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Electric Vehicle Power Consumption Modelling Method Based on Improved Ant Colony Optimization-Support Vector Regression

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  • Jiaan Zhang

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
    School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Wenxin Liu

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
    School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Zhenzhen Wang

    (State Grid Tianjin Wuqing Elect Power Supply Company, Tianjin 301700, China)

  • Ruiqing Fan

    (State Grid Tianjin Wuqing Elect Power Supply Company, Tianjin 301700, China)

Abstract

Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective.

Suggested Citation

  • Jiaan Zhang & Wenxin Liu & Zhenzhen Wang & Ruiqing Fan, 2024. "Electric Vehicle Power Consumption Modelling Method Based on Improved Ant Colony Optimization-Support Vector Regression," Energies, MDPI, vol. 17(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4339-:d:1467392
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    References listed on IDEAS

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    3. Qiang Xing & Zhong Chen & Ziqi Zhang & Xiao Xu & Tian Zhang & Xueliang Huang & Haiwei Wang, 2020. "Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior," Energies, MDPI, vol. 13(6), pages 1-32, March.
    4. Boyacı, Burak & Zografos, Konstantinos G., 2019. "Investigating the effect of temporal and spatial flexibility on the performance of one-way electric carsharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 244-272.
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