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

Jiaan Zhang (), Wenxin Liu, Zhenzhen Wang and Ruiqing Fan
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Jiaan Zhang: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Wenxin Liu: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, 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

Energies, 2024, vol. 17, issue 17, 1-17

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.

Keywords: electric vehicle; power consumption per unit mile; statistical methods; ant colony optimisation algorithm; support vector regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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