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Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles

Author

Listed:
  • Nikita V. Martyushev

    (Department of Advanced Technologies, 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

In this paper, a mathematical simulation model of an electric vehicle traction battery has been developed, in which the battery was studied during the dynamic modes of its charge and discharge for heavy electric vehicles in various driving conditions—the conditions of the urban cycle and movement outside the city. The state of a lithium-ion battery is modeled based on operational factors, including changes in battery temperature. The simulation results will be useful for the implementation of real-time systems that take into account the processes of changing the characteristics of traction batteries. The developed mathematical model can be used in battery management systems to monitor the state of charge and battery degradation using the assessment of the state of charge (SOC) and the state of health (SOH). This is especially important when designing and operating a smart battery management system (BMS) in virtually any application of lithium-ion batteries, providing information on how long the device will run before it needs to be charged (SOC value) and when the battery should be replaced due to loss of battery capacity (SOH value). Based on the battery equivalent circuit and the system of equations, a simulation model was created to calculate the electrical and thermal characteristics. The equivalent circuit includes active and reactive elements, each of which imitates the physicochemical parameter of the battery under study or the structural element of the electrochemical battery. The input signals of the mathematical model are the current and ambient temperatures obtained during the tests of the electric vehicle, and the output signals are voltage, electrolyte temperature and degree of charge. The resulting equations make it possible to assign values of internal resistance to a certain temperature value and a certain value of the degree of charge. As a result of simulation modeling, the dependence of battery heating at various ambient temperatures was determined.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:536-:d:1041029
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    References listed on IDEAS

    as
    1. Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.
    2. Kuo-Hsin Tseng & Jin-Wei Liang & Wunching Chang & Shyh-Chin Huang, 2015. "Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 8(4), pages 1-19, April.
    3. 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.
    4. Liudmyla Davydenko & Nina Davydenko & Andrii Bosak & Alla Bosak & Agnieszka Deja & Tygran Dzhuguryan, 2022. "Smart Sustainable Freight Transport for a City Multi-Floor Manufacturing Cluster: A Framework of the Energy Efficiency Monitoring of Electric Vehicle Fleet Charging," Energies, MDPI, vol. 15(10), pages 1-27, May.
    5. Kabir A. Mamun & F. R. Islam & R. Haque & Aneesh A. Chand & Kushal A. Prasad & Krishneel K. Goundar & Krishneel Prakash & Sidharth Maharaj, 2022. "Systematic Modeling and Analysis of On-Board Vehicle Integrated Novel Hybrid Renewable Energy System with Storage for Electric Vehicles," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    6. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    7. Jiaming Zhou & Chunxiao Feng & Qingqing Su & Shangfeng Jiang & Zhixian Fan & Jiageng Ruan & Shikai Sun & Leli Hu, 2022. "The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    8. Hensher, David A. & Wei, Edward & Liu, Wen, 2021. "Battery electric vehicles in cities: Measurement of some impacts on traffic and government revenue recovery," Journal of Transport Geography, Elsevier, vol. 94(C).
    9. 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.
    10. Zoltán Pusztai & Péter Kőrös & Ferenc Szauter & Ferenc Friedler, 2022. "Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle," Energies, MDPI, vol. 15(10), pages 1-20, May.
    11. 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. 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.
    2. 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.
    3. Tri-Cuong Do & Hoai-An Trinh & Kyoung-Kwan Ahn, 2023. "Hierarchical Control Strategy with Battery Dynamic Consideration for a Dual Fuel Cell/Battery Tramway," Mathematics, MDPI, vol. 11(10), pages 1-19, May.
    4. 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.
    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. 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.
    7. 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.
    8. Sizu Hou & Yisu Hou & Baikui Li & Ziqi Wang, 2023. "Fault Recovery Strategy for Power–Communication Coupled Distribution Network Considering Uncertainty," Energies, MDPI, vol. 16(12), pages 1-21, June.
    9. Pranjal Barman & Lachit Dutta & Brian Azzopardi, 2023. "Electric Vehicle Battery Supply Chain and Critical Materials: A Brief Survey of State of the Art," Energies, MDPI, vol. 16(8), pages 1-23, April.
    10. Alexander Gusev & Alexander Chervyakov & Anna Alexeenko & Evgeny Nikulchev, 2023. "Particle Swarm Training of a Neural Network for the Lower Upper Bound Estimation of the Prediction Intervals of Time Series," Mathematics, MDPI, vol. 11(20), pages 1-12, October.

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