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A study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation

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  • Song, Jingeun
  • Cha, Junepyo
  • Choi, Mingi

Abstract

This paper is a study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation. It aims to develop a prediction model for the energy efficiency of electric vehicles and compare it with the results of chassis dynamometer tests. Driving tests were conducted on a chassis dynamometer, which included various test cycles of the 5-cycle test. Using MATLAB Simulink, a vehicle dynamics analysis model for electric vehicles was constructed, and analytical research was conducted focusing on light-duty electric vehicles. By comparing and analyzing the experimental data obtained from the chassis dynamometer with the data calculated through simulation, it is possible to verify the accuracy of the electric vehicle energy efficiency prediction model. And the reliability of the simulation model was secured by comparing the experimental data obtained through chassis dynamometer test with the simulation results of the developed electric vehicle energy efficiency prediction model. There is good agreement between the experimental and simulation results. In most areas, the error rate remains below 3 %, indicating that the simulation model closely follows the experimental results from the chassis dynamometer. Based on these results, it is expected that the developed simulation model can be utilized for measuring the energy efficiency of electric vehicles.

Suggested Citation

  • Song, Jingeun & Cha, Junepyo & Choi, Mingi, 2024. "A study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation," Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:energy:v:309:y:2024:i:c:s0360544224029645
    DOI: 10.1016/j.energy.2024.133189
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    References listed on IDEAS

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    1. Jingeun Song & Junepyo Cha, 2021. "Analysis of Driving Dynamics Considering Driving Resistances in On-Road Driving," Energies, MDPI, vol. 14(12), pages 1-16, June.
    2. Dwivedi, Shalini & Akula, Aparna & Pecht, Michael, 2024. "Predictive analytics for prolonging lithium-ion battery lifespan through informed storage conditions," Energy, Elsevier, vol. 308(C).
    3. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    4. Bamdezh, M.A. & Molaeimanesh, G.R., 2024. "The effect of active and passive battery thermal management systems on energy consumption, battery degradation, and carbon emissions of an electric vehicle," Energy, Elsevier, vol. 304(C).
    5. Song, Jingeun & Cha, Junepyo, 2022. "Development of prediction methodology for CO2 emissions and fuel economy of light duty vehicle," Energy, Elsevier, vol. 244(PB).
    Full references (including those not matched with items on IDEAS)

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