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Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles

Author

Listed:
  • Nikita V. Martyushev

    (Department of Materials Science, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Boris V. Malozyomov

    (Department of Electrotechnical Complexes, Novosibirsk State Technical University, 20, Karla Marksa Ave., 630073 Novosibirsk, Russia)

  • Svetlana N. Sorokova

    (Department of Mechanical Engineering, Tomsk Polytechnic University, 30, Lenin Ave., 634050 Tomsk, Russia)

  • Egor A. Efremenkov

    (Department of Mechanical Engineering, Tomsk Polytechnic University, 30, Lenin Ave., 634050 Tomsk, Russia)

  • Mengxu Qi

    (Department of Mechanical Engineering, Tomsk Polytechnic University, 30, Lenin Ave., 634050 Tomsk, Russia)

Abstract

Currently, the estimated range of an electric vehicle is a variable value. The assessment of this power reserve is possible by various methods, and the results of the assessment by these methods will be quite different. Thus, building a model based on these cycles is an extremely important task for manufacturers of electric vehicles. In this paper, a simulation model was developed to determine the range of an electric vehicle by cycles of movement. A mathematical model was created to study the power reserve of an electric vehicle, taking into account four driving cycles, in which the lengths of cycles and the forces acting on the electric vehicle are determined; the calculation of the forces of resistance to movement was carried out taking into account the efficiency of the electric motor; thus, the energy consumption of an electric vehicle is determined. The modeling of the study of motion cycles on the presented model was carried out. The mathematical evaluation of battery life was based on simulation results. Simulation modeling of an electric vehicle in the MATLAB Simulink software environment was performed. An assessment of the power reserve of the developed electric vehicle was completed. The power reserve was estimated using the four most common driving cycles—NEDC, WLTC, JC08, US06. Studies have shown that the highest speed of the presented US06 cycle provides the shortest range of an electric vehicle. The JC08 and NEDC cycles have similar developed speeds in urban conditions, while in NEDC there is a phase of out-of-town traffic; therefore, due to the higher speed, the electric vehicle covers a greater distance in equal time compared to JC08. At the same time, the NEDC cycle is the least dynamic and the acceleration values do not exceed 1 m/s 2 . Low dynamics allow for a longer range of an electric vehicle; however, the actual urban operation of an electric vehicle requires more dynamics. The cycles of movement presented in the article provide a sufficient variety and variability of the load of an electric vehicle and its battery over a wide range, which made it possible to conduct effective studies of the energy consumed, taking into account the recovery of electricity to the battery in a wide range of loads. It was determined that frequent braking, taking into account operation including in urban traffic, provides a significant return of electricity to the battery.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2586-:d:1164352
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    References listed on IDEAS

    as
    1. 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.
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    9. Saeed Sepasi & Leon R. Roose & Marc M. Matsuura, 2015. "Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    10. Nickolay I. Shchurov & Sergey V. Myatezh & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergei I. Dedov, 2021. "Determination of Inactive Powers in a Single-Phase AC Network," Energies, MDPI, vol. 14(16), pages 1-13, August.
    11. Allafi, Walid & Uddin, Kotub & Zhang, Cheng & Mazuir Raja Ahsan Sha, Raja & Marco, James, 2017. "On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model," Applied Energy, Elsevier, vol. 204(C), pages 497-508.
    12. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.
    13. Uddin, Kotub & Jackson, Tim & Widanage, Widanalage D. & Chouchelamane, Gael & Jennings, Paul A. & Marco, James, 2017. "On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system," Energy, Elsevier, vol. 133(C), pages 710-722.
    14. Madina E. Isametova & Rollan Nussipali & Nikita V. Martyushev & Boris V. Malozyomov & Egor A. Efremenkov & Aysen Isametov, 2022. "Mathematical Modeling of the Reliability of Polymer Composite Materials," Mathematics, MDPI, vol. 10(21), pages 1-19, October.
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    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 & 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.
    3. 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.
    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. 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.
    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. 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.
    8. 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.
    9. 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.

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