IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i4p577-d1338815.html
   My bibliography  Save this article

Mathematical Modelling of Traction Equipment Parameters of Electric Cargo Trucks

Author

Listed:
  • Boris V. Malozyomov

    (Department of Electrotechnical Complexes, Novosibirsk State Technical University, 630073 Novosibirsk, Russia)

  • Nikita V. Martyushev

    (Department of Advanced Technologies, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Svetlana N. Sorokova

    (Department of Mechanical Engineering, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Egor A. Efremenkov

    (Department of Mechanical Engineering, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Denis V. Valuev

    (Yurga Technological Institute (Branch), Tomsk Polytechnic University, 652055 Yurga, Russia)

  • Mengxu Qi

    (Department of Advanced Technologies, Tomsk Polytechnic University, 634050 Tomsk, Russia)

Abstract

Electric vehicles are one of the most innovative and promising areas of the automotive industry. The efficiency of traction equipment is an important factor in the operation of an electric vehicle. In electric vehicles, the energy stored in the battery is converted into mechanical energy to drive the vehicle. The higher the efficiency of the battery, the less energy is lost in the conversion process, which improves the overall energy efficiency of the electric vehicle. Determining the performance characteristics of the traction battery of an electric vehicle plays an important role in the selection of the vehicle and its future operation. Using mathematical modelling, it is shown that battery capacity, charging rate, durability and efficiency are essential to ensure the comfortable and efficient operation of an electric vehicle throughout its lifetime. A mathematical model of an electric truck including a traction battery has been developed. It is shown that, with the help of the developed mathematical model, it is possible to calculate the load parameters of the battery in standardised driving cycles. The data verification is carried out by comparing the data obtained during standardised driving with the results of mathematical modelling.

Suggested Citation

  • Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2024. "Mathematical Modelling of Traction Equipment Parameters of Electric Cargo Trucks," Mathematics, MDPI, vol. 12(4), pages 1-32, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:4:p:577-:d:1338815
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/4/577/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/4/577/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Boris V. Malozyomov & Nikita V. Martyushev & Elena V. Voitovich & Roman V. Kononenko & Vladimir Yu. Konyukhov & Vadim Tynchenko & Viktor Alekseevich Kukartsev & Yadviga Aleksandrovna Tynchenko, 2023. "Designing the Optimal Configuration of a Small Power System for Autonomous Power Supply of Weather Station Equipment," Energies, MDPI, vol. 16(13), pages 1-30, June.
    2. Foad H. Gandoman & Emad M. Ahmed & Ziad M. Ali & Maitane Berecibar & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2021. "Reliability Evaluation of Lithium-Ion Batteries for E-Mobility Applications from Practical and Technical Perspectives: A Case Study," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    3. Boris V. Malozyomov & Nikita V. Martyushev & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Nikolay A. Zagorodnii & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    4. Xue Li & Jiuchun Jiang & Caiping Zhang & Le Yi Wang & Linfeng Zheng, 2015. "Robustness of SOC Estimation Algorithms for EV Lithium-Ion Batteries against Modeling Errors and Measurement Noise," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-14, October.
    5. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    6. Denys Baranovskyi & Maryna Bulakh & Adam Michajłyszyn & Sergey Myamlin & Leonty Muradian, 2023. "Determination of the Risk of Failures of Locomotive Diesel Engines in Maintenance," Energies, MDPI, vol. 16(13), pages 1-14, June.
    7. Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of Mechanical Forces and Power Balance in Electromechanical Energy Converter," Mathematics, MDPI, vol. 11(10), pages 1-11, May.
    8. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport," Mathematics, MDPI, vol. 11(15), pages 1-31, July.
    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. Nikita V. Martyushev & Boris V. Malozyomov & Olga A. Filina & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Stochastic Models and Processing Probabilistic Data for Solving the Problem of Improving the Electric Freight Transport Reliability," Mathematics, MDPI, vol. 11(23), pages 1-19, November.
    2. Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2024. "Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks," Mathematics, MDPI, vol. 12(3), pages 1-17, February.
    3. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport," Mathematics, MDPI, vol. 11(15), pages 1-31, July.
    4. Olga A. Filina & Nikita V. Martyushev & Boris V. Malozyomov & Vadim Sergeevich Tynchenko & Viktor Alekseevich Kukartsev & Kirill Aleksandrovich Bashmur & Pavel P. Pavlov & Tatyana Aleksandrovna Panfil, 2023. "Increasing the Efficiency of Diagnostics in the Brush-Commutator Assembly of a Direct Current Electric Motor," Energies, MDPI, vol. 17(1), pages 1-24, December.
    5. Boris V. Malozyomov & Nikita V. Martyushev & Vladislav V. Kukartsev & Vadim S. Tynchenko & Vladimir V. Bukhtoyarov & Xiaogang Wu & Yadviga A. Tyncheko & Viktor A. Kukartsev, 2023. "Overview of Methods for Enhanced Oil Recovery from Conventional and Unconventional Reservoirs," Energies, MDPI, vol. 16(13), pages 1-48, June.
    6. Boris V. Malozyomov & Nikita V. Martyushev & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Nikolay A. Zagorodnii & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    7. Khalid Khan & Inna Samuilik & Amir Ali, 2024. "A Mathematical Model for Dynamic Electric Vehicles: Analysis and Optimization," Mathematics, MDPI, vol. 12(2), pages 1-19, January.
    8. Boris V. Malozyomov & Nikita V. Martyushev & Elena V. Voitovich & Roman V. Kononenko & Vladimir Yu. Konyukhov & Vadim Tynchenko & Viktor Alekseevich Kukartsev & Yadviga Aleksandrovna Tynchenko, 2023. "Designing the Optimal Configuration of a Small Power System for Autonomous Power Supply of Weather Station Equipment," Energies, MDPI, vol. 16(13), pages 1-30, June.
    9. An, Fulai & Zhang, Weige & Sun, Bingxiang & Jiang, Jiuchun & Fan, Xinyuan, 2023. "A novel battery pack inconsistency model and influence degree analysis of inconsistency on output energy," Energy, Elsevier, vol. 271(C).
    10. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    11. Igor P. Boychuk & Anna V. Grinek & Nikita V. Martyushev & Roman V. Klyuev & Boris V. Malozyomov & Vadim S. Tynchenko & Viktor A. Kukartsev & Yadviga A. Tynchenko & Sergey I. Kondratiev, 2023. "A Methodological Approach to the Simulation of a Ship’s Electric Power System," Energies, MDPI, vol. 16(24), pages 1-30, December.
    12. Hridoy Roy & Bimol Nath Roy & Md. Hasanuzzaman & Md. Shahinoor Islam & Ayman S. Abdel-Khalik & Mostaf S. Hamad & Shehab Ahmed, 2022. "Global Advancements and Current Challenges of Electric Vehicle Batteries and Their Prospects: A Comprehensive Review," Sustainability, MDPI, vol. 14(24), pages 1-30, December.
    13. Tingting Li & Shejun Deng & Caoye Lu & Yong Wang & Huajun Liao, 2023. "Optimization of Green Vehicle Paths Considering the Impact of Carbon Emissions: A Case Study of Municipal Solid Waste Collection and Transportation," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    14. Tariq Kamal & Syed Zulqadar Hassan, 2023. "Special Issue “Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems”," Energies, MDPI, vol. 16(22), pages 1-4, November.
    15. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    16. Rahil Parag Sheth & Narendra Singh Ranawat & Ayon Chakraborty & Rajesh Prasad Mishra & Manoj Khandelwal, 2023. "The Lithium-Ion Battery Recycling Process from a Circular Economy Perspective—A Review and Future Directions," Energies, MDPI, vol. 16(7), pages 1-16, April.
    17. Madiyar Aidabulov & Daulet Zhakupov & Khabiba Zhunussova & Aknur Temireyeva & Dhawal Shah & Yerbol Sarbassov, 2023. "Thermal Characterization, Kinetic Analysis and Co-Combustion of Sewage Sludge Coupled with High Ash Ekibastuz Coal," Energies, MDPI, vol. 16(18), pages 1-13, September.
    18. Deng Ma & Kai Gao & Yutao Mu & Ziqi Wei & Ronghua Du, 2022. "An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error," Energies, MDPI, vol. 15(10), pages 1-18, May.
    19. Wendi Xu & Xianpeng Wang & Qingxin Guo & Xiangman Song & Ren Zhao & Guodong Zhao & Dakuo He & Te Xu & Ming Zhang & Yang Yang, 2023. "Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence," Mathematics, MDPI, vol. 11(20), pages 1-11, October.
    20. Yaoyidi Wang & Niansheng Chen & Guangyu Fan & Dingyu Yang & Lei Rao & Songlin Cheng & Xiaoyong Song, 2023. "DLPformer: A Hybrid Mathematical Model for State of Charge Prediction in Electric Vehicles Using Machine Learning Approaches," Mathematics, MDPI, vol. 11(22), pages 1-21, November.

    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:jmathe:v:12:y:2024:i:4:p:577-:d:1338815. 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.