustion engine (ICE) operates at the optimum point. In addition, the electric motor (EM) is activated more frequently at low speeds, and the electric motor can also work as a generator. Then, a new dataset was also created on a traffic-free racetrack with the proposed control method for fuel consumption estimation of a parallel hybrid electric vehicle using ECE-15 (Urban Driving Cycle), EUDC (Extra Urban Driving Cycle), and NEDC (New European Driving Cycle) driving cycles. The data set is dependent on 11 different input variables, which complicates the system. Afterward, the fuel estimation process is made with seven different machine learning methods (ML), and these methods are compared using the obtained data set. To avoid overfitting machine learning, two different test data sets were created. The Random Forest algorithm is the most suitable technique in terms of training and testing the fuel consumption model using correlation coefficient ( R 2 ), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) simulation appropriateness for both test datasets. Moreover, the random forest algorithm achieved an impressive accuracy of 97% and 90% for both test datasets, outperforming the other algorithms. Furthermore, the proposed method consumes 4.72 L of fuel per 100 km, while the gasoline-powered vehicle consumes 7 L of fuel per 100 km. The results show that the proposed method emits 4.69 kg less C O 2 emissions. The effectiveness of the Random Forest Algorithm has been verified by both simulation results and real-world data.

Suggested Citation

  • Yılmaz Seryar Arıkuşu & Nevra Bayhan & Hasan Tiryaki, 2023. "Determination of Energy Savings via Fuel Consumption Estimation with Machine Learning Methods and Rule-Based Control Methods Developed for Experimental Data of Hybrid Electric Vehicles," Energies, MDPI, vol. 16(24), pages 1-25, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:7970-:d:1296752
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/7970/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/24/7970/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bielaczyc, Piotr & Woodburn, Joseph & Szczotka, Andrzej, 2014. "An assessment of regulated emissions and CO2 emissions from a European light-duty CNG-fueled vehicle in the context of Euro 6 emissions regulations," Applied Energy, Elsevier, vol. 117(C), pages 134-141.
    2. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    3. Shabbir, Wassif & Evangelou, Simos A., 2019. "Threshold-changing control strategy for series hybrid electric vehicles," Applied Energy, Elsevier, vol. 235(C), pages 761-775.
    4. Daan Hulshof & Machiel Mulder, 2020. "Willingness to Pay for $$\hbox {CO}_2$$CO2 Emission Reductions in Passenger Car Transport," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(4), pages 899-929, April.
    5. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    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. 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).
    2. 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.
    3. 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).
    4. Wang, Yaxin & Lou, Diming & Xu, Ning & Fang, Liang & Tan, Piqiang, 2021. "Energy management and emission control for range extended electric vehicles," Energy, Elsevier, vol. 236(C).
    5. Paweł Krawczyk & Artur Kopczyński & Jakub Lasocki, 2022. "Modeling and Simulation of Extended-Range Electric Vehicle with Control Strategy to Assess Fuel Consumption and CO 2 Emission for the Expected Driving Range," Energies, MDPI, vol. 15(12), pages 1-41, June.
    6. Vincenzo De Bellis & Marco Piras & Enrica Malfi, 2022. "Assessment of an Adaptive Efficient Thermal/Electric Skipping Control Strategy for the Management of a Parallel Plug-in Hybrid Electric Vehicle," Energies, MDPI, vol. 15(19), pages 1-20, September.
    7. Zhao, Mingjie & Shi, Junhui & Lin, Cheng, 2019. "Optimization of integrated energy management for a dual-motor coaxial coupling propulsion electric city bus," Applied Energy, Elsevier, vol. 243(C), pages 21-34.
    8. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    9. 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.
    10. Schleich, Joachim & Alsheimer, Sven, 2024. "The relationship between willingness to pay and carbon footprint knowledge: Are individuals willing to pay more to offset their carbon footprint if they learn about its size and distance to the 1.5 °C," Ecological Economics, Elsevier, vol. 219(C).
    11. Matteo Vaccargiu & Andrea Pinna & Roberto Tonelli & Luisanna Cocco, 2023. "Blockchain in the Energy Sector for SDG Achievement," Sustainability, MDPI, vol. 15(20), pages 1-23, October.
    12. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    13. Artur Jaworski & Hubert Kuszewski & Krzysztof Balawender & Paweł Woś & Krzysztof Lew & Mirosław Jaremcio, 2024. "Assessment of CH 4 Emissions in a Compressed Natural Gas-Adapted Engine in the Context of Changes in the Equivalence Ratio," Energies, MDPI, vol. 17(9), pages 1-18, April.
    14. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    15. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
    16. 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.
    17. Vitale, F. & Rispoli, N. & Sorrentino, M. & Rosen, M.A. & Pianese, C., 2021. "On the use of dynamic programming for optimal energy management of grid-connected reversible solid oxide cell-based renewable microgrids," Energy, Elsevier, vol. 225(C).
    18. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    19. Guo, Hongqiang & Sun, Qun & Wang, Chong & Wang, Qinpu & Lu, Silong, 2018. "A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle," Energy, Elsevier, vol. 148(C), pages 1006-1017.
    20. Kagiri, Charles & Wanjiru, Evan M. & Zhang, Lijun & Xia, Xiaohua, 2018. "Optimized response to electricity time-of-use tariff of a compressed natural gas fuelling station," Applied Energy, Elsevier, vol. 222(C), pages 244-256.

    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:16:y:2023:i:24:p:7970-:d:1296752. 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.